diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..cfd40c8 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,10 @@ +name: CI +on: [pull_request] +jobs: + ci: + uses: ucgmsim/meta-ci-action/.github/workflows/ci.yml@main + with: + package-dir: imdb + uv-extra-args: "--all-groups" + enable-coverage: true + cov-package: imdb diff --git a/.github/workflows/claude-review.yml b/.github/workflows/claude-review.yml new file mode 100644 index 0000000..14b2c86 --- /dev/null +++ b/.github/workflows/claude-review.yml @@ -0,0 +1,19 @@ +name: Claude PR Review + +# Triggered by a "@claude review" comment on a pull request. +# +# Why issue_comment and not pull_request: GitHub withholds secrets from runs +# triggered by fork pull requests, so a pull_request trigger would silently +# fail on exactly the PRs that most need review. issue_comment runs in the base +# repository's context with secrets available. The action independently checks +# that the commenting user has write access before doing anything, so the gate +# is "a member of this org asked for this review". +on: + issue_comment: + types: [created] + +jobs: + review: + uses: ucgmsim/meta-ci-action/.github/workflows/claude-review.yml@main + secrets: + claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }} diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..68d7a63 --- /dev/null +++ b/.gitignore @@ -0,0 +1,181 @@ +.DS_Store +.idea +*.log +tmp/ +# Created by https://www.toptal.com/developers/gitignore/api/python +# Edit at https://www.toptal.com/developers/gitignore?templates=python + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ + +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +### Python Patch ### +# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration +poetry.toml + +# ruff +.ruff_cache/ + +# LSP config files +pyrightconfig.json + +# End of https://www.toptal.com/developers/gitignore/api/python diff --git a/README.md b/README.md new file mode 100644 index 0000000..fa30faa --- /dev/null +++ b/README.md @@ -0,0 +1,37 @@ +# imdb + +A library for reading and writing intensity measure databases (IMDBs) — DuckDB +databases of simulated ground-motion intensity measures. Schema is documented in +`imdb/schema.py`. + +## Usage + +```python +from imdb import IMDB + +# read +with IMDB("run_set.duckdb") as db: + records = db.get_records(event_ids=["event1"], component="rotd50") + psa = db.get_psa(periods=[0.1, 1.0], event_ids=["event1"]) + scalars = db.get_scalars(ims=["PGA", "PGV"]) + +# write +db = IMDB.create("new.duckdb", periods=[0.1, 0.2, 1.0]) +db.add_events(events_df) +db.add_realisations(realisations_df) +db.add_sites(sites_df) +db.add_site_event(site_event_df) +db.add_records(records_df) # rel_id, site_id, component, pSA, FAS, scalar IM columns +db.validate() +db.close() +``` + +## Development + +``` +uv sync --all-groups +uv run pytest -q +uv run ruff check +uv run ruff format +uv run ty check +``` diff --git a/imdb/__init__.py b/imdb/__init__.py new file mode 100644 index 0000000..ca128c4 --- /dev/null +++ b/imdb/__init__.py @@ -0,0 +1,5 @@ +"""A library for reading and writing intensity measure databases.""" + +from imdb.imdb import IMDB + +__all__ = ["IMDB"] diff --git a/imdb/imdb.py b/imdb/imdb.py new file mode 100644 index 0000000..901c078 --- /dev/null +++ b/imdb/imdb.py @@ -0,0 +1,555 @@ +"""Read and write intensity measure databases (IMDBs).""" + +import datetime +import logging +from importlib.metadata import version +from pathlib import Path +from typing import Any, Self + +import ibis +import numpy as np +import pandas as pd +from ibis import _ +from ibis.backends.duckdb import Backend as DuckDBBackend + +from imdb import schema + +logger = logging.getLogger(__name__) + + +class IMDB: + """A DuckDB-backed intensity measure database. + + Parameters + ---------- + path : Path + Path to the database file. + read_only : bool + Open the database read-only. + """ + + def __init__(self, path: Path, read_only: bool = True) -> None: + """Set up the database path; does not open a connection.""" + self.path = Path(path) + self.read_only = read_only + self._con: DuckDBBackend | None = None + + @property + def con(self) -> DuckDBBackend: + """The underlying ibis connection. Raises if the database is not open.""" + if self._con is None: + raise RuntimeError( + "database is not open; call .open() or use as a context manager" + ) + return self._con + + def open(self) -> Self: + """Open the database connection, if not already open.""" + if self._con is None: + self._con = ibis.duckdb.connect(self.path, read_only=self.read_only) + return self + + @property + def db_meta(self) -> dict[str, str]: + """The `db_meta` table, as a dict.""" + df = self.con.table("db_meta").to_pandas() + return dict(zip(df["key"], df["value"], strict=True)) + + def close(self) -> None: + """Close the database connection.""" + if self._con is not None: + self._con.disconnect() + self._con = None + + def __enter__(self) -> Self: + """Open the database connection.""" + return self.open() + + def __exit__(self, *exc: object) -> None: + """Close the database connection.""" + self.close() + + @classmethod + def create( + cls, + path: Path, + periods: list[float], + frequencies: list[float] | None = None, + components: tuple[str, ...] = schema.COMPONENTS, + db_meta: dict[str, str] | None = None, + ) -> "IMDB": + """Create a new, empty IMDB and return it open for writing. + + Parameters + ---------- + path : Path + Path to the database file to create. Must not already exist. + periods : list of float + Response spectral periods, in seconds, that `pSA` arrays are indexed by. + frequencies : list of float, optional + Frequencies, in Hz, that `FAS` arrays are indexed by. + components : tuple of str + Components this database will hold. + db_meta : dict of str to str, optional + Extra `db_meta` entries, merged over the defaults. + + Returns + ------- + IMDB + The newly created database, open for writing. + """ + frequencies = frequencies or [] + db = cls(path, read_only=False).open() + con = db.con + for statement in schema.DDL.strip().split(";"): + if statement.strip(): + con.raw_sql(statement) + + con.insert( + "periods", + pd.DataFrame( + {"period_index": range(1, len(periods) + 1), "period": periods} + ), + ) + con.insert( + "frequencies", + pd.DataFrame( + {"freq_index": range(1, len(frequencies) + 1), "frequency": frequencies} + ), + ) + con.insert( + "im_units", + pd.DataFrame( + {"im": schema.IM_UNITS.keys(), "unit": schema.IM_UNITS.values()} + ), + ) + con.insert( + "notes", + pd.DataFrame({"topic": schema.NOTES.keys(), "note": schema.NOTES.values()}), + ) + + meta = { + "schema_version": schema.SCHEMA_VERSION, + "components": ",".join(components), + "n_periods": str(len(periods)), + "n_frequencies": str(len(frequencies)), + "created_at": datetime.datetime.now(datetime.UTC).isoformat(), + "imdb_version": version("imdb"), + **(db_meta or {}), + } + con.insert( + "db_meta", pd.DataFrame({"key": meta.keys(), "value": meta.values()}) + ) + return db + + def _next_ids(self, table: str, int_col: str, n: int) -> np.ndarray: + """Return `n` new contiguous integer ids for `table`, starting after the current max.""" + current = self.con.table(table)[int_col].max().to_pandas() + start = 0 if pd.isna(current) else int(current) + 1 # ty: ignore[invalid-argument-type] + return np.arange(start, start + n) + + def _id_map(self, table: str, id_col: str, int_col: str) -> pd.Series: + """Return a `pd.Series` mapping string id to int id for `table`.""" + df = self.con.table(table).select(id_col, int_col).to_pandas() + return df.set_index(id_col)[int_col] + + def add_events(self, df: pd.DataFrame) -> None: + """Insert new events. + + Parameters + ---------- + df : pd.DataFrame + Must have an `event_id` column; other columns match `events`. + """ + df = df.copy() + df["event_int_id"] = self._next_ids("events", "event_int_id", len(df)) + self.con.insert("events", df) + + def add_realisations(self, df: pd.DataFrame) -> None: + """Insert new realisations. + + Parameters + ---------- + df : pd.DataFrame + Must have `rel_id` and `event_id` columns; other columns match + `realisations`. + """ + df = df.copy() + event_int_id = self._id_map("events", "event_id", "event_int_id") + df["event_int_id"] = event_int_id.loc[df["event_id"]].to_numpy() + df = df.drop(columns="event_id") + df["rel_int_id"] = self._next_ids("realisations", "rel_int_id", len(df)) + self.con.insert("realisations", df) + + def add_sites(self, df: pd.DataFrame) -> None: + """Insert new sites. + + Parameters + ---------- + df : pd.DataFrame + Must have `site_id`, `lat` and `lon` columns; other columns match `sites`. + """ + df = df.copy() + df["site_int_id"] = self._next_ids("sites", "site_int_id", len(df)) + self.con.insert("sites", df) + + def add_site_event(self, df: pd.DataFrame) -> None: + """Insert new site-event distance rows. + + Parameters + ---------- + df : pd.DataFrame + Must have `site_id` and `event_id` columns; other columns match + `site_event`. + """ + df = df.copy() + site_int_id = self._id_map("sites", "site_id", "site_int_id") + event_int_id = self._id_map("events", "event_id", "event_int_id") + df["site_int_id"] = site_int_id.loc[df["site_id"]].to_numpy() + df["event_int_id"] = event_int_id.loc[df["event_id"]].to_numpy() + df = df.drop(columns=["site_id", "event_id"]) + self.con.insert("site_event", df) + + def add_records( + self, + df: pd.DataFrame, + pSA: np.ndarray | None = None, # noqa: N803 + FAS: np.ndarray | None = None, # noqa: N803 + ) -> np.ndarray: + """Insert new records, and whichever IM tables the input covers. + + A record whose `pSA`/`FAS` row is entirely NaN is not written to that IM + table at all, matching the schema's row-presence-means-coverage convention. + + Parameters + ---------- + df : pd.DataFrame + Must have `rel_id`, `site_id` and `component` columns, one row per + record. May also have any of the scalar IM columns (`schema.SCALAR_IMS`). + pSA : np.ndarray, optional + Shape `(len(df), n_periods)`. A row of all NaN means that record has + no pSA. + FAS : np.ndarray, optional + Shape `(len(df), n_frequencies)`. A row of all NaN means that record + has no FAS. + + Returns + ------- + np.ndarray + The `record_int_id` assigned to each row of `df`, in input order. + """ + df = df.copy() + unknown = set(df["component"]) - set(self.db_meta["components"].split(",")) + if unknown: + raise ValueError(f"components not in this database: {sorted(unknown)}") + + assert pSA is None or pSA.shape[0] == len(df), ( + "pSA must have one row per record" + ) + assert FAS is None or FAS.shape[0] == len(df), ( + "FAS must have one row per record" + ) + + rel_int_id_mapping = self._id_map("realisations", "rel_id", "rel_int_id") + rel_event_int_id_mapping = self._id_map( + "realisations", "rel_int_id", "event_int_id" + ) + site_int_id_mapping = self._id_map("sites", "site_id", "site_int_id") + + df["rel_int_id"] = rel_int_id_mapping.loc[df["rel_id"]].to_numpy() + df["site_int_id"] = site_int_id_mapping.loc[df["site_id"]].to_numpy() + df["event_int_id"] = rel_event_int_id_mapping.loc[df["rel_int_id"]].to_numpy() + record_int_id = ( + self.con.raw_sql( + f"SELECT nextval('record_int_id_seq') FROM range({len(df)})" + ) + .df()["nextval('record_int_id_seq')"] + .to_numpy() + ) + df["record_int_id"] = record_int_id + + self.con.raw_sql("BEGIN TRANSACTION") + try: + self.con.insert( + "records", + df[ + [ + "record_int_id", + "event_int_id", + "rel_int_id", + "site_int_id", + "component", + ] + ], + ) + if pSA is not None: + mask = ~np.isnan(pSA).all(axis=1) + self.con.insert( + "psa_ims", + pd.DataFrame( + {"record_int_id": record_int_id[mask], "pSA": list(pSA[mask])} + ), + ) + if FAS is not None: + mask = ~np.isnan(FAS).all(axis=1) + self.con.insert( + "fas_ims", + pd.DataFrame( + {"record_int_id": record_int_id[mask], "FAS": list(FAS[mask])} + ), + ) + scalar_cols = [c for c in schema.SCALAR_IMS if c in df] + if scalar_cols: + scalars = df[["record_int_id", *scalar_cols]].copy() + rotd = df["component"].str.startswith("rotd") + for col in schema.ROTD_UNDEFINED & set(scalar_cols): + scalars.loc[rotd, col] = None + self.con.insert("scalars_ims", scalars) + except Exception: + self.con.raw_sql("ROLLBACK") + raise + self.con.raw_sql("COMMIT") + logger.info("inserted %d records", len(df)) + return record_int_id + + def delete_event(self, event_id: str) -> None: + """Delete an event and everything derived from it, for a clean re-ingest. + + Parameters + ---------- + event_id : str + The event to delete. + """ + event_int_id_subquery = "(SELECT event_int_id FROM events WHERE event_id = ?)" + record_subquery = f"(SELECT record_int_id FROM records WHERE event_int_id = {event_int_id_subquery})" + statements = [ + f"DELETE FROM psa_ims WHERE record_int_id IN {record_subquery}", + f"DELETE FROM fas_ims WHERE record_int_id IN {record_subquery}", + f"DELETE FROM scalars_ims WHERE record_int_id IN {record_subquery}", + f"DELETE FROM records WHERE event_int_id = {event_int_id_subquery}", + f"DELETE FROM site_event WHERE event_int_id = {event_int_id_subquery}", + f"DELETE FROM realisations WHERE event_int_id = {event_int_id_subquery}", + ] + for statement in statements: + self.con.raw_sql(statement, parameters=[event_id]) + + def validate(self) -> list[str]: + """Check the invariants the schema itself cannot enforce: unique ids, valid FKs. + + Returns + ------- + list of str + One entry per problem found; empty if the database is consistent. + """ + problems = [] + for table in ("records", "psa_ims", "fas_ims", "scalars_ims"): + t = self.con.table(table) + n_rows = t.count().to_pandas() + n_unique = t.record_int_id.nunique().to_pandas() + if n_rows != n_unique: + problems.append( + f"{table}: record_int_id is not unique ({n_rows} rows, {n_unique} unique)" + ) + + records = self.con.table("records") + fks = { + "rel_int_id": self.con.table("realisations").rel_int_id, + "site_int_id": self.con.table("sites").site_int_id, + "event_int_id": self.con.table("events").event_int_id, + } + for col, valid in fks.items(): + n = records.filter(~records[col].isin(valid)).count().to_pandas() + if n: + problems.append(f"records: {n} rows with an unknown {col}") + + return problems + + # ---- read ------------------------------------------------------------- + + def get_events(self) -> pd.DataFrame: + """Return all events, indexed by `event_id`.""" + return self.con.table("events").to_pandas().set_index("event_id") + + def get_realisations(self) -> pd.DataFrame: + """Return all realisations, indexed by `rel_id`.""" + return self.con.table("realisations").to_pandas().set_index("rel_id") + + def get_sites(self) -> pd.DataFrame: + """Return all sites, indexed by `site_id`.""" + return self.con.table("sites").to_pandas().set_index("site_id") + + def get_site_event( + self, + event_ids: list[str] | None = None, + site_ids: list[str] | None = None, + max_rrup: float | None = None, + ) -> pd.DataFrame: + """Return site-event distance rows. + + Parameters + ---------- + event_ids : list of str, optional + Only these events. + site_ids : list of str, optional + Only these sites. + max_rrup : float, optional + Only rows with `rrup` at most this value. + + Returns + ------- + pd.DataFrame + One row per (site, event), with `site_id` and `event_id` columns. + """ + sites = self.con.table("sites").select("site_int_id", "site_id") + events = self.con.table("events").select("event_int_id", "event_id") + t = ( + self.con.table("site_event") + .join(sites, "site_int_id") + .join(events, "event_int_id") + ) + if event_ids is not None: + t = t.filter(t.event_id.isin(event_ids)) + if site_ids is not None: + t = t.filter(t.site_id.isin(site_ids)) + if max_rrup is not None: + t = t.filter(t.rrup <= max_rrup) + return t.drop("site_int_id", "event_int_id").to_pandas() + + def get_records( + self, + event_ids: list[str] | None = None, + rel_ids: list[str] | None = None, + site_ids: list[str] | None = None, + component: str | None = None, + record_int_ids: list[int] | None = None, + ) -> pd.DataFrame: + """Return record identity rows. + + Parameters + ---------- + event_ids : list of str, optional + Only records for these events. + rel_ids : list of str, optional + Only records for these realisations. + site_ids : list of str, optional + Only records for these sites. + component : str, optional + Only records with this component. + record_int_ids : list of int, optional + Only these `record_int_id` values. + + Returns + ------- + pd.DataFrame + Indexed by `record_int_id`, with `event_id`, `rel_id`, `site_id` and + `component` columns. + """ + events = self.con.table("events").select("event_int_id", "event_id") + realisations = self.con.table("realisations").select("rel_int_id", "rel_id") + sites = self.con.table("sites").select("site_int_id", "site_id") + t = ( + self.con.table("records") + .join(events, "event_int_id") + .join(realisations, "rel_int_id") + .join(sites, "site_int_id") + ) + if event_ids is not None: + t = t.filter(t.event_id.isin(event_ids)) + if rel_ids is not None: + t = t.filter(t.rel_id.isin(rel_ids)) + if site_ids is not None: + t = t.filter(t.site_id.isin(site_ids)) + if component is not None: + t = t.filter(t.component == component) + if record_int_ids is not None: + ids = ibis.memtable({"record_int_id": list(record_int_ids)}) + t = t.semi_join(ids, "record_int_id") + return ( + t.select("record_int_id", "event_id", "rel_id", "site_id", "component") + .to_pandas() + .set_index("record_int_id") + ) + + def get_psa( + self, periods: list[float] | None = None, **filters: Any + ) -> pd.DataFrame: + """Return response spectral acceleration. + + Parameters + ---------- + periods : list of float, optional + Periods to return, in seconds. Defaults to every period on the grid. + **filters + Passed to `get_records` to select which records to return. + + Returns + ------- + pd.DataFrame + Indexed by `record_int_id`, one column per requested period. + """ + period_index = ( + self.con.table("periods").to_pandas().set_index("period")["period_index"] + ) + if periods is None: + periods = period_index.index.tolist() + record_int_ids = self.get_records(**filters).index.tolist() + t = self.con.table("psa_ims").filter(_.record_int_id.isin(record_int_ids)) + cols = {str(p): t.pSA[int(period_index.loc[p]) - 1] for p in periods} + return ( + t.select(record_int_id=t.record_int_id, **cols) + .to_pandas() + .set_index("record_int_id") + ) + + def get_fas( + self, frequencies: list[float] | None = None, **filters: Any + ) -> pd.DataFrame: + """Return Fourier amplitude spectra. + + Parameters + ---------- + frequencies : list of float, optional + Frequencies to return, in Hz. Defaults to every frequency on the grid. + **filters + Passed to `get_records` to select which records to return. + + Returns + ------- + pd.DataFrame + Indexed by `record_int_id`, one column per requested frequency. + """ + freq_index = ( + self.con.table("frequencies") + .to_pandas() + .set_index("frequency")["freq_index"] + ) + if frequencies is None: + frequencies = freq_index.index.tolist() + record_int_ids = self.get_records(**filters).index.tolist() + t = self.con.table("fas_ims").filter(_.record_int_id.isin(record_int_ids)) + cols = {str(f): t.FAS[int(freq_index.loc[f]) - 1] for f in frequencies} + return ( + t.select(record_int_id=t.record_int_id, **cols) + .to_pandas() + .set_index("record_int_id") + ) + + def get_scalars(self, ims: list[str] | None = None, **filters: Any) -> pd.DataFrame: + """Return scalar intensity measures. + + Parameters + ---------- + ims : list of str, optional + Which scalar IMs to return. Defaults to all of `schema.SCALAR_IMS`. + **filters + Passed to `get_records` to select which records to return. + + Returns + ------- + pd.DataFrame + Indexed by `record_int_id`, one column per requested IM. + """ + ims = ims or list(schema.SCALAR_IMS) + record_int_ids = self.get_records(**filters).index.tolist() + t = self.con.table("scalars_ims").filter(_.record_int_id.isin(record_int_ids)) + return t.select("record_int_id", *ims).to_pandas().set_index("record_int_id") diff --git a/imdb/schema.py b/imdb/schema.py new file mode 100644 index 0000000..e5f4aa1 --- /dev/null +++ b/imdb/schema.py @@ -0,0 +1,188 @@ +"""DDL and fixed vocabulary for the IMDB schema, version 0. + +Facts only, no logic. `DDL` is the single source of truth for the schema; nothing +else in this library composes column lists by hand. +""" + +SCHEMA_VERSION = "0" + +DDL = """ +CREATE TABLE db_meta (key VARCHAR PRIMARY KEY, value VARCHAR NOT NULL); +CREATE TABLE notes (topic VARCHAR PRIMARY KEY, note VARCHAR NOT NULL); +CREATE TABLE im_units (im VARCHAR PRIMARY KEY, unit VARCHAR NOT NULL); + +CREATE TABLE periods ( + period_index INTEGER PRIMARY KEY, + period DOUBLE NOT NULL UNIQUE +); + +CREATE TABLE frequencies ( + freq_index INTEGER PRIMARY KEY, + frequency DOUBLE NOT NULL UNIQUE +); + +CREATE TYPE tect_type_t AS ENUM ( + 'ACTIVE_SHALLOW', 'VOLCANIC', 'SUBDUCTION_INTERFACE', 'SUBDUCTION_SLAB' +); + +CREATE TABLE events ( + event_int_id INTEGER PRIMARY KEY, + event_id VARCHAR NOT NULL UNIQUE, + magnitude FLOAT, + tect_type tect_type_t, + dip FLOAT, + dip_dir FLOAT, + dtop FLOAT, + dbottom FLOAT, + length FLOAT, + source_wkt VARCHAR, + trace_wkt VARCHAR, + domain_wkt VARCHAR, + metadata VARCHAR +); + +CREATE TABLE realisations ( + rel_int_id INTEGER PRIMARY KEY, + rel_id VARCHAR NOT NULL UNIQUE, + event_int_id INTEGER NOT NULL REFERENCES events(event_int_id), + magnitude FLOAT, + rake FLOAT, + hypo_lat FLOAT, + hypo_lon FLOAT, + hypo_depth FLOAT, + metadata VARCHAR +); + +CREATE TABLE sites ( + site_int_id INTEGER PRIMARY KEY, + site_id VARCHAR NOT NULL UNIQUE, + lat FLOAT NOT NULL, + lon FLOAT NOT NULL, + vs30 FLOAT, + z1p0 FLOAT, + z2p5 FLOAT, + metadata VARCHAR +); + +CREATE TABLE site_event ( + site_int_id INTEGER NOT NULL, + event_int_id INTEGER NOT NULL, + rrup FLOAT, + rjb FLOAT, + rx FLOAT, + ry FLOAT, + metadata VARCHAR +); + +CREATE SEQUENCE record_int_id_seq START 1; + +CREATE TABLE records ( + record_int_id BIGINT DEFAULT nextval('record_int_id_seq'), + event_int_id INTEGER NOT NULL, + rel_int_id INTEGER NOT NULL, + site_int_id INTEGER NOT NULL, + component VARCHAR NOT NULL +); + +CREATE TABLE psa_ims (record_int_id BIGINT NOT NULL, pSA FLOAT[]); +CREATE TABLE fas_ims (record_int_id BIGINT NOT NULL, FAS FLOAT[]); + +CREATE TABLE scalars_ims ( + record_int_id BIGINT NOT NULL, + PGA FLOAT, + PGV FLOAT, + PGD FLOAT, + CAV FLOAT, + AI FLOAT, + Ds575 FLOAT, + Ds595 FLOAT +); +""" + +COMPONENTS = ("000", "090", "ver", "geom", "rotd0", "rotd50", "rotd100") + +SCALAR_IMS = ("PGA", "PGV", "PGD", "CAV", "AI", "Ds575", "Ds595") + +ROTD_UNDEFINED = frozenset({"CAV", "AI", "Ds575", "Ds595"}) + +TECT_TYPES = ("ACTIVE_SHALLOW", "VOLCANIC", "SUBDUCTION_INTERFACE", "SUBDUCTION_SLAB") + +IM_UNITS = { + "pSA": "g", + "FAS": "g.s", + "PGA": "g", + "PGV": "cm/s", + "PGD": "cm", + "CAV": "m/s", + "AI": "m/s", + "Ds575": "s", + "Ds595": "s", +} + +METADATA_TABLES = { + "events": "event_metadata_keys", + "realisations": "rel_metadata_keys", + "sites": "site_metadata_keys", + "site_event": "site_event_metadata_keys", +} + +NOTES = { + "logical keys": ( + "site_event's logical key is (site_int_id, event_int_id); records' logical key is " + "(rel_int_id, site_int_id, component). Neither is enforced by a constraint; the " + "writer is responsible for not creating duplicates." + ), + "array indexing is 1-based": ( + "periods.period_index and frequencies.freq_index are 1-based, matching DuckDB list " + "indexing, so pSA[period_index] and FAS[freq_index] need no offset. len(pSA) equals " + "the row count of periods; len(FAS) equals the row count of frequencies." + ), + "identity and rebuild stability": ( + "event_id, rel_id and site_id are stable. The integer surrogates event_int_id, " + "rel_int_id, site_int_id and record_int_id are assigned at ingest and change on rebuild; " + "nothing outside the database may reference them. External references use " + "(rel_id, site_id, component)." + ), + "component vocabulary": ( + "000, 090, ver, geom, rotd0, rotd50, rotd100, following IM_calculation. A database " + "may hold any subset; db_meta.components lists which. The writer validates against " + "that list." + ), + "rotd scalars are undefined": ( + "scalars_ims.CAV, AI, Ds575 and Ds595 are NULL for rotd* components. PGA, PGV and " + "PGD are populated for every component." + ), + "units": ( + "Linear, physical units; log is a read-time transform. Every IM unit is a row in " + "im_units. Outside the IM tables: distances km, vs30 m/s, z1p0 and z2p5 km, depths " + "km, angles degrees, coordinates WGS84." + ), + "metadata columns are JSON": ( + "events, realisations, sites and site_event each carry a metadata VARCHAR holding a " + "JSON object. Read with json_extract_string(metadata, '$.key'). Permitted keys are " + "declared in db_meta and validated by the writer." + ), + "distances are event level": ( + "rrup, rjb, rx and ry are measured to the rupture surface and are defined at the " + "event level, shared across all realisations of an event. Hypocentral and " + "epicentral distance are not stored; compute them from realisations.hypo_* and " + "sites.lat/lon." + ), + "synthetic realisations": ( + "Every event has at least one realisation. A dataset with no realisation concept " + "gets exactly one per event, with rel_id equal to event_id." + ), + "record_int_id is file-local": ( + "record_int_id is a surrogate scoped to this database file only; it is not stable " + "across rebuilds or between databases." + ), + "physical sort order": ( + "Rows are written in event order, so event_int_id and record_int_id are monotonic and " + "event filters prune row groups. Site filters do not prune. db_meta.sort_order " + "records the ordering used." + ), + "provenance": ( + "db_meta.source, creator, created_at and imdb_version record where the data in this " + "database came from and what built it." + ), +} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..348efb1 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,95 @@ +[build-system] +requires = ["setuptools", "setuptools-scm"] +build-backend = "setuptools.build_meta" + +[project] +name = "imdb" +authors = [{name="ucgmsim"}] +description = "A library for reading and writing intensity measure databases" +readme = "README.md" +requires-python = ">=3.12" +dynamic = ["version"] +dependencies = [ + "ibis-framework[duckdb]>=9", + "numpy>=2", + "pandas>=3", +] + +[dependency-groups] +test = [ + "pytest", + "pytest-cov", + "coverage[toml]", +] +types = [ + "pandas-stubs", + "ty", +] +dev = ["ruff", "deptry", "ty", "numpydoc"] + +[tool.setuptools_scm] + +[tool.setuptools.packages.find] +include = ["imdb*"] + +[tool.ruff.lint] +extend-select = [ + # isort imports + "I", + # Use r'\s+' rather than '\s+' + "W605", + # All the naming errors, like using camel case for function names. + "N", + # Missing docstrings in classes, methods, and functions + "D101", + "D102", + "D103", + "D105", + "D107", + # Use f-string instead of a format call + "UP032", + # Standard library import is deprecated + "UP035", + # Missing function argument type-annotation + "ANN001", + # Using except without specifying an exception type to catch + "BLE001" +] +ignore = ["D104"] + +[tool.ruff.lint.pydocstyle] +convention = "numpy" + +[tool.ruff.lint.isort] +known-first-party = ["imdb", "qcore", "IM", "workflow", "source_modelling"] + +[tool.ruff.lint.per-file-ignores] +# Ignore no docstring in __init__.py +"__init__.py" = ["D104"] +# Ignore docstring errors and fixture annotations in tests folder +"tests/**.py" = ["D", "ANN001"] + +[tool.numpydoc_validation] +checks = [ + "GL05", + "GL08", + "GL10", + "PR01", + "PR02", + "PR03", + "PR04", + "PR05", + "PR06", + "PR07", + "RT01", + "RT02", + "RT03", + "RT04", + "YD01", +] +# remember to use single quotes for regex in TOML +exclude = [ + '\.__repr__$', + '\.__exit__$', + '\.__enter__$', +] diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..4722d07 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,79 @@ +"""Shared fixtures: one small synthetic IMDB.""" + +import numpy as np +import pandas as pd +import pytest + +from imdb import IMDB + +PERIODS = [0.1, 0.2, 0.5, 1.0, 2.0] +FREQUENCIES = [1.0, 5.0, 10.0] +COMPONENTS = ["000", "090"] +EVENTS = ["eventA", "eventB"] +SITES = ["siteA", "siteB", "siteC"] + + +@pytest.fixture +def db(tmp_path): + """A small, fully populated IMDB: 2 events, 2 realisations each, 3 sites, 2 components.""" + path = tmp_path / "test.duckdb" + db = IMDB.create( + path, periods=PERIODS, frequencies=FREQUENCIES, components=COMPONENTS + ) + + db.add_events(pd.DataFrame({"event_id": EVENTS, "magnitude": [6.0, 7.0]})) + + rel_ids = [f"{e}_rel{i}" for e in EVENTS for i in range(2)] + db.add_realisations( + pd.DataFrame( + { + "rel_id": rel_ids, + "event_id": [e for e in EVENTS for _ in range(2)], + } + ) + ) + + db.add_sites( + pd.DataFrame( + { + "site_id": SITES, + "lat": [-43.5, -43.6, -43.7], + "lon": [172.6, 172.7, 172.8], + } + ) + ) + + db.add_site_event( + pd.DataFrame( + { + "site_id": [s for _ in EVENTS for s in SITES], + "event_id": [e for e in EVENTS for _ in SITES], + "rrup": np.arange(len(EVENTS) * len(SITES), dtype=float), + } + ) + ) + + rng = np.random.default_rng(0) + rows = [ + (rel_id, site_id, component) + for rel_id in rel_ids + for site_id in SITES + for component in COMPONENTS + ] + n = len(rows) + db.add_records( + pd.DataFrame( + { + "rel_id": [r[0] for r in rows], + "site_id": [r[1] for r in rows], + "component": [r[2] for r in rows], + "PGA": rng.uniform(size=n), + "PGV": rng.uniform(size=n), + "PGD": rng.uniform(size=n), + } + ), + pSA=rng.uniform(size=(n, len(PERIODS))), + FAS=rng.uniform(size=(n, len(FREQUENCIES))), + ) + yield db + db.close() diff --git a/tests/test_imdb.py b/tests/test_imdb.py new file mode 100644 index 0000000..fc1b99e --- /dev/null +++ b/tests/test_imdb.py @@ -0,0 +1,37 @@ +"""Basic tests: the library works for its intended, correct usage.""" + +from tests.conftest import COMPONENTS, EVENTS, FREQUENCIES, PERIODS, SITES + + +def test_round_trip(db): + records = db.get_records() + n = len(EVENTS) * 2 * len(SITES) * len(COMPONENTS) + assert len(records) == n + + psa = db.get_psa() + assert list(psa.columns) == [str(p) for p in PERIODS] + assert (psa.index == records.index).all() + + fas = db.get_fas() + assert list(fas.columns) == [str(f) for f in FREQUENCIES] + + scalars = db.get_scalars(ims=["PGA", "PGV", "PGD"]) + assert set(scalars.columns) == {"PGA", "PGV", "PGD"} + assert not scalars.isna().any().any() + + +def test_filter_by_event(db): + records = db.get_records(event_ids=["eventA"]) + assert (records["event_id"] == "eventA").all() + assert len(records) == 2 * len(SITES) * len(COMPONENTS) + + +def test_validate_clean(db): + assert db.validate() == [] + + +def test_delete_and_readd(db): + db.delete_event("eventA") + assert len(db.get_records(event_ids=["eventA"])) == 0 + assert len(db.get_realisations()) == 2 + assert db.validate() == [] diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..53e4d1b --- /dev/null +++ b/uv.lock @@ -0,0 +1,1113 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.14' and sys_platform == 'win32'", + 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