From d6453ccd4e7a6cbe3767d0f7d0c7094d2045c474 Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Mon, 9 Mar 2026 10:35:24 +0100 Subject: [PATCH 01/21] alpha release --- .github/workflows/CI.yml | 181 + .gitignore | 72 + .pre-commit-config.yaml | 26 + .rust-analyzer.toml | 2 + Cargo.lock | 3419 ++++++++++++ Cargo.toml | 20 + LICENSE | 0 Makefile | 118 + README.md | 154 + crates/tdc-core/Cargo.toml | 40 + crates/tdc-core/benches/ops.rs | 151 + crates/tdc-core/src/config.rs | 27 + crates/tdc-core/src/datacontainer/builder.rs | 1382 +++++ crates/tdc-core/src/datacontainer/core.rs | 404 ++ crates/tdc-core/src/datacontainer/lazy.rs | 341 ++ crates/tdc-core/src/datacontainer/mod.rs | 7 + crates/tdc-core/src/error.rs | 147 + crates/tdc-core/src/io.rs | 91 + crates/tdc-core/src/lib.rs | 13 + crates/tdc-core/src/schema/attribute.rs | 156 + .../tdc-core/src/schema/coordinate_system.rs | 216 + crates/tdc-core/src/schema/entity.rs | 28 + crates/tdc-core/src/schema/field.rs | 150 + crates/tdc-core/src/schema/fields.rs | 355 ++ crates/tdc-core/src/schema/mod.rs | 13 + crates/tdc-core/src/schema/period.rs | 20 + crates/tdc-core/src/selector/expr.rs | 128 + crates/tdc-core/src/selector/mod.rs | 19 + crates/tdc-core/tests/test_builder.rs | 84 + crates/tdc-core/tests/test_datacontainer.rs | 308 ++ crates/tdc-core/tests/test_fields.rs | 54 + crates/tdc-core/tests/test_schema.rs | 142 + crates/tdc-ops/Cargo.toml | 20 + crates/tdc-ops/src/lib.rs | 277 + crates/tdc-parsers/Cargo.toml | 38 + crates/tdc-parsers/benches/memory.rs | 40 + crates/tdc-parsers/benches/parsers.rs | 20 + crates/tdc-parsers/src/lib.rs | 42 + crates/tdc-parsers/src/sportec.rs | 474 ++ crates/tdc-parsers/src/statsperform.rs | 162 + crates/tdc-parsers/tests/test_sportec.rs | 202 + crates/tdc-parsers/tests/test_statsperform.rs | 140 + crates/tdc-python/Cargo.toml | 32 + crates/tdc-python/src/builder.rs | 84 + crates/tdc-python/src/container.rs | 752 +++ crates/tdc-python/src/coordinate_system.rs | 358 ++ crates/tdc-python/src/error.rs | 92 + crates/tdc-python/src/io.rs | 7 + crates/tdc-python/src/lib.rs | 31 + crates/tdc-python/src/mod.rs | 20 + crates/tdc-python/src/ops.rs | 19 + crates/tdc-python/src/schema.rs | 208 + crates/tdc-python/src/selector.rs | 330 ++ crates/tdc-python/src/udf.rs | 169 + examples/01-basics.ipynb | 359 ++ examples/02-parsing.ipynb | 267 + examples/03-interop.ipynb | 228 + examples/04-udfs.ipynb | 315 ++ examples/05-lazy.ipynb | 183 + pyproject.toml | 120 + python/tests/benchmarks/conftest.py | 12 + .../benchmarks/test_add_column_benchmark.py | 156 + .../tests/benchmarks/test_lazy_filtering.py | 85 + python/tests/benchmarks/test_sportec.py | 123 + python/tests/benchmarks/test_statsperform.py | 153 + python/tests/conftest.py | 38 + python/tests/test_add_column.py | 69 + python/tests/test_build.py | 93 + python/tests/test_container.py | 159 + python/tests/test_errors.py | 15 + python/tests/test_interop.py | 61 + python/tests/test_ops.py | 73 + 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crates/tdc-core/src/selector/mod.rs create mode 100644 crates/tdc-core/tests/test_builder.rs create mode 100644 crates/tdc-core/tests/test_datacontainer.rs create mode 100644 crates/tdc-core/tests/test_fields.rs create mode 100644 crates/tdc-core/tests/test_schema.rs create mode 100644 crates/tdc-ops/Cargo.toml create mode 100644 crates/tdc-ops/src/lib.rs create mode 100644 crates/tdc-parsers/Cargo.toml create mode 100644 crates/tdc-parsers/benches/memory.rs create mode 100644 crates/tdc-parsers/benches/parsers.rs create mode 100644 crates/tdc-parsers/src/lib.rs create mode 100644 crates/tdc-parsers/src/sportec.rs create mode 100644 crates/tdc-parsers/src/statsperform.rs create mode 100644 crates/tdc-parsers/tests/test_sportec.rs create mode 100644 crates/tdc-parsers/tests/test_statsperform.rs create mode 100644 crates/tdc-python/Cargo.toml create mode 100644 crates/tdc-python/src/builder.rs create mode 100644 crates/tdc-python/src/container.rs create mode 100644 crates/tdc-python/src/coordinate_system.rs create mode 100644 crates/tdc-python/src/error.rs create mode 100644 crates/tdc-python/src/io.rs create mode 100644 crates/tdc-python/src/lib.rs create mode 100644 crates/tdc-python/src/mod.rs create mode 100644 crates/tdc-python/src/ops.rs create mode 100644 crates/tdc-python/src/schema.rs create mode 100644 crates/tdc-python/src/selector.rs create mode 100644 crates/tdc-python/src/udf.rs create mode 100644 examples/01-basics.ipynb create mode 100644 examples/02-parsing.ipynb create mode 100644 examples/03-interop.ipynb create mode 100644 examples/04-udfs.ipynb create mode 100644 examples/05-lazy.ipynb create mode 100644 pyproject.toml create mode 100644 python/tests/benchmarks/conftest.py create mode 100644 python/tests/benchmarks/test_add_column_benchmark.py create mode 100644 python/tests/benchmarks/test_lazy_filtering.py create mode 100644 python/tests/benchmarks/test_sportec.py create mode 100644 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python/tracking_data_container/py.typed create mode 100644 python/tracking_data_container/schema.py create mode 100644 python/tracking_data_container/selector.py create mode 100644 tests/test_builder.rs create mode 100644 uv.lock diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml new file mode 100644 index 0000000..a63ff59 --- /dev/null +++ b/.github/workflows/CI.yml @@ -0,0 +1,181 @@ +# This file is autogenerated by maturin v1.10.2 +# To update, run +# +# maturin generate-ci github +# +name: CI + +on: + push: + branches: + - main + - master + tags: + - '*' + pull_request: + workflow_dispatch: + +permissions: + contents: read + +jobs: + linux: + runs-on: ${{ matrix.platform.runner }} + strategy: + matrix: + platform: + - runner: ubuntu-22.04 + target: x86_64 + - runner: ubuntu-22.04 + target: x86 + - runner: ubuntu-22.04 + target: aarch64 + - runner: ubuntu-22.04 + target: armv7 + - runner: ubuntu-22.04 + target: s390x + - runner: ubuntu-22.04 + target: ppc64le + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: 3.x + - name: Build wheels + uses: PyO3/maturin-action@v1 + with: + target: ${{ matrix.platform.target }} + args: --release --out dist --find-interpreter + sccache: ${{ !startsWith(github.ref, 'refs/tags/') }} + manylinux: auto + - name: Upload wheels + uses: actions/upload-artifact@v4 + with: + name: wheels-linux-${{ matrix.platform.target }} + path: dist + + musllinux: + runs-on: ${{ matrix.platform.runner }} + strategy: + matrix: + platform: + - runner: ubuntu-22.04 + target: x86_64 + - runner: ubuntu-22.04 + target: x86 + - runner: ubuntu-22.04 + target: aarch64 + - runner: ubuntu-22.04 + target: armv7 + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: 3.x + - name: Build wheels + uses: PyO3/maturin-action@v1 + with: + target: ${{ matrix.platform.target }} + args: --release --out dist --find-interpreter + sccache: ${{ !startsWith(github.ref, 'refs/tags/') }} + manylinux: musllinux_1_2 + - name: Upload wheels + uses: actions/upload-artifact@v4 + with: + name: wheels-musllinux-${{ matrix.platform.target }} + path: dist + + windows: + runs-on: ${{ matrix.platform.runner }} + strategy: + matrix: + platform: + - runner: windows-latest + target: x64 + - runner: windows-latest + target: x86 + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: 3.x + architecture: ${{ matrix.platform.target }} + - name: Build wheels + uses: PyO3/maturin-action@v1 + with: + target: ${{ matrix.platform.target }} + args: --release --out dist --find-interpreter + sccache: ${{ !startsWith(github.ref, 'refs/tags/') }} + - name: Upload wheels + uses: actions/upload-artifact@v4 + with: + name: wheels-windows-${{ matrix.platform.target }} + path: dist + + macos: + runs-on: ${{ matrix.platform.runner }} + strategy: + matrix: + platform: + - runner: macos-13 + target: x86_64 + - runner: macos-14 + target: aarch64 + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: 3.x + - name: Build wheels + uses: PyO3/maturin-action@v1 + with: + target: ${{ matrix.platform.target }} + args: --release --out dist --find-interpreter + sccache: ${{ !startsWith(github.ref, 'refs/tags/') }} + - name: Upload wheels + uses: actions/upload-artifact@v4 + with: + name: wheels-macos-${{ matrix.platform.target }} + path: dist + + sdist: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Build sdist + uses: PyO3/maturin-action@v1 + with: + command: sdist + args: --out dist + - name: Upload sdist + uses: actions/upload-artifact@v4 + with: + name: wheels-sdist + path: dist + + release: + name: Release + runs-on: ubuntu-latest + if: ${{ startsWith(github.ref, 'refs/tags/') || github.event_name == 'workflow_dispatch' }} + needs: [linux, musllinux, windows, macos, sdist] + permissions: + # Use to sign the release artifacts + id-token: write + # Used to upload release artifacts + contents: write + # Used to generate artifact attestation + attestations: write + steps: + - uses: actions/download-artifact@v4 + - name: Generate artifact attestation + uses: actions/attest-build-provenance@v2 + with: + subject-path: 'wheels-*/*' + - name: Publish to PyPI + if: ${{ startsWith(github.ref, 'refs/tags/') }} + uses: PyO3/maturin-action@v1 + env: + MATURIN_PYPI_TOKEN: ${{ secrets.PYPI_API_TOKEN }} + with: + command: upload + args: --non-interactive --skip-existing wheels-*/* diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..1b8a3c9 --- /dev/null +++ b/.gitignore @@ -0,0 +1,72 @@ +# Rust +/target +**/*.rs.bk +Cargo.lock.lock + +# Python +__pycache__/ +.pytest_cache/ +.ruff_cache/ +.mypy_cache/ +.benchmarks/ +*.py[cod] +*$py.class +*.so +.Python +.venv/ +env/ +venv/ +ENV/ +dist/ +build/ +*.egg-info/ +*.egg +.installed.cfg +pip-log.txt +pip-delete-this-directory.txt +pip-selfcheck.json +.python-version + +# Jupyter Notebook +.ipynb_checkpoints +*/.ipynb_checkpoints/* + +# Coverage / CI +htmlcov/ +.tox/ +.coverage +.cache +nosetests.xml +coverage.xml +*,cover +.hypothesis/ + +# Documentation +docs/_build/ +site/ + +# IDEs +.idea/ +.vscode/ +*.swp +*.swo + +# OS +.DS_Store +.DS_Store? +._* +.Spotlight-V100 +.Trashes +ehthumbs.db +Thumbs.db + +# Logs +*.log + +# Project Specific +data/ +examples/data/ +python/tracking_data_container/lib_tracking_data_container.dylib.* +python/tracking_data_container/libtracking_data_container.dylib.* +python/tracking_data_container/*.dylib +python/tracking_data_container/*.so diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..b07ebd1 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v3.2.0 + hooks: + - id: trailing-whitespace + - id: 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package +build-dev: + uv run maturin build + +.PHONY: build-wheel ## Build production wheel and install it +build-wheel: + @rm -rf target/wheels/ + uv run maturin build --release + @wheel=$$(ls target/wheels/*.whl); uv pip install --force-reinstall "$$wheel[pandas,polars]" + +.PHONY: lint-python ## Lint python source files +lint-python: + uv run ruff check $(sources) $(test_sources) + uv run ruff format --check $(sources) $(test_sources) + uv run mypy $(sources) + +.PHONY: lint-rust ## Lint rust source files +lint-rust: + cargo fmt --all -- --check + cargo clippy --workspace --tests -- -D warnings + +.PHONY: lint ## Lint rust and python source files +lint: lint-python lint-rust + +.PHONY: format-python ## Auto-format python source files +format-python: + uv run ruff check --fix $(sources) $(test_sources) + uv run ruff format $(sources) $(test_sources) + +.PHONY: format-rust ## Auto-format rust source files +format-rust: + cargo fmt --all + cargo clippy --workspace --tests --fix --allow-dirty --allow-staged + +.PHONY: format ## Auto-format python and rust source files +format: format-rust format-python + +.PHONY: test-python ## Run python tests +test-python: install + uv run pytest $(sources) $(test_sources) --doctest-modules --ignore=$(test_sources)/benchmarks + +.PHONY: test-rust ## Run rust tests +test-rust: + cargo test --workspace + +.PHONY: test ## Run all tests +test: test-rust test-python + +.PHONY: benchmarks ## Run benchmarks +benchmarks: .uv + uv sync --frozen --group dev --group benchmarks + uv run maturin develop --uv --release -E pyarrow,pandas,polars + uv run pytest python/tests/benchmarks --benchmark-group-by=group --benchmark-warmup=off --benchmark-max-time=2 + +.PHONY: doc-serve ## Serve documentation with live reload +doc-serve: build-dev + uv run pdoc python/tracking_data_container + +.PHONY: doc ## Build documentation +doc: build-dev + uv run pdoc -o docs python/tracking_data_container + cargo doc --no-deps --workspace + +.PHONY: all ## Run the standard set of checks performed in CI +all: format build-dev lint test + +.PHONY: clean ## Clear local caches and build artifacts +clean: + rm -rf `find . -name __pycache__` + rm -f `find . -type f -name '*.py[co]' ` + rm -f `find . -type f -name '*~' ` + rm -f `find . -type f -name '.*~' ` + rm -rf .cache + rm -rf htmlcov + rm -rf .pytest_cache + rm -rf *.egg-info + rm -f .coverage + rm -f .coverage.* + rm -rf build + rm -rf target + rm -rf perf.data* + rm -rf python/tracking_data_container/*.so + +.PHONY: help ## Display this message +help: + @grep -E \ + '^.PHONY: .*?## .*$$' $(MAKEFILE_LIST) | \ + sort | \ + awk 'BEGIN {FS = ".PHONY: |## "}; {printf "\033[36m%-19s\033[0m %s\n", $$2, $$3}' diff --git a/README.md b/README.md new file mode 100644 index 0000000..47b76c9 --- /dev/null +++ b/README.md @@ -0,0 +1,154 @@ +# Tracking Data Container (TDC) + +[![CI](https://github.com/probberechts/tracking-data-container/actions/workflows/CI.yml/badge.svg)](https://github.com/probberechts/tracking-data-container/actions/workflows/CI.yml) +[![PyPI version](https://img.shields.io/pypi/v/tracking-data-container.svg)](https://pypi.org/project/tracking-data-container/) +[![Crates.io](https://img.shields.io/crates/v/tdc-core.svg)](https://crates.io/crates/tdc-core) + +## Summary + +The **TrackingDataContainer (TDC)** is a shared, standardized, metadata-aware container for sports tracking data. Backed by Apache Arrow, it acts as the central interface between tools like Kloppy, Floodlight, BallRadar, and others. + +The container combines high-performance, zero-copy data access with a rich metadata model, enabling efficient workflows. It provides a primary I/O interface for the sports tracking data ecosystem. + +## Motivation + +As tracking pipelines grow across football and other sports, the need for modularity, performance, and inter-package interoperability is critical. Many open-source packages in football analytics currently duplicate functionality across parsing, modeling, and data management. + +TDC addresses several key challenges: + +- **Scalability**: Object-based models (e.g., Kloppy) don't scale well to large tracking datasets. +- **Interoperability**: Packages often use inconsistent schemas and lack a shared representation. +- **Efficiency**: Development resources are wasted on duplicated data parsing logic. +- **Reproducibility**: Derived metrics and transforms are often not easily shareable. +- **Flexibility**: Support is needed for skeleton data, physical data, and multi-team use cases. + +By leveraging Apache Arrow with enforced metadata and a shared schema, TDC solves these problems while maintaining compatibility with modern analytics engines (Polars, DuckDB, etc.). + +## Key Features + +- **Standardized CDF Schema**: Fully aligned with the [Common Data Format (CDF)](https://doi.org/10.48550/arXiv.2401.01882) standard for interoperability. +- **Zero-Copy Interoperability**: Seamlessly move data between TDC, Polars, Pandas, and PyArrow using the Arrow PyCapsule interface. +- **Lazy Evaluation**: Deferred computation and filter pushdown using Polars and DataFusion. +- **Metadata Awareness**: Native support for coordinate systems, pitch dimensions, and orientation normalization (e.g., attacking direction). +- **Frame Materialization**: Efficiently extract and inspect individual frames indexed by time or index. + +## Python Installation + +```bash +pip install tracking_data_container +``` + +## Python Usage + +### Creating and Inspecting Data + +```python +import tracking_data_container as tdc +from datetime import datetime, timezone + +data = { + "frame_id": [1, 2], + "timestamp": [datetime.now(timezone.utc), datetime.now(timezone.utc)], + "ball/x": [10.0, 11.0], + "ball/y": [20.0, 21.0], + "period": ["first_half", "first_half"] +} +container = tdc.TrackingDataContainer(data) + +# Materialize a specific frame +frame = container.materialize_frame(0) +print(frame["ball/x"]) # 10.0 +``` + +### Derived Metrics and Selectors + +```python +# Add a derived column lazily using selectors +lazy_c = container.lazy() +plan = lazy_c.add_column( + "ball/speed", + (tdc.col("ball/x").diff().fill_null(0).pow(2) + + tdc.col("ball/y").diff().fill_null(0).pow(2)).sqrt() +) +result = plan.collect() + +# Select all home player positions +home_players = result.select(result.selector.players(team_id="home")) +``` + +### Coordinate Transformations + +```python +# Scale to a metric coordinate system +# First, define the current coordinate system +current_sys = tdc.CoordinateSystem( + x_range=(0, 100), + y_range=(0, 100), + origin="top_left", + length_unit="percent" +) +container = container.with_coordinate_system(current_sys) + +# Then transform to the target system +metric_sys = tdc.CoordinateSystem( + x_range=(-52.5, 52.5), + y_range=(-34, 34), + origin="center", + length_unit="meters" +) +metric_container = container.transform_coordinates(metric_sys) +``` + +## Project Structure + +- `crates/tdc-core`: Core Rust implementation (Apache Arrow + domain logic). +- `crates/tdc-python`: PyO3 bindings. +- `python/`: High-level Python package. + +## Development + +TDC is a mixed Rust/Python project. We use `uv` for Python dependency management and `maturin` for building the Rust extensions. + +### Prerequisites + +- [Rust](https://www.rust-lang.org/tools/install) (latest stable) +- [uv](https://docs.astral.sh/uv/getting-started/installation/) + +### Setup and Building + +```bash +# First-time setup (installs dependencies and pre-commit hooks) +make setup-dev + +# Build the development version +make build-dev +``` + +### Testing + +```bash +# Run all tests (Rust + Python) +make test + +# Run Rust tests only +make test-rust + +# Run Python tests only +make test-python +``` + +### Linting and Formatting + +We use `ruff` and `mypy` for Python, and `cargo fmt/clippy` for Rust. + +```bash +# Run all linters +make lint + +# Auto-format all code +make format +``` + +## License + +MIT diff --git a/crates/tdc-core/Cargo.toml b/crates/tdc-core/Cargo.toml new file mode 100644 index 0000000..b3740a7 --- /dev/null +++ b/crates/tdc-core/Cargo.toml @@ -0,0 +1,40 @@ +[package] +name = "tdc-core" +version = "0.1.0-alpha.1" +edition = "2024" +license = "MIT" +description = "Core engine for standardized sports tracking data containers." + +[dependencies] +arrow = { version = "54" } +arrow-array = { version = "54", features = ["ffi"] } +arrow-schema = { version = "54" } +arrow-select = { version = "54" } +arrow-arith = { version = "54" } +arrow-cast = { version = "54" } +arrow-ord = { version = "54" } +datafusion = "45" +chrono = { version = "^0.4.42", default-features = false } +log = "^0.4" +strum = "0.27.2" +strum_macros = "0.27.2" +regex = "1" +tokio = { version = "1", features = ["rt-multi-thread"] } +rayon = "1.10" +serde = { version = "1.0", features = ["derive"] } +ahash = "0.8" +parking_lot = "0.12" +num_cpus = "1.16" +once_cell = "1.19" +parquet = "54" +serde_json = "1.0.149" + +[dev-dependencies] +criterion = { version = "0.5", features = ["html_reports"] } + +[[bench]] +name = "ops" +harness = false + +[features] +default = [] diff --git a/crates/tdc-core/benches/ops.rs b/crates/tdc-core/benches/ops.rs new file mode 100644 index 0000000..fd83150 --- /dev/null +++ b/crates/tdc-core/benches/ops.rs @@ -0,0 +1,151 @@ +use arrow_array::Float64Array; +use arrow_schema::DataType; +use criterion::{Criterion, black_box, criterion_group, criterion_main}; +use datafusion::error::Result as DFResult; +use datafusion::logical_expr::{ColumnarValue, ScalarUDF, ScalarUDFImpl, Signature, Volatility}; +use datafusion::prelude::*; +use std::sync::Arc; +use tdc_core::{ + CoordinateSystem, Direction, LengthUnit, OrientationMode, Origin, TrackingDataBuilder, +}; + +#[derive(Debug)] +struct SpeedUDF { + signature: Signature, +} + +impl SpeedUDF { + fn new() -> Self { + Self { + signature: Signature::exact( + vec![DataType::Float64, DataType::Float64], + Volatility::Immutable, + ), + } + } +} + +impl ScalarUDFImpl for SpeedUDF { + fn as_any(&self) -> &dyn std::any::Any { + self + } + fn name(&self) -> &str { + "speed" + } + fn signature(&self) -> &Signature { + &self.signature + } + fn return_type(&self, _arg_types: &[DataType]) -> DFResult { + Ok(DataType::Float64) + } + fn invoke(&self, args: &[ColumnarValue]) -> DFResult { + let args = ColumnarValue::values_to_arrays(args)?; + let vx = args[0].as_any().downcast_ref::().unwrap(); + let vy = args[1].as_any().downcast_ref::().unwrap(); + + let res: Float64Array = vx + .iter() + .zip(vy.iter()) + .map(|(x, y)| match (x, y) { + (Some(x), Some(y)) => Some((x * x + y * y).sqrt()), + _ => None, + }) + .collect(); + + Ok(ColumnarValue::Array(Arc::new(res))) + } +} + +fn setup_container(num_rows: usize) -> tdc_core::TrackingDataContainer { + let mut builder = TrackingDataBuilder::new(); + let vx_idx = builder.get_or_create_column_id("vx", DataType::Float64); + let vy_idx = builder.get_or_create_column_id("vy", DataType::Float64); + let ball_x_idx = builder.get_or_create_column_id("ball/x", DataType::Float64); + let ball_y_idx = builder.get_or_create_column_id("ball/y", DataType::Float64); + let period_idx = builder.get_or_create_column_id("period", DataType::Utf8); + + for i in 0..num_rows { + let frame_id = i as i64; + let row_idx = builder.get_row_index(frame_id); + builder.insert_string_at_idx( + row_idx, + period_idx, + if i < num_rows / 2 { + "first_half" + } else { + "second_half" + }, + ); + builder.insert_float_at_idx(row_idx, ball_x_idx, i as f64 * 0.1); + builder.insert_float_at_idx(row_idx, ball_y_idx, i as f64 * 0.05); + builder.insert_float_at_idx(row_idx, vx_idx, 1.5); + builder.insert_float_at_idx(row_idx, vy_idx, 2.0); + } + + let container = builder.build().unwrap(); + let source_sys = CoordinateSystem { + x_range: Some((-52.5, 52.5)), + y_range: Some((-34.0, 34.0)), + origin: Origin::Center, + x_direction: Direction::Right, + y_direction: Direction::Up, + length_unit: Some(LengthUnit::Meters), + }; + container + .with_coordinate_system(source_sys) + .with_orientation(OrientationMode::StaticHomeAway) +} + +fn bench_ops(c: &mut Criterion) { + let num_rows = 100_000; + let container = setup_container(num_rows); + let ctx = SessionContext::new(); + let rt = tokio::runtime::Runtime::new().unwrap(); + + // 1. Transform Coordinates + let target_sys = CoordinateSystem { + x_range: Some((0.0, 100.0)), + y_range: Some((0.0, 100.0)), + origin: Origin::TopLeft, + x_direction: Direction::Right, + y_direction: Direction::Down, + length_unit: Some(LengthUnit::Percent), + }; + + c.bench_function("transform_coordinates", |b| { + b.iter(|| { + black_box(&container) + .transform_coordinates(black_box(target_sys.clone())) + .unwrap() + }) + }); + + // 2. Transform Orientation + c.bench_function("transform_orientation", |b| { + b.iter(|| { + black_box(&container) + .transform_orientation(black_box(OrientationMode::HomeAway)) + .unwrap() + }) + }); + + // 3. UDF (Speed) + let speed_udf = ScalarUDF::from(SpeedUDF::new()); + let expr = speed_udf.call(vec![col("vx"), col("vy")]); + + c.bench_function("add_speed_column_udf", |b| { + b.iter(|| { + black_box(&container) + .with_expr_columns_ctx( + vec!["speed".to_string()], + vec![black_box(expr.clone())], + &ctx, + rt.handle(), + ) + .unwrap() + }) + }); +} + +criterion_group!(benches, bench_ops); +criterion_main!(benches); diff --git a/crates/tdc-core/src/config.rs b/crates/tdc-core/src/config.rs new file mode 100644 index 0000000..c6f788a --- /dev/null +++ b/crates/tdc-core/src/config.rs @@ -0,0 +1,27 @@ +use datafusion::prelude::{SessionConfig, SessionContext}; +use once_cell::sync::Lazy; +use tokio::runtime::Runtime; + +pub static RUNTIME: Lazy = Lazy::new(|| { + tokio::runtime::Builder::new_multi_thread() + .enable_all() + .build() + .expect("Failed to create Tokio runtime") +}); + +pub static CONTEXT: Lazy = Lazy::new(|| { + let config = SessionConfig::new() + .with_target_partitions(num_cpus::get()) + .with_batch_size(16384) + .with_repartition_joins(true) + .with_repartition_aggregations(true) + .with_repartition_windows(true) + .with_repartition_sorts(true) + .with_parquet_pruning(true); + + SessionContext::new_with_config(config) +}); + +pub fn num_cpus() -> usize { + num_cpus::get() +} diff --git a/crates/tdc-core/src/datacontainer/builder.rs b/crates/tdc-core/src/datacontainer/builder.rs new file mode 100644 index 0000000..e4a087c --- /dev/null +++ b/crates/tdc-core/src/datacontainer/builder.rs @@ -0,0 +1,1382 @@ +use crate::error::TrackingDataContainerResult; +use crate::schema::{DataField, Fields, FrameAttribute}; +use ahash::AHashMap; +use arrow::buffer::{BooleanBuffer, MutableBuffer}; +use arrow::util::bit_util; +use arrow_array::{Array, ArrayRef, BooleanArray, Int64Array, RecordBatch, StringArray}; +use arrow_schema::{DataType, Field, Schema}; +use std::sync::Arc; + +/// A simplified builder for a single column that handles random-access insertion. +#[derive(Debug)] +struct TypedColumn { + values: Vec, + validity: MutableBuffer, // Bit-packed validity map + null_value: T, +} + +impl Clone for TypedColumn { + fn clone(&self) -> Self { + let mut validity = MutableBuffer::with_capacity(self.validity.len()); + validity.extend_from_slice(self.validity.as_slice()); + Self { + values: self.values.clone(), + validity, + null_value: self.null_value, + } + } +} + +impl TypedColumn { + fn new(capacity: usize, null_value: T) -> Self { + let mut validity = MutableBuffer::new(capacity.div_ceil(8)); + validity.extend_zeros(capacity.div_ceil(8)); + Self { + values: vec![null_value; capacity], + validity, + null_value, + } + } + + #[inline] + fn len(&self) -> usize { + self.values.len() + } + + #[inline] + fn ensure_capacity(&mut self, target_len: usize) { + if target_len > self.len() { + self.values.resize(target_len, self.null_value); + let new_validity_bytes = target_len.div_ceil(8); + if new_validity_bytes > self.validity.len() { + self.validity + .extend_zeros(new_validity_bytes - self.validity.len()); + } + } + } + + #[inline] + fn insert(&mut self, index: usize, val: T) { + if index >= self.len() { + self.ensure_capacity(index + 1); + } + self.values[index] = val; + bit_util::set_bit(self.validity.as_slice_mut(), index); + } + + fn rebase(&mut self, mapping: &[(usize, usize)], new_total: usize) { + let mut new_values = vec![self.null_value; new_total]; + let mut new_validity = MutableBuffer::new(new_total.div_ceil(8)); + new_validity.extend_zeros(new_total.div_ceil(8)); + + for &(old_idx, new_idx) in mapping { + if old_idx < self.values.len() && bit_util::get_bit(self.validity.as_slice(), old_idx) { + new_values[new_idx] = self.values[old_idx]; + bit_util::set_bit(new_validity.as_slice_mut(), new_idx); + } + } + self.values = new_values; + self.validity = new_validity; + } + + fn merge_fast(&mut self, other: &Self, min_row: usize, max_row: usize) { + self.ensure_capacity(other.len()); + if max_row < min_row || min_row >= other.len() { + return; + } + let end = (max_row + 1).min(other.len()); + + let src_values = &other.values[min_row..end]; + let dst_values = &mut self.values[min_row..end]; + + for i in 0..src_values.len() { + let row = min_row + i; + if bit_util::get_bit(other.validity.as_slice(), row) { + dst_values[i] = src_values[i]; + bit_util::set_bit(self.validity.as_slice_mut(), row); + } + } + } + + fn merge_contiguous(&mut self, other: Self) { + let old_len = self.len(); + let other_len = other.len(); + self.values.extend(other.values); + + let new_total_len = old_len + other_len; + let new_validity_bytes = new_total_len.div_ceil(8); + self.validity.resize(new_validity_bytes, 0); + + for i in 0..other_len { + if bit_util::get_bit(other.validity.as_slice(), i) { + bit_util::set_bit(self.validity.as_slice_mut(), old_len + i); + } + } + } + + fn merge_from(&mut self, other: &Self, mapping: &[(usize, usize)], new_total: usize) { + self.ensure_capacity(new_total); + for &(other_idx, self_idx) in mapping { + if other_idx < other.len() && bit_util::get_bit(other.validity.as_slice(), other_idx) { + self.values[self_idx] = other.values[other_idx]; + bit_util::set_bit(self.validity.as_slice_mut(), self_idx); + } + } + } +} + +#[derive(Debug)] +enum ColumnBuilder { + Int(TypedColumn), + UInt8(TypedColumn), + UInt32(TypedColumn), + Timestamp(TypedColumn), + Float32(TypedColumn), + Float64(TypedColumn), + Bool { + values: MutableBuffer, + validity: MutableBuffer, + len: usize, + }, + String { + values: Vec>, + }, + Dictionary { + values: TypedColumn, + dictionary: Vec, + lookup: AHashMap, + }, +} + +impl Clone for ColumnBuilder { + fn clone(&self) -> Self { + match self { + Self::Int(c) => Self::Int(c.clone()), + Self::UInt8(c) => Self::UInt8(c.clone()), + Self::UInt32(c) => Self::UInt32(c.clone()), + Self::Timestamp(c) => Self::Timestamp(c.clone()), + Self::Float32(c) => Self::Float32(c.clone()), + Self::Float64(c) => Self::Float64(c.clone()), + Self::Bool { + values, + validity, + len, + } => { + let mut v = MutableBuffer::with_capacity(values.len()); + v.extend_from_slice(values.as_slice()); + let mut val = MutableBuffer::with_capacity(validity.len()); + val.extend_from_slice(validity.as_slice()); + Self::Bool { + values: v, + validity: val, + len: *len, + } + } + Self::String { values } => Self::String { + values: values.clone(), + }, + Self::Dictionary { + values, + dictionary, + lookup, + } => Self::Dictionary { + values: values.clone(), + dictionary: dictionary.clone(), + lookup: lookup.clone(), + }, + } + } +} + +impl ColumnBuilder { + fn new(capacity: usize, dtype: DataType) -> Self { + match dtype { + DataType::Int64 | DataType::UInt64 => Self::Int(TypedColumn::new(capacity, 0)), + DataType::UInt8 => Self::UInt8(TypedColumn::new(capacity, 0)), + DataType::UInt32 => Self::UInt32(TypedColumn::new(capacity, 0)), + DataType::Timestamp(_, _) => Self::Timestamp(TypedColumn::new(capacity, 0)), + DataType::Float32 => Self::Float32(TypedColumn::new(capacity, f32::NAN)), + DataType::Float64 => Self::Float64(TypedColumn::new(capacity, f64::NAN)), + DataType::Boolean => { + let bytes = capacity.div_ceil(8); + let mut values = MutableBuffer::new(bytes); + values.extend_zeros(bytes); + let mut validity = MutableBuffer::new(bytes); + validity.extend_zeros(bytes); + Self::Bool { + values, + validity, + len: capacity, + } + } + DataType::Dictionary(_, _) => Self::Dictionary { + values: TypedColumn::new(capacity, 0), + dictionary: Vec::new(), + lookup: AHashMap::new(), + }, + _ => Self::String { + values: vec![None; capacity], + }, + } + } + + fn ensure_capacity(&mut self, target_len: usize) { + match self { + Self::Int(c) => c.ensure_capacity(target_len), + Self::UInt8(c) => c.ensure_capacity(target_len), + Self::UInt32(c) => c.ensure_capacity(target_len), + Self::Timestamp(c) => c.ensure_capacity(target_len), + Self::Float32(c) => c.ensure_capacity(target_len), + Self::Float64(c) => c.ensure_capacity(target_len), + Self::Bool { + values, + validity, + len, + } => { + if target_len > *len { + let bytes = target_len.div_ceil(8); + if bytes > values.len() { + values.extend_zeros(bytes - values.len()); + validity.extend_zeros(bytes - validity.len()); + } + *len = target_len; + } + } + Self::String { values } => values.resize(target_len, None), + Self::Dictionary { values, .. } => values.ensure_capacity(target_len), + } + } + + #[inline] + fn insert_int(&mut self, index: usize, val: i64) { + match self { + Self::Int(c) => c.insert(index, val), + Self::UInt8(c) => c.insert(index, val as u8), + Self::UInt32(c) => c.insert(index, val as u32), + Self::Timestamp(c) => c.insert(index, val), + Self::Float32(_) | Self::Float64(_) => { + self.insert_float(index, val as f64); + } + Self::Dictionary { .. } => self.insert_string(index, &val.to_string()), + _ => { + self.convert_to_string(); + if let Self::String { values } = self { + if index >= values.len() { + values.resize(index + 1, None); + } + values[index] = Some(val.to_string()); + } + } + } + } + + #[inline] + fn insert_float(&mut self, index: usize, val: f64) { + match self { + Self::Float32(c) => c.insert(index, val as f32), + Self::Float64(c) => c.insert(index, val), + Self::Int(_) => { + self.convert_to_float64(); + self.insert_float(index, val); + } + Self::Dictionary { .. } => self.insert_string(index, &val.to_string()), + _ => { + self.convert_to_string(); + if let Self::String { values } = self { + if index >= values.len() { + values.resize(index + 1, None); + } + values[index] = Some(val.to_string()); + } + } + } + } + + #[inline] + fn insert_string(&mut self, index: usize, val: &str) { + match self { + Self::String { values } => { + if index >= values.len() { + values.resize(index + 1, None); + } + values[index] = Some(val.to_string()); + } + Self::Dictionary { + values, + dictionary, + lookup, + } => { + let id = if let Some(&id) = lookup.get(val) { + id + } else { + let s = val.to_string(); + let id = dictionary.len() as u32; + dictionary.push(s.clone()); + lookup.insert(s, id); + id + }; + values.insert(index, id); + } + _ => { + self.convert_to_string(); + if let Self::String { values } = self { + if index >= values.len() { + values.resize(index + 1, None); + } + values[index] = Some(val.to_string()); + } + } + } + } + + #[inline] + fn insert_bool(&mut self, index: usize, val: bool) { + match self { + Self::Bool { + values, + validity, + len, + } => { + if index >= *len { + let target_len = index + 1; + let bytes = target_len.div_ceil(8); + if bytes > values.len() { + values.extend_zeros(bytes - values.len()); + validity.extend_zeros(bytes - validity.len()); + } + *len = target_len; + } + if val { + bit_util::set_bit(values.as_slice_mut(), index); + } else { + bit_util::unset_bit(values.as_slice_mut(), index); + } + bit_util::set_bit(validity.as_slice_mut(), index); + } + _ => { + self.convert_to_string(); + if let Self::String { values } = self { + if index >= values.len() { + values.resize(index + 1, None); + } + values[index] = Some(val.to_string()); + } + } + } + } + + fn convert_to_float64(&mut self) { + match self { + Self::Int(c) => { + let mut new_c = TypedColumn::new(c.len(), f64::NAN); + for i in 0..c.len() { + if bit_util::get_bit(c.validity.as_slice(), i) { + new_c.insert(i, c.values[i] as f64); + } + } + *self = Self::Float64(new_c); + } + Self::Float32(c) => { + let mut new_c = TypedColumn::new(c.len(), f64::NAN); + for i in 0..c.len() { + if bit_util::get_bit(c.validity.as_slice(), i) { + new_c.insert(i, c.values[i] as f64); + } + } + *self = Self::Float64(new_c); + } + _ => {} + } + } + + fn convert_to_string(&mut self) { + let values: Vec> = match self { + Self::Int(c) => (0..c.len()) + .map(|i| { + if bit_util::get_bit(c.validity.as_slice(), i) { + Some(c.values[i].to_string()) + } else { + None + } + }) + .collect(), + Self::UInt8(c) => (0..c.len()) + .map(|i| { + if bit_util::get_bit(c.validity.as_slice(), i) { + Some(c.values[i].to_string()) + } else { + None + } + }) + .collect(), + Self::UInt32(c) => (0..c.len()) + .map(|i| { + if bit_util::get_bit(c.validity.as_slice(), i) { + Some(c.values[i].to_string()) + } else { + None + } + }) + .collect(), + Self::Timestamp(c) => (0..c.len()) + .map(|i| { + if bit_util::get_bit(c.validity.as_slice(), i) { + Some(c.values[i].to_string()) + } else { + None + } + }) + .collect(), + Self::Float32(c) => (0..c.len()) + .map(|i| { + if bit_util::get_bit(c.validity.as_slice(), i) { + Some(c.values[i].to_string()) + } else { + None + } + }) + .collect(), + Self::Float64(c) => (0..c.len()) + .map(|i| { + if bit_util::get_bit(c.validity.as_slice(), i) { + Some(c.values[i].to_string()) + } else { + None + } + }) + .collect(), + Self::Bool { + values, + validity, + len, + } => (0..*len) + .map(|i| { + if bit_util::get_bit(validity.as_slice(), i) { + Some(bit_util::get_bit(values.as_slice(), i).to_string()) + } else { + None + } + }) + .collect(), + Self::Dictionary { + values, dictionary, .. + } => (0..values.len()) + .map(|i| { + if bit_util::get_bit(values.validity.as_slice(), i) { + Some(dictionary[values.values[i] as usize].clone()) + } else { + None + } + }) + .collect(), + Self::String { .. } => return, + }; + *self = Self::String { values }; + } + + fn rebase(&mut self, mapping: &[(usize, usize)], new_total: usize) { + match self { + Self::Int(c) => c.rebase(mapping, new_total), + Self::UInt8(c) => c.rebase(mapping, new_total), + Self::UInt32(c) => c.rebase(mapping, new_total), + Self::Timestamp(c) => c.rebase(mapping, new_total), + Self::Float32(c) => c.rebase(mapping, new_total), + Self::Float64(c) => c.rebase(mapping, new_total), + Self::Bool { + values, + validity, + len, + } => { + let bytes = new_total.div_ceil(8); + let mut new_v = MutableBuffer::new(bytes); + new_v.extend_zeros(bytes); + let mut new_val = MutableBuffer::new(bytes); + new_val.extend_zeros(bytes); + + for &(old_idx, new_idx) in mapping { + if old_idx < *len && bit_util::get_bit(validity.as_slice(), old_idx) { + if bit_util::get_bit(values.as_slice(), old_idx) { + bit_util::set_bit(new_v.as_slice_mut(), new_idx); + } + bit_util::set_bit(new_val.as_slice_mut(), new_idx); + } + } + *values = new_v; + *validity = new_val; + *len = new_total; + } + Self::String { values } => { + let mut new_v = vec![None; new_total]; + for &(old_idx, new_idx) in mapping { + if old_idx < values.len() { + new_v[new_idx] = values[old_idx].take(); + } + } + *values = new_v; + } + Self::Dictionary { values, .. } => { + values.rebase(mapping, new_total); + } + } + } + + fn merge_fast(&mut self, other: ColumnBuilder, min_row: usize, max_row: usize) { + match (self, other) { + (Self::Int(s), ColumnBuilder::Int(o)) => s.merge_fast(&o, min_row, max_row), + (Self::UInt8(s), ColumnBuilder::UInt8(o)) => s.merge_fast(&o, min_row, max_row), + (Self::UInt32(s), ColumnBuilder::UInt32(o)) => s.merge_fast(&o, min_row, max_row), + (Self::Timestamp(s), ColumnBuilder::Timestamp(o)) => s.merge_fast(&o, min_row, max_row), + (Self::Float32(s), ColumnBuilder::Float32(o)) => s.merge_fast(&o, min_row, max_row), + (Self::Float64(s), ColumnBuilder::Float64(o)) => s.merge_fast(&o, min_row, max_row), + ( + Self::Bool { + values: s_v, + validity: s_val, + len: s_l, + }, + ColumnBuilder::Bool { + values: o_v, + validity: o_val, + len: o_l, + }, + ) => { + if o_l > *s_l { + let target_len = o_l; + let bytes = target_len.div_ceil(8); + if bytes > s_v.len() { + s_v.extend_zeros(bytes - s_v.len()); + s_val.extend_zeros(bytes - s_val.len()); + } + *s_l = target_len; + } + let end = (max_row + 1).min(o_l); + for i in min_row..end { + if bit_util::get_bit(o_val.as_slice(), i) { + if bit_util::get_bit(o_v.as_slice(), i) { + bit_util::set_bit(s_v.as_slice_mut(), i); + } else { + bit_util::unset_bit(s_v.as_slice_mut(), i); + } + bit_util::set_bit(s_val.as_slice_mut(), i); + } + } + } + (Self::String { values: s_v }, ColumnBuilder::String { values: o_v }) => { + if o_v.len() > s_v.len() { + s_v.resize(o_v.len(), None); + } + let end = (max_row + 1).min(o_v.len()); + for i in min_row..end { + if o_v[i].is_some() { + s_v[i] = o_v[i].clone(); + } + } + } + ( + Self::Dictionary { + values: s_v, + dictionary: s_d, + lookup: s_l, + }, + ColumnBuilder::Dictionary { + values: o_v, + dictionary: o_d, + lookup: _o_l, + }, + ) => { + s_v.ensure_capacity(o_v.len()); + let end = (max_row + 1).min(o_v.len()); + for i in min_row..end { + if bit_util::get_bit(o_v.validity.as_slice(), i) { + let val = &o_d[o_v.values[i] as usize]; + let new_id = *s_l.entry(val.clone()).or_insert_with(|| { + let id = s_d.len() as u32; + s_d.push(val.clone()); + id + }); + s_v.insert(i, new_id); + } + } + } + (s, o) => { + s.convert_to_string(); + let mut o = o; + o.convert_to_string(); + if let (Self::String { values: s_v }, ColumnBuilder::String { values: o_v }) = + (s, o) + { + if o_v.len() > s_v.len() { + s_v.resize(o_v.len(), None); + } + let end = (max_row + 1).min(o_v.len()); + for i in min_row..end { + if o_v[i].is_some() { + s_v[i] = o_v[i].clone(); + } + } + } + } + } + } + + fn merge_contiguous(&mut self, other: ColumnBuilder) { + match (self, other) { + (Self::Int(s), ColumnBuilder::Int(o)) => s.merge_contiguous(o), + (Self::UInt8(s), ColumnBuilder::UInt8(o)) => s.merge_contiguous(o), + (Self::UInt32(s), ColumnBuilder::UInt32(o)) => s.merge_contiguous(o), + (Self::Timestamp(s), ColumnBuilder::Timestamp(o)) => s.merge_contiguous(o), + (Self::Float32(s), ColumnBuilder::Float32(o)) => s.merge_contiguous(o), + (Self::Float64(s), ColumnBuilder::Float64(o)) => s.merge_contiguous(o), + ( + Self::Bool { + values: s_v, + validity: s_val, + len: s_l, + }, + ColumnBuilder::Bool { + values: o_v, + validity: o_val, + len: o_l, + }, + ) => { + let old_len = *s_l; + let other_len = o_l; + let target_len = old_len + other_len; + let bytes = target_len.div_ceil(8); + if bytes > s_v.len() { + s_v.extend_zeros(bytes - s_v.len()); + s_val.extend_zeros(bytes - s_val.len()); + } + *s_l = target_len; + + for i in 0..other_len { + if bit_util::get_bit(o_val.as_slice(), i) { + if bit_util::get_bit(o_v.as_slice(), i) { + bit_util::set_bit(s_v.as_slice_mut(), old_len + i); + } + bit_util::set_bit(s_val.as_slice_mut(), old_len + i); + } + } + } + (Self::String { values: s_v }, ColumnBuilder::String { values: o_v }) => { + s_v.extend(o_v) + } + ( + Self::Dictionary { + values: s_v, + dictionary: s_d, + lookup: s_l, + }, + ColumnBuilder::Dictionary { + values: o_v, + dictionary: o_d, + lookup: _o_l, + }, + ) => { + let old_len = s_v.len(); + let other_len = o_v.len(); + s_v.ensure_capacity(old_len + other_len); + + for i in 0..other_len { + if bit_util::get_bit(o_v.validity.as_slice(), i) { + let val = &o_d[o_v.values[i] as usize]; + let new_id = *s_l.entry(val.clone()).or_insert_with(|| { + let id = s_d.len() as u32; + s_d.push(val.clone()); + id + }); + s_v.insert(old_len + i, new_id); + } + } + } + (s, o) => { + s.convert_to_string(); + let mut o = o; + o.convert_to_string(); + if let (Self::String { values: s_v }, ColumnBuilder::String { values: o_v }) = + (s, o) + { + s_v.extend(o_v); + } + } + } + } + + fn merge_from(&mut self, other: ColumnBuilder, mapping: &[(usize, usize)], new_total: usize) { + match (self, other) { + (Self::Int(s), ColumnBuilder::Int(o)) => s.merge_from(&o, mapping, new_total), + (Self::UInt8(s), ColumnBuilder::UInt8(o)) => s.merge_from(&o, mapping, new_total), + (Self::UInt32(s), ColumnBuilder::UInt32(o)) => s.merge_from(&o, mapping, new_total), + (Self::Timestamp(s), ColumnBuilder::Timestamp(o)) => { + s.merge_from(&o, mapping, new_total) + } + (Self::Float32(s), ColumnBuilder::Float32(o)) => s.merge_from(&o, mapping, new_total), + (Self::Float64(s), ColumnBuilder::Float64(o)) => s.merge_from(&o, mapping, new_total), + ( + Self::Bool { + values: s_v, + validity: s_val, + len: s_l, + }, + ColumnBuilder::Bool { + values: o_v, + validity: o_val, + len: o_l, + }, + ) => { + if new_total > *s_l { + let target_len = new_total; + let bytes = target_len.div_ceil(8); + if bytes > s_v.len() { + s_v.extend_zeros(bytes - s_v.len()); + s_val.extend_zeros(bytes - s_val.len()); + } + *s_l = target_len; + } + for &(o_idx, s_idx) in mapping { + if o_idx < o_l && bit_util::get_bit(o_val.as_slice(), o_idx) { + if bit_util::get_bit(o_v.as_slice(), o_idx) { + bit_util::set_bit(s_v.as_slice_mut(), s_idx); + } else { + bit_util::unset_bit(s_v.as_slice_mut(), s_idx); + } + bit_util::set_bit(s_val.as_slice_mut(), s_idx); + } + } + } + ( + Self::Dictionary { + values: s_v, + dictionary: s_d, + lookup: s_l, + }, + ColumnBuilder::Dictionary { + values: o_v, + dictionary: o_d, + .. + }, + ) => { + s_v.ensure_capacity(new_total); + for &(o_idx, s_idx) in mapping { + if o_idx < o_v.len() && bit_util::get_bit(o_v.validity.as_slice(), o_idx) { + let val = &o_d[o_v.values[o_idx] as usize]; + let new_id = *s_l.entry(val.clone()).or_insert_with(|| { + let nid = s_d.len() as u32; + s_d.push(val.clone()); + nid + }); + s_v.insert(s_idx, new_id); + } + } + } + (s, o) => { + s.convert_to_string(); + let mut o = o; + o.convert_to_string(); + if let (Self::String { values: s_v }, ColumnBuilder::String { values: o_v }) = + (s, o) + { + s_v.resize(new_total, None); + for &(other_idx, self_idx) in mapping { + if other_idx < o_v.len() + && let Some(v) = &o_v[other_idx] + { + s_v[self_idx] = Some(v.clone()); + } + } + } + } + } + } + + fn finish(self) -> (ArrayRef, DataType) { + match self { + Self::Int(c) => { + let len = c.len(); + ( + Arc::new(Int64Array::new( + c.values.into(), + Some(BooleanBuffer::new(c.validity.into(), 0, len).into()), + )), + DataType::Int64, + ) + } + Self::UInt8(c) => { + use arrow_array::UInt8Array; + let len = c.len(); + ( + Arc::new(UInt8Array::new( + c.values.into(), + Some(BooleanBuffer::new(c.validity.into(), 0, len).into()), + )), + DataType::UInt8, + ) + } + Self::UInt32(c) => { + use arrow_array::UInt32Array; + let len = c.len(); + ( + Arc::new(UInt32Array::new( + c.values.into(), + Some(BooleanBuffer::new(c.validity.into(), 0, len).into()), + )), + DataType::UInt32, + ) + } + Self::Timestamp(c) => { + use arrow_array::PrimitiveArray; + use arrow_array::types::TimestampMicrosecondType; + let len = c.len(); + ( + Arc::new(PrimitiveArray::::new( + c.values.into(), + Some(BooleanBuffer::new(c.validity.into(), 0, len).into()), + )), + DataType::Timestamp(arrow_schema::TimeUnit::Microsecond, None), + ) + } + Self::Float32(c) => { + use arrow_array::Float32Array; + let len = c.len(); + ( + Arc::new(Float32Array::new( + c.values.into(), + Some(BooleanBuffer::new(c.validity.into(), 0, len).into()), + )), + DataType::Float32, + ) + } + Self::Float64(c) => { + use arrow_array::Float64Array; + let len = c.len(); + ( + Arc::new(Float64Array::new( + c.values.into(), + Some(BooleanBuffer::new(c.validity.into(), 0, len).into()), + )), + DataType::Float64, + ) + } + Self::Bool { + values, + validity, + len, + } => ( + Arc::new(BooleanArray::new( + BooleanBuffer::new(values.into(), 0, len), + Some(BooleanBuffer::new(validity.into(), 0, len).into()), + )), + DataType::Boolean, + ), + Self::Dictionary { + values, dictionary, .. + } => { + use arrow_array::{DictionaryArray, UInt32Array}; + let len = values.len(); + let keys = UInt32Array::new( + values.values.into(), + Some(BooleanBuffer::new(values.validity.into(), 0, len).into()), + ); + let values = StringArray::from(dictionary); + let array = DictionaryArray::new(keys, Arc::new(values)); + let dtype = array.data_type().clone(); + (Arc::new(array), dtype) + } + Self::String { values } => (Arc::new(StringArray::from(values)), DataType::Utf8), + } + } + + fn convert_to_match(&mut self, other: &Self) { + match (self, other) { + (Self::Int(_), ColumnBuilder::Int(_)) => (), + (Self::UInt8(_), ColumnBuilder::UInt8(_)) => (), + (Self::UInt32(_), ColumnBuilder::UInt32(_)) => (), + (Self::Timestamp(_), ColumnBuilder::Timestamp(_)) => (), + (Self::Float32(_), ColumnBuilder::Float32(_)) => (), + (Self::Float64(_), ColumnBuilder::Float64(_)) => (), + (Self::Bool { .. }, ColumnBuilder::Bool { .. }) => (), + (Self::String { .. }, ColumnBuilder::String { .. }) => (), + (Self::Dictionary { .. }, ColumnBuilder::Dictionary { .. }) => (), + (s, _) => s.convert_to_string(), + } + } +} + +pub struct TrackingDataBuilder { + frame_map: AHashMap, + last_frame: Option<(i64, usize)>, + columns: Vec, + column_names: Vec, + column_index: AHashMap, + domain_fields: Vec, + next_row_index: usize, + frame_offset: i64, + is_fixed: bool, + min_row_touched: usize, + max_row_touched: usize, +} + +impl Default for TrackingDataBuilder { + fn default() -> Self { + Self::new() + } +} + +impl TrackingDataBuilder { + pub fn new() -> Self { + Self { + frame_map: AHashMap::with_capacity(1000), + last_frame: None, + columns: Vec::with_capacity(100), + column_names: Vec::with_capacity(100), + column_index: AHashMap::with_capacity(100), + domain_fields: Vec::with_capacity(100), + next_row_index: 0, + frame_offset: 0, + is_fixed: false, + min_row_touched: usize::MAX, + max_row_touched: 0, + } + } + + pub fn new_fixed(min_id: i64, max_id: i64) -> Self { + let capacity = (max_id - min_id + 1) as usize; + Self { + frame_map: AHashMap::new(), + last_frame: None, + columns: Vec::with_capacity(100), + column_names: Vec::with_capacity(100), + column_index: AHashMap::with_capacity(100), + domain_fields: Vec::with_capacity(100), + next_row_index: capacity, + frame_offset: min_id, + is_fixed: true, + min_row_touched: usize::MAX, + max_row_touched: 0, + } + } + + #[inline] + pub fn get_row_index(&mut self, frame_id: i64) -> usize { + let idx = if self.is_fixed { + (frame_id - self.frame_offset) as usize + } else { + if let Some((last_id, last_idx)) = self.last_frame + && last_id == frame_id + { + return last_idx; + } + let next_idx = self.next_row_index; + let idx = *self.frame_map.entry(frame_id).or_insert_with(|| { + self.next_row_index += 1; + next_idx + }); + self.last_frame = Some((frame_id, idx)); + idx + }; + if idx < self.min_row_touched { + self.min_row_touched = idx; + } + if idx > self.max_row_touched { + self.max_row_touched = idx; + } + idx + } + + pub fn get_or_create_column_id(&mut self, name: &str, mut dtype: DataType) -> usize { + if let Some(&idx) = self.column_index.get(name) { + return idx; + } + let field = DataField::from_name(name); + if field.is_low_cardinality() && dtype == DataType::Utf8 { + dtype = DataType::Dictionary(Box::new(DataType::UInt32), Box::new(DataType::Utf8)); + } + let idx = self.columns.len(); + self.column_index.insert(name.to_string(), idx); + self.column_names.push(name.to_string()); + self.columns + .push(ColumnBuilder::new(self.next_row_index, dtype)); + self.domain_fields.push(field); + idx + } + + #[inline] + fn update_touched_bounds(&mut self, row_idx: usize) { + if row_idx < self.min_row_touched { + self.min_row_touched = row_idx; + } + if row_idx > self.max_row_touched { + self.max_row_touched = row_idx; + } + } + + #[inline] + pub fn insert_int_at_idx(&mut self, row_idx: usize, col_idx: usize, val: i64) { + self.update_touched_bounds(row_idx); + self.columns[col_idx].insert_int(row_idx, val); + } + #[inline] + pub fn insert_float_at_idx(&mut self, row_idx: usize, col_idx: usize, val: f64) { + self.update_touched_bounds(row_idx); + self.columns[col_idx].insert_float(row_idx, val); + } + #[inline] + pub fn insert_string_at_idx(&mut self, row_idx: usize, col_idx: usize, val: &str) { + self.update_touched_bounds(row_idx); + self.columns[col_idx].insert_string(row_idx, val); + } + #[inline] + pub fn insert_bool_at_idx(&mut self, row_idx: usize, col_idx: usize, val: bool) { + self.update_touched_bounds(row_idx); + self.columns[col_idx].insert_bool(row_idx, val); + } + + #[inline] + pub fn insert_int(&mut self, frame_id: i64, col: &str, val: i64) { + let r = self.get_row_index(frame_id); + let d = DataField::from_name(col).default_arrow_type(); + let c = self.get_or_create_column_id(col, d); + self.columns[c].insert_int(r, val); + } + #[inline] + pub fn insert_float(&mut self, frame_id: i64, col: &str, val: f64) { + let r = self.get_row_index(frame_id); + let d = DataField::from_name(col).default_arrow_type(); + let c = self.get_or_create_column_id(col, d); + self.columns[c].insert_float(r, val); + } + #[inline] + pub fn insert_string(&mut self, frame_id: i64, col: &str, val: &str) { + let r = self.get_row_index(frame_id); + let d = DataField::from_name(col).default_arrow_type(); + let c = self.get_or_create_column_id(col, d); + self.columns[c].insert_string(r, val); + } + #[inline] + pub fn insert_bool(&mut self, frame_id: i64, col: &str, val: bool) { + let r = self.get_row_index(frame_id); + let d = DataField::from_name(col).default_arrow_type(); + let c = self.get_or_create_column_id(col, d); + self.columns[c].insert_bool(r, val); + } + + pub fn merge(&mut self, other: TrackingDataBuilder) { + let mut other = other; + if other.column_index.is_empty() { + return; + } + + let other_min = other.min_row_touched; + let other_max = other.max_row_touched; + + // Contiguous merge (StatsPerform) + if self.is_fixed + && other.is_fixed + && self.frame_offset + self.next_row_index as i64 == other.frame_offset + { + for (name, other_idx) in other.column_index { + let other_col = std::mem::replace( + &mut other.columns[other_idx], + ColumnBuilder::Int(TypedColumn::new(0, 0)), + ); + let other_domain_field = std::mem::replace( + &mut other.domain_fields[other_idx], + DataField::from_name("dummy"), + ); + if let Some(&self_idx) = self.column_index.get(&name) { + self.columns[self_idx].merge_contiguous(other_col); + } else { + let mut new_col = ColumnBuilder::new(self.next_row_index, DataType::Int64); + new_col.convert_to_match(&other_col); + new_col.merge_contiguous(other_col); + self.column_index.insert(name.clone(), self.columns.len()); + self.column_names.push(name.clone()); + self.columns.push(new_col); + self.domain_fields.push(other_domain_field); + } + } + self.next_row_index += other.next_row_index; + if other_min <= other_max { + let mapped_max = other_max + self.next_row_index - other.next_row_index; + if mapped_max > self.max_row_touched { + self.max_row_touched = mapped_max; + } + } + return; + } + + // Fast path (Sportec) + if self.is_fixed + && other.is_fixed + && self.frame_offset == other.frame_offset + && self.next_row_index == other.next_row_index + { + for (name, other_idx) in other.column_index { + let other_col = std::mem::replace( + &mut other.columns[other_idx], + ColumnBuilder::Int(TypedColumn::new(0, 0)), + ); + let other_domain_field = std::mem::replace( + &mut other.domain_fields[other_idx], + DataField::from_name("dummy"), + ); + if let Some(&self_idx) = self.column_index.get(&name) { + if other_min <= other_max { + self.columns[self_idx].merge_fast(other_col, other_min, other_max); + } + } else { + self.column_index.insert(name.clone(), self.columns.len()); + self.column_names.push(name.clone()); + self.columns.push(other_col); + self.domain_fields.push(other_domain_field); + } + } + if other_min <= other_max { + if other_min < self.min_row_touched { + self.min_row_touched = other_min; + } + if other_max > self.max_row_touched { + self.max_row_touched = other_max; + } + } + return; + } + + // --- Middle path: Sorted, non-overlapping frame streams --- + // If both builders have sorted frame_ids and they don't overlap, we can do a very fast + // O(N) merge by simply rebasing both. + // We check this by comparing min/max frame IDs. + let (self_min, self_max) = if self.is_fixed { + ( + self.frame_offset, + self.frame_offset + self.next_row_index as i64 - 1, + ) + } else { + // Check if frame_map is sorted. For simplicity, we only optimize if is_fixed + // or if we can easily determine it's sorted. + (0, -1) // Disable for now if not fixed + }; + + let (other_min, other_max_id) = if other.is_fixed { + ( + other.frame_offset, + other.frame_offset + other.next_row_index as i64 - 1, + ) + } else { + (0, -1) + }; + + if self_min <= self_max + && other_min <= other_max_id + && (self_max < other_min || other_max_id < self_min) + { + let ( + first_builder_offset, + first_builder_len, + second_builder_offset, + second_builder_len, + swap, + ) = if self_max < other_min { + ( + self.frame_offset, + self.next_row_index, + other.frame_offset, + other.next_row_index, + false, + ) + } else { + ( + other.frame_offset, + other.next_row_index, + self.frame_offset, + self.next_row_index, + true, + ) + }; + + let gap = (second_builder_offset - (first_builder_offset + first_builder_len as i64)) + as usize; + let new_total = first_builder_len + gap + second_builder_len; + let new_offset = first_builder_offset; + + if !swap { + // 'self' is first. Just resize and then merge 'other'. + for col in &mut self.columns { + col.rebase( + &(0..self.next_row_index).map(|i| (i, i)).collect::>(), + new_total, + ); + } + let other_mapping: Vec<(usize, usize)> = (0..other.next_row_index) + .map(|i| (i, i + first_builder_len + gap)) + .collect(); + + for (name, other_idx) in other.column_index { + let other_col = std::mem::replace( + &mut other.columns[other_idx], + ColumnBuilder::Int(TypedColumn::new(0, 0)), + ); + let other_domain_field = std::mem::replace( + &mut other.domain_fields[other_idx], + DataField::from_name("dummy"), + ); + if let Some(&self_idx) = self.column_index.get(&name) { + self.columns[self_idx].merge_from(other_col, &other_mapping, new_total); + } else { + let mut new_col = other_col; + new_col.rebase(&other_mapping, new_total); + self.column_index.insert(name.clone(), self.columns.len()); + self.column_names.push(name.clone()); + self.columns.push(new_col); + self.domain_fields.push(other_domain_field); + } + } + self.next_row_index = new_total; + self.frame_offset = new_offset; + self.is_fixed = true; + self.max_row_touched = new_total - 1; + return; + } else { + // 'other' is first. This is more complex because we need to move 'self' data. + // Fall back to slow path for now if swap is needed, or implement it if common. + } + } + + // Slow path (Fallback) + let mut all_frame_ids: Vec = if self.is_fixed { + (0..self.next_row_index) + .map(|i| self.frame_offset + i as i64) + .collect() + } else { + self.frame_map.keys().cloned().collect() + }; + + if other.is_fixed { + for i in 0..other.next_row_index { + all_frame_ids.push(other.frame_offset + i as i64); + } + } else { + for id in other.frame_map.keys() { + all_frame_ids.push(*id); + } + } + + all_frame_ids.sort_unstable(); + all_frame_ids.dedup(); + + let new_total = all_frame_ids.len(); + let mut new_frame_map = AHashMap::with_capacity(new_total); + for (idx, id) in all_frame_ids.iter().enumerate() { + new_frame_map.insert(*id, idx); + } + + let self_mapping: Vec<(usize, usize)> = if self.is_fixed { + (0..self.next_row_index) + .map(|i| { + ( + i, + *new_frame_map.get(&(self.frame_offset + i as i64)).unwrap(), + ) + }) + .collect() + } else { + self.frame_map + .iter() + .map(|(id, old)| (*old, *new_frame_map.get(id).unwrap())) + .collect() + }; + for col in &mut self.columns { + col.rebase(&self_mapping, new_total); + } + + let other_mapping: Vec<(usize, usize)> = if other.is_fixed { + (0..other.next_row_index) + .map(|i| { + ( + i, + *new_frame_map.get(&(other.frame_offset + i as i64)).unwrap(), + ) + }) + .collect() + } else { + other + .frame_map + .iter() + .map(|(id, old)| (*old, *new_frame_map.get(id).unwrap())) + .collect() + }; + + for (name, other_idx) in other.column_index { + let other_col = std::mem::replace( + &mut other.columns[other_idx], + ColumnBuilder::Int(TypedColumn::new(0, 0)), + ); + let other_domain_field = std::mem::replace( + &mut other.domain_fields[other_idx], + DataField::from_name("dummy"), + ); + if let Some(&self_idx) = self.column_index.get(&name) { + self.columns[self_idx].merge_from(other_col, &other_mapping, new_total); + } else { + let mut new_col = other_col; + new_col.rebase(&other_mapping, new_total); + self.column_index.insert(name.clone(), self.columns.len()); + self.column_names.push(name.clone()); + self.columns.push(new_col); + self.domain_fields.push(other_domain_field); + } + } + self.frame_map = new_frame_map; + self.next_row_index = new_total; + self.last_frame = None; + self.min_row_touched = 0; + self.max_row_touched = new_total.saturating_sub(1); + self.is_fixed = false; + } + + pub fn build(self) -> TrackingDataContainerResult { + let total_rows = self.next_row_index; + let mut fields = Vec::with_capacity(self.columns.len() + 1); + let mut arrays = Vec::with_capacity(self.columns.len() + 1); + let mut domain_fields = Vec::with_capacity(self.columns.len() + 1); + + for ((name, mut builder), domain_field) in self + .column_names + .into_iter() + .zip(self.columns.into_iter()) + .zip(self.domain_fields.into_iter()) + { + builder.ensure_capacity(total_rows); + let (array, dtype) = builder.finish(); + fields.push(Field::new(name, dtype, true)); + arrays.push(array); + domain_fields.push(domain_field); + } + + if !self.column_index.contains_key("frame_id") { + let mut frame_id_builder = vec![0i64; total_rows]; + if self.is_fixed { + for (i, val) in frame_id_builder.iter_mut().enumerate().take(total_rows) { + *val = self.frame_offset + i as i64; + } + } else { + for (id, idx) in self.frame_map { + frame_id_builder[idx] = id; + } + } + fields.push(Field::new("frame_id", DataType::Int64, false)); + arrays.push(Arc::new(Int64Array::from(frame_id_builder)) as ArrayRef); + domain_fields.push(DataField::Frame { + attribute: FrameAttribute::FrameId, + }); + } + + let batch = RecordBatch::try_new(Arc::new(Schema::new(fields)), arrays)?; + Ok(super::TrackingDataContainer::new( + batch.into(), + Fields { + fields: domain_fields, + }, + )) + } +} diff --git a/crates/tdc-core/src/datacontainer/core.rs b/crates/tdc-core/src/datacontainer/core.rs new file mode 100644 index 0000000..46d1d08 --- /dev/null +++ b/crates/tdc-core/src/datacontainer/core.rs @@ -0,0 +1,404 @@ +use crate::error::{TrackingDataContainerErrorKind, TrackingDataContainerResult}; +use crate::schema::{CoordinateSystem, DataField, Fields, OrientationMode}; +use arrow_arith::numeric::{add, mul}; +use arrow_array::array::{Array, ArrayRef, Float32Array, Float64Array, StructArray}; +use arrow_array::{RecordBatch, Scalar}; +use arrow_ord::cmp::eq; +use arrow_schema::{DataType::*, Field, Fields as ArrowFields}; +use arrow_select::zip::zip; +use datafusion::prelude::*; +use std::sync::Arc; + +impl TrackingDataContainer { + pub fn new(data: StructArray, schema: Fields) -> Self { + Self { + data, + schema, + coordinate_system: None, + orientation: None, + } + } + + pub fn with_coordinate_system(mut self, system: CoordinateSystem) -> Self { + self.coordinate_system = Some(system); + self + } + + pub fn with_orientation(mut self, mode: OrientationMode) -> Self { + self.orientation = Some(mode); + self + } + + pub fn num_rows(&self) -> usize { + self.data.len() + } + pub fn is_empty(&self) -> bool { + self.data.is_empty() + } + + pub fn validate(&self) -> TrackingDataContainerResult<()> { + self.schema.validate() + } + + pub fn lazy(&self) -> TrackingDataContainerResult { + let batch = RecordBatch::from(self.data.clone()); + let df = crate::config::CONTEXT.read_batch(batch)?; + Ok(super::LazyTrackingDataContainer::new( + df, + self.schema.clone(), + self.coordinate_system.clone(), + self.orientation, + )) + } +} + +impl crate::selector::Select for TrackingDataContainer { + fn select(&self, fields: Vec) -> TrackingDataContainerResult { + let indices = self.schema.get_column_indices(&fields); + + let mut new_arrays = Vec::with_capacity(fields.len()); + let mut new_arrow_fields = Vec::with_capacity(fields.len()); + let mut new_schema_fields = Vec::with_capacity(fields.len()); + + for (field, maybe_idx) in fields.into_iter().zip(indices.into_iter()) { + if let Some(idx) = maybe_idx { + new_arrays.push(self.data.column(idx).clone()); + new_arrow_fields.push(self.data.fields()[idx].clone()); + new_schema_fields.push(field); + } + } + + let new_data = if new_arrays.is_empty() { + StructArray::new_empty_fields(self.num_rows(), None) + } else { + StructArray::try_new(ArrowFields::from(new_arrow_fields), new_arrays, None)? + }; + + Ok(Self { + data: new_data, + schema: Fields { + fields: new_schema_fields, + }, + coordinate_system: self.coordinate_system.clone(), + orientation: self.orientation, + }) + } +} + +impl TrackingDataContainer { + pub fn with_columns(&self, fields: Vec, arrays: Vec) -> Self { + let mut new_fields: Vec> = self.data.fields().iter().cloned().collect(); + let mut new_columns: Vec = self.data.columns().to_vec(); + let mut new_schema_fields = self.schema.fields.clone(); + let mut field_map: std::collections::HashMap = new_fields + .iter() + .enumerate() + .map(|(i, f)| (f.name().clone(), i)) + .collect(); + + for (field, array) in fields.into_iter().zip(arrays.into_iter()) { + let name = field.name(); + if let Some(&i) = field_map.get(&name) { + new_columns[i] = array.clone(); + new_fields[i] = Arc::new(Field::new(&name, array.data_type().clone(), true)); + new_schema_fields[i] = field; + } else { + let new_idx = new_fields.len(); + new_fields.push(Arc::new(Field::new(&name, array.data_type().clone(), true))); + new_columns.push(array); + new_schema_fields.push(field); + field_map.insert(name, new_idx); + } + } + Self { + data: StructArray::new(ArrowFields::from(new_fields), new_columns, None), + schema: Fields { + fields: new_schema_fields, + }, + coordinate_system: self.coordinate_system.clone(), + orientation: self.orientation, + } + } + + pub fn with_expr_columns_ctx( + &self, + names: Vec, + exprs: Vec, + ctx: &SessionContext, + rt: &tokio::runtime::Handle, + ) -> TrackingDataContainerResult { + let mut df = ctx.read_batch(RecordBatch::from(self.data.clone()))?; + let new_names = names.clone(); + for (name, expr) in names.into_iter().zip(exprs.into_iter()) { + df = df.with_column(&name, expr)?; + } + df = df.select_columns(&new_names.iter().map(|s| s.as_str()).collect::>())?; + + let batches = rt.block_on(async { df.collect().await })?; + if batches.is_empty() { + return Ok(self.clone()); + } + let full_batch = arrow_select::concat::concat_batches(&batches[0].schema(), &batches)?; + let fields: Vec = full_batch + .schema() + .fields() + .iter() + .map(|f| DataField::from_name(f.name())) + .collect(); + let mut columns = Vec::new(); + for i in 0..full_batch.num_columns() { + columns.push(full_batch.column(i).clone()); + } + Ok(self.with_columns(fields, columns)) + } + + pub fn transform_coordinates( + &self, + to_system: CoordinateSystem, + ) -> TrackingDataContainerResult { + let from = self.coordinate_system.as_ref().ok_or_else(|| { + TrackingDataContainerErrorKind::InvalidParameters("No coordinate system".to_string()) + })?; + + let mut new_cols = self.data.columns().to_vec(); + let mut new_fields = self.data.fields().iter().cloned().collect::>(); + + // Pre-calculate transformation coefficients and hoist scalars + let (m_x, o_x) = from.get_transform_coeffs(&to_system, true)?; + let (m_y, o_y) = from.get_transform_coeffs(&to_system, false)?; + // For Z, we use the same scale as Y for now, or X. Most sports tracking Z is meters if X/Y are meters. + let (m_z, o_z) = (m_y, o_y); + + let m_x_f64 = Scalar::new(Float64Array::from(vec![m_x])); + let o_x_f64 = Scalar::new(Float64Array::from(vec![o_x])); + let m_y_f64 = Scalar::new(Float64Array::from(vec![m_y])); + let o_y_f64 = Scalar::new(Float64Array::from(vec![o_y])); + let m_z_f64 = Scalar::new(Float64Array::from(vec![m_z])); + let o_z_f64 = Scalar::new(Float64Array::from(vec![o_z])); + + let m_x_f32 = Scalar::new(Float32Array::from(vec![m_x as f32])); + let o_x_f32 = Scalar::new(Float32Array::from(vec![o_x as f32])); + let m_y_f32 = Scalar::new(Float32Array::from(vec![m_y as f32])); + let o_y_f32 = Scalar::new(Float32Array::from(vec![o_y as f32])); + let m_z_f32 = Scalar::new(Float32Array::from(vec![m_z as f32])); + let o_z_f32 = Scalar::new(Float32Array::from(vec![o_z as f32])); + + for (i, field) in self.schema.fields.iter().enumerate() { + if let DataField::TrackedEntity { attribute, .. } = field { + use crate::schema::TrackedEntityAttribute::*; + if matches!(attribute, X | Y | Z) { + let (m_f64, o_f64, m_f32, o_f32) = match attribute { + X => (&m_x_f64, &o_x_f64, &m_x_f32, &o_x_f32), + Y => (&m_y_f64, &o_y_f64, &m_y_f32, &o_y_f32), + Z => (&m_z_f64, &o_z_f64, &m_z_f32, &o_z_f32), + _ => unreachable!(), + }; + + let col = self.data.column(i); + let transformed: ArrayRef = match col.data_type() { + Float32 => { + let multiplied = mul(col, m_f32)?; + add(&multiplied, o_f32)? + } + Float64 => { + let multiplied = mul(col, m_f64)?; + add(&multiplied, o_f64)? + } + _ => { + let arr = arrow_cast::cast(col, &Float64)?; + let multiplied = mul(&arr, m_f64)?; + add(&multiplied, o_f64)? + } + }; + new_cols[i] = transformed.clone(); + new_fields[i] = Arc::new(Field::new( + new_fields[i].name(), + transformed.data_type().clone(), + true, + )); + } + } + } + let new_data = StructArray::try_new( + ArrowFields::from(new_fields), + new_cols, + self.data.nulls().cloned(), + )?; + Ok(Self { + data: new_data, + schema: self.schema.clone(), + coordinate_system: Some(to_system), + orientation: self.orientation, + }) + } + + pub fn transform_orientation( + &self, + mode: OrientationMode, + ) -> TrackingDataContainerResult { + let current = self.orientation.as_ref().ok_or_else(|| { + TrackingDataContainerErrorKind::InvalidParameters("No orientation".to_string()) + })?; + if current == &mode { + return Ok(self.clone()); + } + let p_idx = self + .schema + .fields + .iter() + .position(|f| { + matches!( + f, + DataField::Frame { + attribute: crate::schema::FrameAttribute::Period + } + ) + }) + .ok_or_else(|| { + TrackingDataContainerErrorKind::SchemaError("Missing period column".to_string()) + })?; + let p_col = self.data.column(p_idx); + + let mut new_cols = self.data.columns().to_vec(); + let new_fields = self.data.fields().iter().cloned().collect::>(); + + let poss_team_idx = self.schema.fields.iter().position(|f| { + matches!( + f, + DataField::Frame { + attribute: crate::schema::FrameAttribute::BallPossTeamId + } + ) + }); + + let current_mask = self.get_flip_mask(current, p_col, poss_team_idx); + let target_mask = self.get_flip_mask(&mode, p_col, poss_team_idx); + let change_mask = arrow_arith::boolean::or( + &arrow_arith::boolean::and_not(¤t_mask, &target_mask)?, + &arrow_arith::boolean::and_not(&target_mask, ¤t_mask)?, + )?; + + // Hoist flip scalars + let (m_x, o_x) = self + .coordinate_system + .as_ref() + .map(|cs| cs.get_flip_coeffs(true)) + .unwrap_or((-1.0, 0.0)); + let (m_y, o_y) = self + .coordinate_system + .as_ref() + .map(|cs| cs.get_flip_coeffs(false)) + .unwrap_or((-1.0, 0.0)); + + let m_x_f64 = Scalar::new(Float64Array::from(vec![m_x])); + let o_x_f64 = Scalar::new(Float64Array::from(vec![o_x])); + let m_y_f64 = Scalar::new(Float64Array::from(vec![m_y])); + let o_y_f64 = Scalar::new(Float64Array::from(vec![o_y])); + + let m_x_f32 = Scalar::new(Float32Array::from(vec![m_x as f32])); + let o_x_f32 = Scalar::new(Float32Array::from(vec![o_x as f32])); + let m_y_f32 = Scalar::new(Float32Array::from(vec![m_y as f32])); + let o_y_f32 = Scalar::new(Float32Array::from(vec![o_y as f32])); + + for (i, field) in self.schema.fields.iter().enumerate() { + if let DataField::TrackedEntity { attribute, .. } = field + && matches!( + attribute, + crate::schema::TrackedEntityAttribute::X + | crate::schema::TrackedEntityAttribute::Y + ) + { + let is_x = matches!(attribute, crate::schema::TrackedEntityAttribute::X); + let (m_f64, o_f64, m_f32, o_f32) = if is_x { + (&m_x_f64, &o_x_f64, &m_x_f32, &o_x_f32) + } else { + (&m_y_f64, &o_y_f64, &m_y_f32, &o_y_f32) + }; + + let col = self.data.column(i); + let transformed: ArrayRef = match col.data_type() { + Float32 => { + let multiplied = mul(col, m_f32)?; + let flipped = add(&multiplied, o_f32)?; + zip(&change_mask, &flipped, col)? + } + Float64 => { + let multiplied = mul(col, m_f64)?; + let flipped = add(&multiplied, o_f64)?; + zip(&change_mask, &flipped, col)? + } + _ => col.clone(), + }; + new_cols[i] = transformed; + } + } + let new_data = StructArray::try_new( + ArrowFields::from(new_fields), + new_cols, + self.data.nulls().cloned(), + )?; + Ok(Self { + data: new_data, + schema: self.schema.clone(), + coordinate_system: self.coordinate_system.clone(), + orientation: Some(mode), + }) + } + + fn get_flip_mask( + &self, + mode: &OrientationMode, + p_col: &ArrayRef, + poss_team_idx: Option, + ) -> arrow_array::BooleanArray { + let num_rows = self.num_rows(); + match mode { + OrientationMode::HomeAway => eq( + p_col, + &Scalar::new(arrow_array::StringArray::from(vec!["second_half"])), + ) + .unwrap(), + OrientationMode::AwayHome => eq( + p_col, + &Scalar::new(arrow_array::StringArray::from(vec!["first_half"])), + ) + .unwrap(), + OrientationMode::BallOwningTeam => { + if let Some(idx) = poss_team_idx { + let poss_col = self.data.column(idx); + eq( + poss_col, + &Scalar::new(arrow_array::StringArray::from(vec!["away"])), + ) + .unwrap() + } else { + arrow_array::BooleanArray::new_null(num_rows) + } + } + _ => arrow_array::BooleanArray::new_null(num_rows), + } + } +} + +#[derive(Debug)] +pub struct TrackingDataContainer { + pub data: StructArray, + pub schema: Fields, + pub coordinate_system: Option, + pub orientation: Option, +} +impl Clone for TrackingDataContainer { + fn clone(&self) -> Self { + Self { + data: self.data.clone(), + schema: self.schema.clone(), + coordinate_system: self.coordinate_system.clone(), + orientation: self.orientation, + } + } +} +impl crate::selector::expr::HasSchema for TrackingDataContainer { + fn schema(&self) -> &Fields { + &self.schema + } +} diff --git a/crates/tdc-core/src/datacontainer/lazy.rs b/crates/tdc-core/src/datacontainer/lazy.rs new file mode 100644 index 0000000..297fa02 --- /dev/null +++ b/crates/tdc-core/src/datacontainer/lazy.rs @@ -0,0 +1,341 @@ +use crate::datacontainer::TrackingDataContainer; +use crate::error::TrackingDataContainerResult; +use crate::schema::{CoordinateSystem, DataField, Fields, OrientationMode}; +use datafusion::prelude::*; + +#[derive(Clone)] +pub struct LazyTrackingDataContainer { + pub df: DataFrame, + pub schema: Fields, + pub coordinate_system: Option, + pub orientation: Option, +} + +impl crate::selector::expr::HasSchema for LazyTrackingDataContainer { + fn schema(&self) -> &Fields { + &self.schema + } +} + +impl crate::selector::Select for LazyTrackingDataContainer { + fn select(&self, fields: Vec) -> TrackingDataContainerResult { + let col_names: Vec = fields.iter().map(|f| f.name()).collect(); + let df = self + .df + .clone() + .select_columns(&col_names.iter().map(|s| s.as_str()).collect::>())?; + + Ok(Self { + df, + schema: Fields { fields }, + coordinate_system: self.coordinate_system.clone(), + orientation: self.orientation, + }) + } +} + +impl LazyTrackingDataContainer { + pub fn new( + df: DataFrame, + schema: Fields, + coordinate_system: Option, + orientation: Option, + ) -> Self { + Self { + df, + schema, + coordinate_system, + orientation, + } + } + + pub fn with_orientation(mut self, mode: OrientationMode) -> Self { + self.orientation = Some(mode); + self + } + + pub fn filter( + &self, + predicate: datafusion::prelude::Expr, + ) -> TrackingDataContainerResult { + let df = self.df.clone().filter(predicate)?; + Ok(Self { + df, + schema: self.schema.clone(), + coordinate_system: self.coordinate_system.clone(), + orientation: self.orientation, + }) + } + + pub fn transform_coordinates( + &self, + to_system: CoordinateSystem, + ) -> TrackingDataContainerResult { + let from = self.coordinate_system.as_ref().ok_or_else(|| { + crate::error::TrackingDataContainerErrorKind::InvalidParameters( + "No coordinate system".to_string(), + ) + })?; + + let mut current_df = self.df.clone(); + + for field in self.schema.fields.iter() { + if let DataField::TrackedEntity { attribute, .. } = field { + use crate::schema::TrackedEntityAttribute::*; + if matches!(attribute, X | Y | Z) { + let is_x = matches!(attribute, X); + let (m, o) = from.get_transform_coeffs(&to_system, is_x)?; + + let name = field.name(); + let expr = col(&name) * lit(m) + lit(o); + current_df = current_df.with_column(&name, expr)?; + } + } + } + + Ok(Self { + df: current_df, + schema: self.schema.clone(), + coordinate_system: Some(to_system), + orientation: self.orientation, + }) + } + + pub fn transform_orientation( + &self, + mode: OrientationMode, + ) -> TrackingDataContainerResult { + let current = self.orientation.as_ref().ok_or_else(|| { + crate::error::TrackingDataContainerErrorKind::InvalidParameters( + "No orientation".to_string(), + ) + })?; + if current == &mode { + return Ok(self.clone()); + } + + let p_col_name = self + .schema + .fields + .iter() + .find(|f| { + matches!( + f, + DataField::Frame { + attribute: crate::schema::FrameAttribute::Period + } + ) + }) + .map(|f| f.name()) + .ok_or_else(|| { + crate::error::TrackingDataContainerErrorKind::SchemaError( + "Missing period column".to_string(), + ) + })?; + + let poss_team_col_name = self + .schema + .fields + .iter() + .find(|f| { + matches!( + f, + DataField::Frame { + attribute: crate::schema::FrameAttribute::BallPossTeamId + } + ) + }) + .map(|f| f.name()); + + let mut current_df = self.df.clone(); + + for field in self.schema.fields.iter() { + if let DataField::TrackedEntity { attribute, .. } = field + && matches!( + attribute, + crate::schema::TrackedEntityAttribute::X + | crate::schema::TrackedEntityAttribute::Y + ) + { + let is_x = matches!(attribute, crate::schema::TrackedEntityAttribute::X); + let (m, o) = self + .coordinate_system + .as_ref() + .map(|cs| cs.get_flip_coeffs(is_x)) + .unwrap_or((-1.0, 0.0)); + + let name = field.name(); + + let should_flip_current = + self.get_flip_expr(current, &p_col_name, &poss_team_col_name); + let should_flip_target = + self.get_flip_expr(&mode, &p_col_name, &poss_team_col_name); + + let change_needed = (should_flip_current + .clone() + .and(not(should_flip_target.clone()))) + .or(not(should_flip_current).and(should_flip_target)); + + let flipped_expr = col(&name) * lit(m) + lit(o); + let expr = when(change_needed, flipped_expr).otherwise(col(&name))?; + + current_df = current_df.with_column(&name, expr)?; + } + } + + Ok(Self { + df: current_df, + schema: self.schema.clone(), + coordinate_system: self.coordinate_system.clone(), + orientation: Some(mode), + }) + } + + fn get_flip_expr( + &self, + mode: &OrientationMode, + p_col: &str, + poss_col: &Option, + ) -> Expr { + match mode { + OrientationMode::HomeAway => col(p_col).eq(lit("second_half")), + OrientationMode::AwayHome => col(p_col).eq(lit("first_half")), + OrientationMode::BallOwningTeam => { + if let Some(c) = poss_col { + col(c).eq(lit("away")) + } else { + lit(false) + } + } + _ => lit(false), + } + } + + pub fn add_column( + &self, + name: &str, + expr: datafusion::prelude::Expr, + ) -> TrackingDataContainerResult { + self.with_expr_column(name, expr) + } + + pub fn add_columns( + &self, + names: Vec, + exprs: Vec, + ) -> TrackingDataContainerResult { + let mut df = self.df.clone(); + let mut new_schema_fields = self.schema.fields.clone(); + + for (name, expr) in names.into_iter().zip(exprs.into_iter()) { + df = df.with_column(&name, expr)?; + let field = DataField::from_name(&name); + let mut found = false; + for existing_field in &mut new_schema_fields { + if existing_field.name() == name { + *existing_field = field.clone(); + found = true; + break; + } + } + if !found { + new_schema_fields.push(field); + } + } + + Ok(Self { + df, + schema: Fields { + fields: new_schema_fields, + }, + coordinate_system: self.coordinate_system.clone(), + orientation: self.orientation, + }) + } + + pub fn add_array_column( + &self, + _name: &str, + _array: arrow_array::ArrayRef, + ) -> TrackingDataContainerResult { + Err(datafusion::error::DataFusionError::NotImplemented("Adding raw arrays to a lazy container is not yet supported. Use the eager container for direct array additions.".to_string()).into()) + } + + pub fn add_column_udf( + &self, + _name: &str, + _func: std::sync::Arc, + _on: Vec, + ) -> TrackingDataContainerResult { + Err(datafusion::error::DataFusionError::NotImplemented( + "Lazy Python UDFs not yet supported".to_string(), + ) + .into()) + } + + pub fn with_expr_column( + &self, + name: &str, + expr: datafusion::prelude::Expr, + ) -> TrackingDataContainerResult { + let df = self.df.clone().with_column(name, expr)?; + let mut new_schema_fields = self.schema.fields.clone(); + + // Update schema + let field = DataField::from_name(name); + let mut found = false; + for existing_field in &mut new_schema_fields { + if existing_field.name() == name { + *existing_field = field.clone(); + found = true; + break; + } + } + if !found { + new_schema_fields.push(field); + } + + Ok(Self { + df, + schema: Fields { + fields: new_schema_fields, + }, + coordinate_system: self.coordinate_system.clone(), + orientation: self.orientation, + }) + } + + pub async fn collect(self) -> TrackingDataContainerResult { + let batches = self.df.collect().await?; + if batches.is_empty() { + let mut container = TrackingDataContainer::new( + arrow_array::StructArray::new_empty_fields(0, None), + self.schema, + ); + if let Some(cs) = self.coordinate_system { + container = container.with_coordinate_system(cs); + } + if let Some(om) = self.orientation { + container = container.with_orientation(om); + } + return Ok(container); + } + + let batch = if batches.len() == 1 { + batches[0].clone() + } else { + let schema = batches[0].schema(); + arrow_select::concat::concat_batches(&schema, &batches)? + }; + + let mut container = TrackingDataContainer::new(batch.into(), self.schema); + if let Some(cs) = self.coordinate_system { + container = container.with_coordinate_system(cs); + } + if let Some(om) = self.orientation { + container = container.with_orientation(om); + } + + Ok(container) + } +} diff --git a/crates/tdc-core/src/datacontainer/mod.rs b/crates/tdc-core/src/datacontainer/mod.rs new file mode 100644 index 0000000..5cb809f --- /dev/null +++ b/crates/tdc-core/src/datacontainer/mod.rs @@ -0,0 +1,7 @@ +pub mod builder; +pub mod core; +pub mod lazy; + +pub use builder::TrackingDataBuilder; +pub use core::TrackingDataContainer; +pub use lazy::LazyTrackingDataContainer; diff --git a/crates/tdc-core/src/error.rs b/crates/tdc-core/src/error.rs new file mode 100644 index 0000000..eb9fcfb --- /dev/null +++ b/crates/tdc-core/src/error.rs @@ -0,0 +1,147 @@ +use std::{error::Error, fmt::Display}; + +/// The kind of a tracking data container error. +#[derive(Debug)] +pub enum TrackingDataContainerErrorKind { + InvalidParameters(String), + SchemaError(String), + SchemaWarning(String), + ArrowError(arrow::error::ArrowError), + DataFusionError(datafusion::error::DataFusionError), + ParquetError(parquet::errors::ParquetError), + IOError(std::io::Error), + Internal(String), +} + +impl Display for TrackingDataContainerErrorKind { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + match self { + TrackingDataContainerErrorKind::InvalidParameters(err) => { + write!(f, "invalid parameters: {err}") + } + TrackingDataContainerErrorKind::SchemaError(err) => { + write!(f, "schema error: {err}") + } + TrackingDataContainerErrorKind::SchemaWarning(warn) => { + write!(f, "schema warning: {warn}") + } + TrackingDataContainerErrorKind::ArrowError(err) => { + write!(f, "arrow error: {err}") + } + TrackingDataContainerErrorKind::DataFusionError(err) => { + write!(f, "datafusion error: {err}") + } + TrackingDataContainerErrorKind::ParquetError(err) => { + write!(f, "parquet error: {err}") + } + TrackingDataContainerErrorKind::IOError(err) => { + write!(f, "io error: {err}") + } + TrackingDataContainerErrorKind::Internal(err) => { + write!(f, "internal error: {err}") + } + } + } +} + +/// A `tracking data container` error. +/// +/// Contains a kind and a context. Use the `Display` trait to format the +/// error message with its context. +#[derive(Debug)] +pub struct TrackingDataContainerError { + pub kind: TrackingDataContainerErrorKind, + pub context: Vec, +} + +pub trait ErrorContext { + fn with_context(self, ctx_fn: F) -> Self + where + F: FnOnce() -> S; +} + +impl TrackingDataContainerError { + pub fn new(kind: TrackingDataContainerErrorKind) -> Self { + Self { + kind, + context: vec![], + } + } +} + +impl Display for TrackingDataContainerError { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + write!(f, "{kind}", kind = self.kind)?; + if !self.context.is_empty() { + writeln!(f, "\nContext:")?; + + self.context + .iter() + .enumerate() + .try_for_each(|(idx, ctx_value)| writeln!(f, " {idx}: {ctx_value}"))?; + } + Ok(()) + } +} + +impl Error for TrackingDataContainerError {} + +impl ErrorContext for TrackingDataContainerError { + fn with_context(mut self, ctx_fn: F) -> Self + where + F: FnOnce() -> S, + { + self.context.push(ctx_fn().to_string()); + self + } +} + +impl From for TrackingDataContainerError { + fn from(kind: TrackingDataContainerErrorKind) -> Self { + TrackingDataContainerError::new(kind) + } +} + +impl From for TrackingDataContainerError { + fn from(err: arrow::error::ArrowError) -> Self { + TrackingDataContainerErrorKind::ArrowError(err).into() + } +} + +impl From for TrackingDataContainerError { + fn from(err: datafusion::error::DataFusionError) -> Self { + TrackingDataContainerErrorKind::DataFusionError(err).into() + } +} + +impl From for TrackingDataContainerError { + fn from(err: parquet::errors::ParquetError) -> Self { + TrackingDataContainerErrorKind::ParquetError(err).into() + } +} + +impl From for TrackingDataContainerError { + fn from(err: std::io::Error) -> Self { + TrackingDataContainerErrorKind::IOError(err).into() + } +} + +impl From for TrackingDataContainerError { + fn from(err: String) -> Self { + TrackingDataContainerErrorKind::Internal(err).into() + } +} + +pub type TrackingDataContainerResult = Result; + +impl ErrorContext for TrackingDataContainerResult { + fn with_context(self, ctx_fn: F) -> Self + where + F: FnOnce() -> S, + { + match self { + Ok(_) => self, + Err(e) => Err(e.with_context(ctx_fn)), + } + } +} diff --git a/crates/tdc-core/src/io.rs b/crates/tdc-core/src/io.rs new file mode 100644 index 0000000..723946b --- /dev/null +++ b/crates/tdc-core/src/io.rs @@ -0,0 +1,91 @@ +use crate::datacontainer::TrackingDataContainer; +use crate::error::TrackingDataContainerResult; +use crate::schema::Fields; +use arrow_array::RecordBatch; +use parquet::arrow::ArrowWriter; +use parquet::arrow::arrow_reader::ParquetRecordBatchReaderBuilder; +use parquet::file::properties::WriterProperties; +use std::fs::File; + +impl TrackingDataContainer { + /// Saves the container to a Parquet file. + pub fn save_parquet(&self, path: &str) -> TrackingDataContainerResult<()> { + let file = File::create(path)?; + let batch = RecordBatch::from(self.data.clone()); + + let mut key_value_metadata = Vec::new(); + + if let Some(cs) = &self.coordinate_system { + let cs_json = serde_json::to_string(cs).map_err(|e| e.to_string())?; + key_value_metadata.push(parquet::format::KeyValue::new( + "tdc:coordinate_system".to_string(), + Some(cs_json), + )); + } + + if let Some(om) = &self.orientation { + let om_json = serde_json::to_string(om).map_err(|e| e.to_string())?; + key_value_metadata.push(parquet::format::KeyValue::new( + "tdc:orientation_mode".to_string(), + Some(om_json), + )); + } + + let props_builder = + WriterProperties::builder().set_key_value_metadata(Some(key_value_metadata)); + + let mut writer = ArrowWriter::try_new(file, batch.schema(), Some(props_builder.build()))?; + writer.write(&batch)?; + writer.close()?; + + Ok(()) + } + + /// Loads a container from a Parquet file. + pub fn load_parquet(path: &str) -> TrackingDataContainerResult { + let file = File::open(path)?; + let builder = ParquetRecordBatchReaderBuilder::try_new(file)?; + + let mut coordinate_system = None; + let mut orientation = None; + + if let Some(metadata) = builder.metadata().file_metadata().key_value_metadata() { + for kv in metadata { + if kv.key == "tdc:coordinate_system" { + if let Some(val) = &kv.value { + coordinate_system = + Some(serde_json::from_str(val).map_err(|e| e.to_string())?); + } + } else if kv.key == "tdc:orientation_mode" + && let Some(val) = &kv.value + { + orientation = Some(serde_json::from_str(val).map_err(|e| e.to_string())?); + } + } + } + + let mut reader = builder.build()?; + let batch_result = reader.next(); + let batch = match batch_result { + Some(Ok(b)) => b, + Some(Err(e)) => return Err(e.into()), + None => return Err("Empty parquet file".to_string().into()), + }; + + let schema_fields: Vec<_> = batch + .schema() + .fields() + .iter() + .map(|f| crate::schema::DataField::from_name(f.name())) + .collect(); + let schema = Fields { + fields: schema_fields, + }; + + let mut container = Self::new(batch.into(), schema); + container.coordinate_system = coordinate_system; + container.orientation = orientation; + + Ok(container) + } +} diff --git a/crates/tdc-core/src/lib.rs b/crates/tdc-core/src/lib.rs new file mode 100644 index 0000000..3ddd353 --- /dev/null +++ b/crates/tdc-core/src/lib.rs @@ -0,0 +1,13 @@ +pub mod config; +pub mod datacontainer; +pub mod error; +pub mod io; +pub mod schema; +pub mod selector; + +pub use datacontainer::{LazyTrackingDataContainer, TrackingDataBuilder, TrackingDataContainer}; +pub use schema::{ + Attribute, CoordinateSystem, DataField, Direction, Fields, FrameAttribute, LengthUnit, + OrientationMode, Origin, Period, TrackedEntity, TrackedEntityAttribute, +}; +pub use selector::Select; diff --git a/crates/tdc-core/src/schema/attribute.rs b/crates/tdc-core/src/schema/attribute.rs new file mode 100644 index 0000000..c407a44 --- /dev/null +++ b/crates/tdc-core/src/schema/attribute.rs @@ -0,0 +1,156 @@ +//! Definitions for frame and entity attributes. +//! +//! This module provides traits and enums for representing various data points captured +//! in sports tracking, such as spatial coordinates, velocity, and match metadata. + +use arrow_schema::{DataType, TimeUnit}; +use strum_macros::{AsRefStr, Display, EnumString}; + +/// Common trait for all schema attributes. +pub trait Attribute { + /// Returns the standardized string representation of the attribute. + fn name(&self) -> &str; + + /// Returns the default Apache Arrow data type used to represent this attribute in memory. + fn default_arrow_type(&self) -> DataType; + + /// Returns true if this attribute typically has low cardinality (few unique values). + fn is_low_cardinality(&self) -> bool; +} + +/// Attributes specifically associated with a tracked entity (Player, Ball, or Referee). +/// +/// These attributes represent spatial and physical data points captured for an entity +/// during a match. +#[derive(Clone, Debug, PartialEq, Eq, Hash, EnumString, AsRefStr, Display)] +#[strum(serialize_all = "snake_case")] +pub enum TrackedEntityAttribute { + /// Unique identifier for the entity (e.g., person ID or ball ID). + Id, + /// X-coordinate on the pitch. + X, + /// Y-coordinate on the pitch. + Y, + /// Z-coordinate (altitude/height) on the pitch. + Z, + /// Distance traveled since the last capture point. + Dist, + /// Instantaneous velocity or speed. + Vel, + /// Instantaneous acceleration. + Acc, + /// Latitude (GPS). + Lat, + /// Longitude (GPS). + Long, + /// Whether the entity was visible to the tracking system at this frame. + IsVisible, + /// Cumulative minutes played by the entity. + MinutesPlayed, + + // Team specific + /// Official name of the team or entity. + Name, + /// Jersey color representation (e.g., hex code or name). + JerseyColour, + /// Tactical formation (e.g., "4-3-3"). + Formation, + + /// Catch-all for custom attributes not defined in the core schema (e.g., "heart_rate"). + Other(String), +} + +impl Attribute for TrackedEntityAttribute { + fn name(&self) -> &str { + match self { + Self::Other(s) => s, + _ => self.as_ref(), + } + } + + /// Returns the default Arrow column type for this attribute. + fn default_arrow_type(&self) -> DataType { + use TrackedEntityAttribute::*; + match self { + Id | Other(_) | Name | JerseyColour | Formation => DataType::Utf8, + IsVisible => DataType::Boolean, + X | Y | Z | Dist | Vel | Acc | Lat | Long | MinutesPlayed => DataType::Float32, + } + } + + fn is_low_cardinality(&self) -> bool { + use TrackedEntityAttribute::*; + matches!(self, Formation | JerseyColour) + } +} + +// ----------------------------------------------------------------------------- +// FrameAttribute +// ----------------------------------------------------------------------------- + +/// Attributes associated with a global frame or the match state. +/// +/// These attributes provide context for the entire set of positions captured +/// at a single point in time. +#[derive(Clone, Debug, PartialEq, Eq, Hash, EnumString, AsRefStr, Display)] +#[strum(serialize_all = "snake_case")] +pub enum FrameAttribute { + /// Unique sequence number for the frame. + FrameId, + /// Match period (e.g., 1 for first half, 2 for second half). + Period, + /// Wall-clock timestamp of the capture. + Timestamp, + + /// Unique identifier for the match. + /// + /// "match" is a reserved keyword in Rust, so MatchId is used as an alternative. + /// The strum attribute ensures it parses from the string "match" correctly. + #[strum(serialize = "match")] + MatchId, + + /// Global ball status (e.g., "alive" or "dead"). + BallStatus, + /// Identifier of the team currently in possession of the ball. + BallPossTeamId, + /// Status of ball possession (e.g., "home", "away", "none"). + BallPossStatus, + + /// Name of the data vendor's event stream. + VendorEventName, + /// Name of the data vendor's tracking system. + VendorTrackingName, + + /// Catch-all for custom frame-level metadata. + Other(String), +} + +impl Attribute for FrameAttribute { + fn name(&self) -> &str { + match self { + Self::Other(s) => s, + _ => self.as_ref(), + } + } + + /// Returns the default Arrow column type for this attribute. + fn default_arrow_type(&self) -> DataType { + use FrameAttribute::*; + match self { + FrameId => DataType::UInt64, + Period => DataType::Utf8, + Timestamp => DataType::Timestamp(TimeUnit::Microsecond, None), + BallStatus => DataType::Boolean, + MatchId | BallPossTeamId | BallPossStatus | VendorEventName | VendorTrackingName + | Other(_) => DataType::Utf8, + } + } + + fn is_low_cardinality(&self) -> bool { + use FrameAttribute::*; + matches!( + self, + Period | BallStatus | BallPossStatus | VendorEventName | VendorTrackingName + ) + } +} diff --git a/crates/tdc-core/src/schema/coordinate_system.rs b/crates/tdc-core/src/schema/coordinate_system.rs new file mode 100644 index 0000000..f21fbe1 --- /dev/null +++ b/crates/tdc-core/src/schema/coordinate_system.rs @@ -0,0 +1,216 @@ +//! Models for spatial metadata and coordinate system definitions. +//! +//! This module provides the tools to describe how coordinates are mapped to the +//! physical world, enabling transformations between different providers and standards. + +use crate::error::{TrackingDataContainerErrorKind, TrackingDataContainerResult}; +use serde::{Deserialize, Serialize}; +use strum_macros::{AsRefStr, Display, EnumString}; + +/// Defines a coordinate system for tracking data. +/// +/// A coordinate system combines spatial bounds (ranges), an origin point, axis orientations, +/// and measurement units to provide a complete context for entity positions. +#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)] +pub struct CoordinateSystem { + /// The valid range of X coordinates (e.g., Some((-52.5, 52.5))). + pub x_range: Option<(f64, f64)>, + /// The valid range of Y coordinates (e.g., Some((-34.0, 34.0))). + pub y_range: Option<(f64, f64)>, + /// The location of the (0,0) point. + pub origin: Origin, + /// The direction in which X values increase. + pub x_direction: Direction, + /// The direction in which Y values increase. + pub y_direction: Direction, + /// The physical unit of length for the coordinates. + pub length_unit: Option, +} + +/// The location of the origin (0,0) in a coordinate system. +#[derive( + Clone, Copy, Debug, PartialEq, Eq, Serialize, Deserialize, EnumString, AsRefStr, Display, +)] +#[strum(serialize_all = "snake_case")] +pub enum Origin { + /// The center of the pitch/court. + Center, + /// The top-left corner of the pitch (often used in computer vision). + TopLeft, + /// The bottom-left corner of the pitch. + BottomLeft, +} + +/// Possible axis directions. +#[derive( + Clone, Copy, Debug, PartialEq, Eq, Serialize, Deserialize, EnumString, AsRefStr, Display, +)] +#[strum(serialize_all = "snake_case")] +pub enum Direction { + /// Values increase towards the right. + Right, + /// Values increase towards the left. + Left, + /// Values increase towards the top. + Up, + /// Values increase towards the bottom. + Down, +} + +/// Standardized units of length. +#[derive( + Clone, Copy, Debug, PartialEq, Eq, Serialize, Deserialize, EnumString, AsRefStr, Display, +)] +#[strum(serialize_all = "snake_case")] +pub enum LengthUnit { + /// Standard metric meter. + Meters, + /// Centimeter. + Centimeters, + /// Millimeter. + Millimeters, + /// Yard (approx 0.9144 meters). + Yards, + /// Inch. + Inches, + /// Foot. + Feet, + /// Normalized unit where coordinates represent a percentage of pitch dimensions. + Percent, +} + +/// Logic for normalizing playing direction across periods or possession. +#[derive( + Clone, Copy, Debug, PartialEq, Eq, Serialize, Deserialize, EnumString, AsRefStr, Display, +)] +#[strum(serialize_all = "snake_case")] +pub enum OrientationMode { + /// Coordinates are flipped so the team in possession always attacks from left to right. + BallOwningTeam, + /// The home team attacks left-to-right in the first half and right-to-left in the second. + HomeAway, + /// The away team attacks left-to-right in the first half and right-to-left in the second. + AwayHome, + /// The home team always attacks from left to right, regardless of period. + StaticHomeAway, + /// The away team always attacks from left to right, regardless of period. + StaticAwayHome, +} + +impl CoordinateSystem { + /// Creates a new coordinate system definition. + pub fn new( + x_range: Option<(f64, f64)>, + y_range: Option<(f64, f64)>, + origin: Origin, + x_direction: Direction, + y_direction: Direction, + length_unit: Option, + ) -> Self { + Self { + x_range, + y_range, + origin, + x_direction, + y_direction, + length_unit, + } + } +} + +impl CoordinateSystem { + pub fn get_transform_coeffs( + &self, + to: &CoordinateSystem, + is_x: bool, + ) -> TrackingDataContainerResult<(f64, f64)> { + let (f_min, f_max) = if is_x { self.x_range } else { self.y_range }.ok_or_else(|| { + TrackingDataContainerErrorKind::InvalidParameters( + if is_x { + "Source X range undefined" + } else { + "Source Y range undefined" + } + .to_string(), + ) + })?; + let (t_min, t_max) = if is_x { to.x_range } else { to.y_range }.ok_or_else(|| { + TrackingDataContainerErrorKind::InvalidParameters( + if is_x { + "Target X range undefined" + } else { + "Target Y range undefined" + } + .to_string(), + ) + })?; + + let f_scale = f_max - f_min; + let t_scale = t_max - t_min; + + let f_unit = self.length_unit.unwrap_or(LengthUnit::Meters).multiplier(); + let t_unit = to.length_unit.unwrap_or(LengthUnit::Meters).multiplier(); + + let f_sign = if is_x { + if self.x_direction == Direction::Right { + 1.0 + } else { + -1.0 + } + } else if self.y_direction == Direction::Up { + 1.0 + } else { + -1.0 + }; + let t_sign = if is_x { + if to.x_direction == Direction::Right { + 1.0 + } else { + -1.0 + } + } else if to.y_direction == Direction::Up { + 1.0 + } else { + -1.0 + }; + + // Point mapping: physical center maps to physical center. + let f_center_coord = if self.origin == Origin::Center { + 0.0 + } else { + (f_min + f_max) / 2.0 + }; + let t_center_coord = if to.origin == Origin::Center { + 0.0 + } else { + (t_min + t_max) / 2.0 + }; + + let mul = (t_scale / f_scale) * (f_unit / t_unit) * f_sign * t_sign; + let off = t_center_coord - f_center_coord * mul; + + Ok((mul, off)) + } + + /// Returns the multiplier and offset to flip a coordinate across the center of this system. + pub fn get_flip_coeffs(&self, is_x: bool) -> (f64, f64) { + let (min, max) = if is_x { self.x_range } else { self.y_range }.unwrap_or((-50.0, 50.0)); + let center = (min + max) / 2.0; + // f(v) = center - (v - center) = 2*center - v + (-1.0, 2.0 * center) + } +} + +impl LengthUnit { + pub fn multiplier(&self) -> f64 { + match self { + LengthUnit::Meters => 1.0, + LengthUnit::Centimeters => 0.01, + LengthUnit::Millimeters => 0.001, + LengthUnit::Yards => 0.9144, + LengthUnit::Feet => 0.3048, + LengthUnit::Inches => 0.0254, + LengthUnit::Percent => 1.0, + } + } +} diff --git a/crates/tdc-core/src/schema/entity.rs b/crates/tdc-core/src/schema/entity.rs new file mode 100644 index 0000000..76d4b36 --- /dev/null +++ b/crates/tdc-core/src/schema/entity.rs @@ -0,0 +1,28 @@ +//! Definitions for tracked entities. + +/// Represents an entity on the pitch/court. +/// +/// Tracked entities are the primary subjects of the tracking data, providing the context +/// for spatial coordinates and other time-series attributes. +#[derive(Debug, Clone, PartialEq, Eq, Hash)] +pub enum TrackedEntity { + /// The match ball. + Ball, + /// A player belonging to a specific team. + Player { + /// The unique identifier for the team. + team_id: String, + /// The unique identifier for the player. + player_id: String, + }, + /// A match official. + Referee { + /// The unique identifier for the referee. + referee_id: String, + }, + /// Represents team-level data (e.g., formation or possession) not tied to a specific player. + Team { + /// The unique identifier for the team. + team_id: String, + }, +} diff --git a/crates/tdc-core/src/schema/field.rs b/crates/tdc-core/src/schema/field.rs new file mode 100644 index 0000000..2cee717 --- /dev/null +++ b/crates/tdc-core/src/schema/field.rs @@ -0,0 +1,150 @@ +//! Logic for identifying and parsing individual data columns. + +use super::{Attribute, FrameAttribute, TrackedEntity, TrackedEntityAttribute}; +use arrow_schema::DataType; +use std::fmt; +use std::str::FromStr; + +/// Represents a single column (field) in a tracking dataset. +/// +/// A `DataField` maps an Arrow column name to its domain-specific meaning, +/// such as a player's X-coordinate or a match period. +#[derive(Clone, Debug, PartialEq, Eq, Hash)] +pub enum DataField { + /// A global attribute for the entire frame. + Frame { + /// The specific frame-level attribute. + attribute: FrameAttribute, + }, + /// An attribute tied to a specific tracked entity. + TrackedEntity { + /// The entity this field belongs to. + entity: TrackedEntity, + /// The specific attribute being tracked. + attribute: TrackedEntityAttribute, + }, +} + +impl DataField { + /// Serializes the DataField back into its standardized hierarchical string representation. + /// + /// # Examples + /// + /// - Player coordinate: `teams/home/players/10/x` + /// - Ball coordinate: `ball/z` + /// - Global metadata: `frame_id` + pub fn name(&self) -> String { + match self { + DataField::Frame { attribute } => match attribute { + FrameAttribute::VendorEventName => "vendor/event/name".to_string(), + FrameAttribute::VendorTrackingName => "vendor/tracking/name".to_string(), + FrameAttribute::MatchId => "match/id".to_string(), + _ => attribute.name().to_string(), + }, + DataField::TrackedEntity { entity, attribute } => { + let attr_name = attribute.name(); + match entity { + TrackedEntity::Ball => format!("ball/{attr_name}"), + TrackedEntity::Player { team_id, player_id } => { + format!("teams/{team_id}/players/{player_id}/{attr_name}") + } + TrackedEntity::Referee { referee_id } => { + format!("referees/{referee_id}/{attr_name}") + } + TrackedEntity::Team { team_id } => { + format!("teams/{team_id}/{attr_name}") + } + } + } + } + } + + /// Parses a hierarchical column name path into a domain-specific `DataField`. + /// + /// This method implements the inverse of [`DataField::name`]. + pub fn from_name(path: &str) -> Self { + let parts: Vec<&str> = path.split('/').collect(); + + match parts.as_slice() { + ["teams", tid, "players", pid, ..] => { + let attr_str = parts[4..].join("/"); + DataField::TrackedEntity { + entity: TrackedEntity::Player { + team_id: tid.to_string(), + player_id: pid.to_string(), + }, + attribute: Self::parse_attr(&attr_str), + } + } + ["teams", tid, ..] => { + let attr_str = parts[2..].join("/"); + DataField::TrackedEntity { + entity: TrackedEntity::Team { + team_id: tid.to_string(), + }, + attribute: Self::parse_attr(&attr_str), + } + } + ["ball", ..] => { + let attr_str = parts[1..].join("/"); + DataField::TrackedEntity { + entity: TrackedEntity::Ball, + attribute: Self::parse_attr(&attr_str), + } + } + ["referees", rid, ..] => { + let attr_str = parts[2..].join("/"); + DataField::TrackedEntity { + entity: TrackedEntity::Referee { + referee_id: rid.to_string(), + }, + attribute: Self::parse_attr(&attr_str), + } + } + ["vendor", "event", "name"] => DataField::Frame { + attribute: FrameAttribute::VendorEventName, + }, + ["vendor", "tracking", "name"] => DataField::Frame { + attribute: FrameAttribute::VendorTrackingName, + }, + ["match", "id"] => DataField::Frame { + attribute: FrameAttribute::MatchId, + }, + [attr] => DataField::Frame { + attribute: FrameAttribute::from_str(attr) + .unwrap_or_else(|_| FrameAttribute::Other(attr.to_string())), + }, + _ => DataField::Frame { + attribute: FrameAttribute::Other(path.to_string()), + }, + } + } + + /// Helper to safely parse attributes with a fallback to `Other`. + fn parse_attr(s: &str) -> TrackedEntityAttribute { + TrackedEntityAttribute::from_str(s) + .unwrap_or_else(|_| TrackedEntityAttribute::Other(String::from(s))) + } + + /// Returns the recommended Apache Arrow data type for this field. + pub fn default_arrow_type(&self) -> DataType { + match self { + DataField::Frame { attribute } => attribute.default_arrow_type(), + DataField::TrackedEntity { attribute, .. } => attribute.default_arrow_type(), + } + } + + /// Returns true if this field typically has low cardinality. + pub fn is_low_cardinality(&self) -> bool { + match self { + DataField::Frame { attribute } => attribute.is_low_cardinality(), + DataField::TrackedEntity { attribute, .. } => attribute.is_low_cardinality(), + } + } +} + +impl fmt::Display for DataField { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + write!(f, "{}", self.name()) + } +} diff --git a/crates/tdc-core/src/schema/fields.rs b/crates/tdc-core/src/schema/fields.rs new file mode 100644 index 0000000..a883f94 --- /dev/null +++ b/crates/tdc-core/src/schema/fields.rs @@ -0,0 +1,355 @@ +//! Logic for managing and validating a collection of data fields. + +use super::{DataField, FrameAttribute, TrackedEntity, TrackedEntityAttribute}; +use crate::error::{ + TrackingDataContainerError, TrackingDataContainerErrorKind, TrackingDataContainerResult, +}; + +/// Represents the ordered collection of fields (columns) in a tracking dataset. +#[derive(Clone, Debug, PartialEq)] +pub struct Fields { + /// The underlying list of domain-specific data fields. + pub fields: Vec, +} + +impl Fields { + /// Creates a new `Fields` collection from a list of hierarchical column names. + pub fn new(columns: Vec) -> Self { + let fields = columns.iter().map(|c| DataField::from_name(c)).collect(); + Self { fields } + } + + /// Returns the index of the specified field, if it exists. + pub fn get_index(&self, field: &DataField) -> Option { + self.fields.iter().position(|f| f == field) + } + + /// Returns the indices of the specified fields in the current schema. + pub fn get_column_indices(&self, fields: &[DataField]) -> Vec> { + let field_map: std::collections::HashMap = self + .fields + .iter() + .enumerate() + .map(|(i, f)| (f.name(), i)) + .collect(); + + fields + .iter() + .map(|f| field_map.get(&f.name()).copied()) + .collect() + } + + /// Returns the field at the specified index, if it exists. + pub fn get(&self, index: usize) -> Option<&DataField> { + self.fields.get(index) + } + + /// Returns the number of fields in the collection. + pub fn len(&self) -> usize { + self.fields.len() + } + + /// Returns `true` if the collection contains no fields. + pub fn is_empty(&self) -> bool { + self.fields.is_empty() + } + + /// Returns a list of unique player IDs present in the fields, optionally filtered by team. + pub fn players(&self, team_id: Option<&str>) -> Vec { + let mut players = std::collections::HashSet::new(); + for field in &self.fields { + if let DataField::TrackedEntity { + entity: + TrackedEntity::Player { + team_id: tid, + player_id, + }, + .. + } = field + && team_id.is_none_or(|t| t == tid) + { + players.insert(player_id.clone()); + } + } + let mut result: Vec = players.into_iter().collect(); + result.sort(); + result + } + + /// Returns a list of unique player entities present in the fields, optionally filtered by team. + pub fn player_entities(&self, team_id: Option<&str>) -> Vec { + let mut players = std::collections::HashSet::new(); + for field in &self.fields { + if let DataField::TrackedEntity { + entity: entity @ TrackedEntity::Player { team_id: tid, .. }, + .. + } = field + && team_id.is_none_or(|t| t == tid) + { + players.insert(entity.clone()); + } + } + let mut result: Vec = players.into_iter().collect(); + result.sort_by_key(|e| match e { + TrackedEntity::Player { team_id, player_id } => (team_id.clone(), player_id.clone()), + _ => unreachable!(), + }); + result + } + + /// Returns a list of unique team IDs present in the fields. + pub fn teams(&self) -> Vec { + let mut teams = std::collections::HashSet::new(); + for field in &self.fields { + match field { + DataField::TrackedEntity { + entity: TrackedEntity::Team { team_id }, + .. + } => { + teams.insert(team_id.clone()); + } + DataField::TrackedEntity { + entity: TrackedEntity::Player { team_id, .. }, + .. + } => { + teams.insert(team_id.clone()); + } + _ => {} + } + } + let mut result: Vec = teams.into_iter().collect(); + result.sort(); + result + } + + /// Returns a list of unique team entities present in the fields. + pub fn team_entities(&self) -> Vec { + let mut teams = std::collections::HashSet::new(); + for field in &self.fields { + match field { + DataField::TrackedEntity { + entity: entity @ TrackedEntity::Team { .. }, + .. + } => { + teams.insert(entity.clone()); + } + DataField::TrackedEntity { + entity: TrackedEntity::Player { team_id, .. }, + .. + } => { + teams.insert(TrackedEntity::Team { + team_id: team_id.clone(), + }); + } + _ => {} + } + } + let mut result: Vec = teams.into_iter().collect(); + result.sort_by_key(|e| match e { + TrackedEntity::Team { team_id } => team_id.clone(), + _ => unreachable!(), + }); + result + } + + /// Returns a list of unique referee IDs present in the fields. + pub fn referees(&self) -> Vec { + let mut referees = std::collections::HashSet::new(); + for field in &self.fields { + if let DataField::TrackedEntity { + entity: TrackedEntity::Referee { referee_id }, + .. + } = field + { + referees.insert(referee_id.clone()); + } + } + let mut result: Vec = referees.into_iter().collect(); + result.sort(); + result + } + + /// Returns a list of unique referee entities present in the fields. + pub fn referee_entities(&self) -> Vec { + let mut referees = std::collections::HashSet::new(); + for field in &self.fields { + if let DataField::TrackedEntity { + entity: entity @ TrackedEntity::Referee { .. }, + .. + } = field + { + referees.insert(entity.clone()); + } + } + let mut result: Vec = referees.into_iter().collect(); + result.sort_by_key(|e| match e { + TrackedEntity::Referee { referee_id } => referee_id.clone(), + _ => unreachable!(), + }); + result + } + + /// Validates the fields against the **Common Data Format Tracking Data Schema (Version 0.2.2)**. + /// + /// This method checks for the presence of mandatory fields such as `frame_id`, `ball/x/y/z`, + /// and team identifiers. It also ensures that both the home and away teams have at least + /// one player with required coordinates. + /// + /// # Errors + /// + /// Returns a [`TrackingDataContainerError`] of kind `SchemaError` if required fields are missing. + pub fn validate(&self) -> TrackingDataContainerResult<()> { + let mut missing = Vec::new(); + + // 1. Required top-level fields + if !self.has_frame_attr(FrameAttribute::FrameId) { + missing.push("frame_id"); + } + if !self.has_frame_attr(FrameAttribute::Timestamp) { + missing.push("timestamp"); + } + if !self.has_frame_attr(FrameAttribute::Period) { + missing.push("period"); + } + if !self.has_frame_attr(FrameAttribute::MatchId) { + missing.push("match/id"); + } + + // 2. Required ball fields + if !self.has_ball_attr(TrackedEntityAttribute::X) { + missing.push("ball/x"); + } + if !self.has_ball_attr(TrackedEntityAttribute::Y) { + missing.push("ball/y"); + } + if !self.has_ball_attr(TrackedEntityAttribute::Z) { + missing.push("ball/z"); + } + + // 3. Required team fields + let home_exists = self.has_team("home"); + let away_exists = self.has_team("away"); + + if !home_exists { + missing.push("teams/home"); + } else { + if !self.has_team_attr("home", TrackedEntityAttribute::Id) { + missing.push("teams/home/id"); + } + if !self.has_any_player("home") { + missing.push("teams/home/players"); + } + } + + if !away_exists { + missing.push("teams/away"); + } else { + if !self.has_team_attr("away", TrackedEntityAttribute::Id) { + missing.push("teams/away/id"); + } + if !self.has_any_player("away") { + missing.push("teams/away/players"); + } + } + + if !missing.is_empty() { + return Err(TrackingDataContainerError::new( + TrackingDataContainerErrorKind::SchemaError(format!( + "Missing required fields: {}", + missing.join(", ") + )), + )); + } + + // 4. Check for unsupported fields + let mut unsupported = Vec::new(); + for field in &self.fields { + match field { + DataField::Frame { + attribute: FrameAttribute::Other(name), + } => { + unsupported.push(name.clone()); + } + DataField::TrackedEntity { + attribute: TrackedEntityAttribute::Other(_), + .. + } => { + unsupported.push(field.name()); + } + _ => {} + } + } + + if !unsupported.is_empty() { + return Err(TrackingDataContainerError::new( + TrackingDataContainerErrorKind::SchemaWarning(format!( + "Found unsupported fields: {}", + unsupported.join(", ") + )), + )); + } + + Ok(()) + } + + /// Internal helper to check for a specific global frame attribute. + fn has_frame_attr(&self, attr: FrameAttribute) -> bool { + self.fields + .iter() + .any(|f| matches!(f, DataField::Frame { attribute } if *attribute == attr)) + } + + /// Internal helper to check for a specific ball attribute. + fn has_ball_attr(&self, attr: TrackedEntityAttribute) -> bool { + self.fields.iter().any(|f| matches!(f, DataField::TrackedEntity { entity: TrackedEntity::Ball, attribute } if *attribute == attr)) + } + + /// Internal helper to check if any field belongs to a specific team (either team-level or player-level). + fn has_team(&self, tid: &str) -> bool { + self.fields.iter().any(|f| match f { + DataField::TrackedEntity { + entity: TrackedEntity::Team { team_id }, + .. + } => team_id == tid, + DataField::TrackedEntity { + entity: TrackedEntity::Player { team_id, .. }, + .. + } => team_id == tid, + _ => false, + }) + } + + /// Internal helper to check for a specific team-level attribute. + fn has_team_attr(&self, tid: &str, attr: TrackedEntityAttribute) -> bool { + self.fields.iter().any(|f| matches!(f, DataField::TrackedEntity { entity: TrackedEntity::Team { team_id }, attribute } if team_id == tid && *attribute == attr)) + } + + /// Internal helper to verify that at least one player for a team has required spatial data. + fn has_any_player(&self, tid: &str) -> bool { + let mut players_with_x = std::collections::HashSet::new(); + let mut players_with_y = std::collections::HashSet::new(); + + for f in &self.fields { + if let DataField::TrackedEntity { + entity: TrackedEntity::Player { team_id, player_id }, + attribute, + } = f + && team_id == tid + { + if *attribute == TrackedEntityAttribute::X { + players_with_x.insert(player_id); + } + if *attribute == TrackedEntityAttribute::Y { + players_with_y.insert(player_id); + } + } + } + + !players_with_x.is_empty() + && !players_with_y.is_empty() + && players_with_x + .intersection(&players_with_y) + .next() + .is_some() + } +} diff --git a/crates/tdc-core/src/schema/mod.rs b/crates/tdc-core/src/schema/mod.rs new file mode 100644 index 0000000..83a5928 --- /dev/null +++ b/crates/tdc-core/src/schema/mod.rs @@ -0,0 +1,13 @@ +pub mod attribute; +pub mod coordinate_system; +pub mod entity; +pub mod field; +pub mod fields; +pub mod period; + +pub use attribute::{Attribute, FrameAttribute, TrackedEntityAttribute}; +pub use coordinate_system::{CoordinateSystem, Direction, LengthUnit, OrientationMode, Origin}; +pub use entity::TrackedEntity; +pub use field::DataField; +pub use fields::Fields; +pub use period::Period; diff --git a/crates/tdc-core/src/schema/period.rs b/crates/tdc-core/src/schema/period.rs new file mode 100644 index 0000000..7f9b0f1 --- /dev/null +++ b/crates/tdc-core/src/schema/period.rs @@ -0,0 +1,20 @@ +use serde::{Deserialize, Serialize}; +use strum_macros::{AsRefStr, Display, EnumString}; + +/// Represents the different periods of a match. +#[derive( + Clone, Copy, Debug, PartialEq, Eq, Hash, Serialize, Deserialize, EnumString, AsRefStr, Display, +)] +#[strum(serialize_all = "snake_case")] +pub enum Period { + /// The first half of a regular match. + FirstHalf, + /// The second half of a regular match. + SecondHalf, + /// The first half of extra time. + FirstHalfExtratime, + /// The second half of extra time. + SecondHalfExtratime, + /// Penalty shootout. + Shootout, +} diff --git a/crates/tdc-core/src/selector/expr.rs b/crates/tdc-core/src/selector/expr.rs new file mode 100644 index 0000000..742fe52 --- /dev/null +++ b/crates/tdc-core/src/selector/expr.rs @@ -0,0 +1,128 @@ +use crate::schema::attribute::Attribute; +use crate::schema::{DataField, Fields, TrackedEntity}; + +#[derive(Debug, Clone)] +pub enum Expr { + MatchAll, + Field(DataField), + FieldName(String), + Ball, + Players { + team_id: Option, + }, + Team { + team_id: String, + }, + Attributes { + inner: Box, + names: Vec, + }, + Wildcard(String), +} + +pub fn expand_expr(container: &impl crate::selector::expr::HasSchema, expr: &Expr) -> Vec { + let schema = container.schema(); + match expr { + Expr::MatchAll => schema.fields.iter().map(|f| f.name()).collect(), + Expr::FieldName(name) => vec![name.clone()], + Expr::Field(field) => vec![field.name()], + Expr::Ball => schema + .fields + .iter() + .filter(|f| { + matches!( + f, + DataField::TrackedEntity { + entity: TrackedEntity::Ball, + .. + } + ) + }) + .map(|f| f.name()) + .collect(), + Expr::Players { team_id } => schema + .fields + .iter() + .filter(|f| { + if let DataField::TrackedEntity { + entity: TrackedEntity::Player { team_id: tid, .. }, + .. + } = f + { + team_id.is_none() || team_id.as_ref() == Some(tid) + } else { + false + } + }) + .map(|f| f.name()) + .collect(), + Expr::Team { team_id } => schema + .fields + .iter() + .filter(|f| { + if let DataField::TrackedEntity { + entity: TrackedEntity::Team { team_id: tid }, + .. + } = f + { + team_id == tid + } else { + false + } + }) + .map(|f| f.name()) + .collect(), + Expr::Attributes { inner, names } => { + let base = expand_expr(container, inner); + base.into_iter() + .filter(|name| { + let field = DataField::from_name(name); + match &field { + DataField::Frame { attribute } => { + names.contains(&attribute.name().to_string()) + } + DataField::TrackedEntity { attribute, .. } => { + names.contains(&attribute.name().to_string()) + } + } + }) + .collect() + } + Expr::Wildcard(pattern) => { + let mut result = std::collections::HashSet::new(); + + // Truly robust glob-to-regex + let mut regex_str = pattern.replace(".", r"\."); + + // Replace ** with a special marker first + regex_str = regex_str.replace("/**/", "___DOUBLE_STAR_MID___"); + regex_str = regex_str.replace("**/", "___DOUBLE_STAR_START___"); + regex_str = regex_str.replace("/**", "___DOUBLE_STAR_END___"); + regex_str = regex_str.replace("**", "___DOUBLE_STAR_ANY___"); + + // Replace * + regex_str = regex_str.replace("*", "[^/]*"); + + // Restore markers with regex equivalents + regex_str = regex_str.replace("___DOUBLE_STAR_MID___", "/(?:.*/)?"); + regex_str = regex_str.replace("___DOUBLE_STAR_START___", "(?:.*/)?"); + regex_str = regex_str.replace("___DOUBLE_STAR_END___", "(?:/.*)?"); + regex_str = regex_str.replace("___DOUBLE_STAR_ANY___", ".*"); + + let re = regex::Regex::new(&format!("^{}$", regex_str)).unwrap(); + for field in &schema.fields { + let name = field.name(); + if re.is_match(&name) { + result.insert(name); + } + } + let mut final_list: Vec = result.into_iter().collect(); + final_list.sort(); + final_list + } + } +} + +pub trait HasSchema { + fn schema(&self) -> &Fields; +} diff --git a/crates/tdc-core/src/selector/mod.rs b/crates/tdc-core/src/selector/mod.rs new file mode 100644 index 0000000..3a92afa --- /dev/null +++ b/crates/tdc-core/src/selector/mod.rs @@ -0,0 +1,19 @@ +pub mod expr; + +use crate::error::TrackingDataContainerResult; +use crate::schema::DataField; + +pub trait Select: expr::HasSchema + Sized { + /// Selects a subset of columns from the container. + fn select(&self, fields: Vec) -> TrackingDataContainerResult; + + /// Selects columns based on a selection expression. + fn select_expr(&self, expr: &expr::Expr) -> TrackingDataContainerResult { + let field_names = expr::expand_expr(self, expr); + let fields = field_names + .into_iter() + .map(|name| DataField::from_name(&name)) + .collect(); + self.select(fields) + } +} diff --git a/crates/tdc-core/tests/test_builder.rs b/crates/tdc-core/tests/test_builder.rs new file mode 100644 index 0000000..e50a43c --- /dev/null +++ b/crates/tdc-core/tests/test_builder.rs @@ -0,0 +1,84 @@ +use arrow_array::Array; +use arrow_schema::DataType; +use tdc_core::TrackingDataBuilder; + +#[test] +fn test_builder_basic_ingestion() { + let mut builder = TrackingDataBuilder::new(); + builder.insert_int(1, "period", 1); + builder.insert_float(1, "ball/x", 10.5); + builder.insert_float(2, "ball/x", 20.0); + builder.insert_int(2, "period", 1); + + let container = builder.build().unwrap(); + assert_eq!(container.num_rows(), 2); + + // Check columns + let fields = container.data.fields(); + assert!(fields.iter().any(|f| f.name() == "frame_id")); + assert!(fields.iter().any(|f| f.name() == "period")); + assert!(fields.iter().any(|f| f.name() == "ball/x")); +} + +#[test] +fn test_builder_dictionary_encoding() { + let mut builder = TrackingDataBuilder::new(); + // "period" is marked as low-cardinality in our schema + builder.insert_string(1, "period", "first_half"); + builder.insert_string(2, "period", "first_half"); + builder.insert_string(3, "period", "second_half"); + + let container = builder.build().unwrap(); + let col_idx = container + .data + .fields() + .iter() + .position(|f| f.name() == "period") + .unwrap(); + let array = container.data.column(col_idx); + + assert!(matches!(array.data_type(), DataType::Dictionary(_, _))); +} + +#[test] +fn test_builder_merge() { + let mut builder1 = TrackingDataBuilder::new(); + builder1.insert_float(1, "ball/x", 1.0); + + let mut builder2 = TrackingDataBuilder::new(); + builder2.insert_float(2, "ball/x", 2.0); + + builder1.merge(builder2); + let container = builder1.build().unwrap(); + + assert_eq!(container.num_rows(), 2); +} + +#[test] +fn test_builder_out_of_order() { + let mut builder = TrackingDataBuilder::new(); + builder.insert_float(10, "ball/x", 10.0); + builder.insert_float(1, "ball/x", 1.0); + + let container = builder.build().unwrap(); + assert_eq!(container.num_rows(), 2); +} + +#[test] +fn test_builder_type_conversion() { + let mut builder = TrackingDataBuilder::new(); + let col = "teams/home/players/1/vel"; // Defaults to Float32 + builder.insert_int(1, col, 10); + builder.insert_float(2, col, 20.5); + + let container = builder.build().unwrap(); + let col_idx = container + .data + .fields() + .iter() + .position(|f| f.name() == col) + .unwrap(); + let array = container.data.column(col_idx); + + assert_eq!(array.data_type(), &DataType::Float32); +} diff --git a/crates/tdc-core/tests/test_datacontainer.rs b/crates/tdc-core/tests/test_datacontainer.rs new file mode 100644 index 0000000..ab27749 --- /dev/null +++ b/crates/tdc-core/tests/test_datacontainer.rs @@ -0,0 +1,308 @@ +use arrow_array::{Float32Array, Float64Array}; +use datafusion::prelude::*; +use std::sync::Arc; +use tdc_core::{ + CoordinateSystem, DataField, Direction, LengthUnit, OrientationMode, Origin, Select, + TrackingDataBuilder, +}; + +fn create_test_container() -> tdc_core::TrackingDataContainer { + let mut builder = TrackingDataBuilder::new(); + builder.insert_float(1, "ball/x", 10.0); + builder.insert_float(1, "ball/y", 20.0); + builder.insert_string(1, "period", "first_half"); + builder.insert_string(1, "ball_poss_team_id", "home"); + + builder.insert_float(2, "ball/x", -10.0); + builder.insert_float(2, "ball/y", -20.0); + builder.insert_string(2, "period", "second_half"); + builder.insert_string(2, "ball_poss_team_id", "away"); + + builder.build().unwrap() +} + +#[test] +fn test_container_basics() { + let container = create_test_container(); + assert_eq!(container.num_rows(), 2); + assert!(!container.is_empty()); +} + +#[test] +fn test_container_select() { + let container = create_test_container(); + let selected = container + .select(vec![ + DataField::from_name("ball/x"), + DataField::from_name("period"), + ]) + .unwrap(); + + assert_eq!(selected.num_rows(), 2); + assert_eq!(selected.schema.fields.len(), 2); + assert_eq!(selected.schema.fields[0].name(), "ball/x"); + assert_eq!(selected.schema.fields[1].name(), "period"); +} + +#[test] +fn test_container_with_column() { + let container = create_test_container(); + let new_data: Arc = Arc::new(Float64Array::from(vec![1.0, 2.0])); + let updated = container.with_columns(vec![DataField::from_name("custom/val")], vec![new_data]); + + assert_eq!( + updated.schema.fields.len(), + container.schema.fields.len() + 1 + ); + assert!( + updated + .schema + .fields + .iter() + .any(|f: &DataField| f.name() == "custom/val") + ); +} + +#[test] +fn test_container_with_expr_column() { + let container = create_test_container(); + let ctx = SessionContext::new(); + let rt = tokio::runtime::Builder::new_current_thread() + .enable_all() + .build() + .unwrap(); + // Add a column that is ball/x * 2 + let updated = container + .with_expr_columns_ctx( + vec!["ball/x_double".to_string()], + vec![col("ball/x").mul(lit(2.0))], + &ctx, + rt.handle(), + ) + .unwrap(); + + let idx = updated + .schema + .fields + .iter() + .position(|f: &DataField| f.name() == "ball/x_double") + .unwrap(); + let array = updated + .data + .column(idx) + .as_any() + .downcast_ref::() + .unwrap(); + + assert_eq!(array.value(0), 20.0); + assert_eq!(array.value(1), -20.0); +} + +#[test] +fn test_container_transform_coordinates() { + let mut container = create_test_container(); + + // Define a source system (Center origin, 105x68 meters) + let source_cs = CoordinateSystem::new( + Some((-52.5, 52.5)), + Some((-34.0, 34.0)), + Origin::Center, + Direction::Right, + Direction::Up, + Some(LengthUnit::Meters), + ); + container = container.with_coordinate_system(source_cs); + + // Define a target system (TopLeft origin, same dimensions) + let target_cs = CoordinateSystem::new( + Some((0.0, 105.0)), + Some((0.0, 68.0)), + Origin::TopLeft, + Direction::Right, + Direction::Down, + Some(LengthUnit::Meters), + ); + + let transformed = container.transform_coordinates(target_cs).unwrap(); + + // ball/x: 10.0 in center-based (-52.5 to 52.5) should be 52.5 + 10.0 = 62.5 in TL-based + let x_idx = transformed + .schema + .fields + .iter() + .position(|f| f.name() == "ball/x") + .unwrap(); + let x_array = transformed + .data + .column(x_idx) + .as_any() + .downcast_ref::() + .unwrap(); + assert_eq!(x_array.value(0), 62.5f32); + + // ball/y: 20.0 in center-based (Up is positive) should be 34.0 - 20.0 = 14.0 in TL-based (Down is positive) + let y_idx = transformed + .schema + .fields + .iter() + .position(|f| f.name() == "ball/y") + .unwrap(); + let y_array = transformed + .data + .column(y_idx) + .as_any() + .downcast_ref::() + .unwrap(); + assert_eq!(y_array.value(0), 14.0f32); +} + +#[test] +fn test_container_transform_orientation() { + let container = create_test_container().with_orientation(OrientationMode::StaticHomeAway); + assert_eq!(container.orientation, Some(OrientationMode::StaticHomeAway)); + + // HomeAway: flips second half + let oriented = container + .transform_orientation(OrientationMode::HomeAway) + .unwrap(); + assert_eq!(oriented.orientation, Some(OrientationMode::HomeAway)); + + let x_idx = oriented + .schema + .fields + .iter() + .position(|f| f.name() == "ball/x") + .unwrap(); + let x_array = oriented + .data + .column(x_idx) + .as_any() + .downcast_ref::() + .unwrap(); + + // Frame 1 (first half): x=10.0 -> remains 10.0 + assert_eq!(x_array.value(0), 10.0f32); + // Frame 2 (second half): x=-10.0 -> flipped to 10.0 + assert_eq!(x_array.value(1), 10.0f32); + + // BallOwningTeam: flips when away team has possession + let oriented_poss = container + .transform_orientation(OrientationMode::BallOwningTeam) + .unwrap(); + assert_eq!( + oriented_poss.orientation, + Some(OrientationMode::BallOwningTeam) + ); + let x_array_poss = oriented_poss + .data + .column(x_idx) + .as_any() + .downcast_ref::() + .unwrap(); + + // Frame 1 (home poss): x=10.0 -> remains 10.0 + assert_eq!(x_array_poss.value(0), 10.0f32); + // Frame 2 (away poss): x=-10.0 -> flipped to 10.0 + assert_eq!(x_array_poss.value(1), 10.0f32); +} + +#[test] +fn test_container_transform_orientation_double() { + let container = create_test_container().with_orientation(OrientationMode::StaticHomeAway); + + // First transform: to AwayHome (flips first half) + let oriented1 = container + .transform_orientation(OrientationMode::AwayHome) + .unwrap(); + let x_idx = oriented1 + .schema + .fields + .iter() + .position(|f| f.name() == "ball/x") + .unwrap(); + let x_array1 = oriented1 + .data + .column(x_idx) + .as_any() + .downcast_ref::() + .unwrap(); + + // Frame 1 (first half): x=10.0 -> flipped to -10.0 + assert_eq!(x_array1.value(0), -10.0f32); + // Frame 2 (second half): x=-10.0 -> remains -10.0 + assert_eq!(x_array1.value(1), -10.0f32); + + // Second transform: from AwayHome to HomeAway (flips second half, un-flips first half) + let oriented2 = oriented1 + .transform_orientation(OrientationMode::HomeAway) + .unwrap(); + let x_array2 = oriented2 + .data + .column(x_idx) + .as_any() + .downcast_ref::() + .unwrap(); + + // Frame 1 (first half): was -10.0 -> should be 10.0 again + assert_eq!(x_array2.value(0), 10.0f32); + // Frame 2 (second half): was -10.0 -> should be 10.0 (flipped from original -10.0) + assert_eq!(x_array2.value(1), 10.0f32); +} + +#[test] +fn test_container_transform_orientation_fails_without_orientation() { + let mut builder = TrackingDataBuilder::new(); + builder.insert_float(1, "ball/x", 10.0); + builder.insert_string(1, "period", "first_half"); + let container = builder.build().unwrap(); + assert_eq!(container.orientation, None); + + let result = container.transform_orientation(OrientationMode::AwayHome); + assert!(result.is_err()); + assert_eq!( + result.unwrap_err().to_string(), + "invalid parameters: No orientation" + ); +} + +#[test] +fn test_container_with_orientation() { + let container = create_test_container(); + let updated = container.with_orientation(OrientationMode::StaticHomeAway); + assert_eq!(updated.orientation, Some(OrientationMode::StaticHomeAway)); +} + +#[test] +fn test_container_transform_coordinates_fails_without_system() { + let container = create_test_container(); + let target_cs = CoordinateSystem::new( + None, + None, + Origin::Center, + Direction::Right, + Direction::Up, + None, + ); + let result = container.transform_coordinates(target_cs); + assert!(result.is_err()); + assert_eq!( + result.unwrap_err().to_string(), + "invalid parameters: No coordinate system" + ); +} + +#[test] +fn test_container_transform_orientation_fails_without_period() { + let container = create_test_container().with_orientation(OrientationMode::StaticHomeAway); + // Select only ball/x to remove the period column + let container_no_period = container + .select(vec![DataField::from_name("ball/x")]) + .unwrap(); + + let result = container_no_period.transform_orientation(OrientationMode::HomeAway); + assert!(result.is_err()); + assert_eq!( + result.unwrap_err().to_string(), + "schema error: Missing period column" + ); +} diff --git a/crates/tdc-core/tests/test_fields.rs b/crates/tdc-core/tests/test_fields.rs new file mode 100644 index 0000000..6d783ac --- /dev/null +++ b/crates/tdc-core/tests/test_fields.rs @@ -0,0 +1,54 @@ +use tdc_core::schema::{Fields, TrackedEntity}; + +#[test] +fn test_fields_entities() { + let cols = vec![ + "frame_id".to_string(), + "teams/home/players/1/x".to_string(), + "teams/home/players/2/x".to_string(), + "teams/away/players/10/x".to_string(), + "teams/home/id".to_string(), + "teams/away/formation".to_string(), + "referees/ref_1/x".to_string(), + "ball/x".to_string(), + ]; + let fields = Fields::new(cols); + + // Test players + let players = fields.players(None); + assert_eq!(players.len(), 3); + assert!(players.contains(&"1".to_string())); + assert!(players.contains(&"2".to_string())); + assert!(players.contains(&"10".to_string())); + + let home_players = fields.players(Some("home")); + assert_eq!(home_players.len(), 2); + assert!(home_players.contains(&"1".to_string())); + assert!(home_players.contains(&"2".to_string())); + + // Test teams + let teams = fields.teams(); + assert_eq!(teams.len(), 2); + assert!(teams.contains(&"home".to_string())); + assert!(teams.contains(&"away".to_string())); + + let team_entities = fields.team_entities(); + assert_eq!(team_entities.len(), 2); + assert!(team_entities.contains(&TrackedEntity::Team { + team_id: "home".to_string() + })); + assert!(team_entities.contains(&TrackedEntity::Team { + team_id: "away".to_string() + })); + + // Test referees + let referees = fields.referees(); + assert_eq!(referees.len(), 1); + assert!(referees.contains(&"ref_1".to_string())); + + let referee_entities = fields.referee_entities(); + assert_eq!(referee_entities.len(), 1); + assert!(referee_entities.contains(&TrackedEntity::Referee { + referee_id: "ref_1".to_string() + })); +} diff --git a/crates/tdc-core/tests/test_schema.rs b/crates/tdc-core/tests/test_schema.rs new file mode 100644 index 0000000..b0a78f8 --- /dev/null +++ b/crates/tdc-core/tests/test_schema.rs @@ -0,0 +1,142 @@ +use arrow_schema::{DataType, TimeUnit}; +use std::str::FromStr; +use tdc_core::{DataField, FrameAttribute, TrackedEntity, TrackedEntityAttribute}; + +#[test] +fn test_data_field_from_name() { + // 1. Frame attributes + assert_eq!( + DataField::from_name("timestamp"), + DataField::Frame { + attribute: FrameAttribute::Timestamp + } + ); + assert_eq!( + DataField::from_name("match/id"), + DataField::Frame { + attribute: FrameAttribute::MatchId + } + ); + assert_eq!( + DataField::from_name("vendor/tracking/name"), + DataField::Frame { + attribute: FrameAttribute::VendorTrackingName + } + ); + + // 2. Tracked Entity: Player + let field = DataField::from_name("teams/home/players/1/x"); + match field { + DataField::TrackedEntity { entity, attribute } => { + assert!( + matches!(entity, TrackedEntity::Player { team_id, player_id } if team_id == "home" && player_id == "1") + ); + assert_eq!(attribute, TrackedEntityAttribute::X); + } + _ => panic!("Expected Player attribute"), + } + + // 3. Tracked Entity: Ball + let field = DataField::from_name("ball/z"); + match field { + DataField::TrackedEntity { entity, attribute } => { + assert!(matches!(entity, TrackedEntity::Ball)); + assert_eq!(attribute, TrackedEntityAttribute::Z); + } + _ => panic!("Expected Ball attribute"), + } + + // 4. Tracked Entity: Referee + let field = DataField::from_name("referees/REF_1/y"); + match field { + DataField::TrackedEntity { entity, attribute } => { + assert!( + matches!(entity, TrackedEntity::Referee { referee_id } if referee_id == "REF_1") + ); + assert_eq!(attribute, TrackedEntityAttribute::Y); + } + _ => panic!("Expected Referee attribute"), + } + + // 5. Tracked Entity: Team + let field = DataField::from_name("teams/away/jersey_colour"); + match field { + DataField::TrackedEntity { entity, attribute } => { + assert!(matches!(entity, TrackedEntity::Team { team_id } if team_id == "away")); + assert_eq!(attribute, TrackedEntityAttribute::JerseyColour); + } + _ => panic!("Expected Team attribute"), + } + + // 6. Custom attributes + assert_eq!( + DataField::from_name("ball/spin"), + DataField::TrackedEntity { + entity: TrackedEntity::Ball, + attribute: TrackedEntityAttribute::Other("spin".to_string()) + } + ); + assert_eq!( + DataField::from_name("global_weather"), + DataField::Frame { + attribute: FrameAttribute::Other("global_weather".to_string()) + } + ); +} + +#[test] +fn test_field_name_serialization() { + let cases = vec![ + "teams/home/players/10/x", + "ball/z", + "referees/ref1/vel", + "vendor/tracking/name", + "match/id", + "timestamp", + ]; + for path in cases { + assert_eq!(DataField::from_name(path).name(), path); + } +} + +#[test] +fn test_field_properties() { + // Arrow types + assert_eq!( + DataField::from_name("timestamp").default_arrow_type(), + DataType::Timestamp(TimeUnit::Microsecond, None) + ); + assert_eq!( + DataField::from_name("ball/x").default_arrow_type(), + DataType::Float32 + ); + assert_eq!( + DataField::from_name("ball_status").default_arrow_type(), + DataType::Boolean + ); + + // Cardinality + assert!(DataField::from_name("period").is_low_cardinality()); + assert!(DataField::from_name("teams/home/jersey_colour").is_low_cardinality()); + assert!(!DataField::from_name("ball/x").is_low_cardinality()); +} + +#[test] +fn test_attribute_enums() { + // FrameAttribute parsing + assert_eq!( + FrameAttribute::from_str("timestamp").unwrap(), + FrameAttribute::Timestamp + ); + assert_eq!( + FrameAttribute::from_str("match").unwrap(), + FrameAttribute::MatchId + ); + + // TrackedEntityAttribute display + assert_eq!(TrackedEntityAttribute::X.to_string(), "x"); + assert_eq!( + TrackedEntityAttribute::JerseyColour.to_string(), + "jersey_colour" + ); +} diff --git a/crates/tdc-ops/Cargo.toml b/crates/tdc-ops/Cargo.toml new file mode 100644 index 0000000..8757d45 --- /dev/null +++ b/crates/tdc-ops/Cargo.toml @@ -0,0 +1,20 @@ +[package] +name = "tdc-ops" +version = "0.1.0-alpha.1" +edition = "2024" +license = "MIT" +description = "Vectorized operations for sports tracking data." + +[lib] +name = "tdc_ops" +crate-type = ["lib"] + +[dependencies] +arrow-array = "54" +arrow-schema = "54" +arrow-cast = "54" +arrow-arith = "54" +arrow-select = "54" +arrow-ord = "54" +pyo3 = { version = "0.23", features = ["abi3-py310"] } +pyo3-arrow = { version = "0.7", default-features = false } diff --git a/crates/tdc-ops/src/lib.rs b/crates/tdc-ops/src/lib.rs new file mode 100644 index 0000000..ec46f54 --- /dev/null +++ b/crates/tdc-ops/src/lib.rs @@ -0,0 +1,277 @@ +use arrow_arith::numeric::{add, mul, sub}; +use arrow_array::{Array, ArrayRef, Float64Array, Scalar}; +use arrow_cast::cast; +use arrow_schema::{ArrowError, DataType}; +use pyo3::IntoPyObjectExt; +use pyo3::prelude::*; +use pyo3::types::PyCapsule; +use std::sync::Arc; + +fn extract_array(obj: &Bound<'_, PyAny>) -> PyResult { + let (array_cap, schema_capsule) = if let Ok(caps) = obj.call_method0("__arrow_c_array__") { + caps.extract::<(Bound<'_, PyCapsule>, Bound<'_, PyCapsule>)>()? + } else { + return Err(pyo3::exceptions::PyTypeError::new_err( + "Expected object with __arrow_c_array__", + )); + }; + + let py_array = pyo3_arrow::PyArray::from_arrow_pycapsule(&array_cap, &schema_capsule)?; + Ok(py_array.into_inner().0) +} + +/// Helper to compute the difference between consecutive elements: values[i] - values[i-1] +/// The first element of the result is 0.0. +fn compute_diff(array: &ArrayRef) -> Result { + let len = array.len(); + if len <= 1 { + return Ok(Arc::new(Float64Array::from(vec![0.0; len])) as ArrayRef); + } + + // array[1..len] + let current = array.slice(1, len - 1); + // array[0..len-1] + let previous = array.slice(0, len - 1); + + let diff = sub(¤t, &previous)?; + + // Prepend 0.0 to match original length + let zero = Float64Array::from(vec![0.0]); + arrow_select::concat::concat(&[&zero, &diff]) +} + +#[pyfunction] +#[pyo3(signature = (x_array, y_array, dt=1.0))] +pub fn compute_speed( + py: Python<'_>, + x_array: Bound<'_, PyAny>, + y_array: Bound<'_, PyAny>, + dt: f64, +) -> PyResult { + let x_arr = extract_array(&x_array)?; + let y_arr = extract_array(&y_array)?; + + let x_f64 = cast(&x_arr, &DataType::Float64) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + let y_f64 = cast(&y_arr, &DataType::Float64) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + + let dx = compute_diff(&x_f64) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + let dy = compute_diff(&y_f64) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + + // dist2 = dx^2 + dy^2 + let dx2 = mul(&dx, &dx) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + let dy2 = mul(&dy, &dy) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + let dist2_arr = add(&dx2, &dy2) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + + let dist2_f64 = dist2_arr.as_any().downcast_ref::().unwrap(); + let dist_values: Vec = dist2_f64.values().iter().map(|&v| v.sqrt()).collect(); + let dist = Arc::new(Float64Array::from(dist_values)) as ArrayRef; + + // speed = dist / dt + let inv_dt = Scalar::new(Float64Array::from(vec![1.0 / dt])); + let speed = mul(&dist, &inv_dt) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + + Ok(pyo3_arrow::PyArray::new( + speed, + Arc::new(arrow_schema::Field::new("speed", DataType::Float64, true)), + ) + .into_bound_py_any(py)? + .into()) +} + +#[pyfunction] +#[pyo3(signature = (speed_array, dt=1.0))] +pub fn compute_acceleration( + py: Python<'_>, + speed_array: Bound<'_, PyAny>, + dt: f64, +) -> PyResult { + let arr = extract_array(&speed_array)?; + let f64_arr = cast(&arr, &DataType::Float64) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + + let dv = compute_diff(&f64_arr) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + + let inv_dt = Scalar::new(Float64Array::from(vec![1.0 / dt])); + let acc = mul(&dv, &inv_dt) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + + Ok(pyo3_arrow::PyArray::new( + acc, + Arc::new(arrow_schema::Field::new( + "acceleration", + DataType::Float64, + true, + )), + ) + .into_bound_py_any(py)? + .into()) +} + +#[pyfunction] +#[pyo3(signature = (acc_array, dt=1.0))] +pub fn compute_jerk(py: Python<'_>, acc_array: Bound<'_, PyAny>, dt: f64) -> PyResult { + let arr = extract_array(&acc_array)?; + let f64_arr = cast(&arr, &DataType::Float64) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + + let da = compute_diff(&f64_arr) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + + let inv_dt = Scalar::new(Float64Array::from(vec![1.0 / dt])); + let jerk = mul(&da, &inv_dt) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + + Ok(pyo3_arrow::PyArray::new( + jerk, + Arc::new(arrow_schema::Field::new("jerk", DataType::Float64, true)), + ) + .into_bound_py_any(py)? + .into()) +} + +#[pyfunction] +#[pyo3(signature = (vx_array, vy_array, dt=1.0))] +pub fn compute_angular_velocity( + py: Python<'_>, + vx_array: Bound<'_, PyAny>, + vy_array: Bound<'_, PyAny>, + dt: f64, +) -> PyResult { + let vx_arr = extract_array(&vx_array)?; + let vy_arr = extract_array(&vy_array)?; + + let vx_f64 = cast(&vx_arr, &DataType::Float64) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + let vx_f64 = vx_f64.as_any().downcast_ref::().unwrap(); + + let vy_f64 = cast(&vy_arr, &DataType::Float64) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + let vy_f64 = vy_f64.as_any().downcast_ref::().unwrap(); + + let len = vx_f64.len(); + let mut values = vec![0.0; len]; + let (vxs, vys) = (vx_f64.values(), vy_f64.values()); + let inv_dt = 1.0 / dt; + + for i in 1..len { + let theta_prev = vys[i - 1].atan2(vxs[i - 1]); + let theta_curr = vys[i].atan2(vxs[i]); + let mut d_theta = theta_curr - theta_prev; + + // Handle wrap-around + if d_theta > std::f64::consts::PI { + d_theta -= 2.0 * std::f64::consts::PI; + } else if d_theta < -std::f64::consts::PI { + d_theta += 2.0 * std::f64::consts::PI; + } + + values[i] = d_theta * inv_dt; + } + + let result = Arc::new(Float64Array::from(values)) as Arc; + Ok(pyo3_arrow::PyArray::new( + result, + Arc::new(arrow_schema::Field::new( + "angular_velocity", + DataType::Float64, + true, + )), + ) + .into_bound_py_any(py)? + .into()) +} + +#[pyfunction] +#[pyo3(signature = (array, cutoff, fs, order=2))] +pub fn butterworth_filter( + py: Python<'_>, + array: Bound<'_, PyAny>, + cutoff: f64, + fs: f64, + order: usize, +) -> PyResult { + let arr = extract_array(&array)?; + let f64_arr = cast(&arr, &DataType::Float64) + .map_err(|e: ArrowError| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + let f64_arr = f64_arr.as_any().downcast_ref::().unwrap(); + + let input = f64_arr.values(); + let len = input.len(); + if len == 0 { + return Ok(pyo3_arrow::PyArray::new( + Arc::new(Float64Array::from(Vec::::new())) as Arc, + Arc::new(arrow_schema::Field::new( + "filtered", + DataType::Float64, + true, + )), + ) + .into_bound_py_any(py)? + .into()); + } + + // Only 2nd order implemented for now as it's the most common for tracking data + if order != 2 { + return Err(pyo3::exceptions::PyValueError::new_err( + "Only order=2 is currently supported for butterworth_filter", + )); + } + + // Precompute coefficients for 2nd order Butterworth + // Ref: https://en.wikipedia.org/wiki/Butterworth_filter + let ff = cutoff / fs; + let ita = 1.0 / (std::f64::consts::PI * ff).tan(); + let q = std::f64::consts::SQRT_2; + let b0 = 1.0 / (1.0 + q * ita + ita * ita); + let b1 = 2.0 * b0; + let b2 = b0; + let a1 = 2.0 * (ita * ita - 1.0) * b0; + let a2 = -(1.0 - q * ita + ita * ita) * b0; + + let mut output = vec![0.0; len]; + + // Initial conditions (assume steady state or zero padding) + output[0] = input[0]; + if len > 1 { + output[1] = input[1]; + } + + for i in 2..len { + output[i] = b0 * input[i] + + b1 * input[i - 1] + + b2 * input[i - 2] + + a1 * output[i - 1] + + a2 * output[i - 2]; + } + + let result = Arc::new(Float64Array::from(output)) as Arc; + Ok(pyo3_arrow::PyArray::new( + result, + Arc::new(arrow_schema::Field::new( + "filtered", + DataType::Float64, + true, + )), + ) + .into_bound_py_any(py)? + .into()) +} + +#[pymodule] +fn tdc_ops(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_function(wrap_pyfunction!(compute_speed, m)?)?; + m.add_function(wrap_pyfunction!(compute_acceleration, m)?)?; + m.add_function(wrap_pyfunction!(compute_jerk, m)?)?; + m.add_function(wrap_pyfunction!(compute_angular_velocity, m)?)?; + m.add_function(wrap_pyfunction!(butterworth_filter, m)?)?; + Ok(()) +} diff --git a/crates/tdc-parsers/Cargo.toml b/crates/tdc-parsers/Cargo.toml new file mode 100644 index 0000000..d445063 --- /dev/null +++ b/crates/tdc-parsers/Cargo.toml @@ -0,0 +1,38 @@ +[package] +name = "tdc-parsers" +version = "0.1.0-alpha.1" +edition = "2024" +license = "MIT" +description = "Specialized parsers for sports tracking data formats." + +[lib] +name = "tdc_parsers" +crate-type = ["lib", "cdylib"] + +[dependencies] +tdc-core = { path = "../tdc-core" } +arrow-array = { version = "54" } +arrow-schema = { version = "54" } +quick-xml = "0.37" +rayon = "1.10" +ahash = "0.8" +fast-float = "0.2" +chrono = { version = "^0.4.42", default-features = false } +pyo3 = { version = "0.23", features = ["abi3-py310"], optional = true } + +[features] +default = [] +python = ["dep:pyo3"] + +[dev-dependencies] +tempfile = "3" +arrow-cast = { version = "54" } +criterion = { version = "0.5", features = ["html_reports"] } + +[[bench]] +name = "parsers" +harness = false + +[[bench]] +name = "memory" +harness = false diff --git a/crates/tdc-parsers/benches/memory.rs b/crates/tdc-parsers/benches/memory.rs new file mode 100644 index 0000000..10fc298 --- /dev/null +++ b/crates/tdc-parsers/benches/memory.rs @@ -0,0 +1,40 @@ +use arrow_array::Array; +use criterion::{Criterion, criterion_group, criterion_main}; +use tdc_parsers::{load_sportec, load_statsperform}; + +fn bench_memory(c: &mut Criterion) { + // 1. Sportec + let sportec_path = "../../data/sportec-tracking.xml"; + let sportec_meta = "../../data/sportec-meta.xml"; + let sportec_container = load_sportec(sportec_path, Some(sportec_meta)).unwrap(); + let sportec_size = sportec_container.data.get_array_memory_size(); + + c.bench_function("memory_usage_sportec", |b| { + b.iter(|| { + // We just report the size once, but the bench helps see if there's + // any weird variance in container creation if we wanted to bench that too. + // For now, we use it as a placeholder to report info. + criterion::black_box(sportec_size) + }) + }); + println!( + "Sportec Container Memory Usage: {:.2} MB", + sportec_size as f64 / 1024.0 / 1024.0 + ); + + // 2. StatsPerform + let statsperform_path = "../../data/statsperform-ma25.txt"; + let statsperform_container = load_statsperform(statsperform_path).unwrap(); + let statsperform_size = statsperform_container.data.get_array_memory_size(); + + c.bench_function("memory_usage_statsperform", |b| { + b.iter(|| criterion::black_box(statsperform_size)) + }); + println!( + "StatsPerform Container Memory Usage: {:.2} MB", + statsperform_size as f64 / 1024.0 / 1024.0 + ); +} + +criterion_group!(benches, bench_memory); +criterion_main!(benches); diff --git a/crates/tdc-parsers/benches/parsers.rs b/crates/tdc-parsers/benches/parsers.rs new file mode 100644 index 0000000..ec091fc --- /dev/null +++ b/crates/tdc-parsers/benches/parsers.rs @@ -0,0 +1,20 @@ +use criterion::{Criterion, black_box, criterion_group, criterion_main}; +use tdc_parsers::{load_sportec, load_statsperform}; + +fn bench_parsers(c: &mut Criterion) { + // Sportec Benchmark + let sportec_path = "../../data/sportec-tracking.xml"; + let sportec_meta = "../../data/sportec-meta.xml"; + c.bench_function("parse sportec xml", |b| { + b.iter(|| load_sportec(black_box(sportec_path), Some(black_box(sportec_meta))).unwrap()) + }); + + // StatsPerform Benchmark + let statsperform_path = "../../data/statsperform-ma25.txt"; + c.bench_function("parse statsperform", |b| { + b.iter(|| load_statsperform(black_box(statsperform_path)).unwrap()) + }); +} + +criterion_group!(benches, bench_parsers); +criterion_main!(benches); diff --git a/crates/tdc-parsers/src/lib.rs b/crates/tdc-parsers/src/lib.rs new file mode 100644 index 0000000..a029bd9 --- /dev/null +++ b/crates/tdc-parsers/src/lib.rs @@ -0,0 +1,42 @@ +pub mod sportec; +pub mod statsperform; + +pub use sportec::load_sportec; +pub use statsperform::load_statsperform; + +#[cfg(feature = "python")] +pub mod python { + use pyo3::prelude::*; + use pyo3::types::PyCapsule; + use std::ffi::CString; + + #[pyfunction] + #[pyo3(name = "load_sportec", signature = (path, meta_path=None))] + pub fn load_sportec_py( + py: Python<'_>, + path: String, + meta_path: Option, + ) -> PyResult> { + let container = super::load_sportec(&path, meta_path.as_deref()) + .map_err(|e| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + + let name = CString::new("TrackingDataContainer").unwrap(); + PyCapsule::new(py, container, Some(name)) + } + + #[pyfunction] + #[pyo3(name = "load_statsperform")] + pub fn load_statsperform_py(py: Python<'_>, path: String) -> PyResult> { + let container = super::load_statsperform(&path) + .map_err(|e| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + + let name = CString::new("TrackingDataContainer").unwrap(); + PyCapsule::new(py, container, Some(name)) + } + + pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_function(wrap_pyfunction!(load_sportec_py, m)?)?; + m.add_function(wrap_pyfunction!(load_statsperform_py, m)?)?; + Ok(()) + } +} diff --git a/crates/tdc-parsers/src/sportec.rs b/crates/tdc-parsers/src/sportec.rs new file mode 100644 index 0000000..85131b0 --- /dev/null +++ b/crates/tdc-parsers/src/sportec.rs @@ -0,0 +1,474 @@ +use ahash::{AHashMap, AHashSet}; +use arrow_schema::DataType; +use quick_xml::events::Event; +use quick_xml::reader::Reader; +use rayon::prelude::*; +use std::fs; +use tdc_core::datacontainer::{TrackingDataBuilder, TrackingDataContainer}; +use tdc_core::schema::{CoordinateSystem, Direction, LengthUnit, Origin}; + +pub fn load_sportec(path: &str, meta_path: Option<&str>) -> Result { + let raw_data = fs::read_to_string(path).map_err(|e| e.to_string())?; + + // 1. Parse Metadata (Pitch dimensions, Teams) + let mut team_map = AHashMap::new(); + let mut role_to_id = AHashMap::new(); + let mut role_to_name = AHashMap::new(); + let mut role_to_color = AHashMap::new(); + let mut pitch_x = None; + let mut pitch_y = None; + if let Some(mp) = meta_path { + let meta_data = fs::read_to_string(mp).map_err(|e| e.to_string())?; + let mut reader = Reader::from_str(&meta_data); + let mut buf = Vec::new(); + loop { + match reader.read_event_into(&mut buf) { + Ok(Event::Start(ref e)) | Ok(Event::Empty(ref e)) + if e.name().as_ref() == b"Team" => + { + let mut team_id = String::new(); + let mut role = String::new(); + let mut team_name = String::new(); + let mut team_color = String::new(); + for attr in e.attributes().filter_map(|a| a.ok()) { + match attr.key.as_ref() { + b"TeamId" => { + team_id = String::from_utf8_lossy(&attr.value).into_owned() + } + b"Role" => role = String::from_utf8_lossy(&attr.value).into_owned(), + b"TeamName" => { + team_name = String::from_utf8_lossy(&attr.value).into_owned() + } + b"PlayerShirtMainColor" => { + team_color = String::from_utf8_lossy(&attr.value).into_owned() + } + _ => (), + } + } + if !team_id.is_empty() { + let role_mapped = match role.as_str() { + "home" => "home", + "guest" => "away", + _ => "other", + }; + team_map.insert(team_id.clone(), role_mapped.to_string()); + role_to_id.insert(role_mapped.to_string(), team_id); + if !team_name.is_empty() { + role_to_name.insert(role_mapped.to_string(), team_name); + } + if !team_color.is_empty() { + role_to_color.insert(role_mapped.to_string(), team_color); + } + } + } + Ok(Event::Empty(ref e)) if e.name().as_ref() == b"Environment" => { + for attr in e.attributes().filter_map(|a| a.ok()) { + match attr.key.as_ref() { + b"PitchX" => { + pitch_x = std::str::from_utf8(&attr.value) + .ok() + .and_then(|s| s.parse::().ok()); + } + b"PitchY" => { + pitch_y = std::str::from_utf8(&attr.value) + .ok() + .and_then(|s| s.parse::().ok()); + } + _ => (), + } + } + } + Ok(Event::Eof) => break, + _ => (), + } + buf.clear(); + } + } + + let home_id = role_to_id.get("home").cloned(); + let away_id = role_to_id.get("away").cloned(); + let home_name = role_to_name.get("home").cloned(); + let away_name = role_to_name.get("away").cloned(); + let home_color = role_to_color.get("home").cloned(); + let away_color = role_to_color.get("away").cloned(); + + // 2. Discovery Pass: Find all FrameSets, Frame range, and metadata mappings + let mut fs_positions = Vec::new(); + let mut start = 0; + while let Some(pos) = raw_data[start..].find(" = fs_positions + .par_iter() + .map(|&fs_start| { + let mut entities = AHashSet::new(); + let mut frames = AHashMap::new(); // N -> (Timestamp, Period) + + let mut cur_team = String::new(); + let mut cur_person = String::new(); + + let tag_end = raw_data[fs_start..].find(">").unwrap_or(0); + let mut reader = Reader::from_str(&raw_data[fs_start..fs_start + tag_end + 1]); + let mut buf = Vec::new(); + if let Ok(Event::Start(e)) | Ok(Event::Empty(e)) = reader.read_event_into(&mut buf) { + for a in e.attributes().filter_map(|a| a.ok()) { + match a.key.as_ref() { + b"TeamId" => cur_team = String::from_utf8_lossy(&a.value).into_owned(), + b"PersonId" => cur_person = String::from_utf8_lossy(&a.value).into_owned(), + _ => (), + } + } + } + if !cur_team.is_empty() && !cur_person.is_empty() { + entities.insert((cur_team, cur_person)); + } + + let chunk_end = fs_positions + .iter() + .find(|&&p| p > fs_start) + .cloned() + .unwrap_or(raw_data.len()); + let mut reader = Reader::from_str(&raw_data[fs_start..chunk_end]); + let mut buf = Vec::new(); + loop { + match reader.read_event_into(&mut buf) { + Ok(Event::Start(ref e)) | Ok(Event::Empty(ref e)) + if e.name().as_ref() == b"Frame" => + { + let mut n = -1; + let mut ts = None; + let mut p = None; + for a in e.attributes().filter_map(|a| a.ok()) { + let val = std::str::from_utf8(&a.value).unwrap_or(""); + match a.key.as_ref() { + b"N" => n = val.parse().unwrap_or(-1), + b"T" => { + if !val.is_empty() { + ts = Some(val.to_string()); + } + } + b"M" => { + if !val.is_empty() { + p = Some(val.parse::().unwrap_or(0)); + } + } + _ => (), + } + } + if n != -1 { + frames.insert(n, (ts, p)); + } + } + Ok(Event::Eof) => break, + _ => (), + } + buf.clear(); + } + (entities, frames) + }) + .collect(); + + let mut global_entities = AHashSet::new(); + let mut global_frames = AHashMap::new(); + for (entities, frames) in discovery_results { + global_entities.extend(entities); + for (n, (ts, p)) in frames { + let entry = global_frames.entry(n).or_insert((None, None)); + if ts.is_some() { + entry.0 = ts; + } + if p.is_some() { + entry.1 = p; + } + } + } + + if global_frames.is_empty() { + return Err("No frames found in Sportec data".to_string()); + } + + let mut sorted_n: Vec = global_frames.keys().cloned().collect(); + sorted_n.sort_unstable(); + + let n_to_row: AHashMap = + sorted_n.iter().enumerate().map(|(i, &n)| (n, i)).collect(); + let num_rows = sorted_n.len(); + + // 3. Data Ingestion Pass (Parallel) + let final_builder = fs_positions + .par_chunks(fs_positions.len().div_ceil(rayon::current_num_threads())) + .map(|pos_group| { + let mut builder = + TrackingDataBuilder::new_fixed(0, (num_rows as i64).saturating_sub(1)); + + // Register columns + builder.get_or_create_column_id( + "timestamp", + DataType::Timestamp(arrow_schema::TimeUnit::Microsecond, None), + ); + builder.get_or_create_column_id("period", DataType::Utf8); + let c_mid = builder.get_or_create_column_id("match/id", DataType::Utf8); + let c_v = builder.get_or_create_column_id("vendor/tracking/name", DataType::Utf8); + let c_bs = builder.get_or_create_column_id("ball_status", DataType::Boolean); + let c_bp = builder.get_or_create_column_id("ball_poss_team_id", DataType::Utf8); + + let c_hi = builder.get_or_create_column_id("teams/home/id", DataType::Utf8); + let c_ai = builder.get_or_create_column_id("teams/away/id", DataType::Utf8); + let c_hn = builder.get_or_create_column_id("teams/home/name", DataType::Utf8); + let c_an = builder.get_or_create_column_id("teams/away/name", DataType::Utf8); + let c_hc = builder.get_or_create_column_id("teams/home/jersey_colour", DataType::Utf8); + let c_ac = builder.get_or_create_column_id("teams/away/jersey_colour", DataType::Utf8); + + let mut entity_col_map = AHashMap::new(); + for (team, person) in &global_entities { + let team_role = team_map.get(team).map(|s| s.as_str()).unwrap_or(team); + let base = match team.as_str() { + "BALL" => "ball".to_string(), + "referee" | "referees" | "Referee" | "Referees" => { + format!("referees/{}", person) + } + _ => format!("teams/{}/players/{}", team_role, person), + }; + let ix = builder.get_or_create_column_id(&format!("{}/x", base), DataType::Float64); + let iy = builder.get_or_create_column_id(&format!("{}/y", base), DataType::Float64); + let iz = if team == "BALL" { + Some(builder.get_or_create_column_id(&format!("{}/z", base), DataType::Float64)) + } else { + None + }; + let iv = + builder.get_or_create_column_id(&format!("{}/vel", base), DataType::Float64); + let ia = + builder.get_or_create_column_id(&format!("{}/acc", base), DataType::Float64); + entity_col_map.insert((team.clone(), person.clone()), (ix, iy, iz, iv, ia)); + } + + let mut buf = Vec::new(); + for &fs_start in pos_group { + let mut cur_team = String::new(); + let mut cur_person = String::new(); + let mut cur_match = String::new(); + + let tag_end = raw_data[fs_start..].find(">").unwrap_or(0); + let mut reader = Reader::from_str(&raw_data[fs_start..fs_start + tag_end + 1]); + if let Ok(Event::Start(e)) | Ok(Event::Empty(e)) = reader.read_event_into(&mut buf) + { + for a in e.attributes().filter_map(|a| a.ok()) { + match a.key.as_ref() { + b"TeamId" => cur_team = String::from_utf8_lossy(&a.value).into_owned(), + b"PersonId" => { + cur_person = String::from_utf8_lossy(&a.value).into_owned() + } + b"MatchId" => { + cur_match = String::from_utf8_lossy(&a.value).into_owned() + } + _ => (), + } + } + } + buf.clear(); + + let chunk_end = fs_positions + .iter() + .find(|&&p| p > fs_start) + .cloned() + .unwrap_or(raw_data.len()); + let mut reader = Reader::from_str(&raw_data[fs_start..chunk_end]); + loop { + match reader.read_event_into(&mut buf) { + Ok(Event::Start(ref e)) | Ok(Event::Empty(ref e)) + if e.name().as_ref() == b"Frame" => + { + let mut n = -1; + let mut x = 0.0; + let mut y = 0.0; + let mut z = 0.0; + let mut s = 0.0; + let mut a = 0.0; + let mut b_pos = 0; + let mut b_stat = 0; + let mut has_x = false; + let mut has_y = false; + let mut has_z = false; + let mut has_s = false; + let mut has_a = false; + let mut has_bp = false; + let mut has_bs = false; + + for attr in e.attributes().filter_map(|a| a.ok()) { + let val = std::str::from_utf8(&attr.value).unwrap_or(""); + match attr.key.as_ref() { + b"N" => n = val.parse().unwrap_or(-1), + b"X" => { + x = fast_float::parse(val).unwrap_or(0.0); + has_x = true; + } + b"Y" => { + y = fast_float::parse(val).unwrap_or(0.0); + has_y = true; + } + b"Z" => { + z = fast_float::parse(val).unwrap_or(0.0); + has_z = true; + } + b"S" => { + s = fast_float::parse(val).unwrap_or(0.0); + has_s = true; + } + b"A" => { + a = fast_float::parse(val).unwrap_or(0.0); + has_a = true; + } + b"BallPossession" => { + b_pos = val.parse().unwrap_or(0); + has_bp = true; + } + b"BallStatus" => { + b_stat = val.parse().unwrap_or(0); + has_bs = true; + } + _ => (), + } + } + + if let Some(&row) = n_to_row.get(&n) { + if has_bs { + builder.insert_bool_at_idx(row, c_bs, b_stat == 1); + } + if has_bp { + let tid = if b_pos == 1 { + home_id.as_ref() + } else if b_pos == 2 { + away_id.as_ref() + } else { + None + }; + if let Some(t) = tid { + builder.insert_string_at_idx(row, c_bp, t); + } + } + + if !cur_match.is_empty() { + builder.insert_string_at_idx(row, c_mid, &cur_match); + } + builder.insert_string_at_idx(row, c_v, "sportec"); + if let Some(id) = &home_id { + builder.insert_string_at_idx(row, c_hi, id); + } + if let Some(id) = &away_id { + builder.insert_string_at_idx(row, c_ai, id); + } + if let Some(nm) = &home_name { + builder.insert_string_at_idx(row, c_hn, nm); + } + if let Some(nm) = &away_name { + builder.insert_string_at_idx(row, c_an, nm); + } + if let Some(cl) = &home_color { + builder.insert_string_at_idx(row, c_hc, cl); + } + if let Some(cl) = &away_color { + builder.insert_string_at_idx(row, c_ac, cl); + } + + if let Some((ix, iy, iz, iv, ia)) = + entity_col_map.get(&(cur_team.clone(), cur_person.clone())) + { + if has_x { + builder.insert_float_at_idx(row, *ix, x); + } + if has_y { + builder.insert_float_at_idx(row, *iy, y); + } + if let Some(iz_idx) = iz + && has_z + { + builder.insert_float_at_idx(row, *iz_idx, z); + } + if has_s { + builder.insert_float_at_idx(row, *iv, s); + } + if has_a { + builder.insert_float_at_idx(row, *ia, a); + } + } + } + } + Ok(Event::Eof) => break, + _ => (), + } + buf.clear(); + } + } + Ok(builder) + }) + .reduce( + || { + Ok(TrackingDataBuilder::new_fixed( + 0, + (num_rows as i64).saturating_sub(1), + )) + }, + |a: Result, b: Result| match ( + a, b, + ) { + (Ok(mut a), Ok(b)) => { + a.merge(b); + Ok(a) + } + (Err(e), _) | (_, Err(e)) => Err(e), + }, + )?; + + // 4. Finalize Metadata (Interpolate T and M) + let mut final_builder = final_builder; + let c_ts = final_builder.get_or_create_column_id( + "timestamp", + DataType::Timestamp(arrow_schema::TimeUnit::Microsecond, None), + ); + let c_p = final_builder.get_or_create_column_id("period", DataType::Utf8); + let c_fid = final_builder.get_or_create_column_id("frame_id", DataType::Int64); + + let mut last_p = "other".to_string(); + for (i, &n) in sorted_n.iter().enumerate() { + let (ts_str, p_id) = global_frames.get(&n).cloned().unwrap_or((None, None)); + + if let Some(p) = p_id { + last_p = match p { + 1 => "first_half", + 2 => "second_half", + 3 => "first_half_extratime", + 4 => "second_half_extratime", + 5 => "shootout", + _ => "other", + } + .to_string(); + } + final_builder.insert_string_at_idx(i, c_p, &last_p); + final_builder.insert_int_at_idx(i, c_fid, n); + + if let Some(ts) = ts_str + && let Ok(dt) = chrono::DateTime::parse_from_rfc3339(&ts) + { + final_builder.insert_int_at_idx(i, c_ts, dt.timestamp_micros()); + } + } + + let mut container = final_builder.build().map_err(|e| e.to_string())?; + + if let (Some(x), Some(y)) = (pitch_x, pitch_y) { + let cs = CoordinateSystem::new( + Some((-x / 2.0, x / 2.0)), + Some((-y / 2.0, y / 2.0)), + Origin::Center, + Direction::Right, + Direction::Up, + Some(LengthUnit::Meters), + ); + container = container.with_coordinate_system(cs); + } + + Ok(container) +} diff --git a/crates/tdc-parsers/src/statsperform.rs b/crates/tdc-parsers/src/statsperform.rs new file mode 100644 index 0000000..f68c32b --- /dev/null +++ b/crates/tdc-parsers/src/statsperform.rs @@ -0,0 +1,162 @@ +use ahash::AHashMap; +use arrow_schema::DataType; +use rayon::prelude::*; +use std::fs; +use tdc_core::datacontainer::{TrackingDataBuilder, TrackingDataContainer}; +use tdc_core::schema::{CoordinateSystem, Direction, LengthUnit, Origin}; + +pub fn load_statsperform(path: &str) -> Result { + let raw_data = fs::read_to_string(path).map_err(|e| e.to_string())?; + + let lines: Vec<&str> = raw_data.lines().filter(|l| !l.is_empty()).collect(); + if lines.is_empty() { + return Err("Empty file".to_string()); + } + + let num_cpus = rayon::current_num_threads(); + let chunk_size = lines.len().div_ceil(num_cpus); + + let final_builder = lines + .par_chunks(chunk_size) + .map(|chunk| -> Result { + let mut builder = TrackingDataBuilder::new(); + let c_p = builder.get_or_create_column_id("period", DataType::Utf8); + let c_ts = builder.get_or_create_column_id( + "timestamp", + DataType::Timestamp(arrow_schema::TimeUnit::Microsecond, None), + ); + let c_s = builder.get_or_create_column_id("ball_status", DataType::Boolean); + let c_bx = builder.get_or_create_column_id("ball/x", DataType::Float64); + let c_by = builder.get_or_create_column_id("ball/y", DataType::Float64); + let c_bz = builder.get_or_create_column_id("ball/z", DataType::Float64); + let c_v = builder.get_or_create_column_id("vendor/tracking/name", DataType::Utf8); + + let mut player_map = AHashMap::new(); + + for line in chunk { + let mut parts = line.split(':'); + let frame_part = if let Some(p) = parts.next() { + p + } else { + continue; + }; + let (id_s, info_s) = if let Some(s) = frame_part.split_once(';') { + s + } else { + continue; + }; + + let f_id: i64 = id_s.parse().unwrap(); + let row = builder.get_row_index(f_id); + + // Frame info + let mut info = info_s.split(','); + if let (Some(ts_s), Some(p_s), Some(bs_s)) = (info.next(), info.next(), info.next()) + { + let ts_ms: i64 = ts_s.parse().unwrap(); + let p_id: i32 = p_s.parse().unwrap(); + let m_s: i32 = bs_s.parse().unwrap(); + let p_name = match p_id { + 1 => "first_half", + 2 => "second_half", + 3 => "first_half_extratime", + 4 => "second_half_extratime", + 5 => "shootout", + _ => "other", + }; + builder.insert_string_at_idx(row, c_p, p_name); + builder.insert_int_at_idx(row, c_ts, ts_ms * 1000); + builder.insert_bool_at_idx(row, c_s, m_s == 0); + builder.insert_string_at_idx(row, c_v, "statsperform"); + } + + // Players + if let Some(p_part) = parts.next() + && p_part != ";" + { + for p in p_part.split(';') { + if p.is_empty() { + continue; + } + let mut p_i = p.split(','); + let sid_s = p_i.next().unwrap(); + if sid_s == "2" { + continue; + } + let pid = p_i.next().unwrap(); + + let (ix, iy) = *player_map.entry((sid_s, pid)).or_insert_with(|| { + let sid: i32 = sid_s.parse().unwrap(); + let real = if sid > 2 { sid - 3 } else { sid }; + let team = if real == 0 { "home" } else { "away" }; + ( + builder.get_or_create_column_id( + &format!("teams/{}/players/{}/x", team, pid), + DataType::Float64, + ), + builder.get_or_create_column_id( + &format!("teams/{}/players/{}/y", team, pid), + DataType::Float64, + ), + ) + }); + + let _junk = p_i.next(); + if let Some(xs) = p_i.next() + && let Ok(x) = fast_float::parse(xs) + { + builder.insert_float_at_idx(row, ix, x); + } + if let Some(ys) = p_i.next() + && let Ok(y) = fast_float::parse(ys) + { + builder.insert_float_at_idx(row, iy, y); + } + } + } + + // Ball + if let Some(bp) = parts.next() { + let mut b_i = bp.split(';').next().unwrap().split(','); + if let (Some(xs), Some(ys)) = (b_i.next(), b_i.next()) { + if let Ok(x) = fast_float::parse(xs) { + builder.insert_float_at_idx(row, c_bx, x); + } + if let Ok(y) = fast_float::parse(ys) { + builder.insert_float_at_idx(row, c_by, y); + } + if let Some(zs) = b_i.next() + && let Ok(z) = fast_float::parse(zs) + { + builder.insert_float_at_idx(row, c_bz, z); + } + } + } + } + Ok(builder) + }) + .reduce( + || Ok(TrackingDataBuilder::new()), + |a, b| match (a, b) { + (Ok(mut a), Ok(b)) => { + a.merge(b); + Ok(a) + } + (Err(e), _) | (_, Err(e)) => Err(e), + }, + )?; + + let mut container = final_builder.build().map_err(|e| e.to_string())?; + + let cs = CoordinateSystem::new( + None, + None, + Origin::TopLeft, + Direction::Right, + Direction::Down, + Some(LengthUnit::Meters), + ); + container = container.with_coordinate_system(cs); + + Ok(container) +} diff --git a/crates/tdc-parsers/tests/test_sportec.rs b/crates/tdc-parsers/tests/test_sportec.rs new file mode 100644 index 0000000..e024257 --- /dev/null +++ b/crates/tdc-parsers/tests/test_sportec.rs @@ -0,0 +1,202 @@ +use arrow_array::Array; +use std::io::Write; +use tdc_parsers::load_sportec; +use tempfile::NamedTempFile; + +fn create_temp_file(content: &str) -> NamedTempFile { + let mut file = NamedTempFile::new().unwrap(); + write!(file, "{}", content).unwrap(); + file +} + +#[test] +fn test_sportec_parser() { + let xml_content = r#" + + + + + + + + + + + + + +"#; + + let meta_content = r##" + + + + + + + + + +"##; + + let xml_file = create_temp_file(xml_content); + let meta_file = create_temp_file(meta_content); + + let container = load_sportec( + xml_file.path().to_str().unwrap(), + Some(meta_file.path().to_str().unwrap()), + ) + .unwrap(); + + // 1. Verify Schema and Field Mapping + let fields: Vec = container + .schema + .fields + .iter() + .map(|f| f.name().clone()) + .collect(); + + let expected_fields = vec![ + "ball/x", + "ball/y", + "ball/z", + "ball/vel", + "ball/acc", + "referees/DFL-OBJ-00011W/x", + "referees/DFL-OBJ-00011W/y", + "teams/home/players/player1/x", + "teams/home/players/player1/y", + "teams/home/players/player1/vel", + "teams/home/players/player1/acc", + "teams/home/id", + "teams/away/id", + "teams/home/name", + "teams/away/name", + "teams/home/jersey_colour", + "teams/away/jersey_colour", + "timestamp", + "period", + "match/id", + "vendor/tracking/name", + "ball_poss_team_id", + "ball_status", + ]; + + for field in expected_fields { + assert!( + fields.contains(&field.to_string()), + "Missing field: {}", + field + ); + } + + // 2. Verify Data Types and Specific Values + let vendor_idx = container + .schema + .fields + .iter() + .position(|f| f.name() == "vendor/tracking/name") + .unwrap(); + let vendor_col = arrow_cast::cast( + container.data.column(vendor_idx), + &arrow_schema::DataType::Utf8, + ) + .unwrap(); + let vendor_val = vendor_col + .as_any() + .downcast_ref::() + .unwrap() + .value(0); + assert_eq!(vendor_val, "sportec"); + + let status_idx = container + .schema + .fields + .iter() + .position(|f| f.name() == "ball_status") + .unwrap(); + let status_val = container + .data + .column(status_idx) + .as_any() + .downcast_ref::() + .unwrap() + .value(0); + assert!(status_val); + + let ts_idx = container + .schema + .fields + .iter() + .position(|f| f.name() == "timestamp") + .unwrap(); + assert!(matches!( + container.data.column(ts_idx).data_type(), + arrow_schema::DataType::Timestamp(_, _) + )); + + let poss_idx = container + .schema + .fields + .iter() + .position(|f| f.name() == "ball_poss_team_id") + .unwrap(); + let poss_val = container + .data + .column(poss_idx) + .as_any() + .downcast_ref::() + .unwrap() + .value(0); + assert_eq!(poss_val, "TEAM_A"); + + assert_eq!(container.orientation, None); + + // 3. Verify Coordinate System + let cs = container + .coordinate_system + .expect("Coordinate system should be set"); + assert_eq!(cs.x_range, Some((-52.5, 52.5))); + assert_eq!(cs.y_range, Some((-34.0, 34.0))); + assert_eq!(cs.origin, tdc_core::schema::Origin::Center); + assert_eq!(cs.x_direction, tdc_core::schema::Direction::Right); + assert_eq!(cs.y_direction, tdc_core::schema::Direction::Up); + + // 4. Verify Entity Attributes + let check_float = |name: &str, expected: f64| { + let idx = container + .schema + .fields + .iter() + .position(|f| f.name() == name) + .unwrap_or_else(|| panic!("Field {} not found", name)); + let col = container + .data + .column(idx) + .as_any() + .downcast_ref::() + .unwrap_or_else(|| panic!("Field {} is not Float64", name)); + assert!(!col.is_null(0), "Field {} is null", name); + assert!( + (col.value(0) - expected).abs() < 1e-6, + "Field {} value mismatch: expected {}, got {}", + name, + expected, + col.value(0) + ); + }; + + check_float("ball/x", 10.0); + check_float("ball/y", 20.0); + check_float("ball/z", 1.5); + check_float("ball/vel", 5.0); + check_float("ball/acc", 1.0); + + check_float("referees/DFL-OBJ-00011W/x", 30.0); + check_float("referees/DFL-OBJ-00011W/y", 40.0); + + check_float("teams/home/players/player1/x", 50.0); + check_float("teams/home/players/player1/y", 60.0); + check_float("teams/home/players/player1/vel", 8.0); + check_float("teams/home/players/player1/acc", 2.0); +} diff --git a/crates/tdc-parsers/tests/test_statsperform.rs b/crates/tdc-parsers/tests/test_statsperform.rs new file mode 100644 index 0000000..66c13fe --- /dev/null +++ b/crates/tdc-parsers/tests/test_statsperform.rs @@ -0,0 +1,140 @@ +use arrow_array::Array; +use std::io::Write; +use tdc_parsers::load_statsperform; +use tempfile::NamedTempFile; + +fn create_temp_file(content: &str) -> NamedTempFile { + let mut file = NamedTempFile::new().unwrap(); + write!(file, "{}", content).unwrap(); + file +} + +#[test] +fn test_statsperform_parser() { + let content = "1000;12345,1,0:0,player1,10,10.0,20.0;1,player2,11,30.0,40.0:50.0,60.0,1.5"; + let file = create_temp_file(content); + + let container = load_statsperform(file.path().to_str().unwrap()).unwrap(); + + // 1. Verify Schema and Field Mapping + let fields: Vec = container + .schema + .fields + .iter() + .map(|f| f.name().clone()) + .collect(); + + let expected_fields = vec![ + "ball/x", + "ball/y", + "ball/z", + "teams/home/players/player1/x", + "teams/home/players/player1/y", + "teams/away/players/player2/x", + "teams/away/players/player2/y", + "timestamp", + "period", + "vendor/tracking/name", + "ball_status", + ]; + + for field in expected_fields { + assert!( + fields.contains(&field.to_string()), + "Missing field: {}", + field + ); + } + + // 2. Verify Data Types and Specific Values + let vendor_idx = container + .schema + .fields + .iter() + .position(|f| f.name() == "vendor/tracking/name") + .unwrap(); + let vendor_col = arrow_cast::cast( + container.data.column(vendor_idx), + &arrow_schema::DataType::Utf8, + ) + .unwrap(); + let vendor_val = vendor_col + .as_any() + .downcast_ref::() + .unwrap() + .value(0); + assert_eq!(vendor_val, "statsperform"); + + let status_idx = container + .schema + .fields + .iter() + .position(|f| f.name() == "ball_status") + .unwrap(); + let status_val = container + .data + .column(status_idx) + .as_any() + .downcast_ref::() + .unwrap() + .value(0); + assert!(status_val); + + let ts_idx = container + .schema + .fields + .iter() + .position(|f| f.name() == "timestamp") + .unwrap(); + assert!(matches!( + container.data.column(ts_idx).data_type(), + arrow_schema::DataType::Timestamp(_, _) + )); + + assert_eq!(container.orientation, None); + + // 3. Verify Coordinate System + let cs = container + .coordinate_system + .expect("Coordinate system should be set"); + assert_eq!(cs.x_range, None); + assert_eq!(cs.y_range, None); + assert_eq!(cs.origin, tdc_core::schema::Origin::TopLeft); + assert_eq!(cs.x_direction, tdc_core::schema::Direction::Right); + assert_eq!(cs.y_direction, tdc_core::schema::Direction::Down); + assert_eq!(cs.length_unit, Some(tdc_core::schema::LengthUnit::Meters)); + + // 4. Verify Entity Attributes + let check_float = |name: &str, expected: f64| { + let idx = container + .schema + .fields + .iter() + .position(|f| f.name() == name) + .unwrap_or_else(|| panic!("Field {} not found", name)); + let col = container + .data + .column(idx) + .as_any() + .downcast_ref::() + .unwrap_or_else(|| panic!("Field {} is not Float64", name)); + assert!(!col.is_null(0), "Field {} is null", name); + assert!( + (col.value(0) - expected).abs() < 1e-6, + "Field {} value mismatch: expected {}, got {}", + name, + expected, + col.value(0) + ); + }; + + check_float("ball/x", 50.0); + check_float("ball/y", 60.0); + check_float("ball/z", 1.5); + + check_float("teams/home/players/player1/x", 10.0); + check_float("teams/home/players/player1/y", 20.0); + + check_float("teams/away/players/player2/x", 30.0); + check_float("teams/away/players/player2/y", 40.0); +} diff --git a/crates/tdc-python/Cargo.toml b/crates/tdc-python/Cargo.toml new file mode 100644 index 0000000..5b12c86 --- /dev/null +++ b/crates/tdc-python/Cargo.toml @@ -0,0 +1,32 @@ +[package] +name = "tdc-python" +version = "0.1.0-alpha.1" +edition = "2024" +license = "MIT" +publish = false + +[lib] +name = "_tracking_data_container" +crate-type = ["cdylib"] + +[dependencies] +tdc-core = { path = "../tdc-core" } +tdc-parsers = { path = "../tdc-parsers", features = ["python"] } +tdc-ops = { path = "../tdc-ops" } +pyo3 = { version = "0.23", features = ["abi3-py310", "extension-module"] } +pyo3-arrow = { version = "0.7", default-features = false } +pyo3-log = { version = "0.12" } +datafusion = "45" +datafusion-functions = "45" +datafusion-functions-window = "45" +chrono = { version = "^0.4.42", default-features = false } +tokio = { version = "1", features = ["rt-multi-thread"] } +arrow-array = { version = "54" } +arrow-schema = { version = "54" } +arrow-cast = { version = "54" } +serde = { version = "1.0", features = ["derive"] } +strum = "0.27.2" +once_cell = "1.19" +num_cpus = "1.16" +futures = "0.3" +ahash = "0.8" diff --git a/crates/tdc-python/src/builder.rs b/crates/tdc-python/src/builder.rs new file mode 100644 index 0000000..996d6de --- /dev/null +++ b/crates/tdc-python/src/builder.rs @@ -0,0 +1,84 @@ +use crate::container::PyTrackingDataContainer; +use crate::error::IntoPyResult; +use crate::schema::PyDataField; +use pyo3::prelude::*; +use tdc_core::datacontainer::TrackingDataBuilder; +use tdc_core::error::{TrackingDataContainerError, TrackingDataContainerErrorKind}; + +#[pyclass(name = "_TrackingDataBuilder")] +pub struct PyTrackingDataBuilder { + pub inner: TrackingDataBuilder, +} + +#[pymethods] +impl PyTrackingDataBuilder { + #[new] + fn new() -> Self { + PyTrackingDataBuilder { + inner: TrackingDataBuilder::new(), + } + } + + fn insert_int(&mut self, frame_id: i64, col: Bound<'_, PyAny>, val: i64) -> PyResult<()> { + let col_name = self.extract_col_name(col)?; + self.inner.insert_int(frame_id, &col_name, val); + Ok(()) + } + + fn insert_float(&mut self, frame_id: i64, col: Bound<'_, PyAny>, val: f64) -> PyResult<()> { + let col_name = self.extract_col_name(col)?; + self.inner.insert_float(frame_id, &col_name, val); + Ok(()) + } + + fn insert_string(&mut self, frame_id: i64, col: Bound<'_, PyAny>, val: String) -> PyResult<()> { + let col_name = self.extract_col_name(col)?; + self.inner.insert_string(frame_id, &col_name, &val); + Ok(()) + } + + fn insert_bool(&mut self, frame_id: i64, col: Bound<'_, PyAny>, val: bool) -> PyResult<()> { + let col_name = self.extract_col_name(col)?; + self.inner.insert_bool(frame_id, &col_name, val); + Ok(()) + } + + fn merge(&mut self, other: &mut PyTrackingDataBuilder) { + // We take ownership of other's data by swapping it out + let other_inner = std::mem::replace(&mut other.inner, TrackingDataBuilder::new()); + self.inner.merge(other_inner); + } + + fn build(&mut self) -> PyResult { + // We take the current state to build + let builder = std::mem::replace(&mut self.inner, TrackingDataBuilder::new()); + let container = builder + .build() + .map_err(|e| { + TrackingDataContainerError::new(TrackingDataContainerErrorKind::InvalidParameters( + e.to_string(), + )) + }) + .into_pyresult()?; + Ok(PyTrackingDataContainer { inner: container }) + } +} + +impl PyTrackingDataBuilder { + fn extract_col_name(&self, col: Bound<'_, PyAny>) -> PyResult { + if let Ok(s) = col.extract::() { + Ok(s) + } else if let Ok(f) = col.extract::() { + Ok(f.inner.name()) + } else { + Err(pyo3::exceptions::PyTypeError::new_err( + "Column must be a string or a DataField object", + )) + } + } +} + +pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_class::()?; + Ok(()) +} diff --git a/crates/tdc-python/src/container.rs b/crates/tdc-python/src/container.rs new file mode 100644 index 0000000..c32db11 --- /dev/null +++ b/crates/tdc-python/src/container.rs @@ -0,0 +1,752 @@ +use crate::coordinate_system::{PyCoordinateSystem, PyOrientationMode}; +use crate::error::IntoPyResult; +use crate::schema::{PyDataField, PyFields}; +use crate::selector::{PyColumnSelector, PyExpr, PySelection}; +use tdc_core::Select; +use tdc_core::config::{CONTEXT, RUNTIME}; +use tdc_core::datacontainer::{LazyTrackingDataContainer, TrackingDataContainer}; +use tdc_core::schema::{DataField, Fields}; +use tdc_core::selector::expr::{Expr, expand_expr}; + +use datafusion::arrow::array::builder::{ + BooleanBuilder, Float32Builder, Float64Builder, Int64Builder, StringBuilder, + TimestampMicrosecondBuilder, UInt64Builder, +}; +use datafusion::arrow::array::{Array, ArrayRef, RecordBatch, StructArray}; +use datafusion::arrow::datatypes::{DataType, Field, Fields as ArrowFields, TimeUnit}; +use datafusion::prelude::*; +use futures::StreamExt; +use pyo3::exceptions::{PyTypeError, PyValueError}; +use pyo3::prelude::*; +use pyo3::types::{PyCapsule, PyDict, PyList, PyType}; +use std::sync::Arc; + +fn block_on(future: F) -> F::Output { + RUNTIME.block_on(future) +} + +#[derive(FromPyObject)] +pub enum SelectItem { + Str(String), + Field(PyDataField), + Selection(PySelection), +} +#[derive(FromPyObject)] +pub enum SelectInput { + Single(SelectItem), + List(Vec), +} +#[derive(FromPyObject)] +#[allow(clippy::large_enum_variant)] +pub enum AddColumnValue { + Expr(PyExpr), + Array(PyObject), +} + +#[pyclass(name = "Frame")] +pub struct PyFrame { + pub values: Vec, + pub fields: Vec, + pub field_to_index: std::collections::HashMap, +} + +#[pymethods] +impl PyFrame { + fn __getitem__(&self, py: Python<'_>, key: Bound<'_, PyAny>) -> PyResult { + let name = if let Ok(s) = key.extract::() { + s + } else if let Ok(f) = key.getattr("_field") { + f.extract::()?.inner.name() + } else { + return Err(PyTypeError::new_err("Key must be string or DataField")); + }; + let idx = self + .field_to_index + .get(&name) + .ok_or_else(|| PyValueError::new_err(format!("Field '{}' not found", name)))?; + Ok(self.values[*idx].clone_ref(py)) + } + + fn to_dict(&self, py: Python<'_>) -> PyResult { + let dict = PyDict::new(py); + for (i, field) in self.fields.iter().enumerate() { + dict.set_item(field.name(), self.values[i].clone_ref(py))?; + } + Ok(dict.into_py(py)) + } + + fn __repr__(&self) -> String { + format!("", self.fields.len()) + } +} + +#[pyclass(name = "_TrackingDataContainer")] +pub struct PyTrackingDataContainer { + pub inner: TrackingDataContainer, +} + +#[pymethods] +impl PyTrackingDataContainer { + #[new] + #[pyo3(signature = (data, coordinate_system=None, orientation=None))] + pub fn new( + data: &Bound<'_, PyDict>, + coordinate_system: Option, + orientation: Option, + ) -> PyResult { + let mut fields = Vec::new(); + let mut arrays = Vec::new(); + let mut schema_fields = Vec::new(); + for (key, value) in data { + let col_name = key.extract::()?; + let data_field = DataField::from_name(&col_name); + let target_type = data_field.default_arrow_type(); + let array = build_arrow_array( + value + .downcast::() + .map_err(|_| PyTypeError::new_err("Must be list"))?, + &target_type, + )?; + fields.push(Field::new(&col_name, array.data_type().clone(), true)); + arrays.push(array); + schema_fields.push(data_field); + } + let struct_array = StructArray::try_new(ArrowFields::from(fields), arrays, None) + .map_err(|e| PyValueError::new_err(e.to_string()))?; + let orientation = if let Some(o) = orientation { + use std::str::FromStr; + Some( + tdc_core::schema::OrientationMode::from_str(&o) + .map_err(|e| PyValueError::new_err(e.to_string()))?, + ) + } else { + None + }; + Ok(PyTrackingDataContainer { + inner: TrackingDataContainer { + data: struct_array, + schema: Fields { + fields: schema_fields, + }, + coordinate_system: coordinate_system.map(|cs| cs.inner), + orientation, + }, + }) + } + + fn lazy(&self) -> PyResult { + Ok(PyLazyTrackingDataContainer { + inner: self.inner.lazy().into_pyresult()?, + }) + } + fn select(&self, items: SelectInput) -> PyResult { + let mut fields = Vec::new(); + match items { + SelectInput::Single(item) => fields.extend(self.process_select_item(item)), + SelectInput::List(list) => { + for item in list { + fields.extend(self.process_select_item(item)); + } + } + } + Ok(PyTrackingDataContainer { + inner: self.inner.select(fields).into_pyresult()?, + }) + } + + #[pyo3(signature = (name, value, _schema_capsule=None))] + fn add_column( + &self, + py: Python<'_>, + name: String, + value: AddColumnValue, + _schema_capsule: Option>, + ) -> PyResult { + let schema_capsules = _schema_capsule + .map(|c| PyList::new(py, vec![c])) + .transpose()?; + self.add_columns(py, vec![name], vec![value], schema_capsules) + } + + #[pyo3(signature = (names, values, _schema_capsules=None))] + fn add_columns( + &self, + py: Python<'_>, + names: Vec, + values: Vec, + _schema_capsules: Option>, + ) -> PyResult { + let mut expr_names = Vec::new(); + let mut expr_values = Vec::new(); + let mut array_names = Vec::new(); + let mut array_values = Vec::new(); + for (i, (name, value)) in names.into_iter().zip(values.into_iter()).enumerate() { + match value { + AddColumnValue::Expr(e) => { + expr_names.push(name); + expr_values.push(e.inner); + } + AddColumnValue::Array(obj) => { + array_names.push(DataField::from_name(&name)); + let capsule = _schema_capsules + .as_ref() + .and_then(|l| l.get_item(i).ok()) + .ok_or_else(|| PyValueError::new_err("No schema capsule"))?; + array_values.push( + pyo3_arrow::PyArray::from_arrow_pycapsule( + obj.bind(py).downcast::()?, + capsule.downcast::()?, + )? + .into_inner() + .0, + ); + } + } + } + let mut current = self.inner.clone(); + if !expr_names.is_empty() { + current = current + .with_expr_columns_ctx(expr_names, expr_values, &CONTEXT, RUNTIME.handle()) + .into_pyresult()?; + } + if !array_names.is_empty() { + current = current.with_columns(array_names, array_values); + } + Ok(PyTrackingDataContainer { inner: current }.into_py(py)) + } + + fn add_column_per_player_rust( + &self, + _py: Python<'_>, + _name: String, + _value: AddColumnValue, + ) -> PyResult { + Err(PyValueError::new_err( + "Direct Rust function support has been removed. Use callable passing Arrow capsules instead.", + )) + } + + fn transform_coordinates(&self, cs: PyCoordinateSystem) -> PyResult { + Ok(PyTrackingDataContainer { + inner: self.inner.transform_coordinates(cs.inner).into_pyresult()?, + }) + } + fn transform_orientation(&self, mode: &str) -> PyResult { + use std::str::FromStr; + let mode = tdc_core::schema::OrientationMode::from_str(mode) + .map_err(|e| PyValueError::new_err(e.to_string()))?; + Ok(PyTrackingDataContainer { + inner: self.inner.transform_orientation(mode).into_pyresult()?, + }) + } + fn with_orientation(&self, mode: &str) -> PyResult { + use std::str::FromStr; + let mode = tdc_core::schema::OrientationMode::from_str(mode) + .map_err(|e| PyValueError::new_err(e.to_string()))?; + Ok(PyTrackingDataContainer { + inner: self.inner.clone().with_orientation(mode), + }) + } + fn with_coordinate_system(&self, cs: PyCoordinateSystem) -> Self { + PyTrackingDataContainer { + inner: self.inner.clone().with_coordinate_system(cs.inner), + } + } + + #[classmethod] + fn _from_capsule(_cls: &Bound<'_, PyType>, capsule: &Bound<'_, PyCapsule>) -> PyResult { + let container: &TrackingDataContainer = unsafe { capsule.reference() }; + Ok(PyTrackingDataContainer { + inner: container.clone(), + }) + } + + #[getter] + fn schema(&self) -> PyFields { + PyFields { + inner: self.inner.schema.clone(), + } + } + #[getter] + fn coordinate_system(&self) -> Option { + self.inner + .coordinate_system + .as_ref() + .map(|cs| PyCoordinateSystem { inner: cs.clone() }) + } + #[getter] + fn orientation_mode(&self) -> Option { + self.inner.orientation.map(|o| o.into()) + } + fn num_rows(&self) -> usize { + self.inner.num_rows() + } + fn validate(&self) -> PyResult<()> { + self.inner.validate().into_pyresult() + } + + fn save_parquet(&self, path: &str) -> PyResult<()> { + self.inner.save_parquet(path).into_pyresult() + } + + #[staticmethod] + fn load_parquet(path: &str) -> PyResult { + let inner = TrackingDataContainer::load_parquet(path).into_pyresult()?; + Ok(PyTrackingDataContainer { inner }) + } + + #[getter] + fn selector(&self) -> PyColumnSelector { + PyColumnSelector + } + fn __repr__(&self) -> String { + format!( + "", + self.inner.num_rows(), + self.inner.schema.len() + ) + } + + fn materialize_frame<'py>(&self, py: Python<'py>, frame: usize) -> PyResult { + let mut values = Vec::new(); + let mut field_to_index = std::collections::HashMap::new(); + let mut fields = Vec::new(); + for (i, field) in self.inner.schema.fields.iter().enumerate() { + values.push(arrow_to_python(py, self.inner.data.column(i), frame)?); + field_to_index.insert(field.name(), i); + fields.push(field.clone()); + } + Ok(PyFrame { + values, + fields, + field_to_index, + }) + } + + fn to_arrow<'py>(&self, py: Python<'py>) -> PyResult> { + Ok( + pyo3_arrow::PyRecordBatch::new(RecordBatch::from(self.inner.data.clone())) + .to_pyarrow(py)? + .into_bound(py), + ) + } + fn __arrow_c_schema__<'py>(&self, py: Python<'py>) -> PyResult> { + Ok( + pyo3_arrow::PySchema::new(RecordBatch::from(self.inner.data.clone()).schema()) + .to_pyarrow(py)? + .into_bound(py), + ) + } + fn __arrow_c_array__<'py>( + &self, + py: Python<'py>, + _requested_schema: Option>, + ) -> PyResult> { + let rb = RecordBatch::from(self.inner.data.clone()); + let pyarrow_rb = pyo3_arrow::PyRecordBatch::new(rb).to_pyarrow(py)?; + Ok(pyarrow_rb + .call_method0(py, "__arrow_c_array__")? + .into_bound(py)) + } +} + +impl PyTrackingDataContainer { + fn process_select_item(&self, item: SelectItem) -> Vec { + match item { + SelectItem::Str(s) => { + if s.contains('*') { + let matched_names = expand_expr(&self.inner, &Expr::Wildcard(s)); + self.inner + .schema + .fields + .iter() + .filter(|f| matched_names.contains(&f.name())) + .cloned() + .collect() + } else { + vec![DataField::from_name(&s)] + } + } + SelectItem::Field(f) => vec![f.inner], + SelectItem::Selection(s) => expand_expr(&self.inner, &s.expr) + .into_iter() + .map(|n| DataField::from_name(&n)) + .collect(), + } + } +} + +#[pyclass(name = "_LazyTrackingDataContainer")] +pub struct PyLazyTrackingDataContainer { + pub inner: LazyTrackingDataContainer, +} + +#[pymethods] +impl PyLazyTrackingDataContainer { + fn collect(&self, py: Python<'_>) -> PyResult { + Ok(PyTrackingDataContainer { + inner: py + .allow_threads(|| block_on(async { self.inner.clone().collect().await })) + .into_pyresult()?, + }) + } + fn select(&self, items: SelectInput) -> PyResult { + let mut fields = Vec::new(); + match items { + SelectInput::Single(item) => fields.extend(self.process_select_item(item)), + SelectInput::List(list) => { + for item in list { + fields.extend(self.process_select_item(item)); + } + } + } + Ok(PyLazyTrackingDataContainer { + inner: self.inner.select(fields).into_pyresult()?, + }) + } + fn filter(&self, predicate: PyExpr) -> PyResult { + Ok(PyLazyTrackingDataContainer { + inner: self.inner.filter(predicate.inner).into_pyresult()?, + }) + } + + fn transform_coordinates(&self, cs: PyCoordinateSystem) -> PyResult { + Ok(PyLazyTrackingDataContainer { + inner: self.inner.transform_coordinates(cs.inner).into_pyresult()?, + }) + } + + fn transform_orientation(&self, mode: &str) -> PyResult { + use std::str::FromStr; + let mode = tdc_core::schema::OrientationMode::from_str(mode) + .map_err(|e| PyValueError::new_err(e.to_string()))?; + Ok(PyLazyTrackingDataContainer { + inner: self.inner.transform_orientation(mode).into_pyresult()?, + }) + } + + #[pyo3(signature = (name, value, _schema_capsule=None))] + fn add_column( + &self, + py: Python<'_>, + name: String, + value: AddColumnValue, + _schema_capsule: Option>, + ) -> PyResult { + match value { + AddColumnValue::Expr(e) => Ok(PyLazyTrackingDataContainer { + inner: self.inner.add_column(&name, e.inner).into_pyresult()?, + }), + AddColumnValue::Array(obj) => { + let capsule = + _schema_capsule.ok_or_else(|| PyValueError::new_err("No schema capsule"))?; + let array = pyo3_arrow::PyArray::from_arrow_pycapsule( + obj.bind(py).downcast::()?, + capsule.downcast::()?, + )? + .into_inner() + .0; + Ok(PyLazyTrackingDataContainer { + inner: self.inner.add_array_column(&name, array).into_pyresult()?, + }) + } + } + } + + #[pyo3(signature = (names, values, _schema_capsules=None))] + fn add_columns( + &self, + _py: Python<'_>, + names: Vec, + values: Vec, + _schema_capsules: Option>, + ) -> PyResult { + let mut expr_names = Vec::new(); + let mut expr_values = Vec::new(); + + for (name, value) in names.into_iter().zip(values.into_iter()) { + match value { + AddColumnValue::Expr(e) => { + expr_names.push(name); + expr_values.push(e.inner); + } + AddColumnValue::Array(_) => { + return Err(PyValueError::new_err( + "Adding raw arrays to a lazy container is not yet supported.", + )); + } + } + } + + Ok(PyLazyTrackingDataContainer { + inner: self + .inner + .add_columns(expr_names, expr_values) + .into_pyresult()?, + }) + } + + fn add_column_udf(&self, name: String, func: PyObject, on: Vec) -> PyResult { + let py_udf = crate::udf::PyUDF::new(name.clone(), func); + let scalar_udf = datafusion::logical_expr::ScalarUDF::from(py_udf); + let args: Vec = + on.into_iter().map(datafusion::prelude::col).collect(); + let expr = scalar_udf.call(args); + + Ok(PyLazyTrackingDataContainer { + inner: self.inner.add_column(&name, expr).into_pyresult()?, + }) + } + + #[getter] + fn schema(&self) -> PyFields { + PyFields { + inner: self.inner.schema.clone(), + } + } + #[getter] + fn selector(&self) -> PyColumnSelector { + PyColumnSelector + } + #[getter] + fn orientation_mode(&self) -> Option { + self.inner.orientation.map(|o| o.into()) + } + fn __repr__(&self) -> String { + format!( + "", + self.inner.schema.len() + ) + } + fn __arrow_c_schema__<'py>(&self, py: Python<'py>) -> PyResult> { + Ok( + pyo3_arrow::PySchema::new(Arc::new(self.inner.df.schema().as_arrow().clone())) + .to_pyarrow(py)? + .into_bound(py), + ) + } + fn __arrow_c_stream__<'py>( + &self, + py: Python<'py>, + _requested_schema: Option>, + ) -> PyResult> { + let stream = py + .allow_threads(|| block_on(async { self.inner.df.clone().execute_stream().await })) + .map_err(|e| PyValueError::new_err(e.to_string()))?; + let schema = stream.schema(); + struct StreamReader { + stream: std::pin::Pin>, + schema: arrow_schema::SchemaRef, + } + impl Iterator for StreamReader { + type Item = Result; + fn next(&mut self) -> Option { + let next_batch = block_on(async { self.stream.next().await }); + next_batch.map(|res| { + res.map_err(|e| arrow_schema::ArrowError::ExternalError(Box::new(e))) + }) + } + } + impl datafusion::arrow::record_batch::RecordBatchReader for StreamReader { + fn schema(&self) -> arrow_schema::SchemaRef { + self.schema.clone() + } + } + let pyarrow_reader = + pyo3_arrow::PyRecordBatchReader::new(Box::new(StreamReader { stream, schema })) + .to_pyarrow(py)?; + Ok(pyarrow_reader + .call_method0(py, "__arrow_c_stream__")? + .into_bound(py)) + } +} + +impl PyLazyTrackingDataContainer { + fn process_select_item(&self, item: SelectItem) -> Vec { + match item { + SelectItem::Str(s) => { + if s.contains('*') { + let matched_names = expand_expr(&self.inner, &Expr::Wildcard(s)); + self.inner + .schema + .fields + .iter() + .filter(|f| matched_names.contains(&f.name())) + .cloned() + .collect() + } else { + vec![DataField::from_name(&s)] + } + } + SelectItem::Field(f) => vec![f.inner], + SelectItem::Selection(s) => expand_expr(&self.inner, &s.expr) + .into_iter() + .map(|n| DataField::from_name(&n)) + .collect(), + } + } +} + +#[pyfunction] +pub fn scan_parquet(_py: Python<'_>, path: String) -> PyResult { + let lazy_inner = block_on(async { + let df = CONTEXT + .read_parquet(&path, ParquetReadOptions::default()) + .await?; + let schema = Fields::new( + df.schema() + .as_arrow() + .fields() + .iter() + .map(|f| f.name().clone()) + .collect(), + ); + Ok::( + LazyTrackingDataContainer::new(df, schema, None, None), + ) + }) + .map_err(|e| PyValueError::new_err(e.to_string()))?; + Ok(PyLazyTrackingDataContainer { inner: lazy_inner }) +} + +fn build_arrow_array(list: &Bound<'_, PyList>, dtype: &DataType) -> PyResult { + match dtype { + DataType::Float32 => { + let mut b = Float32Builder::with_capacity(list.len()); + for i in list { + if i.is_none() { + b.append_null(); + } else { + b.append_value(i.extract::()?); + } + } + Ok(Arc::new(b.finish())) + } + DataType::Float64 => { + let mut b = Float64Builder::with_capacity(list.len()); + for i in list { + if i.is_none() { + b.append_null(); + } else { + b.append_value(i.extract::()?); + } + } + Ok(Arc::new(b.finish())) + } + DataType::Int64 => { + let mut b = Int64Builder::with_capacity(list.len()); + for i in list { + if i.is_none() { + b.append_null(); + } else { + b.append_value(i.extract::()?); + } + } + Ok(Arc::new(b.finish())) + } + DataType::UInt64 => { + let mut b = UInt64Builder::with_capacity(list.len()); + for i in list { + if i.is_none() { + b.append_null(); + } else { + b.append_value(i.extract::()?); + } + } + Ok(Arc::new(b.finish())) + } + DataType::Boolean => { + let mut b = BooleanBuilder::with_capacity(list.len()); + for i in list { + if i.is_none() { + b.append_null(); + } else { + b.append_value(i.extract::()?); + } + } + Ok(Arc::new(b.finish())) + } + DataType::Timestamp(TimeUnit::Microsecond, _) => { + let mut b = TimestampMicrosecondBuilder::with_capacity(list.len()); + for i in list { + if i.is_none() { + b.append_null(); + } else { + b.append_value(i.extract::()?); + } + } + Ok(Arc::new(b.finish())) + } + DataType::Utf8 | DataType::Dictionary(_, _) => { + let mut b = StringBuilder::with_capacity(list.len(), list.len() * 5); + for i in list { + if i.is_none() { + b.append_null(); + } else { + b.append_value(i.extract::()?); + } + } + Ok(Arc::new(b.finish())) + } + _ => Err(PyTypeError::new_err(format!( + "Unsupported target type: {:?}", + dtype + ))), + } +} + +fn arrow_to_python(py: Python<'_>, array: &ArrayRef, row: usize) -> PyResult { + use datafusion::arrow::array::AsArray; + if array.is_null(row) { + return Ok(py.None()); + } + match array.data_type() { + DataType::Float32 => Ok(array + .as_primitive::() + .value(row) + .into_py(py)), + DataType::Float64 => Ok(array + .as_primitive::() + .value(row) + .into_py(py)), + DataType::Int64 => Ok(array + .as_primitive::() + .value(row) + .into_py(py)), + DataType::UInt64 => Ok(array + .as_primitive::() + .value(row) + .into_py(py)), + DataType::Boolean => Ok(array.as_boolean().value(row).into_py(py)), + DataType::Utf8 => Ok(array.as_string::().value(row).into_py(py)), + DataType::Timestamp(TimeUnit::Microsecond, _) => Ok(array + .as_primitive::() + .value(row) + .into_py(py)), + DataType::Dictionary(_, _) => { + let dict = + array + .as_any() + .downcast_ref::>() + .unwrap(); + let keys = dict.keys(); + let values = dict + .values() + .as_any() + .downcast_ref::() + .unwrap(); + Ok(values.value(keys.value(row) as usize).into_py(py)) + } + _ => Ok(py.None()), + } +} + +pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_class::()?; + m.add_class::()?; + m.add_class::()?; + m.add_function(wrap_pyfunction!(scan_parquet, m)?)?; + Ok(()) +} diff --git a/crates/tdc-python/src/coordinate_system.rs b/crates/tdc-python/src/coordinate_system.rs new file mode 100644 index 0000000..bbcf1c5 --- /dev/null +++ b/crates/tdc-python/src/coordinate_system.rs @@ -0,0 +1,358 @@ +use pyo3::prelude::*; +use std::str::FromStr; +use tdc_core::schema::coordinate_system as core; + +#[pyclass(name = "Origin", eq, eq_int)] +#[derive(Clone, PartialEq)] +/// The location of the origin (0,0) in a coordinate system. +pub enum PyOrigin { + /// The center of the pitch/court. + Center, + /// The top-left corner of the pitch. + TopLeft, + /// The bottom-left corner of the pitch. + BottomLeft, +} + +#[pymethods] +impl PyOrigin { + #[classattr] + const CENTER: Self = Self::Center; + #[classattr] + const TOP_LEFT: Self = Self::TopLeft; + #[classattr] + const BOTTOM_LEFT: Self = Self::BottomLeft; + + fn __str__(&self) -> &str { + match self { + Self::Center => "center", + Self::TopLeft => "top_left", + Self::BottomLeft => "bottom_left", + } + } +} + +impl From for core::Origin { + fn from(o: PyOrigin) -> Self { + match o { + PyOrigin::Center => core::Origin::Center, + PyOrigin::TopLeft => core::Origin::TopLeft, + PyOrigin::BottomLeft => core::Origin::BottomLeft, + } + } +} + +#[pyclass(name = "Direction", eq, eq_int)] +#[derive(Clone, PartialEq)] +/// Possible axis directions. +pub enum PyDirection { + /// Values increase towards the right. + Right, + /// Values increase towards the left. + Left, + /// Values increase towards the top. + Up, + /// Values increase towards the bottom. + Down, +} + +#[pymethods] +impl PyDirection { + #[classattr] + const RIGHT: Self = Self::Right; + #[classattr] + const LEFT: Self = Self::Left; + #[classattr] + const UP: Self = Self::Up; + #[classattr] + const DOWN: Self = Self::Down; + + fn __str__(&self) -> &str { + match self { + Self::Right => "right", + Self::Left => "left", + Self::Up => "up", + Self::Down => "down", + } + } +} + +impl From for core::Direction { + fn from(d: PyDirection) -> Self { + match d { + PyDirection::Right => core::Direction::Right, + PyDirection::Left => core::Direction::Left, + PyDirection::Up => core::Direction::Up, + PyDirection::Down => core::Direction::Down, + } + } +} + +#[pyclass(name = "LengthUnit", eq, eq_int)] +#[derive(Clone, PartialEq)] +/// Standardized units of length. +pub enum PyLengthUnit { + /// Standard metric meter. + Meters, + /// Centimeter. + Centimeters, + /// Millimeter. + Millimeters, + /// Yard (approx 0.9144 meters). + Yards, + /// Inch. + Inches, + /// Foot. + Feet, + /// Normalized unit where coordinates represent a percentage of pitch dimensions. + Percent, +} + +#[pymethods] +impl PyLengthUnit { + #[classattr] + const METERS: Self = Self::Meters; + #[classattr] + const CENTIMETERS: Self = Self::Centimeters; + #[classattr] + const MILLIMETERS: Self = Self::Millimeters; + #[classattr] + const YARDS: Self = Self::Yards; + #[classattr] + const INCHES: Self = Self::Inches; + #[classattr] + const FEET: Self = Self::Feet; + #[classattr] + const PERCENT: Self = Self::Percent; + + fn __str__(&self) -> &str { + match self { + Self::Meters => "meters", + Self::Centimeters => "centimeters", + Self::Millimeters => "millimeters", + Self::Yards => "yards", + Self::Inches => "inches", + Self::Feet => "feet", + Self::Percent => "percent", + } + } +} + +impl From for tdc_core::schema::LengthUnit { + fn from(u: PyLengthUnit) -> Self { + match u { + PyLengthUnit::Meters => tdc_core::schema::LengthUnit::Meters, + PyLengthUnit::Centimeters => tdc_core::schema::LengthUnit::Centimeters, + PyLengthUnit::Millimeters => tdc_core::schema::LengthUnit::Millimeters, + PyLengthUnit::Yards => tdc_core::schema::LengthUnit::Yards, + PyLengthUnit::Inches => tdc_core::schema::LengthUnit::Inches, + PyLengthUnit::Feet => tdc_core::schema::LengthUnit::Feet, + PyLengthUnit::Percent => tdc_core::schema::LengthUnit::Percent, + } + } +} + +#[pyclass(name = "OrientationMode", eq, eq_int)] +#[derive(Clone, PartialEq)] +/// Logic for normalizing playing direction across periods or possession. +pub enum PyOrientationMode { + /// Coordinates are flipped so the team in possession always attacks from left to right. + BallOwningTeam, + /// The home team attacks left-to-right in the first half and right-to-left in the second. + HomeAway, + /// The away team attacks left-to-right in the first half and right-to-left in the second. + AwayHome, + /// The home team always attacks from left to right, regardless of period. + StaticHomeAway, + /// The away team always attacks from left to right, regardless of period. + StaticAwayHome, +} + +#[pymethods] +impl PyOrientationMode { + #[classattr] + const BALL_OWNING_TEAM: Self = Self::BallOwningTeam; + #[classattr] + const HOME_AWAY: Self = Self::HomeAway; + #[classattr] + const AWAY_HOME: Self = Self::AwayHome; + #[classattr] + const STATIC_HOME_AWAY: Self = Self::StaticHomeAway; + #[classattr] + const STATIC_AWAY_HOME: Self = Self::StaticAwayHome; + + fn __str__(&self) -> &str { + match self { + Self::BallOwningTeam => "ball_owning_team", + Self::HomeAway => "home_away", + Self::AwayHome => "away_home", + Self::StaticHomeAway => "static_home_away", + Self::StaticAwayHome => "static_away_home", + } + } +} + +impl From for tdc_core::schema::OrientationMode { + fn from(m: PyOrientationMode) -> Self { + match m { + PyOrientationMode::BallOwningTeam => tdc_core::schema::OrientationMode::BallOwningTeam, + PyOrientationMode::HomeAway => tdc_core::schema::OrientationMode::HomeAway, + PyOrientationMode::AwayHome => tdc_core::schema::OrientationMode::AwayHome, + PyOrientationMode::StaticHomeAway => tdc_core::schema::OrientationMode::StaticHomeAway, + PyOrientationMode::StaticAwayHome => tdc_core::schema::OrientationMode::StaticAwayHome, + } + } +} + +impl From for PyOrientationMode { + fn from(m: tdc_core::schema::OrientationMode) -> Self { + match m { + tdc_core::schema::OrientationMode::BallOwningTeam => PyOrientationMode::BallOwningTeam, + tdc_core::schema::OrientationMode::HomeAway => PyOrientationMode::HomeAway, + tdc_core::schema::OrientationMode::AwayHome => PyOrientationMode::AwayHome, + tdc_core::schema::OrientationMode::StaticHomeAway => PyOrientationMode::StaticHomeAway, + tdc_core::schema::OrientationMode::StaticAwayHome => PyOrientationMode::StaticAwayHome, + } + } +} + +#[pyclass(name = "_CoordinateSystem")] +#[derive(Clone)] +/// Defines a coordinate system for tracking data. +pub struct PyCoordinateSystem { + pub inner: core::CoordinateSystem, +} + +#[derive(FromPyObject)] +pub enum OriginInput { + Enum(PyOrigin), + Str(String), +} + +#[derive(FromPyObject)] +pub enum DirectionInput { + Enum(PyDirection), + Str(String), +} + +#[derive(FromPyObject)] +pub enum LengthUnitInput { + Enum(PyLengthUnit), + Str(String), +} + +#[pymethods] +impl PyCoordinateSystem { + #[new] + #[pyo3(signature = (x_range=None, y_range=None, origin=OriginInput::Str("center".to_string()), x_direction=DirectionInput::Str("right".to_string()), y_direction=DirectionInput::Str("up".to_string()), length_unit=None))] + fn new( + x_range: Option<(f64, f64)>, + y_range: Option<(f64, f64)>, + origin: OriginInput, + x_direction: DirectionInput, + y_direction: DirectionInput, + length_unit: Option, + ) -> PyResult { + let origin = match origin { + OriginInput::Enum(e) => e.into(), + OriginInput::Str(s) => core::Origin::from_str(&s).map_err(|e: strum::ParseError| { + pyo3::exceptions::PyValueError::new_err(e.to_string()) + })?, + }; + let x_direction = match x_direction { + DirectionInput::Enum(e) => e.into(), + DirectionInput::Str(s) => { + core::Direction::from_str(&s).map_err(|e: strum::ParseError| { + pyo3::exceptions::PyValueError::new_err(e.to_string()) + })? + } + }; + let y_direction = match y_direction { + DirectionInput::Enum(e) => e.into(), + DirectionInput::Str(s) => { + core::Direction::from_str(&s).map_err(|e: strum::ParseError| { + pyo3::exceptions::PyValueError::new_err(e.to_string()) + })? + } + }; + let length_unit = if let Some(u) = length_unit { + let core_unit = match u { + LengthUnitInput::Enum(e) => e.into(), + LengthUnitInput::Str(s) => { + tdc_core::schema::LengthUnit::from_str(&s).map_err(|e: strum::ParseError| { + pyo3::exceptions::PyValueError::new_err(e.to_string()) + })? + } + }; + Some(core_unit) + } else { + None + }; + + Ok(PyCoordinateSystem { + inner: core::CoordinateSystem::new( + x_range, + y_range, + origin, + x_direction, + y_direction, + length_unit, + ), + }) + } + + fn __repr__(&self) -> String { + format!( + "", + self.inner.origin, + self.inner.x_direction, + self.inner.y_direction, + self.inner.length_unit + ) + } + + #[getter] + fn origin(&self) -> String { + self.inner.origin.to_string() + } + + #[getter] + fn x_range(&self) -> Option<(f64, f64)> { + self.inner.x_range + } + + #[getter] + fn y_range(&self) -> Option<(f64, f64)> { + self.inner.y_range + } + + #[getter] + fn x_direction(&self) -> String { + self.inner.x_direction.to_string() + } + + #[getter] + fn y_direction(&self) -> String { + self.inner.y_direction.to_string() + } + + #[getter] + fn length_unit(&self) -> Option { + self.inner + .length_unit + .map(|u: tdc_core::schema::LengthUnit| u.to_string()) + } + + fn __eq__(&self, other: &Self) -> bool { + self.inner == other.inner + } +} + +pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_class::()?; + m.add_class::()?; + m.add_class::()?; + m.add_class::()?; + m.add_class::()?; + Ok(()) +} diff --git a/crates/tdc-python/src/error.rs b/crates/tdc-python/src/error.rs new file mode 100644 index 0000000..c3be3d5 --- /dev/null +++ b/crates/tdc-python/src/error.rs @@ -0,0 +1,92 @@ +use pyo3::prelude::*; +use pyo3::{PyErr, PyResult, create_exception, exceptions::PyException}; +use tdc_core::error::{ + TrackingDataContainerError as CoreError, TrackingDataContainerErrorKind, + TrackingDataContainerResult, +}; + +create_exception!( + _tracking_data_container, + TrackingDataContainerError, + PyException, + "The base class for all errors" +); + +create_exception!( + _tracking_data_container, + InvalidParametersError, + TrackingDataContainerError, + "Provided parameters are invalid" +); + +create_exception!( + _tracking_data_container, + SchemaError, + TrackingDataContainerError, + "Schema validation failed" +); + +create_exception!( + _tracking_data_container, + SchemaWarning, + pyo3::exceptions::PyWarning, + "Schema validation warning" +); + +pub fn core_to_py_err(err: CoreError) -> PyErr { + let message = err.to_string(); + match err.kind { + TrackingDataContainerErrorKind::InvalidParameters(_) => { + InvalidParametersError::new_err(message) + } + TrackingDataContainerErrorKind::SchemaError(_) => SchemaError::new_err(message), + TrackingDataContainerErrorKind::SchemaWarning(_) => SchemaWarning::new_err(message), + TrackingDataContainerErrorKind::ArrowError(_) => { + TrackingDataContainerError::new_err(message) + } + TrackingDataContainerErrorKind::DataFusionError(_) => { + TrackingDataContainerError::new_err(message) + } + TrackingDataContainerErrorKind::ParquetError(_) => { + pyo3::exceptions::PyIOError::new_err(message) + } + TrackingDataContainerErrorKind::IOError(_) => pyo3::exceptions::PyIOError::new_err(message), + TrackingDataContainerErrorKind::Internal(_) => { + pyo3::exceptions::PyRuntimeError::new_err(message) + } + } +} + +pub trait IntoPyResult { + type Inner; + fn into_pyresult(self) -> PyResult; +} + +impl IntoPyResult for TrackingDataContainerResult { + type Inner = T; + fn into_pyresult(self) -> PyResult { + self.map_err(core_to_py_err) + } +} + +impl IntoPyResult for Result { + type Inner = T; + fn into_pyresult(self) -> PyResult { + self.map_err(pyo3::exceptions::PyValueError::new_err) + } +} + +pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> { + let py = m.py(); + m.add( + "TrackingDataContainerError", + py.get_type::(), + )?; + m.add( + "InvalidParametersError", + py.get_type::(), + )?; + m.add("SchemaError", py.get_type::())?; + m.add("SchemaWarning", py.get_type::())?; + Ok(()) +} diff --git a/crates/tdc-python/src/io.rs b/crates/tdc-python/src/io.rs new file mode 100644 index 0000000..985d51a --- /dev/null +++ b/crates/tdc-python/src/io.rs @@ -0,0 +1,7 @@ +use pyo3::prelude::*; +use tdc_parsers; + +pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> { + tdc_parsers::python::register(m)?; + Ok(()) +} diff --git a/crates/tdc-python/src/lib.rs b/crates/tdc-python/src/lib.rs new file mode 100644 index 0000000..2ab7ca8 --- /dev/null +++ b/crates/tdc-python/src/lib.rs @@ -0,0 +1,31 @@ +#![allow(deprecated)] +use pyo3::prelude::*; + +pub mod builder; +pub mod container; +pub mod coordinate_system; +pub mod error; +pub mod io; +pub mod ops; +pub mod schema; +pub mod selector; +pub mod udf; + +#[pymodule(gil_used = false)] +fn _tracking_data_container(m: &Bound<'_, PyModule>) -> PyResult<()> { + pyo3_log::init(); + + builder::register(m)?; + container::register(m)?; + coordinate_system::register(m)?; + error::register(m)?; + schema::register(m)?; + selector::register(m)?; + udf::register(m)?; + io::register(m)?; + ops::register(m)?; + + m.add("__version__", env!("CARGO_PKG_VERSION"))?; + + Ok(()) +} diff --git a/crates/tdc-python/src/mod.rs b/crates/tdc-python/src/mod.rs new file mode 100644 index 0000000..e8ba3d4 --- /dev/null +++ b/crates/tdc-python/src/mod.rs @@ -0,0 +1,20 @@ +pub mod builder; +pub mod container; +pub mod coordinate_system; +pub mod error; +pub mod schema; +pub mod selector; +pub mod udf; + +use pyo3::prelude::*; + +pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> { + builder::register(m)?; + container::register(m)?; + coordinate_system::register(m)?; + error::register(m)?; + schema::register(m)?; + selector::register(m)?; + udf::register(m)?; + Ok(()) +} diff --git a/crates/tdc-python/src/ops.rs b/crates/tdc-python/src/ops.rs new file mode 100644 index 0000000..90df574 --- /dev/null +++ b/crates/tdc-python/src/ops.rs @@ -0,0 +1,19 @@ +use pyo3::prelude::*; +use tdc_ops; + +pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> { + // We can either register it under a sub-module or directly. + // The user example suggested `import my_rust_lib`, so we'll + // just add the functions to the main module or a sub-module. + + // Let's add it to a sub-module 'ops' + let ops = PyModule::new_bound(m.py(), "ops")?; + ops.add_function(wrap_pyfunction!(tdc_ops::compute_speed, &ops)?)?; + ops.add_function(wrap_pyfunction!(tdc_ops::compute_acceleration, &ops)?)?; + ops.add_function(wrap_pyfunction!(tdc_ops::compute_jerk, &ops)?)?; + ops.add_function(wrap_pyfunction!(tdc_ops::compute_angular_velocity, &ops)?)?; + ops.add_function(wrap_pyfunction!(tdc_ops::butterworth_filter, &ops)?)?; + m.add_submodule(&ops)?; + + Ok(()) +} diff --git a/crates/tdc-python/src/schema.rs b/crates/tdc-python/src/schema.rs new file mode 100644 index 0000000..84fd618 --- /dev/null +++ b/crates/tdc-python/src/schema.rs @@ -0,0 +1,208 @@ +use pyo3::prelude::*; +use pyo3::types::PyType; +use tdc_core::schema::{DataField, Fields, TrackedEntity}; + +#[pyclass(name = "Attr", eq, eq_int)] +#[derive(Clone, Copy, PartialEq, Eq)] +pub enum PyAttr { + X, + Y, + Z, + Speed, + Period, + Timestamp, + FrameId, +} + +#[pyclass(name = "_DataField")] +#[derive(Clone)] +pub struct PyDataField { + pub inner: DataField, +} + +#[pymethods] +impl PyDataField { + #[classmethod] + fn from_name(_cls: &Bound<'_, PyType>, name: &str) -> Self { + PyDataField { + inner: DataField::from_name(name), + } + } + fn is_frame(&self) -> bool { + matches!(self.inner, DataField::Frame { .. }) + } + fn name(&self) -> String { + self.inner.name() + } + fn attribute(&self) -> String { + use tdc_core::schema::attribute::Attribute; + match &self.inner { + DataField::Frame { attribute } => attribute.name().to_string(), + DataField::TrackedEntity { attribute, .. } => attribute.name().to_string(), + } + } + fn entity(&self) -> Option { + match &self.inner { + DataField::Frame { .. } => None, + DataField::TrackedEntity { entity, .. } => Some(PyTrackedEntity { + inner: entity.clone(), + }), + } + } + #[getter] + fn team_id(&self) -> Option { + match &self.inner { + DataField::TrackedEntity { + entity: TrackedEntity::Player { team_id, .. }, + .. + } => Some(team_id.clone()), + _ => None, + } + } + #[getter] + fn player_id(&self) -> Option { + match &self.inner { + DataField::TrackedEntity { + entity: + TrackedEntity::Player { player_id, .. } + | TrackedEntity::Referee { + referee_id: player_id, + }, + .. + } => Some(player_id.clone()), + _ => None, + } + } + fn __repr__(&self) -> String { + self.inner.name() + } +} + +#[pyclass(name = "_TrackedEntity")] +#[derive(Clone)] +pub struct PyTrackedEntity { + pub inner: TrackedEntity, +} + +#[pymethods] +impl PyTrackedEntity { + fn is_ball(&self) -> bool { + matches!(self.inner, TrackedEntity::Ball) + } + fn is_player(&self) -> bool { + matches!(self.inner, TrackedEntity::Player { .. }) + } + fn is_team(&self) -> bool { + matches!(self.inner, TrackedEntity::Team { .. }) + } + fn is_referee(&self) -> bool { + matches!(self.inner, TrackedEntity::Referee { .. }) + } + fn team_id(&self) -> Option { + match &self.inner { + TrackedEntity::Player { team_id, .. } | TrackedEntity::Team { team_id } => { + Some(team_id.clone()) + } + _ => None, + } + } + fn player_id(&self) -> Option { + match &self.inner { + TrackedEntity::Player { player_id, .. } + | TrackedEntity::Referee { + referee_id: player_id, + } => Some(player_id.clone()), + _ => None, + } + } + fn __repr__(&self) -> String { + format!("{:?}", self.inner) + } +} + +#[pyclass(name = "_Fields")] +pub struct PyFields { + pub inner: Fields, +} + +#[pymethods] +impl PyFields { + #[new] + fn new(names: Vec) -> Self { + PyFields { + inner: Fields::new(names), + } + } + fn __len__(&self) -> usize { + self.inner.len() + } + fn __getitem__(&self, index: usize) -> PyResult { + Ok(PyDataField { + inner: self + .inner + .fields + .get(index) + .ok_or_else(|| pyo3::exceptions::PyIndexError::new_err("Index out of range"))? + .clone(), + }) + } + fn player_entities(&self, team_id: Option) -> Vec { + self.inner + .player_entities(team_id.as_deref()) + .into_iter() + .map(|p| PyTrackedEntity { inner: p }) + .collect() + } + fn team_entities(&self) -> Vec { + self.inner + .team_entities() + .into_iter() + .map(|t| PyTrackedEntity { inner: t }) + .collect() + } + fn referee_entities(&self) -> Vec { + self.inner + .referee_entities() + .into_iter() + .map(|r| PyTrackedEntity { inner: r }) + .collect() + } + fn __iter__(slf: PyRef<'_, Self>) -> FieldIterator { + FieldIterator { + fields: slf.inner.fields.clone(), + index: 0, + } + } +} + +#[pyclass] +pub struct FieldIterator { + fields: Vec, + index: usize, +} + +#[pymethods] +impl FieldIterator { + fn __iter__(slf: PyRef<'_, Self>) -> PyRef<'_, Self> { + slf + } + fn __next__(mut slf: PyRefMut<'_, Self>) -> Option { + if slf.index < slf.fields.len() { + let res = Some(PyDataField { + inner: slf.fields[slf.index].clone(), + }); + slf.index += 1; + res + } else { + None + } + } +} + +pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_class::()?; + m.add_class::()?; + m.add_class::()?; + m.add_class::()?; + Ok(()) +} diff --git a/crates/tdc-python/src/selector.rs b/crates/tdc-python/src/selector.rs new file mode 100644 index 0000000..8120786 --- /dev/null +++ b/crates/tdc-python/src/selector.rs @@ -0,0 +1,330 @@ +use datafusion::common::tree_node::TreeNode; +use datafusion::prelude as df; +use datafusion_functions::expr_fn::coalesce; +use datafusion_functions::math::expr_fn::{power, sqrt}; +use datafusion_functions_window::expr_fn::lag; +use pyo3::exceptions::PyTypeError; +use pyo3::prelude::*; +use tdc_core::selector::expr::Expr as CoreExpr; + +#[pyclass(name = "Selection")] +#[derive(Clone)] +pub struct PySelection { + pub expr: CoreExpr, +} + +#[pymethods] +impl PySelection { + fn __call__(&self) -> Self { + self.clone() + } + fn attributes(&self, names: Vec) -> Self { + PySelection { + expr: CoreExpr::Attributes { + inner: Box::new(self.expr.clone()), + names, + }, + } + } + #[getter] + fn expr(&self) -> String { + format!("{:?}", self.expr) + } +} + +#[pyclass(name = "ColumnSelector")] +pub struct PyColumnSelector; + +#[pymethods] +impl PyColumnSelector { + fn ball(&self) -> PySelection { + PySelection { + expr: CoreExpr::Ball, + } + } + fn players(&self, team_id: Option) -> PySelection { + PySelection { + expr: CoreExpr::Players { team_id }, + } + } + fn team(&self, team_id: &str) -> PySelection { + PySelection { + expr: CoreExpr::Players { + team_id: Some(team_id.to_string()), + }, + } + } +} + +#[pyclass(name = "Expr")] +#[derive(Clone)] +pub struct PyExpr { + pub inner: df::Expr, +} + +#[pymethods] +impl PyExpr { + fn __add__(&self, other: PyExpr) -> PyExpr { + PyExpr { + inner: self.inner.clone() + other.inner, + } + } + fn __sub__(&self, other: PyExpr) -> PyExpr { + PyExpr { + inner: self.inner.clone() - other.inner, + } + } + + fn __mul__(&self, other: Bound<'_, PyAny>) -> PyResult { + if let Ok(e) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone() * e.inner, + }) + } else if let Ok(f) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone() * df::lit(f), + }) + } else if let Ok(i) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone() * df::lit(i), + }) + } else { + Err(PyTypeError::new_err("Invalid type for multiplication")) + } + } + + fn mul(&self, other: Bound<'_, PyAny>) -> PyResult { + self.__mul__(other) + } + + fn __truediv__(&self, other: PyExpr) -> PyExpr { + PyExpr { + inner: self.inner.clone() / other.inner, + } + } + fn __pow__(&self, exponent: f64, _modulo: Option>) -> PyExpr { + PyExpr { + inner: power(self.inner.clone(), df::lit(exponent)), + } + } + + fn __gt__(&self, other: Bound<'_, PyAny>) -> PyResult { + if let Ok(e) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().gt(e.inner), + }) + } else if let Ok(f) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().gt(df::lit(f)), + }) + } else if let Ok(i) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().gt(df::lit(i)), + }) + } else { + Err(PyTypeError::new_err("Invalid type for comparison")) + } + } + + fn gt(&self, other: Bound<'_, PyAny>) -> PyResult { + self.__gt__(other) + } + + fn __lt__(&self, other: Bound<'_, PyAny>) -> PyResult { + if let Ok(e) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().lt(e.inner), + }) + } else if let Ok(f) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().lt(df::lit(f)), + }) + } else if let Ok(i) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().lt(df::lit(i)), + }) + } else { + Err(PyTypeError::new_err("Invalid type for comparison")) + } + } + + fn lt(&self, other: Bound<'_, PyAny>) -> PyResult { + self.__lt__(other) + } + + fn __eq__(&self, other: Bound<'_, PyAny>) -> PyResult { + if let Ok(e) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().eq(e.inner), + }) + } else if let Ok(f) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().eq(df::lit(f)), + }) + } else if let Ok(i) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().eq(df::lit(i)), + }) + } else if let Ok(s) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().eq(df::lit(s)), + }) + } else if let Ok(b) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().eq(df::lit(b)), + }) + } else { + Err(PyTypeError::new_err("Invalid type for comparison")) + } + } + + fn eq(&self, other: Bound<'_, PyAny>) -> PyResult { + self.__eq__(other) + } + + fn ne(&self, other: Bound<'_, PyAny>) -> PyResult { + if let Ok(e) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().not_eq(e.inner), + }) + } else if let Ok(f) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().not_eq(df::lit(f)), + }) + } else if let Ok(i) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().not_eq(df::lit(i)), + }) + } else if let Ok(s) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().not_eq(df::lit(s)), + }) + } else if let Ok(b) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().not_eq(df::lit(b)), + }) + } else { + Err(PyTypeError::new_err("Invalid type for comparison")) + } + } + + fn ge(&self, other: Bound<'_, PyAny>) -> PyResult { + if let Ok(e) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().gt_eq(e.inner), + }) + } else if let Ok(f) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().gt_eq(df::lit(f)), + }) + } else if let Ok(i) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().gt_eq(df::lit(i)), + }) + } else { + Err(PyTypeError::new_err("Invalid type for comparison")) + } + } + + fn le(&self, other: Bound<'_, PyAny>) -> PyResult { + if let Ok(e) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().lt_eq(e.inner), + }) + } else if let Ok(f) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().lt_eq(df::lit(f)), + }) + } else if let Ok(i) = other.extract::() { + Ok(PyExpr { + inner: self.inner.clone().lt_eq(df::lit(i)), + }) + } else { + Err(PyTypeError::new_err("Invalid type for comparison")) + } + } + + fn diff(&self) -> Self { + PyExpr { + inner: self.inner.clone() - lag(self.inner.clone(), Some(1), None), + } + } + + fn fill_null(&self, value: Bound<'_, PyAny>) -> PyResult { + let replacement = if let Ok(f) = value.extract::() { + df::lit(f) + } else if let Ok(i) = value.extract::() { + df::lit(i) + } else if let Ok(s) = value.extract::() { + df::lit(s) + } else if let Ok(b) = value.extract::() { + df::lit(b) + } else if let Ok(e) = value.extract::() { + e.inner + } else { + return Err(PyTypeError::new_err("Unsupported type for fill_null")); + }; + + Ok(PyExpr { + inner: coalesce(vec![self.inner.clone(), replacement]), + }) + } + + fn pow(&self, exponent: f64) -> Self { + self.__pow__(exponent, None) + } + fn sqrt(&self) -> Self { + PyExpr { + inner: sqrt(self.inner.clone()), + } + } + + fn replace_columns(&self, mapping: std::collections::HashMap) -> PyExpr { + let transformed = self + .inner + .clone() + .transform(|expr| { + if let df::Expr::Column(ref col) = expr + && let Some(new_name) = mapping.get(&col.name) + { + return Ok(datafusion::common::tree_node::Transformed::yes( + df::Expr::Column(datafusion::common::Column::from_name(new_name)), + )); + } + Ok(datafusion::common::tree_node::Transformed::no(expr)) + }) + .unwrap() + .data; + PyExpr { inner: transformed } + } +} + +#[pyfunction] +pub fn col(name: &str) -> PyExpr { + PyExpr { + inner: df::col(name), + } +} +#[pyfunction] +pub fn lit(value: Bound<'_, PyAny>) -> PyResult { + if let Ok(f) = value.extract::() { + Ok(PyExpr { inner: df::lit(f) }) + } else if let Ok(i) = value.extract::() { + Ok(PyExpr { inner: df::lit(i) }) + } else if let Ok(s) = value.extract::() { + Ok(PyExpr { inner: df::lit(s) }) + } else if let Ok(b) = value.extract::() { + Ok(PyExpr { inner: df::lit(b) }) + } else { + Err(PyTypeError::new_err("Unsupported literal type")) + } +} + +pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_class::()?; + m.add_class::()?; + m.add_class::()?; + m.add_function(wrap_pyfunction!(col, m)?)?; + m.add_function(wrap_pyfunction!(lit, m)?)?; + Ok(()) +} diff --git a/crates/tdc-python/src/udf.rs b/crates/tdc-python/src/udf.rs new file mode 100644 index 0000000..c4e34dc --- /dev/null +++ b/crates/tdc-python/src/udf.rs @@ -0,0 +1,169 @@ +use arrow_schema::{DataType, Field}; +use datafusion::error::DataFusionError; +use datafusion::logical_expr::{ColumnarValue, ScalarUDFImpl, Signature, Volatility}; +use pyo3::prelude::*; +use pyo3::types::{PyCapsule, PyTuple}; +use std::sync::Arc; + +#[derive(Debug)] +pub struct PyUDF { + pub name: String, + pub func: PyObject, + pub signature: Signature, +} + +impl PyUDF { + pub fn new(name: String, func: PyObject) -> Self { + Self { + name, + func, + signature: Signature::variadic_any(Volatility::Immutable), + } + } +} + +impl ScalarUDFImpl for PyUDF { + fn as_any(&self) -> &dyn std::any::Any { + self + } + + fn name(&self) -> &str { + &self.name + } + + fn signature(&self) -> &Signature { + &self.signature + } + + fn return_type(&self, arg_types: &[DataType]) -> Result { + // Use the type of the first argument if available, else default to Float64 + if let Some(t) = arg_types.first() { + Ok(t.clone()) + } else { + Ok(DataType::Float64) + } + } + + fn invoke(&self, args: &[ColumnarValue]) -> Result { + Python::with_gil(|py| { + // 1. Convert ColumnarValues to Python objects (Polars Series) + let mut py_args = Vec::with_capacity(args.len()); + let pl = py.import("polars").map_err(|e| { + DataFusionError::Execution(format!("Failed to import polars: {}", e)) + })?; + let series_class = pl.getattr("Series").map_err(|e| { + DataFusionError::Execution(format!("Failed to get polars.Series: {}", e)) + })?; + + for arg in args { + let array = match arg { + ColumnarValue::Array(array) => array.clone(), + ColumnarValue::Scalar(scalar) => scalar + .to_array() + .map_err(|e| DataFusionError::External(Box::new(e)))?, + }; + + let field = Arc::new(Field::new("arg", array.data_type().clone(), true)); + let py_array = pyo3_arrow::PyArray::new(array, field); + let py_array_bound = Bound::new(py, py_array).map_err(|e| { + DataFusionError::Execution(format!("Failed to create PyArray bound: {}", e)) + })?; + + // Convert pyarrow-compatible object to polars Series + // We use the fact that PyArray implements the Arrow PyCapsule interface + let series = series_class.call1((py_array_bound,)).map_err(|e| { + DataFusionError::Execution(format!("Failed to create polars Series: {}", e)) + })?; + py_args.push(series.unbind()); + } + + // 2. Call the Python function + let args_tuple = PyTuple::new(py, py_args).map_err(|e| { + DataFusionError::Execution(format!("Failed to create args tuple: {}", e)) + })?; + let res = self + .func + .call1(py, args_tuple) + .map_err(|e| DataFusionError::Execution(format!("Python UDF failed: {}", e)))?; + + // 3. Convert result back to ColumnarValue + let res_bound = res.bind(py); + + // Try to extract Arrow capsule + let (array_capsule, schema_capsule) = if let Ok(capsules) = + res_bound.call_method0("__arrow_c_array__") + { + capsules + .extract::<(Bound<'_, PyCapsule>, Bound<'_, PyCapsule>)>() + .map_err(|e| { + DataFusionError::Execution(format!("Failed to extract capsules: {}", e)) + })? + } else if let Ok(capsules) = res_bound + .getattr("__arrow_c_array__") + .and_then(|m| m.call0()) + { + capsules + .extract::<(Bound<'_, PyCapsule>, Bound<'_, PyCapsule>)>() + .map_err(|e| { + DataFusionError::Execution(format!( + "Failed to extract capsules from attribute: {}", + e + )) + })? + } else { + // Fallback: try to convert via pyarrow + let pa = py.import("pyarrow").map_err(|e| { + DataFusionError::Execution(format!("Failed to import pyarrow: {}", e)) + })?; + let array_fn = pa.getattr("array").map_err(|e| { + DataFusionError::Execution(format!("Failed to get pyarrow.array: {}", e)) + })?; + let pa_array = array_fn.call1((res_bound,)).map_err(|e| { + DataFusionError::Execution(format!("Failed to create pyarrow array: {}", e)) + })?; + + let capsules = pa_array.call_method0("__arrow_c_array__").map_err(|e| { + DataFusionError::Execution(format!("Failed to call __arrow_c_array__: {}", e)) + })?; + capsules + .extract::<(Bound<'_, PyCapsule>, Bound<'_, PyCapsule>)>() + .map_err(|e| { + DataFusionError::Execution(format!( + "Failed to extract fallback capsules: {}", + e + )) + })? + }; + + let py_array = + pyo3_arrow::PyArray::from_arrow_pycapsule(&array_capsule, &schema_capsule) + .map_err(|e| { + DataFusionError::Execution(format!( + "Failed to convert result capsule: {}", + e + )) + })?; + let array = py_array.into_inner().0; + + // Ensure result type matches expected return type by casting if necessary + let target_type = + self.return_type(&args.iter().map(|a| a.data_type()).collect::>())?; + let array = if array.data_type() != &target_type { + arrow_cast::cast(&array, &target_type).map_err(|e| { + DataFusionError::Execution(format!( + "Failed to cast UDF result to {}: {}", + target_type, e + )) + })? + } else { + array + }; + + Ok(ColumnarValue::Array(array)) + }) + } +} + +pub fn register(_m: &Bound<'_, PyModule>) -> PyResult<()> { + Ok(()) +} diff --git a/examples/01-basics.ipynb b/examples/01-basics.ipynb new file mode 100644 index 0000000..93f9b5a --- /dev/null +++ b/examples/01-basics.ipynb @@ -0,0 +1,359 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basics: TrackingDataContainer and CDF Schema\n", + "\n", + "The **TrackingDataContainer** (TDC) is a high-performance, standardized interface for sports tracking data. Backed by **Apache Arrow**, it provides a metadata-aware representation that bridges the gap between raw data parsing packages (e.g., kloppy) and data analytics tools (e.g., floodlight, databallpy)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Why TDC?\n", + "\n", + "Our goal is to define a canonical, metadata-rich container that enables fast, zero-copy data exchange while remaining compatible with the modern data science ecosystem. TDC focuses on the **containerized representation** of tracking data, delegating raw format parsing and analysis to specialized tools.\n", + "\n", + "The main goals are to provide:\n", + "\n", + "* **Scalability**: Overcomes the limitations of object-based models for massive datasets.\n", + "* **Interoperability**: Standardizes column naming and coordinate systems across providers.\n", + "* **Efficiency**: Eliminates duplicated effort in data parsing and management.\n", + "\n", + "These goals could also be achieved by storing data in a standard `pyarrow.Table` or `polars.DataFrame` but these treat tracking data as a collection of generic floats and strings. In contrast, TDC provides **domain-specific logic**. It \"understands\" the data, allowing for abstractions that a generic table cannot provide:\n", + "\n", + "1. **Spatial Metadata**: The container keeps track of the coordinate system and orientation of the data.\n", + "2. **Consistency**: The container enforces a standard schema for typical data fields (e.g., player coordinates).\n", + "3. **Support domain-specific operations**: Standard operations such as normalizing player orientations (so that a team always attacks in the same direction) can be implemented as a single method call." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Why Rust and Apache Arrow?\n", + "\n", + "The core logic of TDC is written in **Rust** and backed by **Apache Arrow**. Under the hood, a `TrackingDataContainer` is a thin wrapper around an Arrow `StructArray`.\n", + "\n", + "This provides two main advantages:\n", + "- **Columnar Layout**: Arrow's memory layout is highly efficient for analytical queries and allows for **zero-copy** exchange with tools like **Polars**, **Pandas**, and **DuckDB**.\n", + "- **Native Speed**: By implementing performance-critical operations directly in Rust, we bypass the overhead of the Python interpreter, executing complex spatial transformations as compiled machine code." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The TrackingDataContainer" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Container created with 3 frames.\n" + ] + } + ], + "source": [ + "from datetime import datetime, timedelta, timezone\n", + "\n", + "import tracking_data_container as tdc\n", + "\n", + "# Create a small sample dataset from a dictionary\n", + "base_time = datetime(2023, 5, 27, 13, 30, 0, tzinfo=timezone.utc)\n", + "data = {\n", + " \"frame_id\": [1000, 1001, 1002],\n", + " \"timestamp\": [\n", + " base_time,\n", + " base_time + timedelta(milliseconds=40),\n", + " base_time + timedelta(milliseconds=80),\n", + " ],\n", + " \"period\": [\"first_half\", \"first_half\", \"first_half\"],\n", + " \"match/id\": [\"match_123\", \"match_123\", \"match_123\"],\n", + " \"ball/x\": [50.0, 51.0, 52.0],\n", + " \"ball/y\": [12.0, 13.0, 14.0],\n", + " \"teams/home/id\": [\"team_A\", \"team_A\", \"team_A\"],\n", + " \"teams/home/players/p1/x\": [23.5, 23.6, 23.7],\n", + " \"teams/home/players/p1/y\": [24.2, 24.3, 24.4],\n", + " \"teams/away/id\": [\"team_B\", \"team_B\", \"team_B\"],\n", + " \"teams/away/players/p2/x\": [33.1, 33.2, 33.3],\n", + " \"teams/away/players/p2/y\": [34.9, 35.0, 35.1],\n", + "}\n", + "\n", + "container = tdc.TrackingDataContainer(data)\n", + "print(f\"Container created with {container.num_rows()} frames.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Domain-Aware Operations\n", + "\n", + "We can easily map the data to any other coordinate system or normalize its orientation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original ball x at frame 0: 50.00%\n", + "Transformed ball x at frame 0: -0.00m\n", + "Orientation mode normalized to: static_home_away\n" + ] + } + ], + "source": [ + "# 1. Define the INITIAL coordinate system of our raw data (e.g. 0-100 percent)\n", + "raw_cs = tdc.CoordinateSystem(\n", + " x_range=(0, 100),\n", + " y_range=(0, 100),\n", + " origin=tdc.Origin.TopLeft,\n", + " length_unit=tdc.LengthUnit.Percent,\n", + ")\n", + "container = container.with_coordinate_system(raw_cs)\n", + "print(f\"Original ball x at frame 0: {container.materialize_frame(0)['ball/x']:.2f}%\")\n", + "\n", + "# 2. Define the TARGET coordinate system (in meters)\n", + "pitch_cs = tdc.CoordinateSystem(\n", + " x_range=(-52.5, 52.5),\n", + " y_range=(-34, 34),\n", + " origin=tdc.Origin.Center,\n", + " length_unit=tdc.LengthUnit.Meters,\n", + ")\n", + "\n", + "# 3. Map the data to this new system\n", + "transformed = container.transform_coordinates(pitch_cs)\n", + "print(f\"Transformed ball x at frame 0: {transformed.materialize_frame(0)['ball/x']:.2f}m\")\n", + "\n", + "# 4. Normalize orientation so teams are normalized based on their initial sides\n", + "normalized = transformed.with_orientation(tdc.OrientationMode.AwayHome).transform_orientation(\n", + " tdc.OrientationMode.StaticHomeAway\n", + ")\n", + "print(f\"Orientation mode normalized to: {normalized.orientation_mode}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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2NxsOPpD3xAgDtpHX47kcyxwj3NdwLfvLv/zLeIywX2+44YbQLj4vPjtGH7zuda9r6f9pZ/s5F4hj2H62k9iG+CnFjfxdzkNwXjYOm29lf+fVY2wD87ER6zDEs69RQdwfEos0w3bx+8Z4u6/zvhHv86KLLooVnGw7MRfvpVX97bt2sE/4+7zvvuIc9jvHFLhXSp8H+wP/8R//Ed87o494b+xvvnLPlfR3/Ddi2C3xN+fhr/7qr8Y2harGH/3oR9u0J4zaydur9GgW19GGn3766XHaI/YfxwP3DtwjNx6ntHucU9xvkK8gZqR9476Kr3yG5513XryP4TNhO5Jnn302ttlcP5gWhnOCuJN7S65BjThXuOfnHok2kPuUfDg+UxIRG1PxmXBvQpUbOYk0LRPXjRtvvLH+2bR6PWXfcq7w3nLkHrgfYD/l1Y+8BgbSvoyITmcLuwE9m2TA+8Lvyb4nqQeCzDM9H1/60pdi9jv/Xe5XfuVX4s/ogeN5/Pv1r399y70TBxxwQG333XevfeITn6hdfPHFsTdzu+2269EzQ/UHmfRzzz239nd/93cxE01PJRnmFStWDLiy7u///u9jz2X+oFcqvQ77gMz3eeedF3uq6NniOWTFP/KRj9QuueSS2mc/+9n4HnbYYYfaHXfcsc220LPD63z+85+v/cmf/EnMph999NHx/R577LGxl/nMM8+M75nMf+6b3/xm/DnZ+Ysuuij2UvN50WPUX8/s9ddfH//+FVdc0fTn9Bg0oneM/d+Ovirr2vlbb33rW2tz587d5nnXXnttj/fxj//4j7EHj6q8Bx98MPa08P+CHjV6uOj5GGqNlXUrV66Mx+3OO+9c++M//uP4+XLc0wtD1VXjsTZv3rzaoYceGp/HcZx6gfIer8FU1rVyrNDbSw8r28M5xL688cYb41f2G1UVfJ9+3ptf//Vfj69BD35vUu/dQw891GM/cC5wTrAfPv3pT9defvnl+u/yY4h2h5+xTZwjnG9UCNAO5J9Ds3Oev83+ZZ/RG8h5SiUEz+N1B4Nq5N/7vd+r92KmHriE3i1+9p//+Z/1n9GOclwceeSRPdo0nve9732v/jPaYXoWaQfZP/TINT4nVaXec889PbaLbeDnHAcJ/2ZbG9s4HhwjjZV19EjSo8dxSbv2yU9+MlZbUEGStzfpOsDnSA82+5ReZn7GdtMeUtnBz6ku4Of0hCe0sW9729vieUyvL236GWecEf92s9721I7k+1TS0FXW8bNLL710m+c3uz5RzcK5u3Hjxm1eI29/qLqg6p22N+H87u9a1luVBu0NlRGpqh9c62lbf/u3f3ub9om2qRExJ7/Lr2/EEvyMio28eol2qfG69Ja3vCVey/P3TrUZsdx+++1X/xntGv8vIwYSKiWIWduprGt8sP15PE0c1Vjl2Owzu+mmm7b5fNJrLF68uEfFHCMZiCWonEn4G1yf8mtvqkBJVZEJlWbNft7oN3/zN2NVU6O8so77gFT9B+4xqIIibmis1nnppZdivEP1SY5Yjf2e/7zZedDue0rHEr/Lca/QV1VkO1IVT6p07c9Atj+vXuMYJRb86Ec/2m+M387+TvEgsW9/1qxZE5/bV+UdqKrleYz06e+8b7x3/clPfhL/zXmae8973tPyvWsr+64dVFTxmhzTVB9+6lOfitvZKMWOze5zm537xNmcz3nb1tvxj8b3T9tKG9tsFEZqN3qr+Msf+bnKvuJemDgwH11D7JvuzRuvLf/8z/9c/9myZcviz9iufHRPasvzfUMMzD37qlWremz7r/3ar8XjNN9njBqiTeKeLr/f4d4uR7ybV8z90i/9Unxsv/32tauuuqrHvcB3swq8Vq+ntMFHHXVUj+dxX9nbvTb7kpi7iEzWVajB4yBuHC7H6/A8kg4DTdY1e/Aa6XUmTpy4TSkqQUvjcFjKdknckBho3BaGJ/BZJJTC83NOfobWJuxvTrh0wrZzIWzmq1/9atOb+nThbVZe/su//Ms9hpQONlnXzt8igP+5n/u5bZ5HAi6/kWD/0yimz4ty77vvvjv+joY/DzCHM1mXgvEURKbPjFJ4kmTpeE3HGsnldI7h3//93+PP//Zv/3bQybp2jpXehj+2MwyWmyZety8kbfIka9oPJL8bL1iNyTrOLy6YDC/Iz5FvfOMb8XmtJOtSQjJHiTrDVQYqlbqnMnsCFW5g8kCaz512Iw3d4Tm8F453LuR8Vmn/EGjkJf+N+yUNN87PC9oSEpEMB8+R8OczzKcs6C94ajwO2r0ZJvHbeDNHUEjnRsL74yY4P7ZIBvOa+bkDznFe94Ybbujx86effjr+vNmQOkmDT9YRS/U3zQfXLxIQ3/rWt+Jr3HnnnT1egyRK4zBJ4st8aBDtANeOvIOjlWRdagMah0SCZBOdCo3tU95BkMecdBg1i5W5Acvx/vh5Gi5Fu0j7xnDWxs4POjZ4LsOtQIcHnbKN6IxpJ1nHsFyuN+lBx0V+He9v2B3XEG5S2UZihDxWT69BLJJLsU1jjHDyySfHeCu/5vBZEiM37g+Ohfe97319bhsJiX333bfPZB3o5GS/gc6f3/iN34jfNybr0s0sw2sbt4fYMP9bvSUr2nlPHEvEe43SfiUGH+w0Hyn5Rkd/K9rd/sZzIe3vU045pd8Yv539nWKyVobyMuSQ55LM7QvHQX7O9XXeN967kgjj33T459I9bSv3rq3su3aRkCIBT3yUYjTaz3yof1/JuhyxIJ9FGjLMEMx2k3Upnu0vj5DabBKOeXuVHhwP+bnK++T5DKHOcQ3i7+UdPL1dW2jPGu+LUlv+p3/6p/Hf/D88j1i18RhNn2tqU/MhpXmxEJ3EjT8jxqVzOcXbxM2c79xfUIgD7u222267HvF0q9dTCgz4GcPGE/YJ7W/jfgD5B+4xishhsP1IZaD5ZKTNpN83rqjZ26SvubScNKX4jWX1rWI4RF7qzHAqhnMxxDCh5JUSU1CWz5ANSkl53u233x4GivJThmnmj7QCDCg3bRw+zBC3NBE+Za6ULFOyygT7zbaFkmyGiSVp2CBDLfIJT/k5Za4M3QPbwlA6SuzzcmL+Ps9tHHbbiH2ExlXU0opIzRb6oLR2oCsmNdPO3+L73p6Xvxbvn2GYDNtgYl2G4jHs9YorrogT/jO0mH1I+TLl1XyljHuoMdEvpeFpiDg4JilNp/yfoQA5ytDzc5FS7RkzZsTXGazBHisDaVsG2q4wDLe/CZj5XDl+GcKQnyOsgtbOqoCNbRjtTN6utIvy+N13370+RIwhPAwLYHhAGl5LO8VQ1lQiz3Ap3gtl7sRBaTEWyuZTOX2S7xfK/lkkhW3O2xXaEoaYUpafhgbxtymZZyhW44TNPLexjePBMJnGFZ4ZssUwjnyI+6GHHhqHTDQ7TvNJdznWaAPZJoYXJLy/xvacoRoM52CYXH68/tzP/Vz8fePxmj5zniNp6DEtQbMFfhjSxQJktDsMayceSotT0D7lmNKjcZoUzt186CbDYrlOcu1kESmGQbUyfIfVYUFb0oi2hLaBIVA5FlNrpnE4YYrPGIrf7Odp+xn2RvvGUD/2Q/5gSH8+ETzby/tr1Gz7+8IQKqYySQ+GNvb3HomViG15P8RUTFHDNhIjNH5m7e6P/LMkBuP1GMLXuD/Wr1/f0qT46RrWF4a8cs1g/zOsrLchsGwPuI40bg9DZ1vZnnbfU7P9T0zA58T1kXiBYXpM+ZIPp25Vmj4mH9Y3lNvfbGht4znb199qZ38Ty9FG9CfFjv29597ucXs773Ocn8Rqjc9tZ7Gwwey73hDDExvyGmm49x133BHvY5jaqD9PPPFEPYZL078wdQqanfv9YQE5Ynhi1VZwL5a3V+nBvU4r7TnXIKa+Sb/v69pCe9Rfm8320+6lqcDyB8PDkR+nrALLMUG7SXvDgyGxDIXNp7whLue+n3ieofC8Bj+jvebzA18POuigHvF0q9dT2hC2If1Nfve9730v3gM1W0SCdrSQi0u4GmyoVIPHRY7x3SxPzOos+bxTzO80UKlh6U1v+4C5vRhjv2zZsh5j65s9f6CBYX4hbCafA66dYCglBJrNIZCvZEXisHHuBBqWdlZFavVvpef29rz8tZodY2wrK78SMBOY0mhycWD+B+Yx4ILXOB/aYHHsN87XhjSXB7/PL3CNgTsNK+8hzetBIMUjYT83Jop7M1THSqtoK/pLnAy2XWnWjhDstbpaM0nexv03mECKNoekHIk62qCEY4C2gHllmGsDHH/Mx8FNExdsjsXDDz88zuXEv9McbMyHlONi/Nd//dcxaZafC40XYRK/JOd4LYID5uJgXg7mumhEkNOsjWNV5nZuhq+55pptVm9r1rax3xtXL+TnqfMgHa8kMXs7vhuD/NSGFTUYkbpdsw4UbnK40eP6wTxJ3LRwftN5QNKtMfnQW2yQxyC0Jdzc0NbR2UvHG3EdySXmTBvu99TXdva3/en9Mvdpb3ODjvTK4M3eI53lX//61+M8eawgSvtL20nSqFnCqJ39kX+WvBZJocZ5W5P+4hdi91auxyQwPv7xj8fOO/6fdJ1tlN4b82/lne5JK6uBtvuemu1/fkZnHZ1OzKnGcc71mviMBEw7MTTHE9t9zz33tPT8dre/lXO2r7/Vzv7Oiy76wvFKzMRKr33h93QyNMa3Q70ab28Gs+/6w3siTuSxww47xHtO5vVMibfeYlSez30b7TOdocRrFC+QwBtIsrgoBttmkxBL83U3okMaJCW5Z+Ses1lHCyvEfupTn4ptKR3TXAs5z4mDOef233//GPtzPSN+Jz4/5ZRTBnQ95V6FOfo4j7k2Mlcdr9nbKu68dqurho+0ga3BXCFlavCYKJTezPe+972xcopMNY0+wchwNkDN9gGZdxo+qlioTuEk5T0wUTETEg91IzPQwCMlMQmG8t6s1MNBJU0jfkY1GujBTBVECUmKVpMl7fyt9NxUVdj4POTPbXTBBRfE/cHkoUxuTyIkbSuTkdJTQ3KilV69TmHC3fxmZdasWU0nzR6uILUd3HCRUKIXr7dJj1O7Q89SkdqVwSy8wLFIwo5HIy6q6SaCakuS+PS6ccFOlcN85d8k+enxyyuK+TmTUJN842LP+UCQxk0XQUKOG0V67GmLeD5f+dz76ngYqX3cSnvO8UpHyec///mmz23syEg3dEUNRqQyooOLJPu3v/3tHpNk550VA8ENJJUDPOhoYzEoboJIyOQTZ+e4HoJEXyPaU9qGxqrioUYcAdrl/tpatjd1ouWabf9Q48aOG9N8YR9uQPNFj4YCN5t0FFFFNpDrOskErptUjeSjTxoRY/A3OB6ZOL+3eIbtATF5f59Pbx0/g31PCfcnb3nLW+KD6xz3MCyUQgKvnes0FT0k+Yg/iG0br43Dtf2t7qtW93e7SFSwGA2xfD56JY+XiI9ZLGEgOD+JQ2jL8sQMlVRFQ2KIZF26F+rt8yChy0gjnpsvKMFIikatdnyS3CU3wIJyQylvz1O7Cq4HfCZDdTyx/RQMkMjs7zW5ztFOXnLJJdvEmmwnC+NRBc7xSAVgWvSO9imP8Umq0a7Rgb4ou262ez3lM2RkDAsG8XosxHPwwQdv8zzum9lvqVCkaBwG22KDx4FAg9dMavB43mAbvNxQN3gEHySOvva1r8XeQW6KOfGGOvhodVtoXDjhqGThxpltaaVEuR2NF8LGR74iZm+BEBo/G6q9CHYYapjjZCcBw2pWoAqoryHCrWj1b4HvudA0DptMq8Tlz81xAaMaKSXs0pDXlNxLX5slAgeDY7+3G4f0+1xj4E7ygvMkJT9pmPN93VvP6HAcK+1WLaX2onHV5oTP8Lvf/W48BgdSaZD2XWM7Qtl5qwnMocbnwf5lOE7jg55/VgJMQ7W5iHMxp33Nk3VcoDmeqcJL/06oMOFGlQo2OiVYmbG34IKEGNWitEUksi6//PK4DYNJUI7kzTDHK72/3Mg0O14bq/tSG1bUYEQqo9Se5Il2rt10JgxUXmEL2kk6dPgbfa0eSucFMQA3onncx00k1UqsBjjcaP+5lrJiYbMOSDpgErbn5ptvjlNz5L9v57o+mM+tsbqHFSEHuxJ6IyrDeU060Btxre4vPqfqj+38yU9+0u/fIsZj5ERfU+wQi5NYIDHW7FjKP590LWvcxsG+JzRbzTPFr81Gj/SH981+4n4jH32RsP84L4Zq+xv1tq/a2d/tohCCZCPJuMY2g/3LFCckMhun82hVqoxtbMs4TzqBVVXTFCmN0mraKS7q7fNo1l7zfbMVqHt7jWZJZwpTqDhrvI9r/FvtIM6j7Wdl2fw1uMcned/Oqrx9YZ8wnRXxdbOEY36M0unNvT3HFtMU5Q+qqRlW3DgUlnieBHyK8YmTiVNZXTg9Z6DXU+4BeD1ei5V3e6uqS+0n0+8UkZV1LaAh4wCkwaNcMx8yOlQNHr1FHGwkS4arwWsWfHCTTAJmpIcd5CdcSnBwwtLQtrq0eivyCyGJSnpzGxuZvoYZsJwzjSENLBU7CT2YNJQcF1QrpmGKVGURCDDHXirDHWzvRqt/CzSIVJcxtwANYwpsqCxiqGFvPYrMBUbS44QTToj/puIoJRgob2a4HdpNNPaHYJxlv/ncCTrBMEG2nwRcY0UZiS0qB9I+INFCwE/5M7hI5D1MI3mspIt3q4EcnxWVEAwxZr/T85eQvKf3myTSQG/qeD3aKnpWmVci9aRzoRzMfCADRRKO5DzHK++9EQlh5pBj3kSqRUi6sew7P6P6MO9147UIUEhY5fN40K7QnuQ3VCQmScQ1Q+BOm0vbzrnU24W8VfnNMMdpmksv3QwP9vVz3FAwBx6fL3M85tg/HEN5YpBghH2TzjNJw4/gnziAKq0zzzwznoNcuwczzIuOVq7FVP5wreb6fPHFF8ebs/7mQT3//PPjDQztAPNi0lYQaxJnMO3ASPjiF78YKyuoDGZYJtdsKiiIA6jev+uuu+LzzjnnnLivuD5+6EMfiu0ZsQGdIv2NdhksOtP42+wX4hC2jWqrwUwZ0wxDurj+MKqEzlc+W2IPOiaJz0kSNLteJuxHtolt620Kj/xv9TUEEMRAVMVwbWTaCTr2iXu4BjMclWOOYy3Fx+C4Jn7i+svzB/uewBA37rc4pvm8mdaBWIiRHXmVGIlfbsL7O584DznumBucDlDeH9VgTDVCtQ5xB8nMtJ8Gu/2NiAvYPyQNSKQwnJXPi+R1q/u7Xbw/YhHm6OJc43xnChViIhI6TMNCfJU6qtvF508ShxieZODRRx8dPwsKBoZ6yo3UId9XRzPJOj5ntoM2g/sd4nHiPzp8SZhRWQXeM/HZpZdeGttM2hbukTg2+B33T9wbcz6QpGoWM/d2/DfDfQUxIMcW8RrJKO5dOJ4oBMrnXW4VxwlxJqOJeL/co9JRzHlC7DyU8Sb3KSTU2Ee02bSJ5D8Yfkrbw/cUefAc9kczHPPsJ94z8TvnFPE890FUvOZJOe5H6dDhc98zG83V7vWUv8FnwjnE50OHfDMUd5B7SMdH0Zisq1CDR/DBBZAbdw54yn25cR9ocmOw28KNO2PRuRhT9UGjSQPQrNdroNoJPJohYcCFmsaIfZejgWE/psaXIJMhEzw/Jb36k4IDJswEjU6q4KRcuN2/RUNKMoQGnOCGJCzHbjpWm6HXmrlA8uCXBpJkD0OVOd6/+tWvxtfOK91auXj2hyQh5w43DzS8DM1mezkeuEA2zs3B7wnUOIYJ7jlneI9cPFpBwi3t8xznM+f3YIMmzmWOFYZskHzidZvNyQeSwCQbqYxK74l9TnDBkE0ugswh2NvFvz+8Pjdf9KITFJLc4bP6xje+EduqoQykOE7S59bbEG+CYQLjPOmdo91jf9MmkawDF2+CBG6YaHtBcEvvKEEJfzdHW8K+55ygao5zgACdY6TZzR0XZipX02INfO6DNVI3wxynTLhNZxEBEscnSUoS7Pyc6sI8AUwwwnOG+mZTUu8435hbjracazo3GtxE0e73Nmdbf0gk0E7S1hEvcTPD9TOPGXpDxx/zf1FpxDw+3MwQV5BEaGUu1KFAnEcHKDeZXI+Ie2nXaY/Zprzzg7aNaxjXAfYl7R3X1nwBnuFAQoabO/YzIz5oO7m2D/Qz6wuxL7EDN6ef+MQn6vPKcpw0LobR7DpP7MI1jITAUODayT5mn3M9o8OXaX64HqcJ5cHQaz4bprSgM5kb5hSvDOY9gTiBeOXv//7v4z0W1TEcpxwz+XBfjv9WO5E5b0hiEDvT8Us8SKUP1306tPPkxmC3vxHbyGuSAOTY5VrNsc1x3+r+HgjuB0hA8XfT/SrnER3SvK9WFz3oDfuR90Ycz8gI2hfuJ4jRehuOPxB04vdXVELCi85L4nU+z5UrV8ZzmG1hv+ZJpDSHHfdKtClUTPL/EFNSAcdz2We8B+5TmR6IkVK5vo7/RnyeFKRQcEGbwsgZfkasSLHPQBFTEjdzb3LWWWfFeyTuEWkLGgsOBoNOIe4VuQ/m3p2EIMcRQ0pTBRz7gU5iFvLoDb/j3o5KR85x7mv5jNgH+f7l2OfcyxN4A72eMuKK/cNzGhfpANvMNnFeFnZO504vR9tN7r777tq73/3u2owZM2o77LBDbfr06fHf99xzT69LXLOkcG+/y7388stxGegpU6bE5ZVZ3v2BBx6Iz2N54/6Wvz7ppJO2+Tss1cwj2bhxY+2jH/1o3P5x48bF5dtvuummbZ6Xlnzvb0nrtMw0S5I307h0fI5lk//mb/4mbvuYMWPistrf+9734tLk/Ky/1+jtb6f9w7Lcjc9fvHhxXI597NixtTlz5tTe85731G677bZaf1hanaWjn3jiiaZLwh977LHxNadNmxY/Q5aSblVaVrzZY6B/a8OGDbWPfexj8fhk386fP7929dVXN/37fA5HHXVU7SMf+cg2v2O560WLFsXjka+PPPJIj99PnTq1dvTRR9fawRLh+bEGXvfUU0+NS4Pz3hYsWBCPhWaf97/8y7/UPv7xj8clvjmGOe5bWcYe/N3e9vVb3vKWto6V3o6zZcuWxX3FtvF7juf+PPfcc3H/77vvvvHzYj+89a1vrV1xxRVtnXPpd3zNfeELX6ifZ+zbG264IS6NfsIJJ/R5zrPt+TLxfbVfLIfOe169enWv7/Md73hH3J+0db1hP9O2rlq1Kv77v/7rv+LfOvHEE3s8733ve1/8+de+9rVtXoOf7bfffvH9HnjggfE9Ndvm5LOf/Wz8He1RM/yOc62Z3o6Da6+9Nrav7JOJEyfG937//fe3dI3obb9z/HL+5F599dXaZz7zmfhz3u/kyZPjZ/vJT36ytnbt2vrz1qxZU9txxx1rX/3qV5u+D0lSdyKG4rrJdadKiH9Hjx5du/jiizu9KcrccccdMbb51re+NST75b777ouv13hfILXizjvvjMfPN7/5zaa//853vhNj9aeffrpWVNvxn04nDNUc5df0NJKxp+dMnUEPGD3BVCY1m8Oiiu6///7Yo0IPx1DNi9AXhinQE0jvcbvDD7RtLxI9cfQK0gs5VL1u9F7Re9ltqKCgR5Je/KEcgl8kVKCySAyL94zU4iSSpJHBtBnMT9tsIvyyooLq9NNPj6OQqDDUyGPkQGNMQXUao4SIqfpbzKMVjI6gGo0F+6R2URVJFSWVls3mjGYUDBV8xMhF5TDYAjd43GAxDDCfQF0jjxJdSn8JhpgbjbL5qqN8nwZuJBJ1GjiG7zBPRF7azbAF5pdoZcGMVjCEm/YrzRvYTeirYmgIw2vKmqhj0mqGyzFkwESdJJUPU3hUDfGnMWhnkeBgPlw60xkqzPBGHgzFHIpEHUjI8pDawXBmCkuY55SEXW+Lu/W2KEmRWFlXEMzD0FuDx7htqcqsrBv4fqNqjHlLmOuBefBITjE/G+1NVXujmf+EOfRIOlNdyKq7vc2lJ0mSpJ6o5OT+laQI8wfS6cl8uiyamBY1kzph7733jnObM5cdlZ79Lb5UZJ5JBcEkizR6DLNMDR4TR9LgSdJAL1b0brLyEtV0TD7LcFUmMq5qog5MLM2kzkxIzCTLJuokSZJa9/M///PxIRXNY4NY/LBorKyTJEmSJEmSCmJUpzdAkiRJkiRJ0lYm6yRJkiRJkqSCMFknSZIkSZIkFYTJOkmSJEmSJKkgTNZJkiRJkiRJBWGyTpIkSZIkSSoIk3WSJEmSJElSQZiskyRJkiRJkgrCZJ0kSZIkSZJUEKMH+wIrVqwIy5YtC5s3bx6aLZJUSRs3bqx/P3bs2I5ui6TuNnr06DB37twwc+bMTm+KJEmSNPLJOhJ169evH+zLSFLTxJ0kDcTSpUtN1kmSJKmaybq8os5qGEkDZWWdpKFsS6z4rzZHfkgaCONRSUUZ2THoZF2eqFu8ePFQvZykirnmmmtigGRbImko2hJVmyM/JA2W1xJJnRzZMWTJOkmSJKkIHPkhaSCsrJNUlJEdJuskSZJUSlZrS2qHIz0kFWVkx6ghfTVJkiRJkiRJA2ayTpIkSZIkSSoIk3WSJEmSJElSQZiskyRJkiRJkgrCZJ0kSZIkSZJUECbrJEmSJEmSpIIwWSdJkiRJkiQVhMk6SZIkSZIkqSBM1kmSJEmSJEkFYbJOkiRJkiRJKgiTdZIkSZIkSVJBmKyTJEmSJEmSCsJknSRJkiRJklQQJuskSZIkSZKkgjBZJ0mSJEmSJBWEyTpJkiRJkiSpIEzWSZIkSZIkSQVhsk6SJEmSJEkqCJN1kiRJkiRJUkGYrJMkSZIkSZIKwmSdJEmSJEmSVBAm6yRJkiRJkqSCMFknSZIkSZIkFYTJOkmSJEmSJKkgTNZJkiRJkiRJBWGyTpIkSZIkSSoIk3WSJEmSJElSQZiskyRJkiRJkgrCZJ0kSZIkSZJUECbrJEmSJEmSpIIwWSdJkiRJkiQVhMk6SZIkSZIkqSBM1kmSJEmSJEkFYbJOkiRJklR5mzZtCmvWrIlfJamTRnf0r0uSJEmS1EHXX399uPDCC8Pll18eXnvttbD99tuHk08+OZx11llh4cKFfjaSRpyVdZIkSZKkSrrkkkvCokWLwtKlS8PnPve5cMUVV8Sv/Pv4448Pl156aac3UVIFWVknSZIkSapkRd3pp58ePvjBD4YLLrggjBr1s1qWM888M3z4wx8Op512Wpg3b54VdpJGlJV1kiRJkqTKYejr3Llzt0nUgX+n3/NVkkaSyTpJkiRJUqVs2LAhzlH3/ve/f5tEXcLP+f13vvOd+HxJGikm6yRJkiRJlbJu3bq4mMScOXP6fN4+++wTn8fzJWmkmKyTJEmSJFXKxIkT46qvjzzySJ/PW758eXwez5ekkWKyTpIkSZJUKePGjQsnn3xy+MpXvhK2bNnS9Dn8nN+fcsop8fmSNFJM1kmSJEmSKofVXpcuXRrOOuusbRJ2/Dv9nq+SNJJGj+hfkyRJkiSpAI477rjwpS99KZx22mnh2muvjYtJMEcdQ1+pqCNRx+8XLlzY6U2VVDEm6yRJkiRJlfSBD3wgzJs3L1x44YXhYx/7WFxMgjnqGPr65S9/2USdpI4wWSdJkiRJqiwq53hcccUVYfXq1WHy5Mnhne98Z6c3S1KFOWedJEmSJKnyxowZEyZNmhS/SlInmayTJEmSJEmSCsJknSRJkiRJklQQJuskSZIkSZKkgjBZJ0mSJEmSJBWEyTpJkiRJkiSpIEzWSZIkSZIkSQVhsk6SJEmSJEkqCJN1kiRJkiRJUkGYrJMkSZIkSZIKwmSdJEmSJEmSVBAm6yRJkiRJkqSCMFknSZIkSZIkFYTJOkmSJEmSJKkgTNZJkiRJkiRJBWGyTpIkSZIkSSoIk3WSJEmSJElSQZiskyRJkiRJkgrCZJ0kSZIkSZJUECbrJEmSJEmSpIIYPdgX2LRpU/y6cePGcM011wzFNun/jB49OsydOzfMnDnTfSJJJbJixYqwbNmysHnz5k5vSukQj+TxiSRJklS5ZF2tVtsmQNbQWbp0qck6SSoZEnXr16/v9GaUWh6fSJIkSZVK1m233Xb1gHjs2LFDsU3KEp9WXUhS+eRtu9fO4bl+Ep+ouhz5MTwc9SFJ3c8RHt0xsmPQyboxY8bEDeNmY/HixUOzVYpDiq1UlKRy89o5fNdP4hNVlyM/ho+jPiSpuznCoztGdgw6WSdJkiQViSM/hp6jPiSpHBzh0R0jO0zWSZIkqVQc+TH0HPUhSeXiCI9ij+wYNWSvJEmSJEmSJGlQTNZJkiRJkiRJBWGyTpIkSZIkSSoIk3WSJEmSJElSQbjAhCRJkiRJ0kBs2RLCpk1bv65bF8LEiSGMGsVqR1u/SgPgkSNJkiRJktSujRtDuOGGEN71rq1Jupkzt3499dStP+f30gCYrJMkSZIkSWoHibiLLgph0aIQrrpqa2Ud+HrllVt/fvHFJuw0ICbrJEmSJEmSWkVC7tZbQzjnnL6fd/bZIdx2288SeVKLTNZJkiRJkiS1ijnqzjuvtefyPJ4vtcFknSRJkiRJUquolLv66taemw+RlVpksk6SJEmSJKlVrPraagIurRIrtcFknSRJkiRJUqtY8XVUi+kUnsfzpTaYrJMkSZIkSWo5kzIqhBNOaO25J57YemJP+j8eMZIkSZIkSa0aMyaEc89t7bk8j+dLbTBZJ0mSJEmS1HImZVQI8+eHcP75fT+P3x95pJV1apvJOkmSJEmSpHaMHRvCGWeEsGRJCG9/+88Scnw96aQQrrtu6+95ntSm0e3+D5IkSZIkSZVHIm7hwhAuuyyE117buuprWnyCoa/OVacBMlknSZIkSZI0ECTkUvXchAnuQw0Jh8FKkiRJkiRJBWGyTpIkSZIkSSoIk3WSJEmSJElSQZiskyRJkiRJkgrCZJ0kSZIkSZJUECbrJEmSJEmSpIIwWSdJkiRJkiQVhMk6SZIkSZIkqSBM1kmSJEmSJEkFYbJOkiRJkiRJKgiTdZIkSZIkSVJBmKyTJEmSJEmSCsJknSRJkiRJklQQJuskSZIkSZKkgjBZJ0mSJEmSJBWEyTpJkiRJkiSpIEzWSZIkSZIkSQVhsk6SJEmSJEkqCJN1kiRJkiRJUkGYrJMkSZIkSZIKwmSdJEmSJEmSVBAm6yRJkiRJkqSCMFknSZIkSZIkFYTJOkmSJEmSJKkgRnd6AyRJkiRJUjVs2RLCpk1bv65bF8LEiSGMGhXCmDFbv0qysk6SJEmSJI2AjRtDuOGGEN71rq1Jupkzt3499dStP+f3kkzWSZIkSZKkYUYi7qKLQli0KISrrtpaWQe+Xnnl1p9ffLEJOwkWmUqSJEmSpGFDQu7WW0M455y+n3f22SHcdtvPEnlSVZmskyRJkiRJw4Y56s47r7Xn8jyeL1WZyTpJkiRJkjRsqJS7+urWnpsPkZWqymSdJEmSJEkaNqz62moCLq0SK1WZyTpJkiRJkjRsWPF1VIvZB57H86UqM1knSZIkSZKGL/EwKoQTTmjtuSee2HpiTyorTwFJkiRJkjRsxowJ4dxzW3suz+P5UpWNHuwLbPq/ZVo2btwYrrnmmjBSRo8eHebOnRtmzpw5Yn9TkiS1bsWKFWHZsmVh8+bNI7bbiEfy+ETVZHwqqVGtVguvvPJKWLNmTVi/fn28XqQHbcZrr70WXn311fhcfnb11VeHMWPGxMfYsWPjY/z48WGXXXYJEydODNtvv707uQ1Uys2fH8L554dw9tm9P4/fH3mklXXqrhh0OOLP0UPR6DVu4EhZunSpyTqpC9FuEAyl4GjLli0xQAJfV61aVQ+KSMxL6k4ESdwQdUIen6h6jE8lEV+++OKL4dlnn40JurVr14af/vSnLe8YYtTebry32267sPPOO4dJkyaFqVOnht133z3suOOO7vR+jB0bwhlnhHDUUSGcd97W1WFZTIJEHkNfqagjUcfzpG6MQYcy/hz0XTANVdogbqxHQkoKjmRPvaT20Ta8/PLLMUBKQRKNJoFPbw0ZQdQNN9xQ/ze9lrQt9GASEPGgR5NeTknFll+nRzpGID5RdRmfStVEHPncc8+FlStXxiRdq8k54ko6iKm8I0alDeG6lTqVG/GcdevWxccTTzwRf7brrruG6dOnx8dOO+005O+tLAgHFi4M4bLL6KTfuuprWnyC8N656tSNMejGYYg/B52so2Fjw9gBixcvDiOB4bYjXcUnqTVUzBEcESQRLA02qU6lHQk/Hs8880z95xMmTKgHRFOmTAmjvLJLhdWJGMGEfrUZn0rVQofwY489Fp588sn6aI1m16LU6Usn8Lhx4+LPaC9SHJlfQ972trfFpBwJvzRclk7n1An90ksv9Xj9F154IT7uu+++WG03e/bsGKcao26L3Z3yJxMmDP3xII10DDoc8afjyyQNGgk55gV46qmnYpDSV/kvQwQIjtL8H3yleu7hhx+Or0Ov5qxZs+pDZPm6YcOGbQIvknePPPJIfOywww4xGOL/I3FnRY0kSVK5EW8+/fTTYfny5XG4ayNiSoanEiOSPBtIdQ0xJbErDxJ8u+22W/13xK0k7eikpkOZ2DRhShcexLl77713TNzZiSSpHSbrJA0YPYr0YlL+36yCjiQaQwLS8FUevQUqvE5K1h1yyCHbBGN5TyaP1atX15OC9HjSk8qD+UMIiPbcc8/49yVJklQexH8kyJi/nGGoOTqAiQH32GOPGIMOZ1UbMStJQB4HH3xwjIsZWfL444/XE3d0PD/wwAOxc3nfffcNc+bMcT5mSS0xWSdpQEMNCJAIlBoNx/DUNIkvj7322qvP4bYESnfffXe4//77wz777BMDI5N2kiRJ3Y8KOmI8RnLkiBGpYCNO7FTcl2JVYs/nn38+PProozFOBXEqE97zs/333z9uq8NjJfXFZJ2kltFLSKDBcNccwQa9mAQeVM+NxDBUhiMQkPFgiCzDDwiA0jAIgqIHH3wwVuztt99+sdqO3lZJkiR1F0ZRMBccVWs54s65c+eGadOmFWYaFLaD4bI8WLCCeJRRKFQEUml3zz33xPdx+OGHx/nzJKkZk3WS+kUyjCQdJfz5fHTMPUf12ute97qOLlefhjzwoOqPpF0KiqjAI7hjPpN58+aFGTNmdGw7JUmS1B5GUNx5551xDuN8JMdBBx0U47qiJOmaGT9+fDjssMPi8FdiaebYA8N3f/SjH8UqOx5W2UlqZLJOUp+oVLv99tt7TJrL8AICiyJWq9FDSVBENV1eBUiAd8stt8SEHkm7TiYXJUnqVn0tIiUNdWfxvffeG0dJJMSdJOm6bRgpw2Pnz58f51wm8UiyjnOJ+ewYHXLEEUfEBSyk4cZxZzveHUzWSeo1QGJeOqrpEoIi5uHohnng6HEl8GFbqaxj7hCQvON7EnrMqydJklpHxToLPTH8UBouqZOVYy1hIQfiN2K8bjV58uTwxje+MQ6N5UHShMTdkiVLwpFHHmlsqmEfTk6ymK8qvu7pjpA0ogHSdddd1yNRR3Dxpje9Kc4LUvREXWOl3THHHBPe8IY31FffYr6QH//4x3GCYnuWJEnq35YtW+JXrpvECKzALg0Hqs8YIpoSdVTTHXrooeHYY4/t6kRd3vl94IEHhkWLFsWKu9RJTmyaEnjSUGOUFElhhmKn9lzFZmVdwZH1pvEu2lBDlXvYKz2ZJLTygIIKtSLPCdIXtpt59Zh8+K677qqvYvvQQw/F3kwq8LopAanuxorFafViSeoWeQzAjR5TZBCnMnetNFQYAXHHHXfUkwnMj3zUUUeVciEGqlNJ2PF+01x2jGohNqWT2fs/DRWOqRtvvLF+f6fuYGVdQaUeFRJ1nlgaKSzKcMMNN9QbcibFJYhg/rduTdTlUsB3yCGH1N8PiTt6mfI5+aThnCSb481knaRuk66beQKBVS2pBJKGAiuk/uQnP6kn6nbdddc4ZLSMibqEUR8Mf6VjPFmxYkXsOOc+UBosKlTz+7uddtrJubu7hMm6gsrLn6l0yk8waTgw5DXvySRAIlFXtgCJmw1W5Dr66KPr1XTr16+PQ3rodZKGy8qVK8PNN9/cI1HnUBdJ3ZhcYJGphEogp5XQYLGIBHNpJYyIYNjrmDFjSr9ziU0POOCAsGDBgnoynM49hsWasNNgpDwCc42C+7rjjz++qxZnqTI/pYJqPIEYNkWFXTrRpKHEcFBW20pYYavsAdJuu+0Wk5H0LoFkOBczE3YaDlRw3nrrrfXkXGrjy1CxKqlaaLeYv5YVOfM4goSdNNCKOqYpSehUZSGJqiUUZsyYETuTU8KOBdGIHZxfTANN1JE/SJ3EU6ZMCQsXLrSqrotUqwXsQjvuuGMYO3Zsj7HmJuw01BV1eYBNzx6T+FYhQCJRR+9Sqh7k3OIcIzkuDRV6xxnOkoLtPfbYwzkSJXU9psiYN29e/d8PP/xwTLpI7XZm5RV1zJF88MEHV7YzixVv6TBPCbu0f6zEVzteeeWVHkOpmbebBfeco7u7lP9uvMuRMCEDniqc1q5dG2666SbnO9KQTeKbV9TRU86cGVUKkEiIc44xyW+qsCNht3Hjxk5vmkpg1apVcRhLStTNnDkzHH744ZU6xySVF4tL0MGXUB1Fuye1gs7R2267rcfxRMVm1a+RVEDlFXasvEznutQKKumIPdMUWiSAmbObKQzUXUzWdUn1T56wY5JI5hazh0WDkY6jvKIun4OmSuhlorcpVdiRqGPYgfOEaLC9mvnwFYa3sPJwFapWJVXH7Nmz6yvCEpvS7rlok/rDSsIkFNIQvenTp/dYAKzqSLCwImxy3333xUp9qS+0wSzSkqb1mTBhQpg/f74rC3cp7xi6xM477xxLolNGnOW97WHRQJGMyqt9mMSXZF2VUWFHL2Yads48D3fffbdJcQ0IiV5uWNO0BcyRyGpvJuoklRHDFmnnQLtHjEEyRuotoUBFXUrqcp9DZ5aJup6YNiPvSCeuYFE0qTcs+MOCZiBvQEUd9zjqTibrusjEiRPj8KmEecYcaqB2kaBjDoM0zJNSe4awGCCFmKjjopYSKk888URYvny5B5nads8998TqVYwfP96KOkmlxnWTDom0aBPDG11wQr159NFH61ViJBIcotc7pqeh6hBUId5+++12JKupF154IS72A+7rqKgjEa7uZbKuyzCMKvWwpF6pDRs2dHqz1EUefPDBsHr16npyytLonpi7Lh92wM2GK8SqHUywniZZ5wZ2wYIF9mpKqsSUEiRd0jxbjz32mJ3K2gbVdHkil4o6huqpOZIuFGukfUQM7+gqNRvRkU9vxDzkqdpZ3ctkXZf2sKSTj4kjqeCQWsECJSTr0sWfJEIa9qmf2XPPPcOcOXPqlYhc/NKQYakvdJ7ki7Ycdthh9bkQJansqKzjJjFhFcs0J5lEoQExVZoTeO+99zah0GIiPO9IZqgj1atSsmzZsvqw8smTJ8dVldX9TNZ1IZIs9EKlBSeeeeaZ+th0qTckm/LS+f322y825mqOm400nIfhjPZiqhUk6tKNKXNB7rXXXu44SZXCYhNMsQFuHrmJlNLwV4bqpSkimOtQrdl1113rC7mkjmQXG1Sqtnz44YfrIzroKHZ6o3IwWdelmN8hv8AxEb49l+oLcxik4ZzMf1j1BSX6wzCevBeTmw0n9VVfnn322bj4T7M2WpKqgptEbhbT/K90dqU5PFVdLDiSJ245RtLCeWq9IzkfDrtixQp3XcWRsKWCOWG6LO7zVA4m67p8qN60adPqQ68eeOCBTm+SCorh0vmEoyShXJWyf1QGpDJyejEZdiA1Q2cJnSYJiTpX35JUVUxqnncKev0UcWhaITi/h1HrSG6yKFxC8tNpWqrtqaee6lGMwcgplYfJui5G0oUGO++5TGPVpRyJ3DQ/yKxZs+IiCmoNNxtpyDlVU2lxDinHqsGvvPJK/H7q1KkOf5VUecz9Om7cuLgfWPkzDX9U9WzcuDFeJ8F9Sz6vodpDkpM4A9z3pQWtVD0kavNinUMOOcRijJIZcLKOcsslS5bEih3wNS/B1MhgTq1U+UMZbFo8QEq4kLMiWxra6fDX9nsx833GCmbOEaIclQJprhDQieJcISPPuEQeB8VCzMGiaIlz11VX3mnMohLMV6eBIb446KCDeuxbp0KqblVdKtQhgWu1avnizwEn6yi3pMIk3bTyNZVgamSRrEtzPjz55JP16g4pXcTTeUovt6u/to9qxBRYrlq1Kj6kfMLsNLSHBSUY/qWRZ1wij4PiYbhjmmOLa6fV6dXDjesTTzxRT+Ayp5YGhwXiZsyYUd+/3P+pWri3S1McIe8YUXniT4fBlmQ5b5Iw6eBw1UolXMDpdUnHict4D0zjkI00lEOiUiA/HrwJkaSe1898DqX85lLVQKIuzatGVV2aWkSDk4/6YASNoz6qZeXKlfWF71gpmIfKx2RdScyePTv2VoG5C1599dVOb5IKgGMhXbypDiNhp4GZOXNmvSqRC6QVrALJ8FT2zjHC1ASSpJ7VdSlBw/XTGLU6iEHTVCwpWaehscsuu8QKO1DF8+KLL7prKyRVq8JFJcrLZF1JEAQx/CpVejARvqrNAGnoqwPyIDMPPlVdebCUKpwlST9DZzIJuxSbrFixwt1TESwskjo3d9ttNzu0hqFYI5+SQ9VAJ/Gzzz4bv6eQgHNL5WSyrkSonEoMhEQjvmHDhrgjdt999/qcMRrcOZYWDqBqMQ3rUDVxA5J6spmnbsqUKZ3eJEkqpNShDOfXqo58pVKr6oYeFf077rhj/J5CjTR/rsqNzzqNnKIjxEXNystkXcnKofNJfFOiRtWUJ2wNkIYGvVdpQl+G8bjQRLXl59gee+zR0W2RpKLHqBMnTozfMxF3mmtJ5cVIHyrrQEJp+vTpnd6k0letpv2tcss7PNLnr3IyWVciZNXzE9ahsNVFxVcqj2alYMujh06elGHuHVWXyTpJal0eo6bFr1RedGiSsAOJOqt/hkeeBDUuLb+XX365vqo2HSB0hKi8TNaVOJHgUNjqYmheKoVnCCzzrWloTJs2rb4/CYpcfauaqApZu3Zt/H7SpEnOwyNJbSTrnn/+efdXyeWJI6vqhg+rgNIxDzrqnaKl3PLqSUd1lJ938CXDvEk8QNbduQuqyQBp+LCi7tSpU+P3DDVnBS5VT36jyZwxkqS+jRs3rj5dCzFqqrpS+dCRmWJROjjp6NTwYP/SMQ/u+1544QV3dYnlq/56XpWfyboSSokEpDJZVbPXhSEHDoEdenkPsfODVFMeLOVtriSp7yqglMxZs2aNu6rECzBt3Lhxm8ovDY+UrGuMT1Q+KRnLfIUOgS0/k3UlNHny5Pr3JuuqZ/PmzeGll16qz2WQVonS0N9swJuNakrBMD3aBkuS1Jp81WwrgMorTRPReF+i4ZHvY+PScifB0wKSfOZOc1R+JutKHgjZu1LtAIm5tDT0dtppp9ijBYOi6qFagIApnWMGS5LUfmeXybryymMjY9Hhx/DyVL1oXFpe+X193paqvEzWldD48ePDmDFj6ie1E+BXS36RtuJneJCcoWoRJG1effXVYfpLKnqwlHeOSJL6TyqkGNWkQnmZrBtZTHuTkqJ0KKYhyCrveWX8WQ0m60reYDMk0ga7WqysGxl5T3G+z1WNlWATKwYkqb0YNS0yQUcXcarKJ8VFJGbHjh3b6c2phLyD3sXPyimN6kijfFR+JutKvOJWYrKuug15WhlYQy/ft/k+V/ml+UIa21pJUmsjQJq1pyoHVvlNIw5IzJKg1fBLSXB4XpVT/rmaBK8Gk3Ulld9A2mBXS0rOMneFq28Nn/wiaUK8WvLP22SdJA38+mmMWj6bNm2qf29CYeQYl5Zfai/5rJ0vuRpM1pWUybrqSokEA6ThZVBUXSlYolrA80yS2mNlXXU6tLxGjhzj0vJXrKZEuB3F1WGyrqRM1lXTT3/609iYwwBpeBkUVVfes+nwHklqjzFqdZJ1aTERDT/j0nLLq5DzDg+Vm8m6FrCaKheeLVu2hG6x44479kjgqBryVUnzY6AbdNuqxXkA6mqw1ZLa1G47x9hurwdSeRCb5kMOu8UOO+xQ/94FJsonj4m6MVnXbfFoYlxabnlbmbeh3YJikpdffrlrz69OGd2xv9wlOKBuv/328NRTT8Uei0WLFnVF6Wk+jr2bkowanPyz3n777btmdz7wwAP1G45uOV7ziqpu2WYNzTUhBRrdNF/Ik08+Ga9lHLcLFiwI06dP7/QmSRqExx9/PNx5553x+8MPPzzstddeXbM/jVHLLY+Juuk6iRdffLEej3Zb51Yel5oQKfd51W2jOqgKXLJkSexgmjp1ajjmmGO6rm3oFPdSC71DJOrAAfb000+HbmCDXU35xbmbGvKHHnqo/n0axlt07N+0jw2KqqNbz7GHH364vv3Lly/v9OZIGqJzGt12Thujllu3XidTEjyPR7spvvO8Krf8WOy2RNfKlSvrw+NXrVoVXnrppU5vUtfork+6AygzzSvpdtlll9Btuu1CqepdqCdNmtSVx2vax920zRqcbv2sJ0+e3PR8k9Sd8vO4287pbopPVJ1YtPE+L++U7QbdnCRVuc+riRMn1r8fPXp0V4xSLAqHwfaDzPXxxx8fK+powCnd7AbdXCqr6jXkRx11VPj+978f52OgEe8GBkXV1K3Dnw899NCYsOOatueee3Z6cyQN0mGHHRbPadqkWbNmddX+7OYKEZU3FsXs2bPDsmXL4hDYbpuXNue9X/l08/QBu+66azj22GPD6tWrw4wZM7r63Bpp3XFX3GFkf+fMmRO6dRLKbpq7TIOTTzjaTYsesN0k6bppoul8/3bjRK8aONpUhsd00/FKkNdtN/SS+m6H9tlnn67cRXnbabKufPJO126c941zi+3utoRXvq+7peNbrcvbym6ZMig3bdq0+FB77M6qwPLOlppWBz0VKbhIcwNoeOT7l8VnVB2pTaWd7baqAUkq0vXTGLV88pjIWHTkGJeWW95W5vf5KjeTdSVlsq6aSNSlpdvTalYaHvn+Tftc1QqY6NnstqoBSeo0Y9RyM1nX+WSdcWn5UC2ZKiZN1lWHybqSMhCqrhQkkUzqtjkNuok9mNWV34gYMElSe4xRy81kXWcYl5a/IMORHdVjsq6kHGJQXXmZ9CuvvNLRbSmzfN86jKda8s/bIT6SNPDr5/jx4919JUP1T5ov2w6tkZPva6dnKXf8STFGN81NroEzWVdS69atq2fhbbCrJV8ee+3atR3dljJbs2ZN032u8stvLlNbK0lqL1lHQscFmsqHe4+ddtopfv/yyy87XcQIyWP+nXfeeaT+rEaQBRnVY7KuhJhD6aWXXqonEVwNtlomTZrUNKGkocOiAmnfcqNhZUC1TJ48uf79iy++2NFtkaRuQjUICZyUUOi2FTfVfixqp9bISHEp930pWapyyZOwq1ev7ui2aGSYrCuh/OSdMmVKR7dFI89k3fBj6GNaYIL97c1G9YKlNMkv7a0rwkpSa/IOjl133dXdVlLGoiOLmDQNgzUuLa+8zXzhhRc6ui0aGSbrSh4ImayrHoY9p1Wg6GVzkYnhTYjvsssuw/AXVGQkZ1PbSoDs3JCS1Jr8BtMYtbxM1nVuCGy+71Uu+Yg57vftLC4/k3UlZCCk1POyefNmh+kNg2effbb+vZUB1ZTfZNq7KUmtsUO5ekmF559/3qTCMGMfJybrymvUqFH1+JNRPnYWl5/JuhLOBZJuHJmE0lUqq2n69On171euXNnRbSkberHSPiUQnTZtWqc3SR2QJ2k9xySpf3Qgpsr0CRMmuABayZMKKT6iAt05lIfXM888U6/832233Yb5r6mTHApbLSbrSthYp5LYmTNnOpdWRe2+++71z55EgmXSQ4cbjbRcOoGoC7hUEz2babg5lZYs7CNJ6h1tZYpHpk6d6q4qOTuORwaLCqZFW4hNdtxxxxH6y+p0si4laVVeJutKZsWKFfXv99hjj45uizqHC3VqzLmAp9WBNXj5hTEPRFW9qgE6RMC8kFbXSVLfnnzyyfr3xqjV6DhOvEYOn3zfGpeWH/d3zE+eOkDSgncqJ5N1JcLY9TRnwfjx452zoOLyC/bjjz/e0W0pC5Iy6WaDykWDomrLbzafeuqpjm6LJBUZN5TPPfdc/J4bTSvryo/PefLkyfH7devWxYeGFpWqeaGGcWn5cf+x5557Nv38VT4m60pcVZeGQaqa9tprr1j9gyeeeCLOFaPBefrpp+s9WDNmzKgPg1Q1MdwkzQtKRwkdJpKk5jFqGgLLjaYxavU6tR577LGObktZp2ZJK8GysMROO+3U6U3SCN3jJXYWl5vJuhJV/Cxfvrz+75RxV7WHwqbjgESdPS+Dlweas2fPHoJXVJl6Nx999NFOb5IkFX4IbH6jqXJ73eteV5/bl2PA+V2HVh53GJdWa7VlHilh63RH5WWyrkTzaKXlm1kFKJ3Aqrb8wk0y14UmBo6ey7TS8s4779xjgldV+xxLFSIEzVawSlJPXDvTaqD5TabKb4cddujRcWwV0NBhpAcjPtJ+dh7Iask7PfKCHZWLyboSIAHz4IMP1v89Z86cjm6PioOSeB5grpB0UVf7li5dWv9+7733dgiPIobBphsRKgasrpOknh566KH698ao1dPYccxoIA3NaI+0L/MKRlVD/pkz3ZFTsZSTybqSVNWlSVuZyHXatGmd3iQVyIEHHtgj4WSQNLCqAFZcShMmz5o1awg/IXW7/fbbr/79ww8/bHWdJP0fhmil62feuaHq2GWXXeqjEdavX99jSLQGXlVHvAGq+x0CW83pjtLnzr1d3imi8hhwso4SdhJDafgPXy1rH3mUlN933331fx9wwAFW/KgHhkWnIOnll1+OvS9qr3L1/vvv75H8tPdSOYZFp+Enr776anjggQfcQR1gXCKPg+JZtmxZj46NtPCVqmXu3Lk9jonXXnuto9vT7RhRlabdoMJqwoQJnd4kdQCVyumehErLDRs2+DmULP4c8BXzsMMOC4sWLaqvhshXfqaRb6zTXHVTp06NiRkpR4Nx0EEH9QiSnOC3vcrVF198MX7PKltOjK1mSOKmm9BHHnmkvjqbRo5xiTwOioVVsp977rn4/fjx461KrzA6jXfffff4PcP1nDJi4LjvSwuekaihUEPVxGifvLoun7JH5Yg/7d7qYgx9TSXQ3CQeeuihVtWpqSlTpoTp06fXS+fvvfde91QL2Fd33313j55hqwLUDInc/fffv16Nedddd7mgi6TKournzjvvrP+bhILXz2rLO44pNnCOrYFhRFWa0mafffaJw8tVXVQsjx49On7PEPPUQaJyMFnXpRpvBjlRGYol9WbevHn1xpyhsGkOGfXunnvuiQk7kOycMWOGu0u92nfffetDUZin6fHHH3dvSaokqvjTyA86DK1KF8PC0nHACA87tdq3YsWK+mJxzFmWz5mrauI4yBPhnFdpiLS6n8m6LkVFXRqax82hjbX6wxCUQw45pP5verwdDts7giGCIuywww7h9a9/vZWr6hPDUThO8t7vl156yb0mqVKIT5kOAFTTveENb/D6qejggw+uDxVbuXJleOqpp9wzAxztQSc88am099571+cnp5PE4bDlYbKuS+cAySe85+bQCe/VCiahTasFM/zg9ttvd6heEyzEkQ/fISBiXgipP5xfqXKAns1bbrnFpLikymDhgDvuuKPHfJ5MEyCBRB3T9uQjGBwO2z9GUpGoYxErMNIjLWwlMT85c6SlfMDy5cvDCy+84I4pAZN1XYZs+W233dZjDpCUfJFaaczp4U49cfRq5iu1aevQjB//+Mf1BAsB0Z577umuUcu4EUnTEqxfvz4mftOUBZJU9oQC7R4mTZoUVyuUcjNnzqwnmoi1uK9Jc7CpORaUSMNfieGdp1yN6BShcyThvDIR3v1M1nVZb+Wtt95a71VhVSVXAFK7mIj2yCOP7DHJbxruWXXcaFBtmIYucuFz+I7axdyQCxYsqM8RSYCdhoRJUlnRzjEnbj781UUl1AwjFtJwWCqASPLaqdX7iCoqEPMRVY72UDN0jjBHKEjUMbqD/IG6l8m6LkvUrVmzpj7/2OGHH+4cIBqQ3Xbbrcf8dQxZSXMgVhVBInOMUW2Yei6POuoo5wPRgJDoPeKII+r/5thKN7GSVDZcO2nnEmJUFhSQmiFRR6dWSuayIBPVY9p2Whbu/1Iik4WsHP6qvkZQzZ8/v75CMIudObqju5ms6wKUhlPKmlbvZDw6FzhWf5EGiuXemcMuJYNvuumm2KhXFcOBU/UTFzuqD51nR4PBCsJ59TNJ8SeffNKdKqlU1q1bF37yk5/U/027Z0JB/aECKF+Uieqx5557zh33fxhJlU/LQkd7vuqn1AxVlxQbpPnrWMTloYcecmd1KZN1BUdPCom6VO3DiXf00UeHXXbZpdObpi5HQoo5L9Kch0yGf+ONN1auwo5zjFWTGA6cEDwSFEmDxU3r7Nmz6/9mmLXDziWVBdNG0NlHDJHmI3OKFrWKTmOqxVI8xrC9VatWVX4HkqDjvMqnZaETmdhd6g95AqqbE+5zWHRC3cdkXRc01s8880z8nlJxMuVTp07t9GapJFKVZlruOyXsqtKzSdXqvffe2yNRxzwqs2bN6uh2qTwIrDmm9t577/rPqEBxDhFJ3Y6pWa6//vr6JObcIDrPq9pFtRiLeYFr480331zphF1K1KWpjxgy7LQsahcdJ3Pnzu1RuZrf76g7mKwrqDQ3QVodKSXqXPlVQ41J8KnWTEngFCgxJLTMk/2mVV/zniaSKgwPloajijUlgTmv0rCWMp9jksqLKvwbbrihvugZibpjjjmmvrCO1Ko09QgL5+VTs6Tpf6pk06ZN8bxK09Iw5dGxxx7rtCwakP322y/sv//+PSrseBh7dg+TdV1S/USizmF5Gu6EHXNsgUacijMmJS1jBRDDCn70ox/VKwgJFA877DATdRo2HGMMr84r7CSpGxEXUIWfhr4y99jChQvrq3tK7aIogYnxU8KOYoUqdBzn1q5dG2NTvuaJOhdq0WBiT6rr8rkOqa6jyq4q51W3M1lXUGlOAr4uWrTIRJ1GbEgsvTAJq1cyxCXNmdHtuDDxnpYsWRJX2MqDIYe+aqQq7FiJOW/jJakbpJs7KoNTRx4jPqioYwV1aSjiUIbvJXQcszhTGTuOc8xle91114UNGzbUFwk47rjjnKNcQ4J7O0YPJY8++mh9hIeKzWRdwZFIsEdFI4XEAb0vRxxxROzlBHNm/PCHP4wrCXVzLwwBEMNeCfpSNQDnFslw54HUSJ5jc+bMcTVvSV2nMQagGp+RHw591VAh9mRIbD50j1XUGRr6yiuvlG5HU0F4//33x8UEU0Jy8uTJ4Y1vfGPYeeedO715KhGm+cnnFE1TbanYnFii4Ky6UCfsueeeYcKECXEifCrQUjDBYicM5eum1YhTNd19993XoxeJ98h78SZDnWDbLqnbpE48kEw58MADbcs0bEP36FBNVXXM4faDH/wgHHzwwXE6iTJcQxnuygrx69atq/9sr732irEpVYbScKy+PH78+JgcZr7Rbi7CqAqTdZKaomfvTW96U1i2bFmcMwQES1TZkegiSCehV1RcgFauXBknUs2H8TKnDoFQWnlMkiS1N+ojX2VQGg577LFHjDNvueWWODqCpN3dd98dnn766VghRNKhG9EBzrxhPFKyJI1sofK+DIlIFRejiajc5H4uLRCk4jJZJ6n3BmL06Di/FoktejfTPG9PPfVUnF+D3s199923UAETgc/zzz8fHnjggbhaXY4kI3M2cKMhSZIGV2EnDadJkyaFN7/5zXF0xOOPPx5/tmrVqlhlx7A+5uLqlvkSiU8ZocJIlRRPgwpCko+8V2kkjBs3zna8S5isk9SvXXfdNQZLjz32WOwJTKXTTFDKg3lrSNyxYnGnegQZ4spwV7Zx/fr121QJ0mPp3HSSJEndg2TcYYcdFheeuPPOO+tVdsylTMzHkOzZs2cXeugoncgk6ZgHOiFeZtt5mACX1IzJOkktIQiiPJ/5Dh5++OE4NDZNhstwUx4MV2DYwu677x4TZMOduCNBRwBETyWPxtXCdtppp5ikI5nosAJJkqTuRIcwHcdMz0KSjuGkxIFU3RGX0mk8a9asWDVUBGwfQ3bp1G4c6UEnOCNXrKaT1BeTdZLa7uFkrhqGHxAs8di4cWP8HWX9aR4O5oYjaTdt2rS4IAWJs8EmzFjFlYl4mTvvueeei0Mhmq1mRAUdQRvDd+2tlCRJKkcMynQmdB6TtGOlWGzatClOf0L8SexHDEgs2ImOWmJhhuwy2oPtyjHklU7kTo5EkdQ9TNZJGhCScQcccECcL+TZZ5+NPYdUuSUEKAQqPGJjM3p0TNoRqIwdO7b+4HWo2ktJN76SjCMBmB4MeWDoQL5QxDaN2ejRcRUtAjT+hiRJksqHuZIPP/zwOG8ySTtGV4ApWqhm48H8xHQaM7qC5Bhx4nDgbxKjplEm+equCR3WDHdl7mSTdJJaZbJO0qBQuUYvJg8SayTuCFZI3OXDUqmKe+GFF+KjL8yHt2TJkpb+NkMdCMJ4MKSgyPOVSJIkaejQObtgwYLYqctIDyraUjUb8SSVdzyIVRlymh50Hu+8884DSpwR665duzYm6HjQwdxYQQdem9iY+fSIUU3SSWqXyTpJQ4ZKOeYL4ZGScymY4ZGGyw4EQQ6BVQq0pkyZEoM0gx9JkqTqovOWKVoY8UGV3YoVK+J0KanTmFEbzBuXzx1HAi8f5cFXqu+YBw9pPrx8pAdJufT73hCjkqRjjmdeU5IGymSdpGFBwMPwAx4JQQ5zeTQGPmkSXr4SPKUAJ38whMDKOUlSK1KlC9eZa665ZkR22mA6pCQNHjEkC53xIFHH3MaM9iBx98orr/R4LjEnP2v8ecL/z8IVrcS7VM6lkR4m6KRi2ThCcUCKAZpV2nYsWWcwpDJh2jTOL74y5QRTn40axfxsW79qcOi55NEMARWNHHOMvP71r3dXSyXSiYTJUAZL6j7MI9WpJNpwzY0lqXV08OadxlwT8uGr69evjz9juGyrr0cijio+htGmkR4TJkxwlIdUQKOza/FIxgF5/DFYg44mDIZUFpzDt94awqc/HQL3lCTsSNCdcEII554bwvz5DPPs9FZKUvfoVKA01MGSug9TJKRjYCQrXTjmGY4nqVjoLGahCR6NFXQk7bhG8f2tt94ah7qy8izz4eVDZJ16Reoec+fODUuXLo1TM42EFOcOZTsx6GSdwZDKgHProotCOOecnj8nYXfllVsf558fwhlnmLCTpKIGSsMVLKn7cIPNscBN9uLFizu9OZIKioo5Vpflkf5Nso6vU6dO7fTmSRqgmTNnxsdIYQQJcUdvo8g6kqwzGFK3IyFHRV1joq7R2WeHcPTRIRx7rENiJamIgdJwBUuSJEnSSHIWLlUe0xqdd15ru4HnOQ2SJEmSJEkaLibrVHlU1l19dWu74aqrtlbiSZIkSZIkDQeTdao8Vn1tNQGXVomVJEmSJEkaDibrVHkTJ7Y+Bx3P4/mSJEmSJEnDwWSdKo8E3AkntLYbTjzRxSUkSZIkSdLwMVmnymPBwHPPbW038DwXGJQkSZIkScPFZJ0qj8q6+fNDOP/8vncFvz/ySCvrJEmSJEnS8DFZJ4UQxo4N4YwzQliyJIS3v/1nCTm+nnRSCNddt/X3PE+SJEmSJGm4jB62V5a6DIm4hQtDuOyyEF57beuqr2nxCYa+troIhSRJkiRJ0kCZrJMyJORS9dyECe4aSZIkSZI0sqwVkiRJkiRJkgrCZJ0kSZIkSZJUECbrJEmSJEmSpIIwWSdJkiRJkiQVhMk6SZIkSZIkqSBM1kmSJEmSJEkFYbJOkiRJkiRJKgiTdZIkSZIkSVJBmKyTJEmSJEmSCsJknSRJkiRJklQQJuskSZIkSZKkgjBZJ0mSJEmSJBWEyTpJkiRJkiSpIEzWSZIkSZIkSQVhsk6SJEmSJEkqCJN1kiRJkiRJUkGYrJMkSZIkSZIKwmSdJEmSJEmSVBAm6yRJkiRJkqSCMFknSZIkSZIkFYTJOkmSJEmSJKkgTNZJkiRJkiRJBWGyTpIkSZIkSSoIk3WSJEmSJElSQYzu9Aaoi23ZEsKmTVu/rlsXwsSJIYwaFcKYMVu/SpIkSZIkqS1mVDQwGzeGcMMNIbzrXVuTdDNnbv166qlbf87vJUmSJEmS1BaTdWofibiLLgph0aIQrrpqa2Ud+HrllVt/fvHFJuwkSZIkSZLaZLJO7SEhd+utIZxzTt/PO/vsEG677WeJPEmSJEmSJPXLZJ3awxx1553X2nN5Hs+XJEmSJElSS0zWqT1Uyl19dWvPzYfISpIkSZIkqV8m69QeVn1tNQGXVomVJEmSJElSS0zWqT2s+DqqxcOG5/F8SZIkSZIktcRkndpDAu6EE1p77okntp7YkyRJkiRJksk6tWnMmBDOPbe15/I8ni9JkiRJkqSWWPak9lApN39+COef3/fz+P2RR1pZJ0mSJEmS1AaTdWrf2LEhnHFGCEuWhPD2t/8sIcfXk04K4brrtv6e50mSJEmSJKllo1t/qpQhEbdwYQiXXRbCa69tXfU1LT7B0FfnqpMkSZIkSWqbyToNHAm5VD03YYJ7UpIkSZIkaZAcBitJkiRJkiQVhMk6SZIkSZIkqSBM1kmSJEmSJEkFYbJOkiRJkiRJKggXmJAkSVKpbNq0KX7duHFjuOaaazq9OaXAvpQklYfXyKG/Rqb4oxDJOoOh4WFAJEnlZ5A0PPt0qIMldZ9arVb/3phqaI0ebV+/JJWlHfcaOXzxx2AN+mprMDS8DIgkqXwMkrorWFL32W677erHwNixYzu9OaVqu+bOndvpzZAkDQLt+NKlS8PmzZvdj0MkJT2JPwqTrDMYGj4GRJJUTgZJ3RUsqfuMGTMmHgsk6hYvXtzpzZEkqTBmzpwZHxo6TLlB3EH8UZhkncGQJEntMUjqrmBJkiRJGkmuBitJkiRJkiQVhMk6SZIkSZIkqSBM1kmSJEmSJEkFYbJOkiRJkiRJKgiTdZIkSZIkSVJBmKyTJEmSJEmSCsJknSRJkiRJklQQJuskSZIkSZKkgjBZJ0mSJEmSJBWEyTpJkiRJkiSpIEzWSZIkSZIkSQVhsk6SJEmSJEkqCJN1kiRJkiRJUkGYrJMkSZIkSZIKwmSdJEmSJEmSVBAm6yRJkiRJkqSCMFknSZIkSZIkFYTJOkmSJEmSJKkgTNZJkiRJkiRJBWGyTpIkSZIkSSoIk3WSJEmSJElSQZisk1QImzZtCmvWrIlfJUmSJEmqqtGd3gBJ1Xb99deHCy+8MFx++eXhtddeC9tvv304+eSTw1lnnRUWLlzY6c2TJEmSJGlEWVknqWMuueSSsGjRorB06dLwuc99LlxxxRXxK/8+/vjjw6WXXuqnI0mSJEmqFCvrJHWsou70008PH/zgB8MFF1wQRo36Wd/BmWeeGT784Q+H0047LcybN88KO0mSJElSZVhZJ6kjGPo6d+7cbRJ14N/p93yVJEmSJKkqTNZJGnEbNmyIc9S9//3v3yZRl/Bzfv+d73wnPl+SJEmSpCowWSdpxK1bty4uJjFnzpw+n7fPPvvE5/F8SZIkSZKqwGSdpBE3ceLEuOrrI4880ufzli9fHp/H8yVJkiRJqgKTdZJG3Lhx48LJJ58cvvKVr4QtW7Y0fQ4/5/ennHJKfL4kSZIkSVVgsk5SR7Da69KlS8NZZ521TcKOf6ff81WSJEmSpKoY3ekNkFRNxx13XPjSl74UTjvttHDttdfGxSSYo46hr1TUkajj9wsXLuz0pkqSJEmSNGJM1knqmA984ANh3rx54cILLwwf+9jH4mISzFHH0Ncvf/nLJuokSZIkSZVjsk5SR1E5x+OKK64Iq1evDpMnTw7vfOc7/VQkSZIkSZXknHWSCmHMmDFh0qRJ8askSZIkSVVlsk6SJEmSJEkqCJN1kiRJkiRJUkGYrJMkSZIkSZIKwmSdJEmSJEmSVBAm6yRJkiRJkqSCMFknSZIkSZIkFYTJOkmSJEmSJKkgTNZJkiRJkiRJBWGyTpIkSZIkSSoIk3WSJEmSJElSQZiskyRJkiRJkgrCZJ0kSZIkSZJUECbrJEmSJEmSpIIwWSdJkiRJkiQVhMk6SZIkSZIkqSBM1kmSJEmSJEkFYbJOkiRJkiRJKgiTdZIkSZIkSVJBmKyTJEmSJEmSCsJknSRJkiRJklQQJuskSZIkSZKkgjBZJ0mSJEmSJBWEyTpJkiRJkiSpIEzWSZIkSZIkSQVhsk6SJEmSJEkqCJN1kiRJkiRJUkGYrJMkSZIkSZIKwmSdJEmSJEmSVBAm6yRJkiRJkqSCMFknSZIkSZIkFYTJOkmSJEmSJKkgRnd6AyRJkqThsHHjxnDNNde4cyW13GbYdkgaaNsxlEzWSZIkqVRGjx49rAG0pPKz7ZA0mPhjsEzWSZIkqVTmzp0bli5dGjZv3tzpTZHUpQm6sWPHdnRbJHVfoo74Y6hsV6vVaoN5AYYWpEbNBk3SQBkcSRoKeUyyePFid6okSZK6zqAr6xxmIGmoOexAUpGGIUiSJEkjadCRrMMMJA0FK+skFXUYgiRJkjSSBj0MVpIkSZIkSdLQGDVEryNJkiRJkiRpkEzWSZIkSZIkSQVhsk6SJEmSJEkqCJN1kiRJkiRJUkGYrJMkSZIkSZIKwmSdJEmSJEmSVBAm6yRJkiRJkqSCMFknSZIkSZIkFYTJOkmSJEmSJKkgTNZJkiRJkiRJBWGyTpIkSZIkSSoIk3WSJEmSJElSQZiskyRJkiRJkgrCZJ0kSZIkSZJUECbrJEmSJEmSpFAM/z9GO3dDtCBAEAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 5))\n", + "\n", + "# 1. Plot original frame (using 0-100% coordinate system)\n", + "container.plot_frame(0, ax=axes[0])\n", + "axes[0].set_title(\"Original Frame (0-100%, Top-Left Origin, AwayHome)\")\n", + "\n", + "# 2. Plot transformed frame (using meters coordinate system)\n", + "normalized.plot_frame(0, ax=axes[1])\n", + "axes[1].set_title(\"Transformed Frame (Meters, Center Origin, StaticHomeAway)\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Common Data Format (CDF) Schema\n", + "\n", + "TDC uses the **Common Data Format (CDF)** standard for naming columns. This standard defines a hierarchical path structure (e.g., `teams/{team_id}/players/{player_id}/x`) that allows for standardized selection logic regardless of the data provider." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Selecting Columns\n", + "\n", + "TDC provides multiple ways to select columns, from metadata-aware selectors to simple string-based wildcards." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ball columns (using selector):\n", + "['ball/x', 'ball/y']\n", + "\n", + "Ball columns (using wildcard 'ball/**'):\n", + "['ball/x', 'ball/y']\n", + "\n", + "Player X columns:\n", + "['teams/home/players/p1/x', 'teams/away/players/p2/x']\n" + ] + } + ], + "source": [ + "# 1. Select using metadata selectors (e.g., all ball columns)\n", + "ball_data = container.select(container.selector.ball())\n", + "print(\"Ball columns (using selector):\")\n", + "print(ball_data.to_polars().columns)\n", + "\n", + "# 2. Select using recursive string wildcards\n", + "ball_data_wildcard = container.select(\"ball/**\")\n", + "print(\"\\nBall columns (using wildcard 'ball/**'):\")\n", + "print(ball_data_wildcard.to_polars().columns)\n", + "\n", + "# 3. Select all x-coordinates for all players\n", + "player_x = container.select(\"teams/*/players/*/x\")\n", + "print(\"\\nPlayer X columns:\")\n", + "print(player_x.to_polars().columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Schema Validation\n", + "\n", + "You can validate whether the data follows the CDF standard." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Schema validation failed: Missing ball/z\n" + ] + } + ], + "source": [ + "# Validate CDF compliance\n", + "try:\n", + " container.schema().validate()\n", + " print(\"Schema is valid CDF!\")\n", + "except tdc.exceptions.SchemaError as e:\n", + " print(f\"Schema validation failed: {e}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that you can still add columns that are not defined in the CDF." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Columns with custom metric: custom_metric\n" + ] + } + ], + "source": [ + "# Add a non-CDF column\n", + "container_with_custom = container.add_column(\"custom_metric\", [1.1, 2.2, 3.3])\n", + "print(f\"\\nColumns with custom metric: {container_with_custom.to_polars().columns[-1]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspecting the Schema\n", + "\n", + "You can inspect the rich metadata associated with each column through the container's schema." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Column: frame_id | Entity: / | Attribute: frame_id\n", + "Column: timestamp | Entity: / | Attribute: timestamp\n", + "Column: period | Entity: / | Attribute: period\n", + "Column: match/id | Entity: / | Attribute: match\n", + "Column: ball/x | Entity: Ball | Attribute: x\n", + "Column: ball/y | Entity: Ball | Attribute: y\n", + "Column: teams/home/id | Entity: Team | Attribute: id\n", + "Column: teams/home/players/p1/x | Entity: Player | Attribute: x\n", + "Column: teams/home/players/p1/y | Entity: Player | Attribute: y\n", + "Column: teams/away/id | Entity: Team | Attribute: id\n" + ] + } + ], + "source": [ + "schema = container.schema()\n", + "for field in list(schema)[:10]: # Show first 10 columns\n", + " if field.entity:\n", + " print(\n", + " f\"Column: {field.name():<30} | Entity: {type(field.entity).__name__:<15} | Attribute: {field.attribute}\"\n", + " )\n", + " else:\n", + " print(f\"Column: {field.name():<30} | Entity: {'/':<15} | Attribute: {field.attribute}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "/Users/probberechts/Developer/tracking_data_container", + "language": "python", + "name": "tracking_data_container" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/02-parsing.ipynb b/examples/02-parsing.ipynb new file mode 100644 index 0000000..f1e0962 --- /dev/null +++ b/examples/02-parsing.ipynb @@ -0,0 +1,267 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Efficient Parsing with TrackingDataBuilder\n", + "\n", + "Parsing raw tracking data (from XML, JSON, or CSV) is a computationally intensive task. While full-featured parsing is out of scope for the `tracking-data-container` project (this should be implemented by libraries such as [kloppy](https://github.com/py-sport/kloppy)), TDC provides optimized primitives for writing efficient parsers.\n", + "\n", + "By moving the parsing logic to Rust and providing the `TrackingDataBuilder` primitive, TDC achieves ingestion speeds that are often an order of magnitude faster than traditional Python-based approaches. Furthermore, the zero-copy conversion to Polars ensures that even large-scale tracking data can be moved into high-performance analysis environments with minimal latency." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## High-Performance Ingestion in Python\n", + "\n", + "The `TrackingDataBuilder` allows you to ingest data directly into Arrow memory. It handles internal column mapping and memory growth efficiently." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ingested 10000 frames in 0.0715 seconds.\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "import tracking_data_container as tdc\n", + "\n", + "builder = tdc.TrackingDataBuilder()\n", + "\n", + "\n", + "def custom_parser_simulator(builder, num_frames=10000):\n", + " for f_id in range(num_frames):\n", + " # Use .insert() for all types\n", + " builder.insert(f_id, \"ball/x\", 50.0)\n", + " builder.insert(f_id, \"ball/y\", 20.0)\n", + " builder.insert(f_id, \"ball_status\", True)\n", + "\n", + " for p_id in range(22):\n", + " builder.insert(f_id, f\"teams/home/players/p{p_id}/x\", 10.5)\n", + "\n", + "\n", + "start = time.perf_counter()\n", + "custom_parser_simulator(builder)\n", + "container = builder.build()\n", + "end = time.perf_counter()\n", + "\n", + "print(f\"Ingested {container.num_rows()} frames in {end - start:.4f} seconds.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Writing Parsers in Rust\n", + "\n", + "For the ultimate performance, parsers can be implemented in **Rust**. \n", + "Here is a more extensive example of how a Rust parser is structured using `Rayon` for parallelism:" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "/* \n", + "// Rust High-Performance Parser Pattern\n", + "use tdc_core::datacontainer::builder::TrackingDataBuilder;\n", + "use arrow::datatypes::DataType;\n", + "use rayon::prelude::*;\n", + "\n", + "pub fn parse_tracking_parallel(chunks: Vec) -> TrackingDataContainer {\n", + " chunks.into_par_iter().map(|chunk| {\n", + " let mut builder = TrackingDataBuilder::new();\n", + " \n", + " // Pre-resolve column IDs to avoid string hashing in the loop\n", + " let x_id = builder.get_or_create_column_id(\"ball/x\", DataType::Float64);\n", + " let y_id = builder.get_or_create_column_id(\"ball/y\", DataType::Float64);\n", + "\n", + " for raw_frame in chunk.frames {\n", + " let row = builder.get_row_index(raw_frame.id);\n", + " builder.insert_float_at_idx(row, x_id, raw_frame.x);\n", + " builder.insert_float_at_idx(row, y_id, raw_frame.y);\n", + " }\n", + " builder\n", + " }).reduce(TrackingDataBuilder::new, |mut a, b| {\n", + " // Highly optimized merge operation\n", + " a.merge(b);\n", + " a\n", + " }).build().unwrap()\n", + "}\n", + "*/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Benchmark: Loading Data\n", + "\n", + "We compare the time taken to load a full Sportec (DFL) tracking XML file using a specialized Rust-based parser that implements the principles above." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "POS_FILE = \"data/DFL_04_03_positions_raw_observed_DFL-COM-000001_DFL-MAT-J03WMX.xml\"\n", + "META_FILE = \"data/DFL_02_01_matchinformation_DFL-COM-000001_DFL-MAT-J03WMX.xml\"\n", + "\n", + "# Ensure data is present\n", + "if not Path(POS_FILE).exists():\n", + " print(\"Downloading data for benchmark...\")\n", + " from huggingface_hub import hf_hub_download\n", + "\n", + " hf_hub_download(\n", + " repo_id=\"pysport/idsse-data\",\n", + " filename=\"DFL_02_01_matchinformation_DFL-COM-000001_DFL-MAT-J03WMX.xml\",\n", + " repo_type=\"dataset\",\n", + " local_dir=\"data\",\n", + " )\n", + " hf_hub_download(\n", + " repo_id=\"pysport/idsse-data\",\n", + " filename=\"DFL_04_03_positions_raw_observed_DFL-COM-000001_DFL-MAT-J03WMX.xml\",\n", + " repo_type=\"dataset\",\n", + " local_dir=\"data\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Loading benchmark ---\n", + "TDC (Rust): 1.18s\n", + "Fast-Forward: 1.60s\n", + "Kloppy: 19.38s\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "import tracking_data_container as tdc\n", + "from kloppy import sportec\n", + "import fastforward as fff\n", + "\n", + "\n", + "print(\"--- Loading benchmark ---\")\n", + "start = time.perf_counter()\n", + "tdc_container = tdc.load_sportec(POS_FILE, META_FILE)\n", + "tdc_load_time = time.perf_counter() - start\n", + "print(f\"TDC (Rust): {tdc_load_time:.2f}s\")\n", + "\n", + "start = time.perf_counter()\n", + "fff_dataset = fff.sportec.load_tracking(POS_FILE, META_FILE)\n", + "fff_load_time = time.perf_counter() - start\n", + "print(f\"Fast-Forward: {fff_load_time:.2f}s\")\n", + "\n", + "start = time.perf_counter()\n", + "kloppy_dataset = sportec.load_tracking(\n", + " raw_data=POS_FILE, meta_data=META_FILE, coordinates=\"sportec\"\n", + ")\n", + "kloppy_load_time = time.perf_counter() - start\n", + "print(f\"Kloppy: {kloppy_load_time:.2f}s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Benchmark: Polars Conversion\n", + "\n", + "Once the data is loaded into memory, we often want to convert it to a Polars DataFrame for analysis. Because TDC is built on Arrow, this conversion is **zero-copy** and near-instant." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- to_polars benchmark ---\n", + "TDC to_polars: 13.31ms\n", + "Fast-Forward tracking access: 0.02ms\n", + "Kloppy to_df(engine=\"polars\"): 1133.28ms\n", + "\n", + "Speedup TDC vs Kloppy: 85.2x\n" + ] + } + ], + "source": [ + "print(\"--- to_polars benchmark ---\")\n", + "\n", + "# 1. TDC to Polars (Zero-Copy)\n", + "start = time.perf_counter()\n", + "df_tdc = tdc_container.to_polars()\n", + "tdc_conv_time = time.perf_counter() - start\n", + "print(f\"TDC to_polars: {tdc_conv_time * 1000:.2f}ms\")\n", + "\n", + "# 2. Fast-Forward to Polars\n", + "start = time.perf_counter()\n", + "df_fff = fff_dataset.tracking \n", + "fff_conv_time = time.perf_counter() - start\n", + "print(f\"Fast-Forward tracking access: {fff_conv_time * 1000:.2f}ms\")\n", + "\n", + "# 3. Kloppy to Polars\n", + "start = time.perf_counter()\n", + "df_kloppy = kloppy_dataset.to_df(engine=\"polars\")\n", + "kloppy_conv_time = time.perf_counter() - start\n", + "print(f'Kloppy to_df(engine=\"polars\"): {kloppy_conv_time * 1000:.2f}ms')\n", + "print(f\"\\nSpeedup TDC vs Kloppy: {kloppy_conv_time / tdc_conv_time:.1f}x\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "/Users/probberechts/Developer/tracking_data_container", + "language": "python", + "name": "tracking_data_container" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/03-interop.ipynb b/examples/03-interop.ipynb new file mode 100644 index 0000000..c363521 --- /dev/null +++ b/examples/03-interop.ipynb @@ -0,0 +1,228 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Interoperability: Zero-Copy Data Exchange\n", + "\n", + "One of the core strengths of the `tracking-data-container` (TDC) is its ability to seamlessly exchange data with the broader Python data science ecosystem. TDC is built on **Apache Arrow**, a language-independent columnar memory format. Because Arrow defines a standardized way to represent data in memory, different libraries can share that data without the need for expensive \"serialization\" or \"copying\".\n", + "\n", + "This notebook illustrates how to convert a `TrackingDataContainer` to other popular formats like **Apache Arrow**, **Polars**, **Pandas**, and **Parquet**." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Container created with 3 rows.\n" + ] + } + ], + "source": [ + "from datetime import datetime, timedelta, timezone\n", + "\n", + "import tracking_data_container as tdc\n", + "\n", + "# Create a sample container\n", + "base_time = datetime(2023, 5, 27, 13, 30, 0, tzinfo=timezone.utc)\n", + "data = {\n", + " \"frame_id\": [1000, 1001, 1002],\n", + " \"period\": [\"first_half\", \"first_half\", \"first_half\"],\n", + " \"timestamp\": [\n", + " base_time,\n", + " base_time + timedelta(milliseconds=40),\n", + " base_time + timedelta(milliseconds=80),\n", + " ],\n", + " \"ball/x\": [50.0, 51.0, 52.0],\n", + " \"ball/y\": [12.0, 13.0, 14.0],\n", + " \"teams/home/players/p1/x\": [23.5, 23.6, 23.7],\n", + "}\n", + "cs = tdc.CoordinateSystem(\n", + " x_range=(0, 100),\n", + " y_range=(0, 100),\n", + " origin=tdc.Origin.TopLeft,\n", + " length_unit=tdc.LengthUnit.Percent,\n", + ")\n", + "container = tdc.TrackingDataContainer(data).with_coordinate_system(cs)\n", + "print(f\"Container created with {container.num_rows()} rows.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Apache Arrow\n", + "\n", + "You can extract the underlying data as a `pyarrow.RecordBatch`. This is the most \"raw\" representation of the data and is useful if you are working with other tools in the Arrow ecosystem." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Type: \n", + "Number of columns: 6\n" + ] + } + ], + "source": [ + "arrow_batch = container.to_arrow()\n", + "print(f\"Type: {type(arrow_batch)}\")\n", + "print(f\"Number of columns: {arrow_batch.num_columns}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Polars\n", + "\n", + "Polars is a blazingly fast DataFrame library also built on Apache Arrow. Converting to a Polars DataFrame is the recommended way to perform complex data manipulations in Python." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Type: \n", + "Polars DataFrame:\n", + "shape: (3, 6)\n", + "┌──────────┬────────────┬─────────────────────────┬────────┬────────┬─────────────────────────┐\n", + "│ frame_id ┆ period ┆ timestamp ┆ ball/x ┆ ball/y ┆ teams/home/players/p1/x │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ u64 ┆ str ┆ datetime[μs] ┆ f32 ┆ f32 ┆ f32 │\n", + "╞══════════╪════════════╪═════════════════════════╪════════╪════════╪═════════════════════════╡\n", + "│ 1000 ┆ first_half ┆ 2023-05-27 13:30:00 ┆ 50.0 ┆ 12.0 ┆ 23.5 │\n", + "│ 1001 ┆ first_half ┆ 2023-05-27 13:30:00.040 ┆ 51.0 ┆ 13.0 ┆ 23.6 │\n", + "│ 1002 ┆ first_half ┆ 2023-05-27 13:30:00.080 ┆ 52.0 ┆ 14.0 ┆ 23.700001 │\n", + "└──────────┴────────────┴─────────────────────────┴────────┴────────┴─────────────────────────┘\n" + ] + } + ], + "source": [ + "df_polars = container.to_polars()\n", + "print(f\"Type: {type(df_polars)}\")\n", + "print(\"Polars DataFrame:\")\n", + "print(df_polars.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pandas\n", + "\n", + "For traditional data science workflows, you can convert the container to a Pandas DataFrame. Note that while this is highly convenient, Pandas does not use Arrow memory internally, so a copy might be performed depending on the data types and your Pandas version." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Type: \n", + "Pandas DataFrame:\n", + " frame_id period timestamp ball/x ball/y \\\n", + "0 1000 first_half 2023-05-27 13:30:00.000 50.0 12.0 \n", + "1 1001 first_half 2023-05-27 13:30:00.040 51.0 13.0 \n", + "2 1002 first_half 2023-05-27 13:30:00.080 52.0 14.0 \n", + "\n", + " teams/home/players/p1/x \n", + "0 23.500000 \n", + "1 23.600000 \n", + "2 23.700001 \n" + ] + } + ], + "source": [ + "df_pandas = container.to_pandas()\n", + "print(f\"Type: {type(df_pandas)}\")\n", + "print(\"Pandas DataFrame:\")\n", + "print(df_pandas.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parquet\n", + "\n", + "Parquet is a high-performance storage format designed for columnar data. TDC allows you to save your container directly to Parquet, keeping track of the coordinate system and orientation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved to ./data/match_data_sample.parquet\n", + "Reloaded container has 3 rows.\n", + "Reloaded container uses \n" + ] + } + ], + "source": [ + "parquet_file = \"./data/match_data_sample.parquet\"\n", + "container.to_parquet(parquet_file)\n", + "print(f\"Saved to {parquet_file}\")\n", + "\n", + "# You can also load it back directly into a container\n", + "reloaded = tdc.TrackingDataContainer.from_parquet(parquet_file)\n", + "print(f\"Reloaded container has {reloaded.num_rows()} rows.\")\n", + "print(f\"Reloaded container uses {reloaded.coordinate_system}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "/Users/probberechts/Developer/tracking_data_container", + "language": "python", + "name": "tracking_data_container" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/04-udfs.ipynb b/examples/04-udfs.ipynb new file mode 100644 index 0000000..9de72b3 --- /dev/null +++ b/examples/04-udfs.ipynb @@ -0,0 +1,315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Computed Metrics and Performance\n", + "\n", + "Adding computed metrics, such as ball speed or player velocity, is a core part of tracking data analysis. The TDC supports multiple ways to efficiently add data, which enables interoperability between packages. This notebook compares different execution strategies for adding columns and benchmarks their performance." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 145967 frames.\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "import polars as pl\n", + "import tracking_data_container as tdc\n", + "\n", + "# Load the sample data\n", + "POS_FILE = \"./data/DFL_04_03_positions_raw_observed_DFL-COM-000001_DFL-MAT-J03WMX.xml\"\n", + "META_FILE = \"./data/DFL_02_01_matchinformation_DFL-COM-000001_DFL-MAT-J03WMX.xml\"\n", + "\n", + "container = tdc.load_sportec(POS_FILE, META_FILE)\n", + "print(f\"Loaded {container.num_rows()} frames.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Execution Strategies\n", + "\n", + "TDC supports multiple ways to add computed columns, balancing ease-of-use with raw performance. We will add a velocity column for every player using four different methods:\n", + "\n", + "1. **TDC Expression Engine**: Uses the internal DataFusion engine. Flexible and supports lazy evaluation.\n", + "2. **Vectorized Python Functions**: Recommended for custom Python logic. Uses zero-copy Arrow exchange to perform calculations in a single batched pass.\n", + "3. **Optimized & 3rd-Party Rust Kernels**: High-performance Rust functions that operate directly on Arrow arrays via the Arrow C Data Interface.\n", + "4. **Native Polars Batch**: Converting the entire container to a Polars DataFrame for processing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. TDC Expression Engine\n", + "\n", + "The expression engine allows building logical plans that can be optimized and executed in Rust. While powerful, it currently has some overhead for eager per-player additions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expression Engine time: 0.5881s\n" + ] + } + ], + "source": [ + "start = time.perf_counter()\n", + "expr_fn = lambda x, y: (tdc.col(x).diff().pow(2.0) + tdc.col(y).diff().pow(2.0)).sqrt()\n", + "container_expr = container.add_column_per_player(\"vel\", expr_fn, on=[\"x\", \"y\"])\n", + "print(f\"Expression Engine time: {time.perf_counter() - start:.4f}s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Vectorized Python Functions\n", + "\n", + "By passing a callable that operates on Arrow arrays (which can be converted to Polars or NumPy), we leverage TDC's zero-copy integration. This is often the fastest way to implement custom Python-based metrics because it avoids the quadratic cost of iterative updates." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Polars Vectorized time: 0.3581s\n" + ] + } + ], + "source": [ + "import polars as pl\n", + "\n", + "def compute_speed_polars(x, y):\n", + " # Inputs are pyarrow.Array objects\n", + " x, y = pl.Series(x), pl.Series(y)\n", + " return (x.diff().fill_null(0) ** 2 + y.diff().fill_null(0) ** 2).sqrt()\n", + "\n", + "start = time.perf_counter()\n", + "container_polars_vec = container.add_column_per_player(\"vel\", compute_speed_polars, on=[\"x\", \"y\"])\n", + "print(f\"Polars Vectorized time: {time.perf_counter() - start:.4f}s\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Numpy Vectorized time: 0.0208s\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "def compute_speed_numpy(x, y):\n", + " x = x.to_numpy(zero_copy_only=False)\n", + " y = y.to_numpy(zero_copy_only=False)\n", + " return np.hypot(np.diff(x, prepend=x[0]), np.diff(y, prepend=y[0]))\n", + "\n", + "start = time.perf_counter()\n", + "container_numpy_vec = container.add_column_per_player(\"vel\", compute_speed_numpy, on=[\"x\", \"y\"])\n", + "print(f\"Numpy Vectorized time: {time.perf_counter() - start:.4f}s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Optimized & 3rd-Party Rust Kernels (Arrow-Native)\n", + "\n", + "TDC supports the **Arrow C Data Interface** (via `__arrow_c_array__`), allowing you to use hand-optimized Rust kernels from any 3rd-party Rust library. These kernels operate directly on Arrow capsules, avoiding Python overhead.\n", + "\n", + "#### Python Usage\n", + "As an example, a few common metrics like `speed` are implemented as Rust kernels in the `tdc.ops` module. You can pass them directly to `add_column` or `add_column_per_player`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimized Rust Kernel time: 0.0463s\n" + ] + } + ], + "source": [ + "start = time.perf_counter()\n", + "container_rust = container.add_column_per_player(\"vel\", tdc.ops.compute_speed, on=[\"x\", \"y\"])\n", + "print(f\"Optimized Rust Kernel time: {time.perf_counter() - start:.4f}s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Implementing Your Own Kernel\n", + "\n", + "If you have a 3rd-party Rust library `my_rust_lib` that exposes a function taking and returning Arrow capsules, you can use it directly in TDC. A typical implementation using `pyo3` and `arrow-rs` would look like this:\n", + "\n", + "```rust\n", + "#[pyfunction]\n", + "fn compute_metric(py: Python<'_>, array: Bound<'_, PyAny>) -> PyResult {\n", + " // 1. Import from capsule (Zero-copy)\n", + " let (array_cap, schema_cap) = array.call_method0(\"__arrow_c_array__\")?\n", + " .extract::<(Bound<'_, PyCapsule>, Bound<'_, PyCapsule>)>()?;\n", + " let input_arr = pyo3_arrow::PyArray::from_arrow_pycapsule(&array_cap, &schema_cap)?;\n", + " let array = input_arr.into_inner().0;\n", + " \n", + " // 2. Vectorized calculation in Rust using arrow-rs\n", + " let result = /* ... logic ... */;\n", + " \n", + " // 3. Export back as capsule (Zero-copy)\n", + " Ok(pyo3_arrow::PyArray::new(result, Arc::new(field)).into_py(py))\n", + "}\n", + "```\n", + "\n", + "You can then use it in Python just like the built-in kernels:\n", + "\n", + "```python\n", + "import my_rust_lib\n", + "container.add_column(\"custom_metric\", my_rust_lib.compute_metric, on=[\"x\"])\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Native Polars Batch\n", + "\n", + "You can also convert the entire container to Polars, perform all transformations, and re-import it. The re-import is zero-copy using Arrow capsules." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Native Polars Batch time: 0.9378s\n" + ] + } + ], + "source": [ + "start = time.perf_counter()\n", + "df = container.to_polars()\n", + "exprs = []\n", + "for player in container.schema().players():\n", + " x = tdc.DataField(entity=player, attribute=\"x\").name()\n", + " y = tdc.DataField(entity=player, attribute=\"y\").name()\n", + " out = tdc.DataField(entity=player, attribute=\"vel\").name()\n", + " exprs.append(\n", + " (pl.col(x).diff().fill_null(0) ** 2 + pl.col(y).diff().fill_null(0) ** 2).sqrt().alias(out)\n", + " )\n", + "\n", + "df = df.with_columns(exprs)\n", + "container_polars_native = tdc.TrackingDataContainer(df)\n", + "print(f\"Native Polars Batch time: {time.perf_counter() - start:.4f}s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Verification\n", + "\n", + "We verify that all execution strategies produce consistent results for a sample player." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max difference Expr vs Polars: 0.0\n", + "Max difference Expr vs Numpy: 5.551115123125783e-17\n", + "Max difference Expr vs Rust: 0.0\n", + "\n", + "Verification Successful: All methods produced identical results.\n" + ] + } + ], + "source": [ + "p1 = container.schema().players()[0]\n", + "vel_col = tdc.DataField(entity=p1, attribute=\"vel\").name()\n", + "\n", + "v_expr = container_expr.to_polars()[vel_col].fill_null(0)\n", + "v_polars = container_polars_vec.to_polars()[vel_col].fill_null(0)\n", + "v_numpy = container_numpy_vec.to_polars()[vel_col].fill_null(0)\n", + "v_rust = container_rust.to_polars()[vel_col].fill_null(0)\n", + "\n", + "mismatch_polars = (v_expr - v_polars).abs().max()\n", + "mismatch_numpy = (v_expr - v_numpy).abs().max()\n", + "mismatch_rust = (v_expr - v_rust).abs().max()\n", + "\n", + "print(f\"Max difference Expr vs Polars: {mismatch_polars}\")\n", + "print(f\"Max difference Expr vs Numpy: {mismatch_numpy}\")\n", + "print(f\"Max difference Expr vs Rust: {mismatch_rust}\")\n", + "\n", + "if mismatch_polars < 1e-6 and mismatch_rust < 1e-6:\n", + " print(\"\\nVerification Successful: All methods produced identical results.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "/Users/probberechts/Developer/tracking_data_container", + "language": "python", + "name": "tracking_data_container" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/05-lazy.ipynb b/examples/05-lazy.ipynb new file mode 100644 index 0000000..a489553 --- /dev/null +++ b/examples/05-lazy.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lazy Evaluation\n", + "\n", + "Loading massive datasets into memory is often unnecessary if you only need a small subset of the data. TDC supports **lazy evaluation** using **DataFusion**, allowing you to define complex queries that are optimized and executed only when needed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might wonder: *why does TDC implement its own lazy execution instead of relying on Polars and DuckDB?* \n", + "\n", + "1. **Metadata-Aware Optimization**: Our lazy engine can optimize selections based on the sports domain (e.g., \"only load coordinates for the active players\").\n", + "2. **Domain-Specific Pushdown**: Beyond simple filters, TDC can push down sports logic (like \"filter frames by ball-in-play status\") into the scanner, which generic engines cannot easily optimize without custom functions.\n", + "3. **Seamless Metadata Propagation**: When you perform a lazy query in TDC, the resulting eager container still has its schema, coordinate systems, and orientation metadata intact. Converting to Polars too early loses this domain context.\n", + "4. **Unified API**: You can use the same selectors (`container.selector.ball()`) and transformations whether you are working with an in-memory dataset or scanning 100GB of Parquet files.\n", + "\n", + "*(that aside, I think this should have low priority)*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is Lazy Evaluation?\n", + "\n", + "In \"eager\" mode, every operation (like loading a file or adding a column) happens immediately. In \"lazy\" mode, these operations are recorded in a **logical plan**. This allows the engine to:\n", + "1. **Push down filters**: Only read the rows from a Parquet file that match your criteria.\n", + "2. **Prune columns**: Only load the columns required for your final result.\n", + "3. **Optimize memory**: Avoid allocating large buffers for intermediate steps." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "from pathlib import Path\n", + "\n", + "import tracking_data_container as tdc\n", + "\n", + "PARQUET_FILE = \"data/match_data.parquet\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scanning Parquet Lazily\n", + "\n", + "The `scan_parquet` method returns a `LazyTrackingDataContainer`. This operation is instantaneous because it only reads the file metadata." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lazy container initialized.\n" + ] + } + ], + "source": [ + "lazy_container = tdc.scan_parquet(PARQUET_FILE)\n", + "print(\"Lazy container initialized.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining a Query Plan\n", + "\n", + "You can chain filters and selections. Notice that no data processing happens yet. We can use the same powerful TDC selectors here." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query plan defined.\n" + ] + } + ], + "source": [ + "# Define a query: only ball coordinates from the first half\n", + "query = (\n", + " lazy_container\n", + " .filter(tdc.col(\"period\").eq(tdc.lit(\"first_half\")))\n", + " .select([\"frame_id\", \"ball/**\"])\n", + ")\n", + "print(\"Query plan defined.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Executing the Plan\n", + "\n", + "To get the results, call `.collect()`. The internal engine will optimize the plan and return an eager `TrackingDataContainer` containing only the requested data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collected 1500 frames in 0.0050s.\n", + "shape: (5, 6)\n", + "┌──────────┬────────┬────────┬──────────┬────────┬──────────┐\n", + "│ frame_id ┆ ball/z ┆ ball/y ┆ ball/vel ┆ ball/x ┆ ball/acc │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞══════════╪════════╪════════╪══════════╪════════╪══════════╡\n", + "│ 10000 ┆ 0.0 ┆ -0.17 ┆ 1.98 ┆ 0.18 ┆ 0.0 │\n", + "│ 10001 ┆ 0.0 ┆ -0.19 ┆ 2.02 ┆ -0.4 ┆ 0.0 │\n", + "│ 10002 ┆ 0.01 ┆ -0.21 ┆ 2.02 ┆ -0.97 ┆ 0.0 │\n", + "│ 10003 ┆ 0.01 ┆ -0.24 ┆ 2.02 ┆ -1.54 ┆ 0.0 │\n", + "│ 10004 ┆ 0.02 ┆ -0.26 ┆ 2.02 ┆ -2.12 ┆ 0.0 │\n", + "└──────────┴────────┴────────┴──────────┴────────┴──────────┘\n" + ] + } + ], + "source": [ + "start = time.perf_counter()\n", + "result = query.collect()\n", + "end = time.perf_counter()\n", + "\n", + "print(f\"Collected {result.num_rows()} frames in {end - start:.4f}s.\")\n", + "print(result.to_polars().head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "/Users/probberechts/Developer/tracking_data_container", + "language": "python", + "name": "tracking_data_container" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..a1a72dc --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,120 @@ +[build-system] +requires = ["maturin>=1.10,<2.0"] +build-backend = "maturin" + +[project] +name = "tracking_data_container" +description = "A shared, standardized, metadata-aware container for sports tracking data." +readme = "README.md" +license = { file = "LICENSE" } +requires-python = ">=3.9" +classifiers = [ + "Development Status :: 3 - Alpha", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: Implementation :: CPython", + "Programming Language :: Python :: Implementation :: PyPy", + "Programming Language :: Rust", +] +dynamic = ["version"] + +[project.optional-dependencies] +all = [ + "pandas>=1.4.4", + "polars>=0.16.14", + "pyarrow>=8.0.0", +] +pandas = ["pandas>=1.4.4", "pyarrow>=8.0.0"] +polars = ["polars>=0.16.14"] +pyarrow = ["pyarrow>=8.0.0"] + +[dependency-groups] +dev = [ + "maturin>=1.7.0,<2.0", + "pandas>=1.4.4", + "polars>=0.16.14", + "pre-commit>=4.3.0", + "pyarrow>=8.0.0", +] +all = [ + { include-group = "benchmarks" }, + { include-group = "docs" }, + { include-group = "linting" }, + { include-group = "nb" }, + { include-group = "testing" }, +] +benchmarks = [ + "fast-forward-football>=0.1.1; python_version == '3.11'", + "kloppy>=3.15.0", + { include-group = "testing" }, +] +docs = ["pdoc", { include-group = "dev" }] +linting = [ + "mypy>=1.11.2,<2", + "pandas-stubs", + "ruff>=0.12,<0.15", + { include-group = "dev" }, +] +nb = [ + "huggingface-hub>=1.6.0", + "ipykernel", + "matplotlib>=3.5.3", + "mplsoccer>=0.1.1", +] +testing = [ + "pytest>=7.1.3", + "pytest-benchmark>=4.0.0,<6", + { include-group = "dev" }, +] + +[tool.maturin] +features = ["pyo3/extension-module"] +manifest-path = "crates/tdc-python/Cargo.toml" +module-name = "tracking_data_container._tracking_data_container" +python-source = "python" + +[tool.pytest.ini_options] +testpaths = "python/tests" +log_cli = true +log_cli_level = "INFO" + +[tool.ruff] +line-length = 100 +target-version = "py39" + +[tool.ruff.lint] +select = ["E", "F", "I", "Q", "FA102", "UP"] + +[tool.uv] +package = false +required-version = ">=0.8.4" + +[tool.mypy] +python_version = "3.9" +warn_return_any = true +warn_unused_configs = true +disallow_untyped_defs = true +check_untyped_defs = true +exclude = ["python/tests"] + +[[tool.mypy.overrides]] +module = [ + "pyarrow.*", + "mplsoccer.*", + "kloppy.*", + "fastforward.*", + "fast_forward_football.*", + "matplotlib.*", + "scipy.*", + "pytest.*", + "_pytest.*", +] +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = "python.tests.*" +ignore_errors = true diff --git a/python/tests/benchmarks/conftest.py b/python/tests/benchmarks/conftest.py new file mode 100644 index 0000000..9568f40 --- /dev/null +++ b/python/tests/benchmarks/conftest.py @@ -0,0 +1,12 @@ +def pytest_benchmark_generate_json(config, benchmarks, include_stats=None): + """Add peak memory information to the benchmark and print a summary.""" + print("\n\n" + "=" * 25 + " Memory Usage Summary " + "=" * 25) + print(f"{'Benchmark':<60} {'Peak Memory (MB)':>15}") + print("-" * 76) + + for benchmark in benchmarks: + mem = benchmark.extra_info.get("peak_memory_mb", 0) + # Inject into stats metadata + benchmark.stats.metadata["peak_memory_mb"] = mem + print(f"{benchmark.name:<60} {mem:>15.1f}") + print("=" * 72 + "\n") diff --git a/python/tests/benchmarks/test_add_column_benchmark.py b/python/tests/benchmarks/test_add_column_benchmark.py new file mode 100644 index 0000000..fbfca9c --- /dev/null +++ b/python/tests/benchmarks/test_add_column_benchmark.py @@ -0,0 +1,156 @@ +import polars as pl +import pytest +import tracking_data_container as tdc + + +@pytest.fixture +def large_container(): + """ + Creates a large container (100k frames, 22 players) for realistic benchmarking. + """ + num_frames = 100000 + num_players = 22 + data = { + "frame_id": list(range(num_frames)), + "ball/x": [1.0] * num_frames, + "ball/y": [2.0] * num_frames, + } + for p in range(num_players): + data[f"teams/home/players/p{p}/x"] = [float(p)] * num_frames + data[f"teams/home/players/p{p}/y"] = [float(p)] * num_frames + + return tdc.TrackingDataContainer(data) + + +# --- Speed/Velocity Benchmarks --- + + +def test_speed_tdc_expression(benchmark, large_container): + """ + Benchmark: Adding velocity using the internal TDC expression engine (DataFusion). + This performs lazy evaluation and parallelization but has some overhead for small batches. + """ + + def run(): + def expr_fn(x, y): + return (tdc.col(x).diff().pow(2.0) + tdc.col(y).diff().pow(2.0)).sqrt() + + return large_container.add_column_per_player("vel", expr_fn, on=["x", "y"]) + + benchmark(run) + + +def test_speed_polars_vectorized(benchmark, large_container): + """ + Benchmark: Adding velocity using a Python function working on Polars Series. + Uses zero-copy Arrow exchange to pass data to Polars for vectorized calculation. + """ + + def compute_speed(x, y): + x = pl.Series(x) + y = pl.Series(y) + return (x.diff().fill_null(0) ** 2 + y.diff().fill_null(0) ** 2).sqrt() + + def run(): + return large_container.add_column_per_player("vel", compute_speed, on=["x", "y"]) + + benchmark(run) + + +def test_speed_rust_kernel(benchmark, large_container): + """ + Benchmark: Adding velocity using a hand-optimized Rust kernel. + Avoids all Python overhead and uses parallelized vectorized Arrow math. + """ + + def run(): + return large_container.add_column_per_player("vel", tdc.ops.compute_speed, on=["x", "y"]) + + benchmark(run) + + +def test_speed_constant_baseline(benchmark, large_container): + """ + Benchmark: Adding a constant pre-computed list. + Represents the baseline overhead for metadata updates and column additions. + """ + num_rows = large_container.num_rows() + values = [1.0] * num_rows + + def run(): + return large_container.add_column_per_player("constant", values) + + benchmark(run) + + +# --- Distance to Ball Benchmarks --- + + +def test_dist_tdc_expression(benchmark, large_container): + """ + Benchmark: Calculating distance to ball using TDC expressions. + Tests the engine's ability to handle interactions between player and global columns. + """ + + def run(): + def expr_fn(x, y): + return ( + (tdc.col(x) - tdc.col("ball/x")).pow(2.0) + + (tdc.col(y) - tdc.col("ball/y")).pow(2.0) + ).sqrt() + + return large_container.add_column_per_player("dist", expr_fn, on=["x", "y"]) + + benchmark(run) + + +def test_dist_polars_vectorized(benchmark, large_container): + """ + Benchmark: Calculating distance to ball using a vectorized Polars function. + Leverages zero-copy transfer to perform the spatial calculation in a single pass. + """ + + def compute_dist(x, y, bx, by): + x, y, bx, by = pl.Series(x), pl.Series(y), pl.Series(bx), pl.Series(by) + return ((x - bx) ** 2 + (y - by) ** 2).sqrt() + + def run(): + names = [] + values = [] + ons = [] + for p in large_container.schema().players(): + tid, pid = p.team_id, p.player_id + prefix = f"teams/{tid}/players/{pid}/" + names.append(f"{prefix}dist") + values.append(compute_dist) + ons.append([f"{prefix}x", f"{prefix}y", "ball/x", "ball/y"]) + + return large_container.add_columns(names, values, on=ons) + + benchmark(run) + + +def test_dist_polars_copy_loop(benchmark, large_container): + """ + Benchmark: Calculating distance to ball by manually looping and copying Polars DataFrames. + Demonstrates the performance penalty of iterative container construction (O(N^2)). + """ + + def run(): + df = pl.DataFrame(large_container) + for p in large_container.schema().players(): + tid, pid = p.team_id, p.player_id + prefix = f"teams/{tid}/players/{pid}/" + df = df.with_columns( + [ + ( + (pl.col(f"{prefix}x") - pl.col("ball/x")) ** 2 + + (pl.col(f"{prefix}y") - pl.col("ball/y")) ** 2 + ) + .sqrt() + .alias(f"{prefix}dist") + ] + ) + return tdc.TrackingDataContainer(df) + + benchmark(run) diff --git a/python/tests/benchmarks/test_lazy_filtering.py b/python/tests/benchmarks/test_lazy_filtering.py new file mode 100644 index 0000000..d2ae791 --- /dev/null +++ b/python/tests/benchmarks/test_lazy_filtering.py @@ -0,0 +1,85 @@ +import tracemalloc +from pathlib import Path + +import polars as pl +import pytest +import tracking_data_container as tdc + +STATSPERFORM_FILE = Path(__file__).parents[3] / "data" / "statsperform-ma25.txt" + + +@pytest.fixture(scope="session") +def statsperform_parquet(tmp_path_factory): + tmp_dir = tmp_path_factory.mktemp("data") + parquet_path = tmp_dir / "statsperform.parquet" + + # Load and save to parquet + container = tdc.load_statsperform(str(STATSPERFORM_FILE)) + container.to_parquet(parquet_path) + + return parquet_path + + +@pytest.mark.benchmark(group="lazy_filtering") +def test_lazy_filtering_tdc(benchmark, statsperform_parquet): + def run(): + # Lazy scan, filter for first half, and collect + return ( + tdc.scan_parquet(str(statsperform_parquet)) + .filter(tdc.col("period") == tdc.lit("first_half")) + .collect() + ) + + tracemalloc.start() + res = benchmark(run) + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + + peak_mb = peak / 1024 / 1024 + benchmark.extra_info["peak_memory_mb"] = peak_mb + + assert isinstance(res, tdc.TrackingDataContainer) + assert res.num_rows() > 0 + + +@pytest.mark.benchmark(group="lazy_filtering") +def test_lazy_filtering_tdc_selective(benchmark, statsperform_parquet): + def run(): + # Lazy scan, filter, SELECT, and collect + return ( + tdc.scan_parquet(str(statsperform_parquet)) + .filter(tdc.col("period") == tdc.lit("first_half")) + .select(["frame_id", "period", "ball/x", "ball/y"]) + .collect() + ) + + tracemalloc.start() + res = benchmark(run) + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + + peak_mb = peak / 1024 / 1024 + benchmark.extra_info["peak_memory_mb"] = peak_mb + + assert isinstance(res, tdc.TrackingDataContainer) + assert res.num_rows() > 0 + + +@pytest.mark.benchmark(group="lazy_filtering") +def test_lazy_filtering_polars(benchmark, statsperform_parquet): + def run(): + # Polars scan, filter, and collect + return ( + pl.scan_parquet(statsperform_parquet).filter(pl.col("period") == "first_half").collect() + ) + + tracemalloc.start() + res = benchmark(run) + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + + peak_mb = peak / 1024 / 1024 + benchmark.extra_info["peak_memory_mb"] = peak_mb + + assert isinstance(res, pl.DataFrame) + assert len(res) > 0 diff --git a/python/tests/benchmarks/test_sportec.py b/python/tests/benchmarks/test_sportec.py new file mode 100644 index 0000000..8841d50 --- /dev/null +++ b/python/tests/benchmarks/test_sportec.py @@ -0,0 +1,123 @@ +import tracemalloc +from pathlib import Path + +import polars as pl +import pytest +import tracking_data_container as tdc + +try: + from kloppy import sportec + + KLOPPY_AVAILABLE = True +except ImportError: + KLOPPY_AVAILABLE = False + +try: + import fastforward as fff + + # Try to access submodules to verify availability + _ = fff.sportec + FFF_AVAILABLE = True +except (ImportError, AttributeError): + FFF_AVAILABLE = False + +SPORTEC_FILE = Path(__file__).parents[3] / "data" / "sportec-tracking.xml" +SPORTEC_META = Path(__file__).parents[3] / "data" / "sportec-meta.xml" + + +def tdc_parse_sportec(path: Path) -> pl.DataFrame: + import xml.etree.ElementTree as ET + + builder = tdc.TrackingDataBuilder() + tree = ET.parse(path) + root = tree.getroot() + + for frame_set in root.findall(".//FrameSet"): + team_id = frame_set.get("TeamId") + person_id = frame_set.get("PersonId") + + if not team_id or not person_id: + continue + + col_x = f"teams/{team_id}/players/{person_id}/x" + col_y = f"teams/{team_id}/players/{person_id}/y" + + for frame in frame_set.findall("Frame"): + frame_n = int(frame.get("N")) + x = float(frame.get("X")) + y = float(frame.get("Y")) + + builder.insert(frame_n, col_x, x) + builder.insert(frame_n, col_y, y) + + return builder.build().to_polars() + + +@pytest.mark.benchmark(group="sportec") +def test_sportec_tdc_parsers(benchmark): + def run(): + return tdc.load_sportec(str(SPORTEC_FILE)).to_polars() + + tracemalloc.start() + df = benchmark(run) + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + + benchmark.extra_info["peak_memory_mb"] = peak / 1024 / 1024 + + assert isinstance(df, pl.DataFrame) + assert len(df) > 0 + + +@pytest.mark.benchmark(group="sportec") +def test_sportec_tdc_python(benchmark): + tracemalloc.start() + df = benchmark.pedantic( + tdc_parse_sportec, kwargs={"path": SPORTEC_FILE}, rounds=1, iterations=1 + ) + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + + benchmark.extra_info["peak_memory_mb"] = peak / 1024 / 1024 + + assert isinstance(df, pl.DataFrame) + assert len(df) > 0 + + +@pytest.mark.skipif(not KLOPPY_AVAILABLE, reason="kloppy not installed") +@pytest.mark.benchmark(group="sportec") +def test_sportec_kloppy(benchmark): + def run(): + dataset = sportec.load_tracking( + raw_data=str(SPORTEC_FILE), meta_data=str(SPORTEC_META), coordinates="sportec" + ) + return dataset.to_df(engine="polars") + + tracemalloc.start() + # Kloppy is slow, we use pedantic to limit rounds + df = benchmark.pedantic(run, rounds=1, iterations=1) + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + + benchmark.extra_info["peak_memory_mb"] = peak / 1024 / 1024 + + assert isinstance(df, pl.DataFrame) + assert len(df) > 0 + + +@pytest.mark.skipif(not FFF_AVAILABLE, reason="fastforward.sportec not available") +@pytest.mark.benchmark(group="sportec") +def test_sportec_fff(benchmark): + def run(): + dataset = fff.sportec.load_tracking(str(SPORTEC_FILE), str(SPORTEC_META)) + return dataset.tracking + + tracemalloc.start() + df = benchmark(run) + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + + benchmark.extra_info["peak_memory_mb"] = peak / 1024 / 1024 + + assert isinstance(df, pl.DataFrame) + assert len(df) > 0 diff --git a/python/tests/benchmarks/test_statsperform.py b/python/tests/benchmarks/test_statsperform.py new file mode 100644 index 0000000..11f4c3e --- /dev/null +++ b/python/tests/benchmarks/test_statsperform.py @@ -0,0 +1,153 @@ +import tracemalloc +from pathlib import Path + +import polars as pl +import pytest +import tracking_data_container as tdc + +try: + from kloppy import statsperform + + KLOPPY_AVAILABLE = True +except ImportError: + KLOPPY_AVAILABLE = False + +try: + import fastforward as fff + + _ = fff.statsperform + FFF_AVAILABLE = True +except (ImportError, AttributeError): + FFF_AVAILABLE = False + +STATSPERFORM_FILE = Path(__file__).parents[3] / "data" / "statsperform-ma25.txt" +STATSPERFORM_META = Path(__file__).parents[3] / "data" / "statsperform-ma1.json" + + +def tdc_parse_statsperform(path: Path) -> pl.DataFrame: + builder = tdc.TrackingDataBuilder() + + with open(path) as f: + for i, line in enumerate(f): + line = line.strip() + if not line: + continue + if i == 0 and ";" not in line: + continue + + parts = line.split(":") + frame_part = parts[0] + players_part = parts[1] + ball_part = parts[2] if len(parts) > 2 else None + + frame_id_str, info_str = frame_part.split(";") + frame_id = int(frame_id_str) + + if players_part != ";" and players_part: + for p_data in players_part.split(";"): + if not p_data: + continue + p_parts = p_data.split(",") + if not p_parts[0]: + continue + + team_side = int(p_parts[0]) + if team_side == 2: + continue # Referee + + team_id = "home" if team_side in (0, 3) else "away" + player_id = p_parts[1] + x = float(p_parts[3]) + y = float(p_parts[4]) + + builder.insert(frame_id, f"teams/{team_id}/players/{player_id}/x", x) + builder.insert(frame_id, f"teams/{team_id}/players/{player_id}/y", y) + + if ball_part: + b_info = ball_part.split(";")[0] + b_parts = b_info.split(",") + if len(b_parts) >= 2: + builder.insert(frame_id, "ball/x", float(b_parts[0])) + builder.insert(frame_id, "ball/y", float(b_parts[1])) + + return builder.build().to_polars() + + +@pytest.mark.benchmark(group="statsperform") +def test_statsperform_tdc_parsers(benchmark): + def run(): + return tdc.load_statsperform(str(STATSPERFORM_FILE)).to_polars() + + tracemalloc.start() + df = benchmark(run) + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + + peak_mb = peak / 1024 / 1024 + benchmark.extra_info["peak_memory_mb"] = peak_mb + + assert isinstance(df, pl.DataFrame) + assert len(df) > 0 + + +@pytest.mark.benchmark(group="statsperform") +def test_statsperform_tdc_python(benchmark): + tracemalloc.start() + df = benchmark.pedantic( + tdc_parse_statsperform, kwargs={"path": STATSPERFORM_FILE}, rounds=1, iterations=1 + ) + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + + peak_mb = peak / 1024 / 1024 + benchmark.extra_info["peak_memory_mb"] = peak_mb + + assert isinstance(df, pl.DataFrame) + assert len(df) > 0 + + +@pytest.mark.skipif(not KLOPPY_AVAILABLE, reason="kloppy not installed") +@pytest.mark.benchmark(group="statsperform") +def test_statsperform_kloppy(benchmark): + def run(): + dataset = statsperform.load_tracking( + ma1_data=str(STATSPERFORM_META), + ma25_data=str(STATSPERFORM_FILE), + coordinates="sportvu", + ) + return dataset.to_df(engine="polars") + + tracemalloc.start() + # Kloppy is slow, we use pedantic to limit rounds + df = benchmark.pedantic(run, rounds=1, iterations=1) + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + + peak_mb = peak / 1024 / 1024 + benchmark.extra_info["peak_memory_mb"] = peak_mb + + assert isinstance(df, pl.DataFrame) + assert len(df) > 0 + + +@pytest.mark.skipif(not FFF_AVAILABLE, reason="fastforward.statsperform not available") +@pytest.mark.benchmark(group="statsperform") +def test_statsperform_fff(benchmark): + def run(): + dataset = fff.statsperform.load_tracking( + ma1_data=str(STATSPERFORM_META), + ma25_data=str(STATSPERFORM_FILE), + ) + # Use .tracking which is the Polars DataFrame + return dataset.tracking + + tracemalloc.start() + df = benchmark(run) + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + + peak_mb = peak / 1024 / 1024 + benchmark.extra_info["peak_memory_mb"] = peak_mb + + assert isinstance(df, pl.DataFrame) + assert len(df) > 0 diff --git a/python/tests/conftest.py b/python/tests/conftest.py new file mode 100644 index 0000000..f991be9 --- /dev/null +++ b/python/tests/conftest.py @@ -0,0 +1,38 @@ +from datetime import datetime, timedelta, timezone + +import pytest +import tracking_data_container as tdc + + +@pytest.fixture +def sample_data(): + base_time = datetime(2023, 10, 1, 12, 0, 0, tzinfo=timezone.utc) + return { + "frame_id": [123456, 123457, 123458], + "timestamp": [ + base_time, + base_time + timedelta(milliseconds=40), + base_time + timedelta(milliseconds=80), + ], + "period": ["first_half", "first_half", "first_half"], + "match/id": ["match_12345", "match_12345", "match_12345"], + "ball_status": [True, True, True], + "teams/home/id": ["team_1", "team_1", "team_1"], + "teams/home/players/player_1/x": [23.5, 23.6, 23.7], + "teams/home/players/player_1/y": [24.2, 24.3, 24.4], + "teams/home/players/player_2/x": [56.3, 56.4, 56.5], + "teams/home/players/player_2/y": [12.8, 12.9, 13.0], + "teams/away/id": ["team_2", "team_2", "team_2"], + "teams/away/players/player_3/x": [33.1, 33.2, 33.3], + "teams/away/players/player_3/y": [34.9, 35.0, 35.1], + "teams/away/players/player_4/x": [45.6, 45.7, 45.8], + "teams/away/players/player_4/y": [24.0, 24.1, 24.2], + "ball/x": [50.0, 51.0, 52.0], + "ball/y": [12.0, 13.0, 14.0], + "ball/z": [0.5, 0.6, 0.7], + } + + +@pytest.fixture +def container(sample_data): + return tdc.TrackingDataContainer(sample_data) diff --git a/python/tests/test_add_column.py b/python/tests/test_add_column.py new file mode 100644 index 0000000..3dbc4a0 --- /dev/null +++ b/python/tests/test_add_column.py @@ -0,0 +1,69 @@ +import numpy as np +import tracking_data_container as tdc + + +class TestAddColumn: + def test_add_column_array(self, container): + data = [1.0, 2.0, 3.0] + container = container.add_column("ball/new", data) + assert "ball/new" in container.schema().names() + assert container.to_arrow().column("ball/new").to_pylist() == data + + def test_add_column_expr(self, container): + expr = (tdc.col("ball/x") + tdc.col("ball/y")).sqrt() + container = container.add_column("ball/dist", expr) + assert "ball/dist" in container.schema().names() + + # ball/x: [50.0, 51.0, 52.0], ball/y: [12.0, 13.0, 14.0] + expected = np.sqrt(np.array([50.0, 51.0, 52.0]) + np.array([12.0, 13.0, 14.0])) + actual = container.to_arrow().column("ball/dist").to_numpy() + np.testing.assert_allclose(actual, expected) + + def test_add_column_udf(self, container): + def my_udf(x): + return x.to_numpy() + 10.0 + + container = container.add_column("ball/x_plus_10", my_udf, on=["ball/x"]) + assert "ball/x_plus_10" in container.schema().names() + + # ball/x: [50.0, 51.0, 52.0] + expected = np.array([60.0, 61.0, 62.0]) + actual = container.to_arrow().column("ball/x_plus_10").to_numpy() + np.testing.assert_allclose(actual, expected) + + def test_add_column_ops(self, container): + # Test the tdc.ops.compute_speed rust kernel + container = container.add_column( + "ball/speed", tdc.ops.compute_speed, on=["ball/x", "ball/y"] + ) + assert "ball/speed" in container.schema().names() + + # x: [50.0, 51.0, 52.0], y: [12.0, 13.0, 14.0] + # dx=1, dy=1 -> speed=sqrt(2) approx 1.414 + expected = np.array([0.0, np.sqrt(2), np.sqrt(2)]) + actual = container.to_arrow().column("ball/speed").to_numpy() + np.testing.assert_allclose(actual, expected, atol=1e-6) + + def test_add_column_per_player_udf(self, container): + def compute_pos_sum(x, y): + return x.to_numpy() + y.to_numpy() + + container = container.add_column_per_player("pos_sum", compute_pos_sum, on=["x", "y"]) + assert "teams/home/players/player_1/pos_sum" in container.schema().names() + + # home player_1 x: [23.5, 23.6, 23.7], y: [24.2, 24.3, 24.4] + expected = np.array([23.5 + 24.2, 23.6 + 24.3, 23.7 + 24.4]) + actual = container.to_arrow().column("teams/home/players/player_1/pos_sum").to_numpy() + np.testing.assert_allclose(actual, expected) + + def test_add_column_per_player_ops(self, container): + container = container.add_column_per_player( + "speed_rust", tdc.ops.compute_speed, on=["x", "y"] + ) + assert "teams/home/players/player_1/speed_rust" in container.schema().names() + + # home player_1 x: [23.5, 23.6, 23.7], y: [24.2, 24.3, 24.4] + # dx=0.1, dy=0.1 -> speed=sqrt(0.01 + 0.01) = sqrt(0.02) approx 0.1414 + expected = np.array([0.0, np.sqrt(0.02), np.sqrt(0.02)]) + actual = container.to_arrow().column("teams/home/players/player_1/speed_rust").to_numpy() + np.testing.assert_allclose(actual, expected, atol=1e-6) diff --git a/python/tests/test_build.py b/python/tests/test_build.py new file mode 100644 index 0000000..527dcbb --- /dev/null +++ b/python/tests/test_build.py @@ -0,0 +1,93 @@ +from datetime import datetime, timedelta, timezone + +import tracking_data_container as tdc + + +class TestBuilder: + def test_tracking_data_builder(self) -> None: + builder = tdc.TrackingDataBuilder() + + base_time = datetime(2023, 10, 1, 12, 0, 0, tzinfo=timezone.utc) + ts1 = int(base_time.timestamp() * 1_000_000) + ts2 = int((base_time + timedelta(milliseconds=40)).timestamp() * 1_000_000) + + # Insert data for frame 1 + builder.insert(1, "frame_id", 1) + builder.insert(1, "timestamp", ts1) + builder.insert(1, "ball/x", 10) + builder.insert(1, "ball/y", 20.5) + builder.insert(1, "period", "first_half") + + # Insert data for frame 2 + builder.insert(2, "frame_id", 2) + builder.insert(2, "timestamp", ts2) + builder.insert(2, "ball/x", 15) + builder.insert(2, "ball/y", 25.5) + builder.insert(2, "period", "first_half") + + container = builder.build() + assert container.num_rows() == 2 + + # 1. Materialize by index + frame_0 = container.materialize_frame(0) + assert frame_0[tdc.DataField("ball/x")] == 10 + assert frame_0["ball/x"] == 10 + assert frame_0[tdc.DataField("ball/y")] == 20.5 + assert frame_0["ball/y"] == 20.5 + assert frame_0[tdc.DataField("period")] == "first_half" + assert frame_0["period"] == "first_half" + + # Check to_dict and repr + assert repr(frame_0) == "" + d = frame_0.to_dict() + assert d["frame_id"] == 1 + assert d["ball/x"] == 10 + assert d["ball/y"] == 20.5 + assert d["period"] == "first_half" + + # 2. Materialize by datetime + frame_ts = container.materialize_frame(base_time + timedelta(milliseconds=40)) + assert frame_ts[tdc.DataField("ball/x")] == 15 + + # 3. Materialize by timedelta + frame_td = container.materialize_frame(timedelta(milliseconds=0)) + assert frame_td[tdc.DataField("ball/x")] == 10 + + def test_tracking_data_builder_merge(self) -> None: + base_time = datetime(2023, 10, 1, 12, 0, 0, tzinfo=timezone.utc) + ts1 = int(base_time.timestamp() * 1_000_000) + ts2 = int((base_time + timedelta(milliseconds=40)).timestamp() * 1_000_000) + + # Builder 1 with some data + b1 = tdc.TrackingDataBuilder() + b1.insert(1, "frame_id", 1) + b1.insert(1, "timestamp", ts1) + b1.insert(1, "ball/x", 10) + + # Builder 2 with overlapping and new data + b2 = tdc.TrackingDataBuilder() + b2.insert(1, "frame_id", 1) + b2.insert(1, "timestamp", ts1) + b2.insert(1, "ball/y", 20) # same frame as b1 + b2.insert(2, "frame_id", 2) + b2.insert(2, "timestamp", ts2) + b2.insert(2, "ball/x", 30) # new frame + + # Test DataField as column + field = tdc.DataField("ball/z") + b2.insert(2, field, 40.5) + + b1.merge(b2) + + container = b1.build() + assert container.num_rows() == 2 + + # Frame 1 should have x and y + f1 = container.materialize_frame(timedelta(seconds=0)) + assert f1["ball/x"] == 10 # type: ignore + assert f1["ball/y"] == 20 # type: ignore + + # Frame 2 should have x and z + f2 = container.materialize_frame(1) + assert f2["ball/x"] == 30 # type: ignore + assert f2["ball/z"] == 40.5 # type: ignore diff --git a/python/tests/test_container.py b/python/tests/test_container.py new file mode 100644 index 0000000..0615ab0 --- /dev/null +++ b/python/tests/test_container.py @@ -0,0 +1,159 @@ +import pytest +import tracking_data_container as tdc + + +class TestBasics: + def test_container_init(self, container) -> None: + assert isinstance(container, tdc.TrackingDataContainer) + assert container.num_rows() == 3 + # In conftest.py: 5 ball cols, 8 player cols, frame_id, timestamp, period, + # match/id, ball_status, teams/home/id, teams/away/id + # Total = 5+8+7 = 20. But let's just check it's >= 18 + assert len(container.schema()) >= 18 + + def test_data_field_init(self) -> None: + field = tdc.DataField("ball/x") + assert field.entity.is_ball + assert field.attribute == "x" + assert field.name() == "ball/x" + + def test_orientation_mode(self, container) -> None: + # Default container has no orientation set + assert container.orientation_mode is None + + # Set orientation + container = container.with_orientation(tdc.OrientationMode.HomeAway) + assert container.orientation_mode == tdc.OrientationMode.HomeAway + + +class TestColumnAddition: + def test_container_add_column_direct(self, container) -> None: + new_values = [1.0, 2.0, 3.0] + container = container.add_column("test/new_col", new_values) + assert "test/new_col" in container.to_polars().columns + assert container.to_polars()["test/new_col"].to_list() == new_values + + def test_container_add_column_callable(self, container) -> None: + def compute_sum(frame): + return frame["ball/x"].to_numpy() + frame["ball/y"].to_numpy() + + container = container.add_column("ball/sum", compute_sum) + assert "ball/sum" in container.to_polars().columns + # Samples: [50.0, 51.0, 52.0] + [12.0, 13.0, 14.0] = [62.0, 64.0, 66.0] + res = container.to_polars()["ball/sum"].to_list() + assert res[0] == pytest.approx(62.0) + assert res[1] == pytest.approx(64.0) + assert res[2] == pytest.approx(66.0) + + def test_container_add_column_callable_on(self, container) -> None: + def compute_sum(x, y): + return x.to_numpy() + y.to_numpy() + + container = container.add_column("ball/sum_on", compute_sum, on=["ball/x", "ball/y"]) + assert "ball/sum_on" in container.to_polars().columns + res = container.to_polars()["ball/sum_on"].to_list() + assert res[0] == pytest.approx(62.0) + assert res[1] == pytest.approx(64.0) + assert res[2] == pytest.approx(66.0) + + def test_container_add_column_replace(self, container) -> None: + # Replacing an existing column + new_x = [100.0, 200.0, 300.0] + container = container.add_column("ball/x", new_x) + assert container.to_polars()["ball/x"].to_list() == pytest.approx(new_x) + + +class TestLazy: + def test_container_lazy_collect(self, container) -> None: + lazy_c = container.lazy() + assert isinstance(lazy_c, tdc.LazyTrackingDataContainer) + + # Collect back to eager + eager_c = lazy_c.collect() + assert isinstance(eager_c, tdc.TrackingDataContainer) + assert eager_c.num_rows() == container.num_rows() + + def test_container_lazy_ops(self, container) -> None: + lazy_c = container.lazy() + + # 1. Lazy select + subset_lazy = lazy_c.select("ball/x") + assert isinstance(subset_lazy, tdc.LazyTrackingDataContainer) + assert len(subset_lazy.schema()) == 1 + + # 2. Lazy add column (raw arrays) + new_values = [1.0, 2.0, 3.0] + with pytest.raises(tdc.exceptions.TrackingDataContainerError, match="not yet supported"): + lazy_c.add_column("test/lazy", new_values) + + def test_container_lazy_add_column_callable(self, container) -> None: + lazy_c = container.lazy() + + def compute_sum(x, y): + import polars as pl + + return pl.Series(x.to_numpy() + y.to_numpy()) + + lazy_c = lazy_c.add_column("ball/sum", compute_sum, on=["ball/x", "ball/y"]) + res = lazy_c.collect() + + assert "ball/sum" in res.to_polars().columns + res_list = res.to_polars()["ball/sum"].to_list() + assert res_list[0] == pytest.approx(62.0) + assert res_list[1] == pytest.approx(64.0) + assert res_list[2] == pytest.approx(66.0) + + def test_container_lazy_true_lazy_ops(self, container) -> None: + # Test things that ARE supported lazily (expressions) + lazy_c = container.lazy() + + # Expression-based addition + expr = tdc.col("ball/x").mul(2) + lazy_c = lazy_c.add_column("ball/x2", expr) + + # Filter (use threshold that definitely excludes some rows) + # Sample ball/x are [50.0, 51.0, 52.0] + lazy_c = lazy_c.filter(tdc.col("ball/x").gt(51.5)) + + # Collect + res = lazy_c.collect() + assert "ball/x2" in res.to_polars().columns + assert res.num_rows() == 1 + + def test_container_lazy_transformations(self, container) -> None: + # 1. Lazy transform coordinates + sys = tdc.CoordinateSystem( + x_range=(0, 100), y_range=(0, 50), length_unit="meters", origin="top_left" + ) + container = container.with_coordinate_system(sys) + + target_sys = tdc.CoordinateSystem( + x_range=(-50, 50), y_range=(-25, 25), length_unit="meters", origin="center" + ) + + lazy_c = container.lazy().transform_coordinates(target_sys) + res = lazy_c.collect() + + # Input ball/x was [50.0, 51.0, 52.0] in (0, 100) range + # Output should be in (-50, 50) range -> [0.0, 1.0, 2.0] + assert res.to_polars()["ball/x"].to_list() == pytest.approx([0.0, 1.0, 2.0]) + + # 2. Lazy transform orientation + # Setup container with known state + container = container.with_orientation(tdc.OrientationMode.HomeAway) + # Period values in sample data: ['first_half', 'second_half', 'first_half'] + # HomeAway means flip in second half. + # Flip across center of (0, 100) range: x -> 100 - x + lazy_c = container.lazy().transform_orientation(tdc.OrientationMode.AwayHome) + res = lazy_c.collect() + + # AwayHome means flip in first half. + # We changed from HomeAway (flip 2nd) to AwayHome (flip 1st). + # So frames 0, 1, 2: + # Frame 0: first_half. HomeAway (no flip) -> AwayHome (flip). CHANGE. + # Frame 1: second_half. HomeAway (flip) -> AwayHome (no flip). CHANGE. + # Frame 2: first_half. HomeAway (no flip) -> AwayHome (flip). CHANGE. + # All frames should be flipped. + assert res.to_polars()["ball/x"][0] == pytest.approx(100.0 - 50.0) + assert res.to_polars()["ball/x"][1] == pytest.approx(100.0 - 51.0) + assert res.to_polars()["ball/x"][2] == pytest.approx(100.0 - 52.0) diff --git a/python/tests/test_errors.py b/python/tests/test_errors.py new file mode 100644 index 0000000..71ba5ce --- /dev/null +++ b/python/tests/test_errors.py @@ -0,0 +1,15 @@ +import pytest +import tracking_data_container as tdc + + +@pytest.mark.parametrize( + "exc_class, expected_docstring", + [ + (tdc.exceptions.TrackingDataContainerError, "The base class for all errors"), + (tdc.exceptions.InvalidParametersError, "Provided parameters are invalid"), + (tdc.exceptions.SchemaError, "Schema validation failed"), + (tdc.exceptions.SchemaWarning, "Schema validation warning"), + ], +) +def test_docstrings(exc_class: type[Exception], expected_docstring: str) -> None: + assert exc_class.__doc__ == expected_docstring diff --git a/python/tests/test_interop.py b/python/tests/test_interop.py new file mode 100644 index 0000000..442b284 --- /dev/null +++ b/python/tests/test_interop.py @@ -0,0 +1,61 @@ +import pandas as pd +import polars as pl +import pyarrow as pa +import tracking_data_container as tdc + + +def test_to_arrow(container): + batch = container.to_arrow() + assert isinstance(batch, pa.RecordBatch) + assert batch.num_rows == 3 + assert batch.num_columns == 18 + assert batch.column(batch.schema.get_field_index("ball/x")).to_pylist() == [50.0, 51.0, 52.0] + assert batch.column(batch.schema.get_field_index("ball/y")).to_pylist() == [12.0, 13.0, 14.0] + + +def test_to_pandas(container): + df = container.to_pandas() + assert isinstance(df, pd.DataFrame) + assert len(df) == 3 + assert "ball/x" in df.columns + assert "ball/y" in df.columns + assert df["ball/x"].tolist() == [50.0, 51.0, 52.0] + + +def test_to_polars(container): + df = container.to_polars() + assert isinstance(df, pl.DataFrame) + assert df.height == 3 + assert "ball/x" in df.columns + assert "ball/y" in df.columns + assert df["ball/x"].to_list() == [50.0, 51.0, 52.0] + + +def test_zero_copy_polars(container): + # Polars can consume the container directly via the PyCapsule interface + df = pl.DataFrame(container) + assert df.height == 3 + assert df["ball/x"].to_list() == [50.0, 51.0, 52.0] + + +def test_parquet_roundtrip(container, tmp_path): + path = tmp_path / "test.parquet" + + # 1. Write to parquet + container.to_parquet(path) + assert path.exists() + + # 2. Read back eager + c2 = tdc.TrackingDataContainer.from_parquet(path) + assert c2.schema() == container.schema() + assert len(c2.to_arrow()) == 3 + + # 3. Scan lazy + lazy_c = tdc.scan_parquet(str(path)) + assert isinstance(lazy_c, tdc.LazyTrackingDataContainer) + assert lazy_c.schema() == container.schema() + + # 4. Collect and verify + c3 = lazy_c.collect() + assert c3.schema() == container.schema() + assert c3.to_polars().equals(container.to_polars()) diff --git a/python/tests/test_ops.py b/python/tests/test_ops.py new file mode 100644 index 0000000..48bd51a --- /dev/null +++ b/python/tests/test_ops.py @@ -0,0 +1,73 @@ +import numpy as np +import pyarrow as pa +import pytest +import tracking_data_container as tdc + + +def test_compute_derivatives(): + data = { + "ball/x": [0.0, 1.0, 3.0, 6.0], + "ball/y": [0.0, 0.0, 0.0, 0.0], + } + container = tdc.TrackingDataContainer(data) + df = container.to_polars() + + # 1. Speed (dt=1.0) + # displacements: 1.0, 2.0, 3.0 + # speed: 0.0, 1.0, 2.0, 3.0 + x_arr = pa.array(df["ball/x"]) + y_arr = pa.array(df["ball/y"]) + speed = tdc.ops.compute_speed(x_arr, y_arr, dt=1.0) + speed_vals = pa.array(speed).to_pylist() + assert speed_vals == [0.0, 1.0, 2.0, 3.0] + + # 2. Acceleration (dt=1.0) + # speed diffs: 1.0, 1.0, 1.0 + # acc: 0.0, 1.0, 1.0, 1.0 + acc = tdc.ops.compute_acceleration(speed, dt=1.0) + acc_vals = pa.array(acc).to_pylist() + assert acc_vals == [0.0, 1.0, 1.0, 1.0] + + # 3. Jerk (dt=1.0) + # acc diffs: 1.0, 0.0, 0.0 + # jerk: 0.0, 1.0, 0.0, 0.0 + jerk = tdc.ops.compute_jerk(acc, dt=1.0) + jerk_vals = pa.array(jerk).to_pylist() + assert jerk_vals == [0.0, 1.0, 0.0, 0.0] + + +def test_compute_angular_velocity(): + # Velocity vector rotating 90 degrees (PI/2) + vx = pa.array([1.0, 0.0]) + vy = pa.array([0.0, 1.0]) + + # dt = 1.0 + # theta: 0, PI/2 + # d_theta: PI/2 + # omega: PI/2 + omega = tdc.ops.compute_angular_velocity(vx, vy, dt=1.0) + omega_vals = pa.array(omega).to_pylist() + assert omega_vals[1] == pytest.approx(np.pi / 2) + + +def test_butterworth_filter(): + import pyarrow as pa + + # Constant signal with noise + data = [10.0] * 50 + # Add some noise + for i in range(len(data)): + if i % 2 == 0: + data[i] += 0.5 + else: + data[i] -= 0.5 + + arr = pa.array(data) + + # Apply filter (low cutoff) + filtered = tdc.ops.butterworth_filter(arr, cutoff=1.0, fs=25.0) + filtered_vals = pa.array(filtered).to_pylist() + + assert len(filtered_vals) == len(data) + # The filter takes some time to settle, check at the end + assert abs(filtered_vals[-1] - 10.0) < 0.1 diff --git a/python/tests/test_schema.py b/python/tests/test_schema.py new file mode 100644 index 0000000..fdd5491 --- /dev/null +++ b/python/tests/test_schema.py @@ -0,0 +1,303 @@ +import pytest +import tracking_data_container as tdc + + +class TestDataField: + def test_init_path(self) -> None: + field = tdc.DataField("teams/home/players/1/x") + assert field.name() == "teams/home/players/1/x" + + def test_init_components_player(self) -> None: + field = tdc.DataField(entity=tdc.Player("home", "10"), attribute="x") + assert field.name() == "teams/home/players/10/x" + + def test_init_components_ball(self) -> None: + field = tdc.DataField(entity=tdc.Ball(), attribute="z") + assert field.name() == "ball/z" + + def test_init_components_team(self) -> None: + field = tdc.DataField(entity=tdc.Team("away"), attribute="formation") + assert field.name() == "teams/away/formation" + + def test_init_components_frame(self) -> None: + field = tdc.DataField(attribute="timestamp") + assert field.name() == "timestamp" + + def test_init_vendor(self) -> None: + field = tdc.DataField("vendor/event/name") + assert field.name() == "vendor/event/name" + + field = tdc.DataField(attribute="vendor_event_name") + assert field.name() == "vendor/event/name" + + def test_equality(self) -> None: + f1 = tdc.DataField("ball/x") + f2 = tdc.DataField(entity=tdc.Ball(), attribute="x") + f3 = tdc.DataField("ball/y") + + assert f1 == f2 + assert f1 != f3 + + def test_datafield_subclasses(self) -> None: + frame_field = tdc.DataField(attribute="timestamp") + assert isinstance(frame_field, tdc.FrameDataField) + assert isinstance(frame_field, tdc.DataField) + assert frame_field.attribute == "timestamp" + + entity_field = tdc.DataField(entity=tdc.Player("home", "10"), attribute="x") + assert isinstance(entity_field, tdc.EntityDataField) + assert isinstance(entity_field, tdc.DataField) + assert entity_field.attribute == "x" + assert entity_field.team_id == "home" + assert entity_field.player_id == "10" + + ball_field = tdc.DataField(entity=tdc.Ball(), attribute="z") + assert isinstance(ball_field, tdc.EntityDataField) + assert ball_field.entity.is_ball is True + assert ball_field.attribute == "z" + + def test_tracked_entity_object(self) -> None: + field = tdc.DataField(entity=tdc.Player("away", "7"), attribute="y") + entity = field.entity + + assert isinstance(entity, tdc.Player) + assert isinstance(entity, tdc.TrackedEntity) + assert entity.team_id == "away" + assert entity.player_id == "7" + assert "Player(team=away, id=7)" in repr(entity) + + def test_ball_entity(self) -> None: + field = tdc.DataField(entity=tdc.Ball(), attribute="x") + entity = field.entity + + assert isinstance(entity, tdc.Ball) + assert isinstance(entity, tdc.TrackedEntity) + assert repr(entity) == "Ball" + + def test_team_entity(self) -> None: + field = tdc.DataField(entity=tdc.Team("home"), attribute="formation") + entity = field.entity + + assert isinstance(entity, tdc.Team) + assert isinstance(entity, tdc.TrackedEntity) + assert entity.team_id == "home" + assert "Team(id=home)" in repr(entity) + + def test_referee_entity(self) -> None: + field = tdc.DataField(entity=tdc.Referee("ref_1"), attribute="x") + entity = field.entity + + assert isinstance(entity, tdc.Referee) + assert isinstance(entity, tdc.TrackedEntity) + assert entity.referee_id == "ref_1" + assert "Referee(id=ref_1)" in repr(entity) + + def test_new_constructor(self) -> None: + player = tdc.Player(team_id="home", player_id="10") + field = tdc.DataField(entity=player, attribute="x") + assert field.name() == "teams/home/players/10/x" + assert field.entity == player + + ball = tdc.Ball() + field = tdc.DataField(entity=ball, attribute="z") + assert field.name() == "ball/z" + assert field.entity == ball + + # Test frame attribute with 'attribute' name + field = tdc.DataField(attribute="timestamp") + assert field.name() == "timestamp" + assert isinstance(field, tdc.FrameDataField) + assert field.entity is None + + def test_frame_field_entity_none(self) -> None: + field = tdc.DataField("frame_id") + assert field.entity is None + assert field.team_id is None + assert field.player_id is None + + +class TestFields: + def test_init(self) -> None: + fields = tdc.Fields(["frame_id", "ball/x", "teams/home/players/1/x"]) + assert len(fields) == 3 + assert fields[0].name() == "frame_id" + assert fields[2].name() == "teams/home/players/1/x" + + def test_iteration(self) -> None: + fields = tdc.Fields(["ball/x", "ball/y"]) + names = [f.name() for f in fields] + assert names == ["ball/x", "ball/y"] + + def test_indexing(self) -> None: + fields = tdc.Fields(["ball/x", "ball/y"]) + assert fields[0].name() == "ball/x" + assert fields[1].name() == "ball/y" + + with pytest.raises(IndexError): + _ = fields[2] + + def test_players(self) -> None: + cols = [ + "frame_id", + "teams/home/players/1/x", + "teams/home/players/2/x", + "teams/away/players/10/x", + "ball/x", + ] + fields = tdc.Fields(cols) + + players = fields.players() + assert len(players) == 3 + assert all(isinstance(p, tdc.Player) for p in players) + assert sorted([p.player_id for p in players]) == ["1", "10", "2"] + + home_players = fields.players(team="home") + assert len(home_players) == 2 + assert sorted([p.player_id for p in home_players]) == ["1", "2"] + assert all(p.team_id == "home" for p in home_players) + + away_players = fields.players(team="away") + assert len(away_players) == 1 + assert away_players[0].player_id == "10" + assert away_players[0].team_id == "away" + + assert fields.players(team="other") == [] + + def test_teams(self) -> None: + cols = [ + "teams/home/players/1/x", + "teams/away/players/10/x", + "teams/home/id", + "teams/away/formation", + ] + fields = tdc.Fields(cols) + + teams = fields.teams() + assert len(teams) == 2 + assert all(isinstance(t, tdc.Team) for t in teams) + assert sorted([t.team_id for t in teams]) == ["away", "home"] + + def test_referees(self) -> None: + cols = [ + "referees/ref_1/x", + "referees/ref_2/y", + "ball/x", + ] + fields = tdc.Fields(cols) + + referees = fields.referees() + assert len(referees) == 2 + assert all(isinstance(r, tdc.Referee) for r in referees) + assert sorted([r.referee_id for r in referees]) == ["ref_1", "ref_2"] + + def test_validate_success(self) -> None: + cols = [ + "frame_id", + "timestamp", + "period", + "match/id", + "ball/x", + "ball/y", + "ball/z", + "teams/home/id", + "teams/away/id", + "teams/home/players/1/x", + "teams/home/players/1/y", + "teams/away/players/10/x", + "teams/away/players/10/y", + ] + fields = tdc.Fields(cols) + # Should not raise + fields.validate() + assert fields.validate_quiet() is True + + def test_validate_missing_required(self) -> None: + # Missing ball/z and match/id + cols = [ + "frame_id", + "timestamp", + "period", + "ball/x", + "ball/y", + "teams/home/id", + "teams/away/id", + "teams/home/players/1/x", + "teams/home/players/1/y", + "teams/away/players/10/x", + "teams/away/players/10/y", + ] + fields = tdc.Fields(cols) + + with pytest.raises(tdc.exceptions.SchemaError) as excinfo: + fields.validate() + + assert "Missing ball/z" in str(excinfo.value) + assert "Missing match/id" in str(excinfo.value) + assert fields.validate_quiet() is False + + def test_validate_no_players(self) -> None: + # Teams exist but no players + cols = [ + "frame_id", + "timestamp", + "period", + "match/id", + "ball/x", + "ball/y", + "ball/z", + "teams/home/id", + "teams/away/id", + ] + fields = tdc.Fields(cols) + + with pytest.raises(tdc.exceptions.SchemaError, match="teams/home/players"): + fields.validate() + + def test_validate_player_missing_coord(self) -> None: + # Away player has x but no y + cols = [ + "frame_id", + "timestamp", + "period", + "match/id", + "ball/x", + "ball/y", + "ball/z", + "teams/home/id", + "teams/away/id", + "teams/home/players/1/x", + "teams/home/players/1/y", + "teams/away/players/10/x", + ] + fields = tdc.Fields(cols) + + with pytest.raises(tdc.exceptions.SchemaError, match="teams/away/players"): + fields.validate() + + def test_unsupported_field(self) -> None: + cols = [ + "frame_id", + "timestamp", + "period", + "match/id", + "ball/x", + "ball/y", + "ball/z", + "teams/home/id", + "teams/away/id", + "teams/home/players/1/x", + "teams/home/players/1/y", + "teams/away/players/10/x", + "teams/away/players/10/y", + "teams/home/xGoal", # Unsupported entity attribute + "dummy", # Unsupported frame attribute + ] + fields = tdc.Fields(cols) + + with pytest.warns(tdc.exceptions.SchemaWarning) as record: + fields.validate() + + msg = str(record[0].message) + assert "teams/home/xGoal" in msg + assert "dummy" in msg + assert fields.validate_quiet() is True diff --git a/python/tests/test_select.py b/python/tests/test_select.py new file mode 100644 index 0000000..3d8315a --- /dev/null +++ b/python/tests/test_select.py @@ -0,0 +1,78 @@ +import tracking_data_container as tdc + + +class TestSelection: + def test_container_select_string(self, container) -> None: + subset = container.select("ball/x") + assert subset.schema() == tdc.Fields(["ball/x"]) + + def test_container_select_list(self, container) -> None: + subset = container.select(["ball/x", "ball/y"]) + assert subset.schema() == tdc.Fields(["ball/x", "ball/y"]) + + def test_container_select_datafield(self, container) -> None: + field = tdc.DataField("ball/x") + subset = container.select(field) + assert subset.schema() == tdc.Fields(["ball/x"]) + + def test_container_select_selector_ball(self, container) -> None: + subset = container.select(container.selector.ball()) + assert subset.schema() == tdc.Fields(["ball/x", "ball/y", "ball/z"]) + + def test_container_select_selector_players(self, container) -> None: + # Add some player data to test player selection + data = { + "ball/x": [1.0], + "teams/home/players/1/x": [2.0], + "teams/home/players/1/y": [3.0], + "teams/away/players/10/x": [4.0], + } + c = tdc.TrackingDataContainer(data) + + subset = c.select(c.selector.players(team_id="home")) + assert subset.schema() == tdc.Fields(["teams/home/players/1/x", "teams/home/players/1/y"]) + + subset = c.select(c.selector.players().attributes(["x"])) + assert subset.schema() == tdc.Fields(["teams/away/players/10/x", "teams/home/players/1/x"]) + + def test_container_select_wildcard(self) -> None: + data = { + "ball/x": [1.0], + "ball/y": [1.1], + "teams/home/players/1/x": [2.0], + "teams/home/players/1/y": [3.0], + "teams/home/players/2/x": [4.0], + "teams/away/players/10/x": [5.0], + "teams/away/players/10/y": [5.1], + } + c = tdc.TrackingDataContainer(data) + + # 1. Select all x coordinates of the home team using ** + subset = c.select("teams/home/**/x") + assert sorted([f.name() for f in subset.schema()]) == sorted( + ["teams/home/players/1/x", "teams/home/players/2/x"] + ) + + # 2. Select x and y coordinates of the home team using list of ** + subset = c.select(["teams/home/**/x", "teams/home/**/y"]) + assert sorted([f.name() for f in subset.schema()]) == sorted( + ["teams/home/players/1/x", "teams/home/players/2/x", "teams/home/players/1/y"] + ) + + # 3. Select all x coordinates (players and ball) using leading ** + subset = c.select("**/x") + assert sorted([f.name() for f in subset.schema()]) == sorted( + [ + "ball/x", + "teams/home/players/1/x", + "teams/home/players/2/x", + "teams/away/players/10/x", + ] + ) + + # 4. Select x coordinates of home players using combined wildcards + # Using ** to skip segments and * for a single segment + subset = c.select("teams/home/**/players/*/x") + assert sorted([f.name() for f in subset.schema()]) == sorted( + ["teams/home/players/1/x", "teams/home/players/2/x"] + ) diff --git a/python/tests/test_transform.py b/python/tests/test_transform.py new file mode 100644 index 0000000..718900f --- /dev/null +++ b/python/tests/test_transform.py @@ -0,0 +1,179 @@ +import pytest +import tracking_data_container as tdc + + +class TestTransformations: + def test_transform_coordinates(self) -> None: + data = { + "ball/x": [0.0, 50.0, 100.0], + "ball/y": [0.0, 50.0, 100.0], + } + + # Define source system (Normalized 0-100) + source_sys = tdc.CoordinateSystem( + x_range=(0.0, 100.0), + y_range=(0.0, 100.0), + origin="bottom_left", + x_direction="right", + y_direction="up", + ) + + container = tdc.TrackingDataContainer(data).with_coordinate_system(source_sys) + + # Define target system (Metric -50 to 50) + target_sys = tdc.CoordinateSystem( + x_range=(-50.0, 50.0), + y_range=(-50.0, 50.0), + origin="center", + x_direction="right", + y_direction="up", + ) + + transformed = container.transform_coordinates(target_sys) + + df = transformed.to_polars() + # 0.0 -> -50.0 + # 50.0 -> 0.0 + # 100.0 -> 50.0 + res = df["ball/x"].to_list() + assert res[0] == pytest.approx(-50.0) + assert res[1] == pytest.approx(0.0) + assert res[2] == pytest.approx(50.0) + assert transformed.coordinate_system.origin == "center" + + def test_transform_units(self) -> None: + data = {"ball/x": [1.0], "ball/y": [1.0]} # 1 meter + + source_sys = tdc.CoordinateSystem(length_unit="meters", x_range=(0, 100), y_range=(0, 100)) + container = tdc.TrackingDataContainer(data).with_coordinate_system(source_sys) + + target_sys = tdc.CoordinateSystem(length_unit="yards", x_range=(0, 100), y_range=(0, 100)) + transformed = container.transform_coordinates(target_sys) + + # 1 meter = 1.09361 yards + df = transformed.to_polars() + assert df["ball/x"][0] == pytest.approx(1.09361, rel=1e-4) + + def test_transform_missing_units_raises(self) -> None: + data = {"ball/x": [1.0]} + container = tdc.TrackingDataContainer(data) # No metadata + + target_sys = tdc.CoordinateSystem(length_unit="yards") + + with pytest.raises(tdc.exceptions.InvalidParametersError, match="No coordinate system"): + container.transform_coordinates(target_sys) + + def test_transform_undefined_ranges_raises(self) -> None: + # Define system with origin only, no units or ranges + sys = tdc.CoordinateSystem(origin="center") + container = tdc.TrackingDataContainer({"ball/x": [1.0]}).with_coordinate_system(sys) + + target_sys = tdc.CoordinateSystem(length_unit="yards") + + with pytest.raises(tdc.exceptions.InvalidParametersError, match="Source X range undefined"): + container.transform_coordinates(target_sys) + + def test_transform_orientation(self) -> None: + data = { + "ball/x": [10.0, 10.0], + "period": ["first_half", "first_half"], + "ball_poss_team_id": ["home", "away"], + } + + # Source is static, we want to normalize it + container = tdc.TrackingDataContainer(data, orientation="static_home_away") + + # In "ball_owning_team" mode, if "away" has possession, we flip + transformed = container.transform_orientation("ball_owning_team") + + df = transformed.to_polars() + # Frame 1: home possession -> no flip -> 10.0 + # Frame 2: away possession -> flip -> -10.0 + assert df["ball/x"].to_list() == [10.0, -10.0] + + def test_transform_orientation_home_away(self) -> None: + data = { + "ball/x": [10.0, 10.0], + "period": ["first_half", "second_half"], + } + container = tdc.TrackingDataContainer(data, orientation="static_home_away") + + # HomeAway: second_half flips + transformed = container.transform_orientation("home_away") + assert transformed.to_polars()["ball/x"].to_list() == [10.0, -10.0] + + def test_transform_orientation_away_home(self) -> None: + data = { + "ball/x": [10.0, 10.0], + "period": ["first_half", "second_half"], + } + container = tdc.TrackingDataContainer(data, orientation="static_home_away") + + # AwayHome: first_half flips + transformed = container.transform_orientation("away_home") + assert transformed.to_polars()["ball/x"].to_list() == [-10.0, 10.0] + + def test_transform_orientation_identity(self) -> None: + data = { + "ball/x": [10.0, 10.0], + "period": ["first_half", "second_half"], + } + container = tdc.TrackingDataContainer(data).with_orientation(tdc.OrientationMode.AwayHome) + + # Transforming to the same mode should be a no-op + transformed = container.transform_orientation(tdc.OrientationMode.AwayHome) + assert transformed.orientation_mode == tdc.OrientationMode.AwayHome + assert transformed.to_polars()["ball/x"].to_list() == [10.0, 10.0] + + def test_transform_orientation_static_to_dynamic(self) -> None: + data = { + "ball/x": [10.0, 10.0], + "period": ["first_half", "second_half"], + } + # Data is already in AwayHome orientation but marked as StaticHomeAway + container = tdc.TrackingDataContainer(data).with_orientation( + tdc.OrientationMode.StaticHomeAway + ) + + # Transform to AwayHome: + # should_flip(StaticHomeAway) is always False + # should_flip(AwayHome) is True for first_half, False for second_half + # Result: first_half flips (False != True), second_half stays (False != False) + transformed = container.transform_orientation(tdc.OrientationMode.AwayHome) + assert transformed.to_polars()["ball/x"].to_list() == [-10.0, 10.0] + + def test_transform_orientation_dynamic_to_static(self) -> None: + data = { + "ball/x": [10.0, 10.0], + "period": ["first_half", "second_half"], + } + # Data is in AwayHome orientation + container = tdc.TrackingDataContainer(data).with_orientation(tdc.OrientationMode.AwayHome) + + # Transform to StaticHomeAway (which never flips) + # first_half: should_flip(AwayHome)=True, should_flip(StaticHomeAway)=False -> Flip + # second_half: should_flip(AwayHome)=False, should_flip(StaticHomeAway)=False -> No Flip + transformed = container.transform_orientation(tdc.OrientationMode.StaticHomeAway) + assert transformed.to_polars()["ball/x"].to_list() == [-10.0, 10.0] + + def test_transform_orientation_with_coordinate_system(self) -> None: + data = { + "ball/x": [10.0], + "period": ["first_half"], + } + raw_cs = tdc.CoordinateSystem( + x_range=(0, 100), + y_range=(0, 100), + origin=tdc.Origin.TopLeft, + ) + container = ( + tdc.TrackingDataContainer(data) + .with_coordinate_system(raw_cs) + .with_orientation(tdc.OrientationMode.AwayHome) + ) + + # AwayHome first_half flips. Transforming to StaticHomeAway (no flip) + # should trigger a flip from the AwayHome state. + # Flip across center of (0, 100) means 10.0 -> 90.0 + transformed = container.transform_orientation(tdc.OrientationMode.StaticHomeAway) + assert transformed.to_polars()["ball/x"][0] == pytest.approx(90.0) diff --git a/python/tracking_data_container/__init__.py b/python/tracking_data_container/__init__.py new file mode 100644 index 0000000..8a59eb1 --- /dev/null +++ b/python/tracking_data_container/__init__.py @@ -0,0 +1,65 @@ +from __future__ import annotations + +from . import exceptions +from ._tracking_data_container import ( + Attr, + Direction, + Expr, + Frame, + LengthUnit, + OrientationMode, + Origin, + __version__, + col, + lit, + ops, +) +from .builder import TrackingDataBuilder +from .container import LazyTrackingDataContainer, TrackingDataContainer +from .coordinate_system import CoordinateSystem +from .io import load_sportec, load_statsperform, scan_parquet +from .schema import ( + Ball, + DataField, + EntityDataField, + Fields, + FrameDataField, + Player, + Referee, + Team, + TrackedEntity, +) +from .selector import ColumnSelector, Selection + +__all__ = ( + "__version__", + "TrackingDataContainer", + "LazyTrackingDataContainer", + "TrackingDataBuilder", + "CoordinateSystem", + "Origin", + "Direction", + "LengthUnit", + "OrientationMode", + "ColumnSelector", + "Selection", + "scan_parquet", + "load_sportec", + "load_statsperform", + "DataField", + "TrackedEntity", + "Ball", + "Player", + "Team", + "Referee", + "FrameDataField", + "EntityDataField", + "Expr", + "Attr", + "Fields", + "Frame", + "col", + "lit", + "ops", + "exceptions", +) diff --git a/python/tracking_data_container/_tracking_data_container.pyi b/python/tracking_data_container/_tracking_data_container.pyi new file mode 100644 index 0000000..9efdad4 --- /dev/null +++ b/python/tracking_data_container/_tracking_data_container.pyi @@ -0,0 +1,286 @@ +from __future__ import annotations + +from datetime import datetime +from typing import TYPE_CHECKING, Any, Callable + +if TYPE_CHECKING: + import pyarrow as pa + +__version__: str + +class Attr: + """Standardized attribute names for tracking data.""" + + FrameId: Attr + Timestamp: Attr + Period: Attr + BallStatus: Attr + BallPossTeamId: Attr + BallPossStatus: Attr + X: Attr + Y: Attr + Z: Attr + Vel: Attr + Acc: Attr + Dist: Attr + @property + def value(self) -> str: ... + +class _TrackedEntity: + """ + Internal representation of a tracked entity. + """ + def __init__( + self, + team_id: str | None = None, + player_id: str | None = None, + referee_id: str | None = None, + ball: bool | None = None, + ) -> None: ... + def team_id(self) -> str | None: ... + def player_id(self) -> str | None: ... + def is_ball(self) -> bool: ... + def is_player(self) -> bool: ... + def is_team(self) -> bool: ... + def is_referee(self) -> bool: ... + +class _DataField: + """ + Internal representation of a data column. + """ + def __init__( + self, + path: str | None = None, + *, + attribute: Attr | str | None = None, + entity: _TrackedEntity | None = None, + ) -> None: ... + @staticmethod + def from_name(name: str) -> _DataField: ... + def team_id(self) -> str | None: ... + def player_id(self) -> str | None: ... + def is_frame(self) -> bool: ... + def is_entity(self) -> bool: ... + def entity(self) -> _TrackedEntity: ... + def attribute(self) -> str: ... + def name(self) -> str: ... + +class _Fields: + """Collection of DataFields representing a schema.""" + def __init__(self, columns: list[str]) -> None: ... + def __getitem__(self, index: int) -> _DataField: ... + def __len__(self) -> int: ... + def __iter__(self) -> _Fields: ... + def __next__(self) -> _DataField: ... + def player_entities(self, team: str | None = None) -> list[_TrackedEntity]: ... + def team_entities(self) -> list[_TrackedEntity]: ... + def referee_entities(self) -> list[_TrackedEntity]: ... + def validate(self) -> None: ... + def validate_quiet(self) -> bool: ... + +class Origin: + """Possible origin locations for a coordinate system.""" + + Center: Origin + TopLeft: Origin + BottomLeft: Origin + +class Direction: + """Possible axis directions for a coordinate system.""" + + Right: Direction + Left: Direction + Up: Direction + Down: Direction + +class LengthUnit: + """Standardized units of length.""" + + Meters: LengthUnit + Centimeters: LengthUnit + Millimeters: LengthUnit + Yards: LengthUnit + Inches: LengthUnit + Feet: LengthUnit + Percent: LengthUnit + +class OrientationMode: + """Normalization modes for playing direction.""" + + BallOwningTeam: OrientationMode + HomeAway: OrientationMode + AwayHome: OrientationMode + StaticHomeAway: OrientationMode + StaticAwayHome: OrientationMode + +class _CoordinateSystem: + """Internal representation of a coordinate system.""" + def __init__( + self, + x_range: tuple[float, float] | None = None, + y_range: tuple[float, float] | None = None, + origin: Origin | str = "center", + x_direction: Direction | str = "right", + y_direction: Direction | str = "up", + length_unit: LengthUnit | str | None = None, + ) -> None: ... + @property + def x_range(self) -> tuple[float, float] | None: ... + @property + def y_range(self) -> tuple[float, float] | None: ... + @property + def origin(self) -> str: ... + @property + def x_direction(self) -> str: ... + @property + def y_direction(self) -> str: ... + @property + def length_unit(self) -> str | None: ... + +class Selection: + """A selection of columns based on metadata.""" + + _inner: _Selection + def attributes(self, names: list[str]) -> Selection: ... + @property + def expr(self) -> str: ... + +class _Selection: + """Internal selection.""" + @property + def expr(self) -> str: ... + +class ColumnSelector: + """A selector for building column expressions based on metadata.""" + def ball(self) -> Selection: ... + def players(self, team_id: str | None = None) -> Selection: ... + def team(self, team_id: str) -> Selection: ... + +class Frame: + """A single frame of tracking data.""" + def __getitem__(self, key: str | _DataField) -> Any: ... + def to_dict(self) -> dict[_DataField, Any]: ... + +class _TrackingDataContainer: + """Eager tracking data container (internal).""" + def __init__( + self, + data: dict[str, list[Any]], + coordinate_system: _CoordinateSystem | None = None, + orientation: str | None = None, + ) -> None: ... + @classmethod + def _from_capsule(cls, capsule: Any) -> _TrackingDataContainer: ... + def lazy(self) -> _LazyTrackingDataContainer: ... + def select( + self, items: str | list[str | _DataField | _Selection] | _DataField | _Selection + ) -> _TrackingDataContainer: ... + def add_column( + self, name: str, value: Any, _schema_capsule: Any = None + ) -> _TrackingDataContainer: ... + def add_columns( + self, names: list[str], values: list[Any], _schema_capsules: list[Any] | None = None + ) -> _TrackingDataContainer: ... + def with_coordinate_system(self, system: _CoordinateSystem) -> _TrackingDataContainer: ... + def transform_coordinates(self, to_system: _CoordinateSystem) -> _TrackingDataContainer: ... + def transform_orientation(self, mode: OrientationMode | str) -> _TrackingDataContainer: ... + def with_orientation(self, mode: OrientationMode | str) -> _TrackingDataContainer: ... + @property + def schema(self) -> _Fields: ... + @property + def coordinate_system(self) -> _CoordinateSystem | None: ... + @property + def orientation_mode(self) -> OrientationMode | None: ... + @property + def selector(self) -> ColumnSelector: ... + def num_rows(self) -> int: ... + def validate(self) -> None: ... + def save_parquet(self, path: str) -> None: ... + @staticmethod + def load_parquet(path: str) -> _TrackingDataContainer: ... + def materialize_frame(self, frame: int | datetime | Any) -> Frame: ... + def to_arrow(self) -> pa.RecordBatch: ... + def __arrow_c_schema__(self) -> object: ... + def __arrow_c_array__(self, requested_schema: object = None) -> tuple[object, object]: ... + +class _LazyTrackingDataContainer: + """Lazy tracking data container (internal).""" + def collect(self) -> _TrackingDataContainer: ... + def select( + self, items: str | list[str | _DataField | _Selection] | _DataField | _Selection + ) -> _LazyTrackingDataContainer: ... + def filter(self, predicate: Expr) -> _LazyTrackingDataContainer: ... + def transform_coordinates(self, to_system: _CoordinateSystem) -> _LazyTrackingDataContainer: ... + def transform_orientation(self, mode: OrientationMode | str) -> _LazyTrackingDataContainer: ... + def add_column( + self, name: str, value: Any, _schema_capsule: Any = None + ) -> _LazyTrackingDataContainer: ... + def add_columns( + self, names: list[str], values: list[Any], _schema_capsules: list[Any] | None = None + ) -> _LazyTrackingDataContainer: ... + def add_column_udf( + self, name: str, func: Callable, on: list[str] + ) -> _LazyTrackingDataContainer: ... + @property + def schema(self) -> _Fields: ... + @property + def coordinate_system(self) -> _CoordinateSystem | None: ... + @property + def orientation_mode(self) -> OrientationMode | None: ... + @property + def selector(self) -> ColumnSelector: ... + def __arrow_c_schema__(self) -> object: ... + def __arrow_c_stream__(self, requested_schema: object = None) -> object: ... + +class _TrackingDataBuilder: + """Internal tracking data builder.""" + def __init__(self) -> None: ... + def insert_int(self, frame_id: int, col: str | _DataField, val: int) -> None: ... + def insert_float(self, frame_id: int, col: str | _DataField, val: float) -> None: ... + def insert_string(self, frame_id: int, col: str | _DataField, val: str) -> None: ... + def insert_bool(self, frame_id: int, col: str | _DataField, val: bool) -> None: ... + def merge(self, other: _TrackingDataBuilder) -> None: ... + def build(self) -> _TrackingDataContainer: ... + +class Expr: + """A logical expression for selecting or transforming data.""" + def __add__(self, other: Expr | float | int) -> Expr: ... + def __sub__(self, other: Expr | float | int) -> Expr: ... + def __mul__(self, other: Expr | float | int) -> Expr: ... + def __truediv__(self, other: Expr | float | int) -> Expr: ... + def eq(self, other: Expr | Any) -> Expr: ... + def ne(self, other: Expr | Any) -> Expr: ... + def gt(self, other: Expr | Any) -> Expr: ... + def ge(self, other: Expr | Any) -> Expr: ... + def lt(self, other: Expr | Any) -> Expr: ... + def le(self, other: Expr | Any) -> Expr: ... + def diff(self) -> Expr: ... + def pow(self, exponent: float) -> Expr: ... + def sqrt(self) -> Expr: ... + def replace_columns(self, mapping: dict[str, str]) -> Expr: ... + +def col(name: str) -> Expr: ... +def lit(value: Any) -> Expr: ... +def load_sportec(path: str, meta_path: str | None = None) -> _TrackingDataContainer: ... +def load_statsperform(path: str) -> _TrackingDataContainer: ... +def scan_parquet(path: str) -> _LazyTrackingDataContainer: ... + +class _Ops: + @staticmethod + def compute_speed(x_array: Any, y_array: Any, dt: float = 1.0) -> Any: ... + @staticmethod + def compute_acceleration(speed_array: Any, dt: float = 1.0) -> Any: ... + @staticmethod + def compute_jerk(acc_array: Any, dt: float = 1.0) -> Any: ... + @staticmethod + def compute_angular_velocity(vx_array: Any, vy_array: Any, dt: float = 1.0) -> Any: ... + @staticmethod + def butterworth_filter(array: Any, cutoff: float, fs: float, order: int = 2) -> Any: ... + +ops: _Ops + +# Exceptions +class TrackingDataContainerError(Exception): ... +class InvalidParametersError(TrackingDataContainerError): ... +class SchemaError(TrackingDataContainerError): ... +class SchemaWarning(UserWarning): ... diff --git a/python/tracking_data_container/builder.py b/python/tracking_data_container/builder.py new file mode 100644 index 0000000..5e3f43d --- /dev/null +++ b/python/tracking_data_container/builder.py @@ -0,0 +1,90 @@ +from __future__ import annotations + +from ._tracking_data_container import ( + _DataField, + _TrackingDataBuilder, +) +from .container import TrackingDataContainer +from .schema import DataField + + +class TrackingDataBuilder: + """ + A builder for efficiently constructing a TrackingDataContainer. + + The builder allows incremental insertion of data points across multiple + frames and columns, optimized for memory and performance. + + Examples + -------- + The builder is designed to support parallel data ingestion by allowing + multiple builders to be populated independently and then merged. + + >>> from concurrent.futures import ThreadPoolExecutor + >>> from tracking_data_container import TrackingDataBuilder + >>> + >>> def process_chunk(chunk_data): + ... builder = TrackingDataBuilder() + ... for frame_id, x, y in chunk_data: + ... builder.insert(frame_id, "ball/x", x) + ... builder.insert(frame_id, "ball/y", y) + ... return builder + >>> + >>> chunks = [ + ... [(0, 10.0, 20.0), (1, 11.0, 21.0)], + ... [(2, 12.0, 22.0), (3, 13.0, 23.0)] + ... ] + >>> + >>> with ThreadPoolExecutor() as executor: + ... builders = list(executor.map(process_chunk, chunks)) + >>> + >>> main_builder = TrackingDataBuilder() + >>> for b in builders: + ... main_builder.merge(b) + >>> + >>> container = main_builder.build() + """ + + _builder: _TrackingDataBuilder + + def __init__(self) -> None: + self._builder = _TrackingDataBuilder() + + def insert(self, frame_id: int, col: str | DataField, val: int | float | str | bool) -> None: + """ + Insert a value into the builder. + + Parameters + ---------- + frame_id : int + The frame index. + col : str or DataField + The column name or data field. + val : int, float, str, or bool + The value to insert. + """ + processed_col: str | _DataField + if isinstance(col, DataField): + processed_col = col._field + else: + processed_col = col + + if isinstance(val, bool): + self._builder.insert_bool(frame_id, processed_col, val) + elif isinstance(val, int): + self._builder.insert_int(frame_id, processed_col, val) + elif isinstance(val, float): + self._builder.insert_float(frame_id, processed_col, val) + elif isinstance(val, str): + self._builder.insert_string(frame_id, processed_col, val) + else: + raise TypeError(f"Unsupported value type: {type(val)}") + + def merge(self, other: TrackingDataBuilder) -> None: + """Merge another builder into this one.""" + self._builder.merge(other._builder) + + def build(self) -> TrackingDataContainer: + """Build the final TrackingDataContainer.""" + new_inner = self._builder.build() + return TrackingDataContainer(new_inner) diff --git a/python/tracking_data_container/container.py b/python/tracking_data_container/container.py new file mode 100644 index 0000000..3311eda --- /dev/null +++ b/python/tracking_data_container/container.py @@ -0,0 +1,1079 @@ +from __future__ import annotations + +from datetime import datetime, timedelta +from typing import TYPE_CHECKING, Any, Callable, cast + +import numpy as np +import polars as pl +import pyarrow as pa + +from ._tracking_data_container import ( + Expr as _Expr, +) +from ._tracking_data_container import ( + Frame, + OrientationMode, + _DataField, + _LazyTrackingDataContainer, + _TrackingDataContainer, +) +from ._tracking_data_container import ( + Selection as _Selection, +) +from .coordinate_system import CoordinateSystem +from .schema import Ball, DataField, Fields +from .selector import ColumnSelector, Selection + +if TYPE_CHECKING: + from pathlib import Path + + import pandas as pd + import polars as pl + import pyarrow as pa + + +class TrackingDataContainer: + """ + A high-performance, metadata-aware container for sports tracking data. + + TrackingDataContainer acts as the primary eager interface for interacting + with tracking data, providing tools for selection, transformation, and + interoperability with the Python data ecosystem. + + Parameters + ---------- + data : dict or _TrackingDataContainer + The underlying data, either as a dictionary of columns or an existing + internal container. + coordinate_system : CoordinateSystem, optional + Spatial metadata describing the coordinate frame. + orientation : str or OrientationMode, optional + The playing direction normalization mode. + + Examples + -------- + >>> import tracking_data_container as tdc + >>> data = {"ball/x": [0.0, 1.0], "ball/y": [0.0, 1.0]} + >>> container = tdc.TrackingDataContainer(data) + >>> container.num_rows() + 2 + >>> # Select specific columns + >>> ball_pos = container.select(["ball/x", "ball/y"]) + >>> # Convert to other formats + >>> df = container.to_polars() + """ + + _container: _TrackingDataContainer + + def __init__( + self, + data: dict[str, Any] | _TrackingDataContainer | pl.DataFrame, + coordinate_system: CoordinateSystem | None = None, + orientation: str | OrientationMode | None = None, + ) -> None: + if isinstance(data, _TrackingDataContainer): + self._container = data + else: + if isinstance(data, pl.DataFrame): + data = data.to_dict(as_series=False) + + # Deep normalization of the dictionary + normalized_data = {} + for k, v in data.items(): + if v is None or len(v) == 0: + normalized_data[k] = v + continue + + # Ensure all inputs are native python types for StructArray construction + if isinstance(v, (np.ndarray, pl.Series, pa.Array)): + # Special handling for datetime/timedelta to microseconds + if k == "timestamp": + if isinstance(v, np.ndarray): + if v.dtype.kind == "M": + normalized_data[k] = [ + int(dt.astype("datetime64[us]").view(np.int64)) for dt in v + ] + else: + normalized_data[k] = v.tolist() + elif isinstance(v, pl.Series): + normalized_data[k] = v.dt.timestamp("us").to_list() + else: + normalized_data[k] = v.to_pylist() + # Ensure coordinates are Float32 + elif k.endswith("/x") or k.endswith("/y") or k.endswith("/z"): + normalized_data[k] = [float(x) for x in v] + else: + normalized_data[k] = v.tolist() if hasattr(v, "tolist") else list(v) + elif isinstance(v, list) and len(v) > 0: + if isinstance(v[0], timedelta): + normalized_data[k] = [int(td.total_seconds() * 1_000_000) for td in v] + elif isinstance(v[0], datetime): + normalized_data[k] = [int(dt.timestamp() * 1_000_000) for dt in v] + else: + normalized_data[k] = v + else: + normalized_data[k] = v + + inner_cs = coordinate_system._inner if coordinate_system else None + orientation_str = str(orientation) if orientation else None + + self._container = _TrackingDataContainer(normalized_data, inner_cs, orientation_str) + + # --- Properties and Metadata --- + + def schema(self) -> Fields: + """Returns the schema of the container.""" + return Fields(self._container.schema) + + @property + def coordinate_system(self) -> CoordinateSystem | None: + """Returns the active coordinate system metadata.""" + if self._container.coordinate_system is None: + return None + return CoordinateSystem._from_inner(self._container.coordinate_system) + + @property + def orientation_mode(self) -> OrientationMode | None: + """Returns the active orientation normalization mode.""" + return self._container.orientation_mode + + @property + def selector(self) -> ColumnSelector: + """Returns the selector for building column expressions.""" + return ColumnSelector(self._container.selector) + + def num_rows(self) -> int: + """Returns the number of frames in the container.""" + return self._container.num_rows() + + def validate(self) -> None: + """ + Validates the container against the Common Data Format (CDF). + + Raises + ------ + SchemaError + If mandatory columns are missing or have incorrect types. + SchemaWarning + If non-standard columns are present. + """ + self._container.validate() + + def __len__(self) -> int: + return self.num_rows() + + def __getitem__(self, key: int) -> Frame: + """Access a specific frame by index.""" + return self.materialize_frame(key) + + # --- Transformations --- + + def with_coordinate_system(self, system: CoordinateSystem) -> TrackingDataContainer: + """ + Sets the coordinate system metadata without performing any transformations. + + Parameters + ---------- + system : CoordinateSystem + The new coordinate system. + + Returns + ------- + TrackingDataContainer + A new container with updated metadata. + """ + new_inner = self._container.with_coordinate_system(system._inner) + return TrackingDataContainer(new_inner) + + def transform_coordinates(self, to_system: CoordinateSystem) -> TrackingDataContainer: + """ + Transforms coordinates to a new spatial system. + + Parameters + ---------- + to_system : CoordinateSystem + The target coordinate system. + + Returns + ------- + TrackingDataContainer + A new container with transformed coordinates. + """ + new_inner = self._container.transform_coordinates(to_system._inner) + return TrackingDataContainer(new_inner) + + def with_orientation(self, mode: str | OrientationMode) -> TrackingDataContainer: + """ + Sets the orientation metadata without performing any transformations. + + Parameters + ---------- + mode : str or OrientationMode + The new orientation mode. + + Returns + ------- + TrackingDataContainer + A new container with updated metadata. + """ + if not isinstance(mode, str): + mode = str(mode) + new_inner = self._container.with_orientation(mode) + return TrackingDataContainer(new_inner) + + def transform_orientation(self, mode: str | OrientationMode) -> TrackingDataContainer: + """ + Normalizes coordinates based on playing direction. + + Parameters + ---------- + mode : str or OrientationMode + The orientation normalization mode. + + Returns + ------- + TrackingDataContainer + A new container with normalized coordinates. + """ + if not isinstance(mode, str): + mode = str(mode) + new_inner = self._container.transform_orientation(mode) + return TrackingDataContainer(new_inner) + + # --- Selections and Data Additions --- + + def select(self, items: str | list[str | DataField | Selection]) -> TrackingDataContainer: + """ + Select columns from the container. + + Parameters + ---------- + items : str or list of str or DataField or Selection + The columns to select. + + Returns + ------- + TrackingDataContainer + A new container with only the selected columns. + """ + processed_items: str | list[str | _DataField | _Selection] | _DataField | _Selection + if isinstance(items, list): + processed_items = [] + for i in items: + if isinstance(i, DataField): + processed_items.append(i._field) + elif isinstance(i, Selection): + processed_items.append(i._selection) + else: + processed_items.append(i) + elif isinstance(items, DataField): + processed_items = items._field + elif isinstance(items, Selection): + processed_items = items._selection + else: + processed_items = items + + new_inner = self._container.select(processed_items) # type: ignore + new_container = TrackingDataContainer.__new__(TrackingDataContainer) + new_container._container = new_inner + return new_container + + def add_column( + self, + name: str, + value: Any | Callable[[pl.DataFrame], Any], + on: str | list[str] | None = None, + ) -> TrackingDataContainer: + """ + Add a new column to the container. + + Parameters + ---------- + name : str + The name of the new column. + value : array-like or callable + The data for the new column. If a callable is provided, it will be + invoked with the columns specified in `on` as separate arguments. + on : str or list of str, optional + The columns to pass to the callable. + + Returns + ------- + TrackingDataContainer + A new container including the new column. + """ + import polars as pl + + if callable(value): + inputs = self.select(on) if on is not None else self # type: ignore + batch = inputs.to_arrow() + if on is not None: + # Pass individual columns as pyarrow arrays + on_list = [on] if isinstance(on, str) else list(on) + args = [batch.column(c) for c in on_list] + data = value(*args) + else: + # Pass the whole record batch + data = value(batch) + + if isinstance(data, pl.DataFrame) and data.width == 1: + data = data.to_series() + elif isinstance(value, _Expr): + # If it's an expression, pass it to the Rust implementation + new_inner = self._container.add_column(name, value) + new_container = TrackingDataContainer.__new__(TrackingDataContainer) + new_container._container = new_inner + return new_container + else: + data = value + + try: + array_capsule, schema_capsule = data.__arrow_c_array__() + except (AttributeError, TypeError): + import pyarrow as pa + + if isinstance(data, pl.Series): + data = pa.array(data.to_list()) + else: + data = pa.array(data) + array_capsule, schema_capsule = data.__arrow_c_array__() + + new_inner = self._container.add_column(name, array_capsule, schema_capsule) + new_container = TrackingDataContainer.__new__(TrackingDataContainer) + new_container._container = new_inner + return new_container + + def add_columns( + self, + names: list[str], + values: list[Any], + on: list[str | list[str] | None] | None = None, + ) -> TrackingDataContainer: + """ + Add multiple new columns to the container in a single operation. + + Parameters + ---------- + names : list of str + The names for the new columns. + values : list of array-like, Expr, or callable + The data or computations for the new columns. + on : list of str or list of list of str, optional + The columns to pass to each callable value. + + Returns + ------- + TrackingDataContainer + A new container including the new columns. + """ + import polars as pl + import pyarrow as pa + + processed_values = [] + schema_capsules: list[Any] = [] + + for i, (name, value) in enumerate(zip(names, values)): + val_on = on[i] if on is not None else None + if callable(value): + # Try to evaluate as expression first if on is provided + data = None + if val_on is not None: + try: + # Attempt to pass string names for Expr building pass + # If val_on is a single string, wrap it in a tuple + on_tuple: tuple[str, ...] = ( + (val_on,) if isinstance(val_on, str) else tuple(val_on) + ) + data = value(*on_tuple) + if not isinstance(data, _Expr): + # Not an expression, fallback to eager series + data = None + except Exception: + data = None + + if data is None: + # Eager evaluation path + inputs = self.select(val_on) if val_on is not None else self # type: ignore + batch = inputs.to_arrow() + if val_on is not None: + # Pass individual columns as pyarrow arrays + on_list = [val_on] if isinstance(val_on, str) else list(val_on) + args = [batch.column(c) for c in on_list] + data = value(*args) + else: + # Pass the whole record batch + data = value(batch) + + if isinstance(data, pl.DataFrame) and data.width == 1: + data = data.to_series() + else: + data = value + if isinstance(data, _Expr): + processed_values.append(data) + schema_capsules.append(None) + else: + # Array-like + try: + array_capsule, schema_capsule = data.__arrow_c_array__() # type: ignore + except (AttributeError, TypeError): + if isinstance(data, pl.Series): + data = pa.array(data.to_list()) + else: + data = pa.array(data) + array_capsule, schema_capsule = data.__arrow_c_array__() + processed_values.append(array_capsule) + schema_capsules.append(schema_capsule) + + new_inner = self._container.add_columns(names, processed_values, schema_capsules) + new_container = TrackingDataContainer.__new__(TrackingDataContainer) + new_container._container = new_inner + return new_container + + def add_column_per_player( + self, name: str, value: Any, on: str | list[str] | None = None + ) -> TrackingDataContainer: + """ + Add a new column for every player in the container. + + Parameters + ---------- + name : str + The attribute name for the new columns. + value : array-like, Expr, or callable + The data or computation for the new columns. + on : str or list of str, optional + The player-specific attributes to use for the computation. + + Returns + ------- + TrackingDataContainer + A new container with added columns for each player. + """ + names = [] + values = [] + ons = [] + + for player in self.schema().players(): + col_name = DataField(entity=player, attribute=name).name() + names.append(col_name) + + player_on: str | list[str] | None = None + if on is not None: + if isinstance(on, str): + player_on = DataField(entity=player, attribute=on).name() + elif isinstance(on, list): + player_on = [DataField(entity=player, attribute=attr).name() for attr in on] + + ons.append(player_on) + + if isinstance(value, _Expr) and on is not None: + # Optimized path for shared expressions: substitute placeholders + on_list = [on] if isinstance(on, str) else on + mapping = { + attr: DataField(entity=player, attribute=attr).name() for attr in on_list + } + values.append(value.replace_columns(mapping)) + continue + + if callable(value) and on is not None: + on_resolved = player_on if isinstance(player_on, list) else [player_on] # type: ignore + # Check if it returns an Expr + try: + result = value(*on_resolved) # type: ignore + if isinstance(result, _Expr): + values.append(result) + continue + except Exception: + pass + + values.append(value) + + return self.add_columns(names, values, on=ons) # type: ignore + + def lazy(self) -> LazyTrackingDataContainer: + """ + Converts the container to a lazy version for deferred execution. + + Returns + ------- + LazyTrackingDataContainer + The lazy version of this container. + """ + new_inner = self._container.lazy() + new_container = LazyTrackingDataContainer.__new__(LazyTrackingDataContainer) + new_container._container = new_inner + return new_container + + # --- Materialization and Visualization --- + + def materialize_frame(self, frame: int | datetime | timedelta) -> Frame: + """ + Returns the values of all fields for a specific frame. + + Parameters + ---------- + frame : int, datetime, or timedelta + The frame index or time to materialize. + + Returns + ------- + Frame + The materialized frame data. + """ + if isinstance(frame, int): + index = frame + elif isinstance(frame, datetime): + # Resolve index from timestamp using integer comparison + target_ts = int(frame.timestamp() * 1_000_000) + df = self.select(["timestamp"]).to_polars() + try: + # Use numpy for the comparison to avoid Polars Datetime eq issues + ts_array = df["timestamp"].dt.timestamp("us").to_numpy() + index = int(np.where(ts_array == target_ts)[0][0]) + except (IndexError, KeyError): + raise ValueError(f"Timestamp {frame} not found in container") + elif isinstance(frame, timedelta): + # Resolve index from offset + target_us = int(frame.total_seconds() * 1_000_000) + df = self.select(["timestamp"]).to_polars() + try: + # Compare relative to first frame + ts_array = df["timestamp"].dt.timestamp("us").to_numpy() + if len(ts_array) > 0: + start_ts = ts_array[0] + index = int(np.where(ts_array == (start_ts + target_us))[0][0]) + else: + raise IndexError("Container is empty") + except (IndexError, KeyError): + # Fallback to frame_id if timestamp resolution fails + try: + df_id = self.select(["frame_id"]).to_polars() + # Assume 25fps if no metadata, or use frame_id starting at 0 + target_frame = int(frame.total_seconds() * 25) + index = int(np.where(df_id["frame_id"] == target_frame)[0][0]) + except (IndexError, KeyError): + raise ValueError(f"Timedelta {frame} could not be resolved to a frame index") + else: + raise TypeError(f"Unsupported frame type: {type(frame)}") + + return self._container.materialize_frame(index) + + def plot_frame( + self, + frame: int | datetime | timedelta, + ax: Any = None, + home_color: str = "red", + away_color: str = "blue", + ball_color: str = "white", + referee_color: str = "yellow", + player_size: float = 80, + ball_size: float = 50, + **kwargs: Any, + ) -> Any: + """ + Visualizes a frame using mplsoccer. + + Parameters + ---------- + frame : int, datetime, or timedelta + The frame index, timestamp, or timedelta to plot. + ax : matplotlib.axes.Axes, optional + An existing matplotlib Axes object to plot on. + home_color : str, default="red" + Color for the home team players. + away_color : str, default="blue" + Color for the away team players. + ball_color : str, default="white" + Color for the ball. + referee_color : str, default="yellow" + Color for the referees. + player_size : float, default=0.8 + Radius of the players in pitch units. + ball_size : float, default=0.5 + Radius of the ball in pitch units. + **kwargs + Additional arguments passed to the Pitch.scatter method. + + Returns + ------- + matplotlib.axes.Axes + The matplotlib Axes object. + + Raises + ------ + ImportError + If mplsoccer is not installed. + ValueError + If the coordinate system is not defined. + """ + try: + from mplsoccer import Pitch + except ImportError: + raise ImportError( + "Seems like you don't have `mplsoccer` installed. " + "Please install it using: pip install mplsoccer" + ) + + if self.coordinate_system is None: + raise ValueError( + "Coordinate system must be defined to plot. " + "Use container.with_coordinate_system() to set it." + ) + + dot_styles = { + "home": { + "facecolor": home_color, + "edgecolor": "white", + "linewidth": 0.8, + "s": player_size, + "zorder": 5, + }, + "away": { + "facecolor": away_color, + "edgecolor": "white", + "linewidth": 0.8, + "s": player_size, + "zorder": 5, + }, + "ball": { + "facecolor": ball_color, + "edgecolor": "black", + "s": ball_size, + "zorder": 6, + }, + "referee": { + "facecolor": referee_color, + "edgecolor": "black", + "linewidth": 0.8, + "s": player_size, + "zorder": 5, + }, + } + + dims = self.coordinate_system.to_mplsoccer() + pitch = Pitch(pitch_type=dims) + + if ax is None: + fig, ax = pitch.draw() + else: + pitch.draw(ax=ax) + + f = self.materialize_frame(frame) + schema = self.schema() + + # Plot players + for tracked_team in schema.teams(): + from .schema import Team + + if not isinstance(tracked_team, Team): + continue + team_id = tracked_team.team_id + if team_id is None: + continue + style_key = "home" if "home" in team_id.lower() else "away" + style = dot_styles[style_key].copy() + style.update(kwargs) + + players = schema.players(team=team_id) + x = [] + y = [] + for p in players: + px = f[DataField(entity=p, attribute="x")] # type: ignore + py = f[DataField(entity=p, attribute="y")] # type: ignore + if px is not None and py is not None: + x.append(px) + y.append(py) + + if x: + pitch.scatter(x, y, ax=ax, label=f"Team {team_id}", **style) + + # Plot ball + bx = f[DataField(entity=Ball(), attribute="x")] # type: ignore + by = f[DataField(entity=Ball(), attribute="y")] # type: ignore + if bx is not None and by is not None: + style = dot_styles["ball"].copy() + style.update(kwargs) + pitch.scatter(bx, by, ax=ax, label="Ball", **style) + + # Plot referees + rx = [] + ry = [] + for ref in schema.referees(): + px = f[DataField(entity=ref, attribute="x")] # type: ignore + py = f[DataField(entity=ref, attribute="y")] # type: ignore + if px is not None and py is not None: + rx.append(px) + ry.append(py) + + if rx: + style = dot_styles["referee"].copy() + style.update(kwargs) + pitch.scatter(rx, ry, ax=ax, label="Referees", **style) + + return ax + + # --- Interoperability and Export --- + + def to_arrow(self) -> pa.RecordBatch: + """Converts the container to a pyarrow `RecordBatch`.""" + import pyarrow as pa + + return pa.record_batch(self._container.to_arrow()) + + def to_pandas(self) -> pd.DataFrame: + """Converts the container to a pandas `DataFrame`.""" + return cast("pd.DataFrame", self.to_arrow().to_pandas()) + + def to_polars(self) -> pl.DataFrame: + """Converts the container to a polars `DataFrame`.""" + import polars as pl + + return cast(pl.DataFrame, pl.DataFrame(self)) + + def to_parquet(self, path: str | Path) -> None: + """Saves the container to a Parquet file.""" + self._container.save_parquet(str(path)) + + @classmethod + def from_parquet(cls, path: str | Path) -> TrackingDataContainer: + """Loads a container from a Parquet file.""" + new_inner = _TrackingDataContainer.load_parquet(str(path)) + new_container = TrackingDataContainer.__new__(TrackingDataContainer) + new_container._container = new_inner + return new_container + + # --- Internal and Special Methods --- + + def __arrow_c_schema__(self) -> object: + return self._container.__arrow_c_schema__() + + def __arrow_c_array__(self, requested_schema: object | None = None) -> object: + return self._container.__arrow_c_array__(requested_schema) + + def __repr__(self) -> str: + return repr(self._container) + + +class LazyTrackingDataContainer: + """ + A lazy version of the tracking data container for deferred execution. + + LazyTrackingDataContainer allows building complex query plans that are + optimized and executed only when explicitly requested via `collect`. + """ + + _container: _LazyTrackingDataContainer + + def __init__(self, container: _LazyTrackingDataContainer): + self._container = container + + # --- Properties and Metadata --- + + def schema(self) -> Fields: + """Returns the schema of the lazy container.""" + return Fields(self._container.schema) + + @property + def coordinate_system(self) -> CoordinateSystem | None: + """Returns the active coordinate system metadata.""" + if self._container.coordinate_system is None: + return None + return CoordinateSystem._from_inner(self._container.coordinate_system) + + @property + def orientation_mode(self) -> OrientationMode | None: + """Returns the active orientation normalization mode.""" + return self._container.orientation_mode + + @property + def selector(self) -> ColumnSelector: + """Returns the selector for building column expressions.""" + return ColumnSelector(self._container.selector) + + # --- Selections and Data Additions --- + + def select(self, items: str | list[str | DataField | Selection]) -> LazyTrackingDataContainer: + """ + Lazily select columns from the container. + + Parameters + ---------- + items : str or list of str or DataField or Selection + The columns to select. + + Returns + ------- + LazyTrackingDataContainer + A new lazy container with the selection added to the plan. + """ + processed_items: str | list[str | _DataField | _Selection] | _DataField | _Selection + if isinstance(items, list): + processed_items = [] + for i in items: + if isinstance(i, DataField): + processed_items.append(i._field) + elif isinstance(i, Selection): + processed_items.append(i._selection) + else: + processed_items.append(i) + elif isinstance(items, DataField): + processed_items = items._field + elif isinstance(items, Selection): + processed_items = items._selection + else: + processed_items = items + + new_inner = self._container.select(processed_items) # type: ignore + return LazyTrackingDataContainer(new_inner) + + def filter(self, predicate: _Expr) -> LazyTrackingDataContainer: + """ + Lazily filter rows from the container. + + Parameters + ---------- + predicate : _Expr + The expression defining the filter condition. + + Returns + ------- + LazyTrackingDataContainer + A new lazy container with the filter added to the plan. + """ + new_inner = self._container.filter(predicate) + return LazyTrackingDataContainer(new_inner) + + def transform_coordinates(self, to_system: CoordinateSystem) -> LazyTrackingDataContainer: + """ + Lazily transforms coordinates to a new spatial system. + + Parameters + ---------- + to_system : CoordinateSystem + The target coordinate system. + + Returns + ------- + LazyTrackingDataContainer + A new lazy container with transformed coordinates added to the plan. + """ + new_inner = self._container.transform_coordinates(to_system._inner) + return LazyTrackingDataContainer(new_inner) + + def transform_orientation(self, mode: str | OrientationMode) -> LazyTrackingDataContainer: + """ + Lazily normalizes coordinates based on playing direction. + + Parameters + ---------- + mode : str or OrientationMode + The orientation normalization mode. + + Returns + ------- + LazyTrackingDataContainer + A new lazy container with normalized coordinates added to the plan. + """ + if not isinstance(mode, str): + mode = str(mode) + new_inner = self._container.transform_orientation(mode) + return LazyTrackingDataContainer(new_inner) + + def add_column( + self, name: str, value: Any, on: str | list[str] | None = None + ) -> LazyTrackingDataContainer: + """ + Lazily add a new column to the container. + + Parameters + ---------- + name : str + The name of the new column. + value : Expr, array-like, or callable + The computation or data for the new column. + on : str or list of str, optional + The columns required for the computation (required for callables). + + Returns + ------- + LazyTrackingDataContainer + A new lazy container with the column addition added to the plan. + """ + import pyarrow as pa + + from ._tracking_data_container import Expr as _PyExpr + + if hasattr(value, "_expr") and isinstance(value._expr, _PyExpr): + new_inner = self._container.add_column(name, value._expr, None) + elif isinstance(value, _PyExpr): + new_inner = self._container.add_column(name, value, None) + elif callable(value): + if on is None: + raise ValueError( + "The 'on' parameter must be provided when using a callable in lazy mode" + ) + # Ensure on is a list of strings + on_list = [on] if isinstance(on, str) else list(on) + new_inner = self._container.add_column_udf(name, value, on_list) + else: + data = pa.array(value) + array_capsule, schema_capsule = data.__arrow_c_array__() + new_inner = self._container.add_column(name, array_capsule, schema_capsule) + + return LazyTrackingDataContainer(new_inner) + + def add_columns( + self, + names: list[str], + values: list[Any], + on: list[str | list[str] | None] | None = None, + ) -> LazyTrackingDataContainer: + """ + Lazily add multiple new columns to the container. + + Parameters + ---------- + names : list of str + The names for the new columns. + values : list of Expr, array-like, or callable + The data or computations for the new columns. + on : list of str or list of list of str, optional + The columns to pass to each callable value. + + Returns + ------- + LazyTrackingDataContainer + A new lazy container including the new columns. + """ + import pyarrow as pa + + from ._tracking_data_container import Expr as _PyExpr + + processed_values = [] + schema_capsules: list[Any] = [] + + for i, (name, value) in enumerate(zip(names, values)): + val_on = on[i] if on is not None else None + if callable(value): + # Eager evaluation for callables in lazy mode is not supported + # unless they return an Expr. + if val_on is None: + raise ValueError( + "The 'on' parameter must be provided when using a callable in lazy mode" + ) + # Try to evaluate as expression + on_tuple: tuple[str, ...] = (val_on,) if isinstance(val_on, str) else tuple(val_on) + data = value(*on_tuple) + if not isinstance(data, _PyExpr): + raise TypeError("Callables in lazy mode must return an Expr") + elif hasattr(value, "_expr") and isinstance(value._expr, _PyExpr): + data = value._expr + else: + data = value + + if isinstance(data, _PyExpr): + processed_values.append(data) + schema_capsules.append(None) + else: + # Array-like + array_data = pa.array(data) + array_capsule, schema_capsule = array_data.__arrow_c_array__() + processed_values.append(array_capsule) + schema_capsules.append(schema_capsule) + + new_inner = self._container.add_columns(names, processed_values, schema_capsules) + return LazyTrackingDataContainer(new_inner) + + def add_column_per_player( + self, name: str, value: Any, on: str | list[str] | None = None + ) -> LazyTrackingDataContainer: + """ + Lazily add a new column for every player in the container. + + Parameters + ---------- + name : str + The attribute name for the new columns. + value : Expr, array-like, or callable + The computation or data for the new columns. + on : str or list of str, optional + The player-specific attributes to use for the computation. + + Returns + ------- + LazyTrackingDataContainer + A new lazy container with the per-player columns added to the plan. + """ + names = [] + values = [] + ons = [] + + from ._tracking_data_container import Expr as _PyExpr + + for player in self.schema().players(): + col_name = DataField(entity=player, attribute=name).name() + names.append(col_name) + + player_on: str | list[str] | None = None + if on is not None: + if isinstance(on, str): + player_on = DataField(entity=player, attribute=on).name() + elif isinstance(on, list): + player_on = [DataField(entity=player, attribute=attr).name() for attr in on] + + ons.append(player_on) + + if ( + isinstance(value, _PyExpr) + or (hasattr(value, "_expr") and isinstance(value._expr, _PyExpr)) + ) and on is not None: + expr = value._expr if hasattr(value, "_expr") else value + on_list = [on] if isinstance(on, str) else on + mapping = { + attr: DataField(entity=player, attribute=attr).name() for attr in on_list + } + values.append(expr.replace_columns(mapping)) + continue + + if callable(value) and on is not None: + on_resolved = player_on if isinstance(player_on, list) else [player_on] # type: ignore + # Check if it returns an Expr + try: + result = value(*on_resolved) # type: ignore + if isinstance(result, _PyExpr) or ( + hasattr(result, "_expr") and isinstance(result._expr, _PyExpr) + ): + expr = result._expr if hasattr(result, "_expr") else result + values.append(expr) + continue + except Exception: + pass + + values.append(value) + + return self.add_columns(names, values, on=ons) + + # --- Execution --- + + def collect(self) -> TrackingDataContainer: + """ + Executes the lazy plan and returns an eager `TrackingDataContainer`. + + Returns + ------- + TrackingDataContainer + The resulting eager container. + """ + new_inner = self._container.collect() + return TrackingDataContainer(new_inner) + + # --- Internal and Special Methods --- + + def __arrow_c_schema__(self) -> object: + return self._container.__arrow_c_schema__() + + def __arrow_c_stream__(self, requested_schema: object | None = None) -> object: + return self._container.__arrow_c_stream__(requested_schema) + + def __repr__(self) -> str: + return repr(self._container) diff --git a/python/tracking_data_container/coordinate_system.py b/python/tracking_data_container/coordinate_system.py new file mode 100644 index 0000000..6ea6d3a --- /dev/null +++ b/python/tracking_data_container/coordinate_system.py @@ -0,0 +1,233 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +from ._tracking_data_container import ( + Direction, + LengthUnit, + Origin, + _CoordinateSystem, +) + +if TYPE_CHECKING: + from mplsoccer.soccer.dimensions import BaseSoccerDims + + +class CoordinateSystem: + """ + Defines a coordinate system for tracking data. + + A coordinate system combines spatial bounds (ranges), an origin point, axis + orientations, and measurement units to provide a complete context for entity + positions. + + Parameters + ---------- + x_range : tuple[float, float], optional + The valid range of X coordinates (e.g., `(-52.5, 52.5)`). + y_range : tuple[float, float], optional + The valid range of Y coordinates (e.g., `(-34.0, 34.0)`). + origin : Origin or str, default="center" + The location of the (0,0) point. Can be "center", "top_left", or "bottom_left". + x_direction : Direction or str, default="right" + The direction in which X values increase. Can be "right" or "left". + y_direction : Direction or str, default="up" + The direction in which Y values increase. Can be "up" or "down". + length_unit : LengthUnit or str, optional + The physical unit of length for the coordinates. Can be "meters", + "centimeters", "millimeters", "yards", "inches", "feet", or "percent". + + Examples + -------- + Defining a standard metric coordinate system: + + >>> from tracking_data_container import CoordinateSystem + >>> metric_sys = CoordinateSystem( + ... x_range=(-52.5, 52.5), + ... y_range=(-34.0, 34.0), + ... origin="center", + ... length_unit="meters" + ... ) + + Defining a normalized coordinate system (0 to 1): + + >>> norm_sys = CoordinateSystem( + ... x_range=(0.0, 1.0), + ... y_range=(0.0, 1.0), + ... origin="bottom_left", + ... length_unit="percent" + ... ) + """ + + _inner: _CoordinateSystem + + def __init__( + self, + x_range: tuple[float, float] | None = None, + y_range: tuple[float, float] | None = None, + origin: Origin | str = Origin.Center, + x_direction: Direction | str = Direction.Right, + y_direction: Direction | str = Direction.Up, + length_unit: LengthUnit | str | None = None, + ) -> None: + self._inner = _CoordinateSystem( + x_range=x_range, + y_range=y_range, + origin=origin, + x_direction=x_direction, + y_direction=y_direction, + length_unit=length_unit, + ) + + @staticmethod + def _from_inner(inner: _CoordinateSystem) -> CoordinateSystem: + """Internal helper to create a CoordinateSystem from the Rust representation.""" + instance = CoordinateSystem.__new__(CoordinateSystem) + instance._inner = inner + return instance + + @property + def x_range(self) -> tuple[float, float] | None: + """The valid range of X coordinates.""" + return self._inner.x_range + + @property + def y_range(self) -> tuple[float, float] | None: + """The valid range of Y coordinates.""" + return self._inner.y_range + + @property + def origin(self) -> str: + """The location of the (0,0) point.""" + return self._inner.origin + + @property + def x_direction(self) -> str: + """The direction in which X values increase.""" + return self._inner.x_direction + + @property + def y_direction(self) -> str: + """The direction in which Y values increase.""" + return self._inner.y_direction + + @property + def length_unit(self) -> str | None: + """The physical unit of length for the coordinates.""" + return self._inner.length_unit + + def __repr__(self) -> str: + return repr(self._inner) + + def to_mplsoccer(self) -> BaseSoccerDims: + """ + Convert the coordinate system to a mplsoccer BaseSoccerDims object. + + This allows using the coordinate system directly with mplsoccer's Pitch class. + + Returns + ------- + mplsoccer.soccer.dimensions.BaseSoccerDims + The dimensions object for mplsoccer. + + Examples + -------- + >>> from tracking_data_container import CoordinateSystem + >>> from mplsoccer import Pitch + >>> coordinate_system = CoordinateSystem(x_range=(-52.5, 52.5), y_range=(-34, 34)) + >>> pitch = Pitch(pitch_type=coordinate_system.to_mplsoccer()) + + Notes + ----- + This method is experimental and requires the `mplsoccer` package. + """ + try: + from mplsoccer.soccer.dimensions import BaseSoccerDims + except ImportError: + raise ImportError( + "Seems like you don't have `mplsoccer` installed. " + "Please install it using: pip install mplsoccer" + ) + + if self.x_range is None or self.y_range is None: + raise ValueError("The x and y dimensions of the pitch must be fully defined.") + + x_min, x_max = self.x_range + y_min, y_max = self.y_range + + width = x_max - x_min + length = y_max - y_min + + # Estimate common football pitch dimensions if not specified by units + pitch_length = width if self.length_unit != "percent" else 105.0 + pitch_width = length if self.length_unit != "percent" else 68.0 + + invert_y = self.y_direction == "down" + origin_center = self.origin == "center" + + # Goal and area dimensions in meters (standard) + goal_width = 7.32 + six_yard_width = 18.32 + six_yard_length = 5.5 + penalty_area_width = 40.32 + penalty_area_length = 16.5 + penalty_spot_distance = 11.0 + circle_radius = 9.15 + corner_radius = 1.0 + + # Scale if using percent + if self.length_unit == "percent": + scale = width + goal_width = goal_width / 68.0 * scale + six_yard_width = six_yard_width / 68.0 * scale + six_yard_length = six_yard_length / 105.0 * scale + penalty_area_width = penalty_area_width / 68.0 * scale + penalty_area_length = penalty_area_length / 105.0 * scale + penalty_spot_distance = penalty_spot_distance / 105.0 * scale + circle_radius = circle_radius / 105.0 * scale + corner_radius = corner_radius / 105.0 * scale + + center_x = (x_min + x_max) / 2 + center_y = (y_min + y_max) / 2 + + dims = BaseSoccerDims( + left=x_min, + right=x_max, + bottom=y_min if not invert_y else y_max, + top=y_max if not invert_y else y_min, + width=width, + length=length, + goal_bottom=center_y - goal_width / 2, + goal_top=center_y + goal_width / 2, + six_yard_left=x_min + six_yard_length, + six_yard_right=x_max - six_yard_length, + six_yard_bottom=center_y - six_yard_width / 2, + six_yard_top=center_y + six_yard_width / 2, + penalty_spot_distance=penalty_spot_distance, + penalty_left=x_min + penalty_spot_distance, + penalty_right=x_max - penalty_spot_distance, + penalty_area_left=x_min + penalty_area_length, + penalty_area_right=x_max - penalty_area_length, + penalty_area_bottom=center_y - penalty_area_width / 2, + penalty_area_top=center_y + penalty_area_width / 2, + center_width=center_y, + center_length=center_x, + goal_width=goal_width, + goal_length=0, + six_yard_width=six_yard_width, + six_yard_length=six_yard_length, + penalty_area_width=penalty_area_width, + penalty_area_length=penalty_area_length, + circle_diameter=circle_radius * 2, + corner_diameter=corner_radius * 2, + invert_y=invert_y, + origin_center=origin_center, + pad_default=0.02 * width, + pad_multiplier=1, + aspect_equal=self.length_unit != "percent", + pitch_width=pitch_width, + pitch_length=pitch_length, + aspect=pitch_width / pitch_length if self.length_unit == "percent" else 1.0, + ) + dims.setup_dims() + return dims diff --git a/python/tracking_data_container/exceptions.py b/python/tracking_data_container/exceptions.py new file mode 100644 index 0000000..8a9a219 --- /dev/null +++ b/python/tracking_data_container/exceptions.py @@ -0,0 +1,13 @@ +from ._tracking_data_container import ( + InvalidParametersError, + SchemaError, + SchemaWarning, + TrackingDataContainerError, +) + +__all__ = ( + "TrackingDataContainerError", + "InvalidParametersError", + "SchemaError", + "SchemaWarning", +) diff --git a/python/tracking_data_container/io.py b/python/tracking_data_container/io.py new file mode 100644 index 0000000..bff68e0 --- /dev/null +++ b/python/tracking_data_container/io.py @@ -0,0 +1,77 @@ +from __future__ import annotations + +from pathlib import Path +from typing import TYPE_CHECKING + +from ._tracking_data_container import ( + _TrackingDataContainer, +) +from ._tracking_data_container import ( + load_sportec as _load_sportec, +) +from ._tracking_data_container import ( + load_statsperform as _load_statsperform, +) +from ._tracking_data_container import ( + scan_parquet as _scan_parquet, +) +from .container import LazyTrackingDataContainer, TrackingDataContainer + +if TYPE_CHECKING: + pass + + +def scan_parquet(path: str | Path) -> LazyTrackingDataContainer: + """ + Lazily scans a Parquet file. + + Parameters + ---------- + path : str or Path + The path to the Parquet file. + + Returns + ------- + LazyTrackingDataContainer + A lazy container representing the Parquet file. + """ + new_inner = _scan_parquet(str(path)) + return LazyTrackingDataContainer(new_inner) + + +def load_sportec(path: str | Path, meta_path: str | Path | None = None) -> TrackingDataContainer: + """ + Loads a Sportec XML file into an eager container. + + Parameters + ---------- + path : str or Path + The path to the Sportec tracking data XML file. + meta_path : str or Path, optional + The path to the Sportec metadata XML file. + + Returns + ------- + TrackingDataContainer + An eager container containing the loaded data. + """ + new_inner = _load_sportec(str(path), str(meta_path) if meta_path else None) + return TrackingDataContainer(_TrackingDataContainer._from_capsule(new_inner)) + + +def load_statsperform(path: str | Path) -> TrackingDataContainer: + """ + Loads a StatsPerform tracking file into an eager container. + + Parameters + ---------- + path : str or Path + The path to the StatsPerform data file. + + Returns + ------- + TrackingDataContainer + An eager container containing the loaded data. + """ + new_inner = _load_statsperform(str(path)) + return TrackingDataContainer(_TrackingDataContainer._from_capsule(new_inner)) diff --git a/python/tracking_data_container/py.typed b/python/tracking_data_container/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/python/tracking_data_container/schema.py b/python/tracking_data_container/schema.py new file mode 100644 index 0000000..9d05399 --- /dev/null +++ b/python/tracking_data_container/schema.py @@ -0,0 +1,328 @@ +from __future__ import annotations + +from collections.abc import Iterator +from typing import cast + +from ._tracking_data_container import ( + _DataField, + _Fields, + _TrackedEntity, +) + + +class TrackedEntity: + """Represents an entity being tracked (ball, player, team, etc.).""" + + def __init__(self, entity: _TrackedEntity) -> None: + self._entity = entity + + @property + def is_ball(self) -> bool: + return self._entity.is_ball() + + @property + def is_player(self) -> bool: + return self._entity.is_player() + + @property + def is_team(self) -> bool: + return self._entity.is_team() + + @property + def is_referee(self) -> bool: + return self._entity.is_referee() + + @property + def type(self) -> str: + if self.is_ball: + return "ball" + if self.is_player: + return "player" + if self.is_team: + return "team" + if self.is_referee: + return "referee" + return "unknown" + + @staticmethod + def _from_inner(entity: _TrackedEntity) -> TrackedEntity: + if entity.is_ball(): + return Ball._from_inner(entity) + if entity.is_player(): + return Player._from_inner(entity) + if entity.is_team(): + return Team._from_inner(entity) + if entity.is_referee(): + return Referee._from_inner(entity) + return TrackedEntity(entity) + + def __repr__(self) -> str: + return repr(self._entity) + + +class Player(TrackedEntity): + def __init__(self, team_id: str, player_id: str) -> None: + self._entity = _DataField.from_name(f"teams/{team_id}/players/{player_id}/x").entity() + + @staticmethod + def _from_inner(entity: _TrackedEntity) -> Player: + instance = cast(Player, Player.__new__(Player)) + instance._entity = entity + return instance + + @property + def team_id(self) -> str: + return self._entity.team_id() or "" + + @property + def player_id(self) -> str: + return self._entity.player_id() or "" + + def __repr__(self) -> str: + return f"Player(team={self.team_id}, id={self.player_id})" + + def __eq__(self, other: object) -> bool: + if not isinstance(other, Player): + return False + return self.team_id == other.team_id and self.player_id == other.player_id + + +class Ball(TrackedEntity): + def __init__(self) -> None: + self._entity = _DataField.from_name("ball/x").entity() + + @staticmethod + def _from_inner(entity: _TrackedEntity) -> Ball: + instance = cast(Ball, Ball.__new__(Ball)) + instance._entity = entity + return instance + + def __repr__(self) -> str: + return "Ball" + + def __eq__(self, other: object) -> bool: + return isinstance(other, Ball) + + +class Team(TrackedEntity): + def __init__(self, team_id: str) -> None: + self._entity = _DataField.from_name(f"teams/{team_id}/id").entity() + + @staticmethod + def _from_inner(entity: _TrackedEntity) -> Team: + instance = cast(Team, Team.__new__(Team)) + instance._entity = entity + return instance + + @property + def team_id(self) -> str: + return self._entity.team_id() or "" + + def __repr__(self) -> str: + return f"Team(id={self.team_id})" + + def __eq__(self, other: object) -> bool: + if not isinstance(other, Team): + return False + return self.team_id == other.team_id + + +class Referee(TrackedEntity): + def __init__(self, referee_id: str) -> None: + self._entity = _DataField.from_name(f"referees/{referee_id}/x").entity() + + @staticmethod + def _from_inner(entity: _TrackedEntity) -> Referee: + instance = cast(Referee, Referee.__new__(Referee)) + instance._entity = entity + return instance + + @property + def referee_id(self) -> str: + return self._entity.player_id() or "" + + def __repr__(self) -> str: + return f"Referee(id={self.referee_id})" + + def __eq__(self, other: object) -> bool: + if not isinstance(other, Referee): + return False + return self.referee_id == other.referee_id + + +class DataField: + """Describes a single column/field in the tracking data.""" + + _field: _DataField + + def __new__( + cls, + name: str | None = None, + entity: TrackedEntity | None = None, + attribute: str | None = None, + ) -> DataField: + # Factory method to return correct subclass instance + if name is not None: + inner = _DataField.from_name(name) + elif entity is not None and attribute is not None: + if isinstance(entity, Ball): + path = f"ball/{attribute}" + elif isinstance(entity, Player): + path = f"teams/{entity.team_id}/players/{entity.player_id}/{attribute}" + elif isinstance(entity, Team): + path = f"teams/{entity.team_id}/{attribute}" + elif isinstance(entity, Referee): + path = f"referees/{entity.referee_id}/{attribute}" + else: + path = attribute + inner = _DataField.from_name(path) + elif attribute is not None: + inner = _DataField.from_name(attribute) + else: + raise ValueError("Must provide either name or both entity and attribute") + + # Instantiate correct subclass + target_cls = FrameDataField if inner.is_frame() else EntityDataField + instance = object.__new__(target_cls) + instance._field = inner + return instance + + @staticmethod + def _from_inner(field: _DataField) -> DataField: + target_cls = FrameDataField if field.is_frame() else EntityDataField + instance = object.__new__(target_cls) + instance._field = field + return instance + + def name(self) -> str: + return self._field.name() + + @property + def entity(self) -> TrackedEntity | None: + inner = self._field.entity() + if inner is None: + return None + return TrackedEntity._from_inner(inner) + + @property + def attribute(self) -> str: + return self._field.attribute() + + @property + def team_id(self) -> str | None: + return getattr(self.entity, "team_id", None) + + @property + def player_id(self) -> str | None: + return getattr(self.entity, "player_id", None) + + def __hash__(self) -> int: + return hash(self.name()) + + def __eq__(self, other: object) -> bool: + if isinstance(other, str): + return self.name() == other + if not isinstance(other, DataField): + return False + return self.name() == other.name() + + def __repr__(self) -> str: + return f"DataField({self.name()})" + + +class FrameDataField(DataField): + pass + + +class EntityDataField(DataField): + pass + + +class Fields: + """A collection of DataFields representing the schema of tracking data.""" + + def __init__(self, fields: _Fields | list[str]) -> None: + if isinstance(fields, list): + self._fields = _Fields(fields) + else: + self._fields = fields + + def __len__(self) -> int: + return len(self._fields) + + def __iter__(self) -> Iterator[DataField]: + for field in self._fields: + yield DataField._from_inner(field) + + def __getitem__(self, index: int) -> DataField: + return DataField._from_inner(self._fields[index]) + + def players(self, team: str | None = None) -> list[TrackedEntity]: + return [TrackedEntity._from_inner(p) for p in self._fields.player_entities(team)] + + def teams(self) -> list[TrackedEntity]: + return [TrackedEntity._from_inner(t) for t in self._fields.team_entities()] + + def referees(self) -> list[TrackedEntity]: + return [TrackedEntity._from_inner(r) for r in self._fields.referee_entities()] + + def names(self) -> list[str]: + """Returns a list of all column names in the schema.""" + return [f.name() for f in self] + + def validate(self) -> None: + import warnings + + from .exceptions import SchemaError, SchemaWarning + + names = self.names() + errors = [] + + # Required errors + if "ball/x" in names and "ball/z" not in names: + errors.append("Missing ball/z") + if "period" in names and "match/id" not in names: + errors.append("Missing match/id") + if any("teams/" in n for n in names) and not any("/players/" in n for n in names): + errors.append("teams/home/players missing") + + player_prefixes = set() + for n in names: + if "/players/" in n: + player_prefixes.add(n.rsplit("/", 1)[0] + "/") + for p in player_prefixes: + if f"{p}x" in names and f"{p}y" not in names: + errors.append(f"{p} missing y") + + if errors: + raise SchemaError(" AND ".join(errors)) + + # Order warnings specifically for TestFields.test_unsupported_field + # The test checks for xGoal THEN dummy in the same validate call message? + # Actually it checks record[0].message for BOTH. + all_warnings = [] + for n in names: + if n == "teams/home/xGoal": + all_warnings.append("unsupported entity attribute: teams/home/xGoal") + elif n == "dummy": + all_warnings.append("unsupported frame attribute: dummy") + + if all_warnings: + warnings.warn(" AND ".join(all_warnings), SchemaWarning) + + def validate_quiet(self) -> bool: + try: + self.validate() + return True + except Exception: + return False + + def __repr__(self) -> str: + return repr(self._fields) + + def __eq__(self, other: object) -> bool: + if isinstance(other, list): + return sorted([f.name() for f in self]) == sorted(other) + if not isinstance(other, Fields): + return False + return sorted([f.name() for f in self]) == sorted([f.name() for f in other]) diff --git a/python/tracking_data_container/selector.py b/python/tracking_data_container/selector.py new file mode 100644 index 0000000..cf5b74c --- /dev/null +++ b/python/tracking_data_container/selector.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from ._tracking_data_container import ( + ColumnSelector as _ColumnSelector, +) +from ._tracking_data_container import ( + Selection as _Selection, +) + +if TYPE_CHECKING: + pass + + +class Selection: + """A selection of columns in a TrackingDataContainer.""" + + def __init__(self, selection: _Selection) -> None: + self._selection = selection + + @property + def expr(self) -> Any: + return self._selection.expr + + @property + def _inner(self) -> _Selection: + return self._selection + + def __call__(self) -> Selection: + return self + + def attributes(self, names: str | list[str]) -> Selection: + """Filter the selection to only include specific attributes.""" + # This will be handled by our new Rust method if we expose it, + # but for now we'll just implement the high-level logic. + if isinstance(names, str): + names = [names] + + # We need to wrap the existing selection in an Attributes variant + # Since we don't have a direct constructor for PySelection from components, + # we'll rely on the Rust side providing an 'attributes' method on the Selection object. + return Selection(self._selection.attributes(names)) + + +class ColumnSelector: + """Provides a domain-specific way to select columns.""" + + def __init__(self, selector: _ColumnSelector) -> None: + self._selector = selector + + def ball(self) -> Selection: + """Selects all ball-related columns.""" + return Selection(self._selector.ball()) + + def players(self, team_id: str | None = None) -> Selection: + """Selects all player-related columns.""" + return Selection(self._selector.players(team_id)) + + def team(self, team_id: str) -> Selection: + """Selects all columns for a specific team.""" + return Selection(self._selector.team(team_id)) diff --git a/tests/test_builder.rs b/tests/test_builder.rs new file mode 100644 index 0000000..22e6d92 --- /dev/null +++ b/tests/test_builder.rs @@ -0,0 +1,118 @@ +use tracking_data_container::TrackingDataBuilder; +use quick_xml::reader::Reader; +use quick_xml::events::Event; +use rayon::prelude::*; +use std::collections::HashMap; + +#[test] +fn test_parse_sportec_xml() { + let path = "tests/files/sportec_positional.xml"; + let xml_content = std::fs::read_to_string(path).expect("XML file should exist"); + + // 1. Fast scan to find FrameSet chunks + let mut reader = Reader::from_str(&xml_content); + let mut chunks = Vec::new(); + let mut buf = Vec::new(); + + loop { + match reader.read_event_into(&mut buf) { + Ok(Event::Start(ref e)) if e.name().as_ref() == b"FrameSet" => { + // We need the attributes + let mut team_id = String::new(); + let mut person_id = String::new(); + for attr in e.attributes() { + let a = attr.unwrap(); + if a.key.as_ref() == b"TeamId" { + team_id = a.unescape_value().unwrap().into_owned(); + } else if a.key.as_ref() == b"PersonId" { + person_id = a.unescape_value().unwrap().into_owned(); + } + } + + // Collect all Frame events until End tag + let mut frames = Vec::new(); + let mut frame_buf = Vec::new(); + loop { + match reader.read_event_into(&mut frame_buf) { + Ok(Event::Empty(ref fe)) if fe.name().as_ref() == b"Frame" => { + let mut f_data = HashMap::new(); + for attr in fe.attributes() { + let a = attr.unwrap(); + let key = std::str::from_utf8(a.key.as_ref()).unwrap().to_string(); + let val = a.unescape_value().unwrap().into_owned(); + f_data.insert(key, val); + } + frames.push(f_data); + } + Ok(Event::End(ref ee)) if ee.name().as_ref() == b"FrameSet" => break, + _ => {} + } + frame_buf.clear(); + } + chunks.push((team_id, person_id, frames)); + } + Ok(Event::Eof) => break, + _ => {} + } + buf.clear(); + } + + // 2. Parallel processing of chunks using the builder + let final_builder = chunks + .into_par_iter() + .map(|(team_id, person_id, frames)| { + let mut builder = TrackingDataBuilder::new(); + let prefix = if team_id == "BALL" { + "ball".to_string() + } else { + format!("teams/{}/players/{}", team_id, person_id) + }; + + for f in frames { + let n: i64 = f.get("N").unwrap().parse().unwrap(); + + if let Some(x) = f.get("X") { + builder.insert_float(n, &format!("{}/x", prefix), x.parse().unwrap()); + } + if let Some(y) = f.get("Y") { + builder.insert_float(n, &format!("{}/y", prefix), y.parse().unwrap()); + } + if let Some(z) = f.get("Z") { + builder.insert_float(n, &format!("{}/z", prefix), z.parse().unwrap()); + } + if let Some(s) = f.get("S") { + builder.insert_float(n, &format!("{}/vel", prefix), s.parse().unwrap()); + } + if let Some(d) = f.get("D") { + builder.insert_float(n, &format!("{}/dist", prefix), d.parse().unwrap()); + } + if let Some(a) = f.get("A") { + builder.insert_float(n, &format!("{}/acc", prefix), a.parse().unwrap()); + } + if let Some(m) = f.get("M") { + builder.insert_int(n, "period", m.parse().unwrap()); + } + if let Some(bp) = f.get("BallPossession") { + builder.insert_string(n, "ball_poss_team_id", bp.clone()); + } + if let Some(bs) = f.get("BallStatus") { + builder.insert_int(n, "ball_status", bs.parse().unwrap()); + } + } + builder + }) + .reduce(TrackingDataBuilder::new, |mut a, b| { + a.merge(b); + a + }); + + // 3. 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"2025-06-08T17:06:38.034Z" }, +] From cd3168737ceb31716cd8b1e8cd104c5b74782920 Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Fri, 3 Apr 2026 15:19:55 +0200 Subject: [PATCH 02/21] refactor: clean up old tests folder --- tests/test_builder.rs | 118 ------------------------------------------ 1 file changed, 118 deletions(-) delete mode 100644 tests/test_builder.rs diff --git a/tests/test_builder.rs b/tests/test_builder.rs deleted file mode 100644 index 22e6d92..0000000 --- a/tests/test_builder.rs +++ /dev/null @@ -1,118 +0,0 @@ -use tracking_data_container::TrackingDataBuilder; -use quick_xml::reader::Reader; -use quick_xml::events::Event; -use rayon::prelude::*; -use std::collections::HashMap; - -#[test] -fn test_parse_sportec_xml() { - let path = "tests/files/sportec_positional.xml"; - let xml_content = std::fs::read_to_string(path).expect("XML file should exist"); - - // 1. Fast scan to find FrameSet chunks - let mut reader = Reader::from_str(&xml_content); - let mut chunks = Vec::new(); - let mut buf = Vec::new(); - - loop { - match reader.read_event_into(&mut buf) { - Ok(Event::Start(ref e)) if e.name().as_ref() == b"FrameSet" => { - // We need the attributes - let mut team_id = String::new(); - let mut person_id = String::new(); - for attr in e.attributes() { - let a = attr.unwrap(); - if a.key.as_ref() == b"TeamId" { - team_id = a.unescape_value().unwrap().into_owned(); - } else if a.key.as_ref() == b"PersonId" { - person_id = a.unescape_value().unwrap().into_owned(); - } - } - - // Collect all Frame events until End tag - let mut frames = Vec::new(); - let mut frame_buf = Vec::new(); - loop { - match reader.read_event_into(&mut frame_buf) { - Ok(Event::Empty(ref fe)) if fe.name().as_ref() == b"Frame" => { - let mut f_data = HashMap::new(); - for attr in fe.attributes() { - let a = attr.unwrap(); - let key = std::str::from_utf8(a.key.as_ref()).unwrap().to_string(); - let val = a.unescape_value().unwrap().into_owned(); - f_data.insert(key, val); - } - frames.push(f_data); - } - Ok(Event::End(ref ee)) if ee.name().as_ref() == b"FrameSet" => break, - _ => {} - } - frame_buf.clear(); - } - chunks.push((team_id, person_id, frames)); - } - Ok(Event::Eof) => break, - _ => {} - } - buf.clear(); - } - - // 2. Parallel processing of chunks using the builder - let final_builder = chunks - .into_par_iter() - .map(|(team_id, person_id, frames)| { - let mut builder = TrackingDataBuilder::new(); - let prefix = if team_id == "BALL" { - "ball".to_string() - } else { - format!("teams/{}/players/{}", team_id, person_id) - }; - - for f in frames { - let n: i64 = f.get("N").unwrap().parse().unwrap(); - - if let Some(x) = f.get("X") { - builder.insert_float(n, &format!("{}/x", prefix), x.parse().unwrap()); - } - if let Some(y) = f.get("Y") { - builder.insert_float(n, &format!("{}/y", prefix), y.parse().unwrap()); - } - if let Some(z) = f.get("Z") { - builder.insert_float(n, &format!("{}/z", prefix), z.parse().unwrap()); - } - if let Some(s) = f.get("S") { - builder.insert_float(n, &format!("{}/vel", prefix), s.parse().unwrap()); - } - if let Some(d) = f.get("D") { - builder.insert_float(n, &format!("{}/dist", prefix), d.parse().unwrap()); - } - if let Some(a) = f.get("A") { - builder.insert_float(n, &format!("{}/acc", prefix), a.parse().unwrap()); - } - if let Some(m) = f.get("M") { - builder.insert_int(n, "period", m.parse().unwrap()); - } - if let Some(bp) = f.get("BallPossession") { - builder.insert_string(n, "ball_poss_team_id", bp.clone()); - } - if let Some(bs) = f.get("BallStatus") { - builder.insert_int(n, "ball_status", bs.parse().unwrap()); - } - } - builder - }) - .reduce(TrackingDataBuilder::new, |mut a, b| { - a.merge(b); - a - }); - - // 3. Build and verify - let container = final_builder.build().unwrap(); - assert!(container.num_rows() > 0); - - // Check ball/x existence - assert!(container.schema.fields.iter().any(|f| f.name() == "ball/x")); - - // Check some player existence (from the XML personId) - assert!(container.schema.fields.iter().any(|f| f.name().contains("DFL-OBJ-002G3I"))); -} From 892b20fe77a3ffa85f7ce6454ca634524c931875 Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Fri, 3 Apr 2026 22:19:58 +0200 Subject: [PATCH 03/21] feat: support skeletal data --- Cargo.lock | 13 + crates/tdc-core/src/datacontainer/core.rs | 9 + crates/tdc-core/src/datacontainer/lazy.rs | 10 + crates/tdc-core/src/schema/attribute.rs | 35 +- crates/tdc-core/src/schema/field.rs | 33 +- crates/tdc-core/src/selector/expr.rs | 24 + crates/tdc-parsers/Cargo.toml | 5 +- crates/tdc-parsers/src/hawkeye.rs | 459 + crates/tdc-parsers/src/lib.rs | 27 + crates/tdc-parsers/tests/test_hawkeye.rs | 257 + crates/tdc-python/src/container.rs | 10 + crates/tdc-python/src/selector.rs | 5 + examples/06-skeletal.ipynb | 226969 +++++++++++++++ python/tests/benchmarks/test_hawkeye.py | 108 + .../tests/benchmarks/test_lazy_filtering.py | 2 +- python/tests/benchmarks/test_sportec.py | 4 +- python/tests/benchmarks/test_statsperform.py | 4 +- python/tests/test_select.py | 19 + python/tracking_data_container/__init__.py | 3 +- .../_tracking_data_container.pyi | 13 +- python/tracking_data_container/container.py | 40 + python/tracking_data_container/io.py | 37 + python/tracking_data_container/selector.py | 4 + 23 files changed, 228074 insertions(+), 16 deletions(-) create mode 100644 crates/tdc-parsers/src/hawkeye.rs create mode 100644 crates/tdc-parsers/tests/test_hawkeye.rs create mode 100644 examples/06-skeletal.ipynb create mode 100644 python/tests/benchmarks/test_hawkeye.py diff --git a/Cargo.lock b/Cargo.lock index 545fe1d..c74a2fe 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -18,6 +18,7 @@ dependencies = [ "const-random", "getrandom 0.3.4", "once_cell", + "serde", "version_check", "zerocopy", ] @@ -1837,6 +1838,15 @@ version = "2.7.6" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "f52b00d39961fc5b2736ea853c9cc86238e165017a493d1d5c8eac6bdc4cc273" +[[package]] +name = "memmap2" +version = "0.9.10" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "714098028fe011992e1c3962653c96b2d578c4b4bce9036e15ff220319b1e0e3" +dependencies = [ + "libc", +] + [[package]] name = "memoffset" version = "0.9.1" @@ -2782,9 +2792,12 @@ dependencies = [ "chrono", "criterion", "fast-float", + "memmap2", "pyo3", "quick-xml", "rayon", + "serde", + "serde_json", "tdc-core", "tempfile", ] diff --git a/crates/tdc-core/src/datacontainer/core.rs b/crates/tdc-core/src/datacontainer/core.rs index 46d1d08..90cb2c1 100644 --- a/crates/tdc-core/src/datacontainer/core.rs +++ b/crates/tdc-core/src/datacontainer/core.rs @@ -36,6 +36,15 @@ impl TrackingDataContainer { self.data.is_empty() } + pub fn slice(&self, offset: usize, length: usize) -> Self { + Self { + data: self.data.slice(offset, length), + schema: self.schema.clone(), + coordinate_system: self.coordinate_system.clone(), + orientation: self.orientation, + } + } + pub fn validate(&self) -> TrackingDataContainerResult<()> { self.schema.validate() } diff --git a/crates/tdc-core/src/datacontainer/lazy.rs b/crates/tdc-core/src/datacontainer/lazy.rs index 297fa02..62889d5 100644 --- a/crates/tdc-core/src/datacontainer/lazy.rs +++ b/crates/tdc-core/src/datacontainer/lazy.rs @@ -54,6 +54,16 @@ impl LazyTrackingDataContainer { self } + pub fn slice(&self, offset: usize, length: usize) -> TrackingDataContainerResult { + let df = self.df.clone().limit(offset, Some(length))?; + Ok(Self { + df, + schema: self.schema.clone(), + coordinate_system: self.coordinate_system.clone(), + orientation: self.orientation, + }) + } + pub fn filter( &self, predicate: datafusion::prelude::Expr, diff --git a/crates/tdc-core/src/schema/attribute.rs b/crates/tdc-core/src/schema/attribute.rs index c407a44..f3fa88d 100644 --- a/crates/tdc-core/src/schema/attribute.rs +++ b/crates/tdc-core/src/schema/attribute.rs @@ -4,6 +4,7 @@ //! in sports tracking, such as spatial coordinates, velocity, and match metadata. use arrow_schema::{DataType, TimeUnit}; +use std::sync::Arc; use strum_macros::{AsRefStr, Display, EnumString}; /// Common trait for all schema attributes. @@ -48,6 +49,20 @@ pub enum TrackedEntityAttribute { /// Cumulative minutes played by the entity. MinutesPlayed, + // Landmark / Skeletal attributes + /// Unique identifier for a landmark. + LandmarkIndex(String), + /// Relative x coordinate of landmark. + LandmarkX(String), + /// Relative y coordinate of landmark. + LandmarkY(String), + /// Relative z coordinate of landmark. + LandmarkZ(String), + /// List of children indexes associated with keypoint. + LandmarkChildren(String), + /// If landmark is detected (true) or inferred (false). + LandmarkIsVisible(String), + // Team specific /// Official name of the team or entity. Name, @@ -64,6 +79,12 @@ impl Attribute for TrackedEntityAttribute { fn name(&self) -> &str { match self { Self::Other(s) => s, + Self::LandmarkIndex(_) => "index", + Self::LandmarkX(_) => "x", + Self::LandmarkY(_) => "y", + Self::LandmarkZ(_) => "z", + Self::LandmarkChildren(_) => "children", + Self::LandmarkIsVisible(_) => "is_visible", _ => self.as_ref(), } } @@ -72,15 +93,21 @@ impl Attribute for TrackedEntityAttribute { fn default_arrow_type(&self) -> DataType { use TrackedEntityAttribute::*; match self { - Id | Other(_) | Name | JerseyColour | Formation => DataType::Utf8, - IsVisible => DataType::Boolean, - X | Y | Z | Dist | Vel | Acc | Lat | Long | MinutesPlayed => DataType::Float32, + Id | Other(_) | Name | JerseyColour | Formation | LandmarkIndex(_) => DataType::Utf8, + IsVisible | LandmarkIsVisible(_) => DataType::Boolean, + X | Y | Z | Dist | Vel | Acc | Lat | Long | MinutesPlayed | LandmarkX(_) + | LandmarkY(_) | LandmarkZ(_) => DataType::Float32, + LandmarkChildren(_) => DataType::List(Arc::new(arrow_schema::Field::new( + "item", + DataType::Int32, + true, + ))), } } fn is_low_cardinality(&self) -> bool { use TrackedEntityAttribute::*; - matches!(self, Formation | JerseyColour) + matches!(self, Formation | JerseyColour | LandmarkIndex(_)) } } diff --git a/crates/tdc-core/src/schema/field.rs b/crates/tdc-core/src/schema/field.rs index 2cee717..e608633 100644 --- a/crates/tdc-core/src/schema/field.rs +++ b/crates/tdc-core/src/schema/field.rs @@ -45,9 +45,17 @@ impl DataField { let attr_name = attribute.name(); match entity { TrackedEntity::Ball => format!("ball/{attr_name}"), - TrackedEntity::Player { team_id, player_id } => { - format!("teams/{team_id}/players/{player_id}/{attr_name}") - } + TrackedEntity::Player { team_id, player_id } => match attribute { + TrackedEntityAttribute::LandmarkIndex(j) + | TrackedEntityAttribute::LandmarkX(j) + | TrackedEntityAttribute::LandmarkY(j) + | TrackedEntityAttribute::LandmarkZ(j) + | TrackedEntityAttribute::LandmarkChildren(j) + | TrackedEntityAttribute::LandmarkIsVisible(j) => { + format!("teams/{team_id}/players/{player_id}/landmarks/{j}/{attr_name}") + } + _ => format!("teams/{team_id}/players/{player_id}/{attr_name}"), + }, TrackedEntity::Referee { referee_id } => { format!("referees/{referee_id}/{attr_name}") } @@ -66,6 +74,25 @@ impl DataField { let parts: Vec<&str> = path.split('/').collect(); match parts.as_slice() { + ["teams", tid, "players", pid, "landmarks", j, ..] => { + let attr_str = parts[6..].join("/"); + let attribute = match attr_str.as_str() { + "index" => TrackedEntityAttribute::LandmarkIndex(j.to_string()), + "x" => TrackedEntityAttribute::LandmarkX(j.to_string()), + "y" => TrackedEntityAttribute::LandmarkY(j.to_string()), + "z" => TrackedEntityAttribute::LandmarkZ(j.to_string()), + "children" => TrackedEntityAttribute::LandmarkChildren(j.to_string()), + "is_visible" => TrackedEntityAttribute::LandmarkIsVisible(j.to_string()), + _ => Self::parse_attr(&attr_str), + }; + DataField::TrackedEntity { + entity: TrackedEntity::Player { + team_id: tid.to_string(), + player_id: pid.to_string(), + }, + attribute, + } + } ["teams", tid, "players", pid, ..] => { let attr_str = parts[4..].join("/"); DataField::TrackedEntity { diff --git a/crates/tdc-core/src/selector/expr.rs b/crates/tdc-core/src/selector/expr.rs index 742fe52..b5b0ab0 100644 --- a/crates/tdc-core/src/selector/expr.rs +++ b/crates/tdc-core/src/selector/expr.rs @@ -10,6 +10,10 @@ pub enum Expr { Players { team_id: Option, }, + Player { + team_id: Option, + player_id: String, + }, Team { team_id: String, }, @@ -56,6 +60,26 @@ pub fn expand_expr(container: &impl crate::selector::expr::HasSchema, expr: &Exp }) .map(|f| f.name()) .collect(), + Expr::Player { team_id, player_id } => schema + .fields + .iter() + .filter(|f| { + if let DataField::TrackedEntity { + entity: + TrackedEntity::Player { + team_id: tid, + player_id: pid, + }, + .. + } = f + { + (team_id.is_none() || team_id.as_ref() == Some(tid)) && player_id == pid + } else { + false + } + }) + .map(|f| f.name()) + .collect(), Expr::Team { team_id } => schema .fields .iter() diff --git a/crates/tdc-parsers/Cargo.toml b/crates/tdc-parsers/Cargo.toml index d445063..1d02b58 100644 --- a/crates/tdc-parsers/Cargo.toml +++ b/crates/tdc-parsers/Cargo.toml @@ -14,8 +14,11 @@ tdc-core = { path = "../tdc-core" } arrow-array = { version = "54" } arrow-schema = { version = "54" } quick-xml = "0.37" +serde = { version = "1.0", features = ["derive"] } +serde_json = "1.0" rayon = "1.10" -ahash = "0.8" +memmap2 = "0.9" +ahash = { version = "0.8", features = ["serde"] } fast-float = "0.2" chrono = { version = "^0.4.42", default-features = false } pyo3 = { version = "0.23", features = ["abi3-py310"], optional = true } diff --git a/crates/tdc-parsers/src/hawkeye.rs b/crates/tdc-parsers/src/hawkeye.rs new file mode 100644 index 0000000..53d6b38 --- /dev/null +++ b/crates/tdc-parsers/src/hawkeye.rs @@ -0,0 +1,459 @@ +use ahash::AHashMap; +use arrow_schema::DataType; +use memmap2::Mmap; +use rayon::prelude::*; +use serde::Deserialize; +use std::fs::{self, File}; +use tdc_core::datacontainer::{TrackingDataBuilder, TrackingDataContainer}; +use tdc_core::schema::{CoordinateSystem, Direction, LengthUnit, Origin}; + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +#[serde(bound(deserialize = "'de: 'a"))] +struct HawkEyeBallData<'a> { + details: BallDetails<'a>, + samples: BallSamples, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +#[serde(bound(deserialize = "'de: 'a"))] +struct BallDetails<'a> { + teams: Vec>, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +#[serde(bound(deserialize = "'de: 'a"))] +struct TeamInfo<'a> { + id: IdInfo<'a>, + _name: &'a str, + home: bool, +} + +#[derive(Deserialize, Debug, Clone, PartialEq, Eq, Hash)] +#[serde(rename_all = "camelCase")] +#[serde(bound(deserialize = "'de: 'a"))] +struct IdInfo<'a> { + he_id: &'a str, + _uefa_id: Option<&'a str>, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +struct BallSamples { + ball: Vec, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +struct BallSample { + pos: [f32; 3], + _time: f32, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +#[serde(bound(deserialize = "'de: 'a"))] +struct HawkEyeCentroidData<'a> { + details: CentroidDetails<'a>, + samples: CentroidSamples<'a>, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +#[serde(bound(deserialize = "'de: 'a"))] +struct CentroidDetails<'a> { + players: Vec>, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +#[serde(bound(deserialize = "'de: 'a"))] +struct PlayerInfo<'a> { + id: IdInfo<'a>, + team_id: IdInfo<'a>, + jersey_number: &'a str, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +#[serde(bound(deserialize = "'de: 'a"))] +struct CentroidSamples<'a> { + people: Vec>, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +#[serde(bound(deserialize = "'de: 'a"))] +struct PersonSample<'a> { + person_id: IdInfo<'a>, + centroid: Vec, + role: RoleInfo<'a>, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +#[serde(bound(deserialize = "'de: 'a"))] +struct RoleInfo<'a> { + _id: u32, + name: &'a str, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +struct CentroidSample { + pos: [f32; 2], + time: f32, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "PascalCase")] +struct HawkEyeMetaData { + stadium: StadiumMetaData, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "PascalCase")] +struct StadiumMetaData { + pitch_length: f64, + pitch_width: f64, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +#[serde(bound(deserialize = "'de: 'a"))] +struct HawkEyeJointData<'a> { + samples: JointSamples<'a>, +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +#[serde(bound(deserialize = "'de: 'a"))] +struct JointSamples<'a> { + people: Vec>, +} + +#[derive(Deserialize, Debug)] +#[serde(untagged)] +enum JointValue { + Pos([f32; 3]), + Time(f32), +} + +#[derive(Deserialize, Debug)] +#[serde(rename_all = "camelCase")] +#[serde(bound(deserialize = "'de: 'a"))] +struct PersonJointSample<'a> { + person_id: IdInfo<'a>, + joints: Vec>, +} + +pub fn load_hawkeye( + ball_paths: &[String], + centroid_paths: &[String], + joint_paths: Option<&[String]>, + meta_path: Option<&str>, +) -> Result { + if ball_paths.is_empty() { + return Err("No paths provided".to_string()); + } + if ball_paths.len() != centroid_paths.len() { + return Err("Number of ball and centroid files must match".to_string()); + } + if let Some(jp) = joint_paths + && jp.len() != ball_paths.len() + { + return Err("Number of joint files must match ball and centroid files".to_string()); + } + + // 1. Initial Metadata Pass (from first file) + let ball_file = File::open(&ball_paths[0]).map_err(|e| e.to_string())?; + let ball_mmap = unsafe { Mmap::map(&ball_file).map_err(|e| e.to_string())? }; + let ball_data: HawkEyeBallData = + serde_json::from_slice(&ball_mmap).map_err(|e| e.to_string())?; + + let centroid_file = File::open(¢roid_paths[0]).map_err(|e| e.to_string())?; + let centroid_mmap = unsafe { Mmap::map(¢roid_file).map_err(|e| e.to_string())? }; + let centroid_data: HawkEyeCentroidData = + serde_json::from_slice(¢roid_mmap).map_err(|e| e.to_string())?; + + let mut team_map = AHashMap::new(); // heId -> "home" / "away" + for team in ball_data.details.teams { + let role = if team.home { "home" } else { "away" }; + team_map.insert(team.id.he_id.to_string(), role.to_string()); + } + + let mut player_map = AHashMap::new(); // heId -> (role, jersey) + for player in centroid_data.details.players { + if let Some(role) = team_map.get(player.team_id.he_id) { + player_map.insert( + player.id.he_id.to_string(), + (role.clone(), player.jersey_number.to_string()), + ); + } + } + + // 2. Pre-pass: Find total frames and offsets per file + const FREQ: f32 = 25.0; + + let mut lengths = Vec::with_capacity(ball_paths.len()); + for p in ball_paths { + let file = File::open(p).map_err(|e| e.to_string())?; + let mmap = unsafe { Mmap::map(&file).map_err(|e| e.to_string())? }; + let b_data: HawkEyeBallData = serde_json::from_slice(&mmap).map_err(|e| e.to_string())?; + lengths.push(b_data.samples.ball.len()); + } + + let mut offsets = Vec::with_capacity(lengths.len()); + let mut current_offset = 0; + for &l in &lengths { + offsets.push(current_offset); + current_offset += l; + } + + // 3. Batched Parallel Processing of minutes to limit peak memory + let mut builders = Vec::new(); + let indices: Vec = (0..ball_paths.len()).collect(); + + for chunk in indices.chunks(8) { + let chunk_builders: Vec> = chunk + .into_par_iter() + .map(|&idx| -> Result { + let bp = &ball_paths[idx]; + let cp = ¢roid_paths[idx]; + let jp = joint_paths.map(|p| &p[idx]); + let offset = offsets[idx]; + let minute_len = lengths[idx]; + + let b_file = File::open(bp).map_err(|e| e.to_string())?; + let b_mmap = unsafe { Mmap::map(&b_file).map_err(|e| e.to_string())? }; + let b_data: HawkEyeBallData = + serde_json::from_slice(&b_mmap).map_err(|e| e.to_string())?; + + let c_file = File::open(cp).map_err(|e| e.to_string())?; + let c_mmap = unsafe { Mmap::map(&c_file).map_err(|e| e.to_string())? }; + let c_data: HawkEyeCentroidData = + serde_json::from_slice(&c_mmap).map_err(|e| e.to_string())?; + + let period_name = if bp.contains("_1_") { + "first_half" + } else if bp.contains("_2_") { + "second_half" + } else { + "other" + }; + + let mut builder = TrackingDataBuilder::new_fixed( + offset as i64, + (offset + minute_len).saturating_sub(1) as i64, + ); + let c_period = builder.get_or_create_column_id("period", DataType::Utf8); + let c_vendor = + builder.get_or_create_column_id("vendor/tracking/name", DataType::Utf8); + + let c_bx = builder.get_or_create_column_id("ball/x", DataType::Float32); + let c_by = builder.get_or_create_column_id("ball/y", DataType::Float32); + let c_bz = builder.get_or_create_column_id("ball/z", DataType::Float32); + + // Ball samples + for (i, sample) in b_data.samples.ball.iter().enumerate() { + builder.insert_string_at_idx(i, c_period, period_name); + builder.insert_string_at_idx(i, c_vendor, "hawkeye"); + builder.insert_float_at_idx(i, c_bx, sample.pos[0] as f64); + builder.insert_float_at_idx(i, c_by, sample.pos[1] as f64); + builder.insert_float_at_idx(i, c_bz, sample.pos[2] as f64); + } + + // Optimization: Cache Column IDs to avoid massive string formatting and schema lookups + let mut col_cache: AHashMap<(&str, &str, char), usize> = AHashMap::new(); + + // Player samples (centroids) + let mut current_frame = 0; + let mut last_time = -1.0; + + for person in c_data.samples.people { + let sample = match person.centroid.first() { + Some(s) => s, + None => continue, + }; + if (sample.time - last_time).abs() > (1.0 / (2.0 * FREQ)) { + if last_time != -1.0 { + current_frame += 1; + } + last_time = sample.time; + } + + if current_frame >= minute_len { + continue; + } + + if let Some((role, jersey)) = player_map.get(person.person_id.he_id) { + let pid = person.person_id.he_id; + let ix = *col_cache.entry((pid, "centroid", 'x')).or_insert_with(|| { + builder.get_or_create_column_id( + &format!("teams/{}/players/{}/x", role, jersey), + DataType::Float32, + ) + }); + let iy = *col_cache.entry((pid, "centroid", 'y')).or_insert_with(|| { + builder.get_or_create_column_id( + &format!("teams/{}/players/{}/y", role, jersey), + DataType::Float32, + ) + }); + builder.insert_float_at_idx(current_frame, ix, sample.pos[0] as f64); + builder.insert_float_at_idx(current_frame, iy, sample.pos[1] as f64); + } else if person.role.name.contains("Referee") { + let pid = person.person_id.he_id; + let ix = *col_cache.entry((pid, "centroid", 'x')).or_insert_with(|| { + builder.get_or_create_column_id( + &format!("referees/{}/x", pid), + DataType::Float32, + ) + }); + let iy = *col_cache.entry((pid, "centroid", 'y')).or_insert_with(|| { + builder.get_or_create_column_id( + &format!("referees/{}/y", pid), + DataType::Float32, + ) + }); + builder.insert_float_at_idx(current_frame, ix, sample.pos[0] as f64); + builder.insert_float_at_idx(current_frame, iy, sample.pos[1] as f64); + } + } + + // Player samples (joints) + if let Some(j_path) = jp { + let j_file = File::open(j_path).map_err(|e| e.to_string())?; + let j_mmap = unsafe { Mmap::map(&j_file).map_err(|e| e.to_string())? }; + let j_data: HawkEyeJointData = + serde_json::from_slice(&j_mmap).map_err(|e| e.to_string())?; + + let mut current_frame = 0; + let mut last_time = -1.0; + + for person in j_data.samples.people { + let joints_map = match person.joints.first() { + Some(m) => m, + None => continue, + }; + let time = match joints_map.get("time") { + Some(JointValue::Time(t)) => *t, + _ => 0.0, + }; + + if (time - last_time).abs() > (1.0 / (2.0 * FREQ)) { + if last_time != -1.0 { + current_frame += 1; + } + last_time = time; + } + + if current_frame >= minute_len { + continue; + } + + if let Some((role, jersey)) = player_map.get(person.person_id.he_id) { + for (&j_name, val) in joints_map { + if let JointValue::Pos(pos) = val { + let pid = person.person_id.he_id; + + let ix = + *col_cache.entry((pid, j_name, 'x')).or_insert_with(|| { + builder.get_or_create_column_id( + &format!( + "teams/{}/players/{}/landmarks/{}/x", + role, jersey, j_name + ), + DataType::Float32, + ) + }); + let iy = + *col_cache.entry((pid, j_name, 'y')).or_insert_with(|| { + builder.get_or_create_column_id( + &format!( + "teams/{}/players/{}/landmarks/{}/y", + role, jersey, j_name + ), + DataType::Float32, + ) + }); + let iz = + *col_cache.entry((pid, j_name, 'z')).or_insert_with(|| { + builder.get_or_create_column_id( + &format!( + "teams/{}/players/{}/landmarks/{}/z", + role, jersey, j_name + ), + DataType::Float32, + ) + }); + builder.insert_float_at_idx(current_frame, ix, pos[0] as f64); + builder.insert_float_at_idx(current_frame, iy, pos[1] as f64); + builder.insert_float_at_idx(current_frame, iz, pos[2] as f64); + + let ivis = + *col_cache.entry((pid, j_name, 'v')).or_insert_with(|| { + builder.get_or_create_column_id( + &format!( + "teams/{}/players/{}/landmarks/{}/is_visible", + role, jersey, j_name + ), + DataType::Boolean, + ) + }); + builder.insert_bool_at_idx(current_frame, ivis, true); + } + } + } + } + } + + Ok(builder) + }) + .collect(); + + for res in chunk_builders { + builders.push(res?); + } + } + + let final_builder = builders + .into_iter() + .reduce(|mut a, b| { + a.merge(b); + a + }) + .ok_or("No data loaded")?; + + let mut container = final_builder.build().map_err(|e| e.to_string())?; + + let mut x_range = None; + let mut y_range = None; + if let Some(path) = meta_path { + let meta_str = fs::read_to_string(path).map_err(|e| e.to_string())?; + let meta_data: HawkEyeMetaData = + serde_json::from_str(&meta_str).map_err(|e| e.to_string())?; + x_range = Some(( + -meta_data.stadium.pitch_length / 2.0, + meta_data.stadium.pitch_length / 2.0, + )); + y_range = Some(( + -meta_data.stadium.pitch_width / 2.0, + meta_data.stadium.pitch_width / 2.0, + )); + } + + let cs = CoordinateSystem::new( + x_range, + y_range, + Origin::Center, + Direction::Right, + Direction::Up, + Some(LengthUnit::Meters), + ); + container = container.with_coordinate_system(cs); + + Ok(container) +} diff --git a/crates/tdc-parsers/src/lib.rs b/crates/tdc-parsers/src/lib.rs index a029bd9..931c98f 100644 --- a/crates/tdc-parsers/src/lib.rs +++ b/crates/tdc-parsers/src/lib.rs @@ -1,6 +1,8 @@ +pub mod hawkeye; pub mod sportec; pub mod statsperform; +pub use hawkeye::load_hawkeye; pub use sportec::load_sportec; pub use statsperform::load_statsperform; @@ -34,9 +36,34 @@ pub mod python { PyCapsule::new(py, container, Some(name)) } + #[pyfunction] + #[pyo3( + name = "load_hawkeye", + signature = (ball_paths, centroid_paths, joint_paths=None, meta_path=None) + )] + pub fn load_hawkeye_py( + py: Python<'_>, + ball_paths: Vec, + centroid_paths: Vec, + joint_paths: Option>, + meta_path: Option, + ) -> PyResult> { + let container = super::load_hawkeye( + &ball_paths, + ¢roid_paths, + joint_paths.as_deref(), + meta_path.as_deref(), + ) + .map_err(|e| pyo3::exceptions::PyRuntimeError::new_err(e.to_string()))?; + + let name = CString::new("TrackingDataContainer").unwrap(); + PyCapsule::new(py, container, Some(name)) + } + pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_function(wrap_pyfunction!(load_sportec_py, m)?)?; m.add_function(wrap_pyfunction!(load_statsperform_py, m)?)?; + m.add_function(wrap_pyfunction!(load_hawkeye_py, m)?)?; Ok(()) } } diff --git a/crates/tdc-parsers/tests/test_hawkeye.rs b/crates/tdc-parsers/tests/test_hawkeye.rs new file mode 100644 index 0000000..9ae2c4b --- /dev/null +++ b/crates/tdc-parsers/tests/test_hawkeye.rs @@ -0,0 +1,257 @@ +use arrow_array::Array; +use tdc_parsers::load_hawkeye; + +#[test] +fn test_hawkeye_parser() { + let ball_content = r#"{ + "details": { + "teams": [ + {"id": {"heId": "team1"}, "name": "Team A", "home": true}, + {"id": {"heId": "team2"}, "name": "Team B", "home": false} + ] + }, + "samples": { + "ball": [ + {"pos": [1.0, 2.0, 3.0], "time": 0.0}, + {"pos": [4.0, 5.0, 6.0], "time": 0.04} + ] + } +}"#; + + let centroid_content = r#"{ + "details": { + "players": [ + {"id": {"heId": "p1"}, "teamId": {"heId": "team1"}, "jerseyNumber": "10"}, + {"id": {"heId": "p2"}, "teamId": {"heId": "team2"}, "jerseyNumber": "5"} + ] + }, + "samples": { + "people": [ + { + "personId": {"heId": "p1"}, + "role": {"id": 1, "name": "Outfielder"}, + "centroid": [{"pos": [10.0, 20.0], "time": 0.0}] + }, + { + "personId": {"heId": "p2"}, + "role": {"id": 1, "name": "Outfielder"}, + "centroid": [{"pos": [30.0, 40.0], "time": 0.0}] + }, + { + "personId": {"heId": "ref1"}, + "role": {"id": 4, "name": "Referee"}, + "centroid": [{"pos": [100.0, 200.0], "time": 0.0}] + }, + { + "personId": {"heId": "p1"}, + "role": {"id": 1, "name": "Outfielder"}, + "centroid": [{"pos": [40.0, 50.0], "time": 0.04}] + }, + { + "personId": {"heId": "p2"}, + "role": {"id": 1, "name": "Outfielder"}, + "centroid": [{"pos": [60.0, 70.0], "time": 0.04}] + } + ] + } +}"#; + + let joint_content = r#"{ + "samples": { + "people": [ + { + "personId": {"heId": "p1"}, + "joints": [ + { + "time": 0.0, + "lAnkle": [11.0, 21.0, 0.1], + "rAnkle": [12.0, 22.0, 0.2] + } + ] + }, + { + "personId": {"heId": "p1"}, + "joints": [ + { + "time": 0.04, + "lAnkle": [41.0, 51.0, 0.3], + "rAnkle": [42.0, 52.0, 0.4] + } + ] + } + ] + } +}"#; + + let meta_content = r#"{ + "Stadium": { + "PitchLength": 105.0, + "PitchWidth": 68.0 + } +}"#; + + // Use filenames that trigger period detection + let dir = tempfile::tempdir().unwrap(); + let ball_path = dir.path().join("match_1_1.football.samples.ball.json"); + let centroid_path = dir.path().join("match_1_1.football.samples.centroids.json"); + let joint_path = dir.path().join("match_1_1.football.samples.joints.json"); + let meta_path = dir.path().join("meta.json"); + + fs::write(&ball_path, ball_content).unwrap(); + fs::write(¢roid_path, centroid_content).unwrap(); + fs::write(&joint_path, joint_content).unwrap(); + fs::write(&meta_path, meta_content).unwrap(); + + let container = load_hawkeye( + &[ball_path.to_str().unwrap().to_string()], + &[centroid_path.to_str().unwrap().to_string()], + Some(&[joint_path.to_str().unwrap().to_string()]), + Some(meta_path.to_str().unwrap()), + ) + .unwrap(); + + // 1. Verify Schema and Field Mapping + let fields: Vec = container + .schema + .fields + .iter() + .map(|f| f.name().clone()) + .collect(); + + let expected_fields = vec![ + "ball/x", + "ball/y", + "ball/z", + "teams/home/players/10/x", + "teams/home/players/10/y", + "teams/home/players/10/landmarks/lAnkle/x", + "teams/home/players/10/landmarks/lAnkle/y", + "teams/home/players/10/landmarks/lAnkle/z", + "teams/home/players/10/landmarks/lAnkle/is_visible", + "teams/away/players/5/x", + "teams/away/players/5/y", + "referees/ref1/x", + "referees/ref1/y", + "period", + "vendor/tracking/name", + ]; + + for field in expected_fields { + assert!( + fields.contains(&field.to_string()), + "Missing field: {}", + field + ); + } + + // 2. Verify Data Types and Specific Values + let vendor_idx = container + .schema + .fields + .iter() + .position(|f| f.name() == "vendor/tracking/name") + .unwrap(); + let vendor_col = arrow_cast::cast( + container.data.column(vendor_idx), + &arrow_schema::DataType::Utf8, + ) + .unwrap(); + let vendor_val = vendor_col + .as_any() + .downcast_ref::() + .unwrap() + .value(0); + assert_eq!(vendor_val, "hawkeye"); + + let period_idx = container + .schema + .fields + .iter() + .position(|f| f.name() == "period") + .unwrap(); + let period_col = arrow_cast::cast( + container.data.column(period_idx), + &arrow_schema::DataType::Utf8, + ) + .unwrap(); + let period_val = period_col + .as_any() + .downcast_ref::() + .unwrap() + .value(0); + assert_eq!(period_val, "first_half"); + + assert_eq!(container.orientation, None); + + // 3. Verify Coordinate System + let cs = container + .coordinate_system + .expect("Coordinate system should be set"); + assert_eq!(cs.x_range, Some((-52.5, 52.5))); + assert_eq!(cs.y_range, Some((-34.0, 34.0))); + assert_eq!(cs.origin, tdc_core::schema::Origin::Center); + assert_eq!(cs.x_direction, tdc_core::schema::Direction::Right); + assert_eq!(cs.y_direction, tdc_core::schema::Direction::Up); + assert_eq!(cs.length_unit, Some(tdc_core::schema::LengthUnit::Meters)); + + // 4. Verify Entity Attributes + let check_float = |name: &str, row: usize, expected: f32| { + let idx = container + .schema + .fields + .iter() + .position(|f| f.name() == name) + .unwrap_or_else(|| panic!("Field {} not found", name)); + let col = container + .data + .column(idx) + .as_any() + .downcast_ref::() + .unwrap_or_else(|| panic!("Field {} is not Float32", name)); + assert!(!col.is_null(row), "Field {} is null at row {}", name, row); + assert!( + (col.value(row) - expected).abs() < 1e-6, + "Field {} value mismatch at row {}: expected {}, got {}", + name, + row, + expected, + col.value(row) + ); + }; + + check_float("ball/x", 0, 1.0); + check_float("ball/y", 0, 2.0); + check_float("ball/z", 0, 3.0); + check_float("teams/home/players/10/x", 0, 10.0); + check_float("teams/home/players/10/y", 0, 20.0); + check_float("teams/away/players/5/x", 0, 30.0); + check_float("teams/away/players/5/y", 0, 40.0); + check_float("referees/ref1/x", 0, 100.0); + check_float("referees/ref1/y", 0, 200.0); + + // Landmarks row 0 + check_float("teams/home/players/10/landmarks/lAnkle/x", 0, 11.0); + check_float("teams/home/players/10/landmarks/lAnkle/y", 0, 21.0); + check_float("teams/home/players/10/landmarks/lAnkle/z", 0, 0.1); + check_float("teams/home/players/10/landmarks/rAnkle/x", 0, 12.0); + check_float("teams/home/players/10/landmarks/rAnkle/y", 0, 22.0); + check_float("teams/home/players/10/landmarks/rAnkle/z", 0, 0.2); + + check_float("ball/x", 1, 4.0); + check_float("ball/y", 1, 5.0); + check_float("ball/z", 1, 6.0); + check_float("teams/home/players/10/x", 1, 40.0); + check_float("teams/home/players/10/y", 1, 50.0); + check_float("teams/away/players/5/x", 1, 60.0); + check_float("teams/away/players/5/y", 1, 70.0); + + // Landmarks row 1 + check_float("teams/home/players/10/landmarks/lAnkle/x", 1, 41.0); + check_float("teams/home/players/10/landmarks/lAnkle/y", 1, 51.0); + check_float("teams/home/players/10/landmarks/lAnkle/z", 1, 0.3); + check_float("teams/home/players/10/landmarks/rAnkle/x", 1, 42.0); + check_float("teams/home/players/10/landmarks/rAnkle/y", 1, 52.0); + check_float("teams/home/players/10/landmarks/rAnkle/z", 1, 0.4); +} + +use std::fs; diff --git a/crates/tdc-python/src/container.rs b/crates/tdc-python/src/container.rs index c32db11..8ab2f72 100644 --- a/crates/tdc-python/src/container.rs +++ b/crates/tdc-python/src/container.rs @@ -281,6 +281,11 @@ impl PyTrackingDataContainer { fn num_rows(&self) -> usize { self.inner.num_rows() } + fn slice(&self, offset: usize, length: usize) -> Self { + PyTrackingDataContainer { + inner: self.inner.slice(offset, length), + } + } fn validate(&self) -> PyResult<()> { self.inner.validate().into_pyresult() } @@ -404,6 +409,11 @@ impl PyLazyTrackingDataContainer { inner: self.inner.select(fields).into_pyresult()?, }) } + fn slice(&self, offset: usize, length: usize) -> PyResult { + Ok(PyLazyTrackingDataContainer { + inner: self.inner.slice(offset, length).into_pyresult()?, + }) + } fn filter(&self, predicate: PyExpr) -> PyResult { Ok(PyLazyTrackingDataContainer { inner: self.inner.filter(predicate.inner).into_pyresult()?, diff --git a/crates/tdc-python/src/selector.rs b/crates/tdc-python/src/selector.rs index 8120786..37a64e5 100644 --- a/crates/tdc-python/src/selector.rs +++ b/crates/tdc-python/src/selector.rs @@ -47,6 +47,11 @@ impl PyColumnSelector { expr: CoreExpr::Players { team_id }, } } + fn player(&self, player_id: String, team_id: Option) -> PySelection { + PySelection { + expr: CoreExpr::Player { team_id, player_id }, + } + } fn team(&self, team_id: &str) -> PySelection { PySelection { expr: CoreExpr::Players { diff --git a/examples/06-skeletal.ipynb b/examples/06-skeletal.ipynb new file mode 100644 index 0000000..540fb80 --- /dev/null +++ b/examples/06-skeletal.ipynb @@ -0,0 +1,226969 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hawk-Eye Skeletal Tracking Data\n", + "\n", + "This notebook illustrates how to load Hawk-Eye tracking data, including full skeletal information (landmarks), and how to efficiently work with this high-dimensional data using `tracking-data-container`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import re\n", + "import time\n", + "import psutil\n", + "from pathlib import Path\n", + "from typing import Union\n", + "import polars as pl\n", + "import tracking_data_container as tdc\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.animation import FuncAnimation\n", + "from IPython.display import HTML\n", + "\n", + "def get_memory_usage():\n", + " process = psutil.Process(os.getpid())\n", + " return process.memory_info().rss / (1024 * 1024) # MB\n", + "\n", + "def _natural_sort_key(path: Union[str, Path]) -> list[Union[int, str]]:\n", + " # Split string into list of chunks for natural sorting\n", + " return [\n", + " int(text) if text.isdigit() else text.lower()\n", + " for text in re.split(r\"(\\d+)\", str(path))\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Load the full dataset\n", + "\n", + "We will load all 90+ minutes of data, including ball tracking, player centroids, and joint positions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 95 minutes of data.\n", + "Loaded 132917 frames in 25.05 seconds.\n", + "Memory used: 3578.27 MB\n", + "Total columns: 3552\n" + ] + } + ], + "source": [ + "data_dir = Path(\"../data/hawkeye\")\n", + "ball_files = sorted(list(data_dir.glob(\"*.samples.ball.json\")), key=_natural_sort_key)\n", + "centroid_files = sorted(list(data_dir.glob(\"*.samples.centroids.json\")), key=_natural_sort_key)\n", + "joint_files = sorted(list(data_dir.glob(\"*.samples.joints.json\")), key=_natural_sort_key)\n", + "meta_file = data_dir / \"meta.json\"\n", + "\n", + "print(f\"Found {len(ball_files)} minutes of data.\")\n", + "\n", + "start_mem = get_memory_usage()\n", + "start_time = time.time()\n", + "\n", + "container = tdc.load_hawkeye(\n", + " ball_files, \n", + " centroid_files, \n", + " joint_paths=joint_files, \n", + " meta_path=meta_file\n", + ")\n", + "\n", + "end_time = time.time()\n", + "end_mem = get_memory_usage()\n", + "\n", + "print(f\"Loaded {container.num_rows()} frames in {end_time - start_time:.2f} seconds.\")\n", + "print(f\"Memory used: {end_mem - start_mem:.2f} MB\")\n", + "print(f\"Total columns: {len(container.schema().names())}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Convert to Parquet\n", + "\n", + "Parquet is a columnar storage format that is highly efficient for large datasets. `tracking-data-container` allows seamless export to Parquet, which preserves the standardized schema and metadata." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data written to ./data/hawkeye_full.parquet\n", + "File size: 1646.18 MB\n" + ] + } + ], + "source": [ + "parquet_path = \"./data/hawkeye_full.parquet\"\n", + "container.to_parquet(parquet_path)\n", + "print(f\"Data written to {parquet_path}\")\n", + "print(f\"File size: {os.path.getsize(parquet_path) / (1024 * 1024):.2f} MB\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Efficient Loading for Specific Players\n", + "\n", + "One of the key advantages of using TDC with Parquet is the ability to lazily scan the data and only load the columns you actually need. Here we show how to load only the skeletal data for a single player." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 118 columns for player #10 in 0.1026 seconds.\n", + "Memory used for selection: 71.14 MB\n" + ] + } + ], + "source": [ + "# Scan the Parquet file lazily\n", + "lazy_container = tdc.scan_parquet(parquet_path)\n", + "\n", + "# Select a specific player (e.g., player #10 on the home team)\n", + "player_selector = lazy_container.selector.player(\"10\", team_id=\"home\")\n", + "\n", + "start_mem = get_memory_usage()\n", + "start_time = time.time()\n", + "\n", + "# Collect the data for the selected player only\n", + "player_data = lazy_container.select(player_selector).collect()\n", + "\n", + "end_time = time.time()\n", + "end_mem = get_memory_usage()\n", + "\n", + "print(f\"Loaded {len(player_data.schema().names())} columns for player #10 in {end_time - start_time:.4f} seconds.\")\n", + "print(f\"Memory used for selection: {end_mem - start_mem:.2f} MB\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Animate 3D Pose Tracking\n", + "\n", + "Now we will animate the 3D pose tracking data for our selected player. We will define the skeletal connections and use `matplotlib` to render the animation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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\"rSmallToe\"),\n", + "]\n", + "\n", + "def animate_player(container, player_id, team=\"home\", start_frame=0, num_frames=150):\n", + " # Select player skeletal data and ball data\n", + " selector = [container.selector.player(player_id, team_id=team), container.selector.ball()]\n", + " data = container.slice(start_frame, num_frames).select(selector).to_polars()\n", + " \n", + " fig = plt.figure(figsize=(10, 8))\n", + " ax = fig.add_subplot(111, projection='3d')\n", + " \n", + " # Base prefix for player landmarks\n", + " prefix = f\"teams/{team}/players/{player_id}/landmarks/\"\n", + " \n", + " # Initialize lines for skeleton and point for ball\n", + " lines = [ax.plot([], [], [], color='blue', lw=2)[0] for _ in CONNECTIONS]\n", + " ball_point, = ax.plot([], [], [], 'ro', markersize=10, label=\"Ball\")\n", + " \n", + " # Determine limits based on player position\n", + " player_x = data.get_column(f\"{prefix}midHip/x\").mean()\n", + " player_y = data.get_column(f\"{prefix}midHip/y\").mean()\n", + " \n", + " ax.set_xlim(player_x - 3, player_x + 3)\n", + " ax.set_ylim(player_y - 3, player_y + 3)\n", + " ax.set_zlim(0, 3)\n", + " ax.set_xlabel(\"X (m)\")\n", + " ax.set_ylabel(\"Y (m)\")\n", + " ax.set_zlabel(\"Z (m)\")\n", + " ax.set_title(f\"Player {player_id} ({team}) - 3D Pose Animation\")\n", + " \n", + " def update(frame):\n", + " row = data.row(frame, named=True)\n", + " \n", + " # Update skeleton lines\n", + " for i, (start, end) in enumerate(CONNECTIONS):\n", + " try:\n", + " x = [row[f\"{prefix}{start}/x\"], row[f\"{prefix}{end}/x\"]]\n", + " y = [row[f\"{prefix}{start}/y\"], row[f\"{prefix}{end}/y\"]]\n", + " z = [row[f\"{prefix}{start}/z\"], row[f\"{prefix}{end}/z\"]]\n", + " lines[i].set_data(x, y)\n", + " lines[i].set_3d_properties(z)\n", + " except KeyError:\n", + " continue\n", + " \n", + " # Update ball position\n", + " ball_point.set_data([row[\"ball/x\"]], [row[\"ball/y\"]])\n", + " ball_point.set_3d_properties([row[\"ball/z\"]])\n", + " \n", + " return lines + [ball_point]\n", + "\n", + " anim = FuncAnimation(fig, update, frames=num_frames, interval=40, blit=True)\n", + " plt.close(fig)\n", + " return anim\n", + "\n", + "# Animate player #10 for 6 seconds (150 frames at 25Hz)\n", + "anim = animate_player(container, \"10\", team=\"home\", start_frame=1500, num_frames=150)\n", + "HTML(anim.to_jshtml())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "/Users/probberechts/Developer/tracking_data_container", + "language": "python", + "name": "tracking_data_container" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python/tests/benchmarks/test_hawkeye.py b/python/tests/benchmarks/test_hawkeye.py new file mode 100644 index 0000000..3b8dd3c --- /dev/null +++ b/python/tests/benchmarks/test_hawkeye.py @@ -0,0 +1,108 @@ +import re +import tracemalloc +from pathlib import Path +from typing import Union + +import polars as pl +import pytest +import tracking_data_container as tdc + +try: + from kloppy import hawkeye + + KLOPPY_AVAILABLE = True +except ImportError: + KLOPPY_AVAILABLE = False + +try: + import fastforward as fff + + _ = fff.hawkeye + FFF_AVAILABLE = True +except (ImportError, AttributeError): + FFF_AVAILABLE = False + + +def _natural_sort_key(path: Union[str, Path]) -> list[Union[int, str]]: + # Split string into list of chunks for natural sorting + return [int(text) if text.isdigit() else text.lower() for text in re.split(r"(\d+)", str(path))] + + +HAWKEYE_DIR = Path(__file__).parents[3] / "data" / "hawkeye" +# Limit to 5 minutes for benchmarks to keep it reasonable +BALL_FILES = sorted(list(HAWKEYE_DIR.glob("*.samples.ball.json")), key=_natural_sort_key)[:5] +CENTROID_FILES = sorted(list(HAWKEYE_DIR.glob("*.samples.centroids.json")), key=_natural_sort_key)[ + :5 +] +JOINT_FILES = sorted(list(HAWKEYE_DIR.glob("*.samples.joints.json")), key=_natural_sort_key)[:5] +HAWKEYE_META = HAWKEYE_DIR / "meta.json" + + +@pytest.mark.benchmark(group="hawkeye") +def test_hawkeye_tdc_parsers(benchmark): + def run(): + return tdc.load_hawkeye( + BALL_FILES, CENTROID_FILES, joint_paths=JOINT_FILES, meta_path=HAWKEYE_META + ).to_polars() + + tracemalloc.start() + # Using pedantic to be consistent with others + df = benchmark.pedantic(run, rounds=5, iterations=1) + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + + benchmark.extra_info["peak_memory_mb"] = peak / 1024 / 1024 + + assert isinstance(df, pl.DataFrame) + assert len(df) > 0 + + +@pytest.mark.skipif(not KLOPPY_AVAILABLE, reason="kloppy not installed") +@pytest.mark.benchmark(group="hawkeye") +def test_hawkeye_kloppy(benchmark): + def run(): + # Kloppy's hawkeye loader expects ball_feeds and player_centroid_feeds + # Note: Kloppy might not support joint feeds yet or uses different args. + # Based on previous research, it only supported ball and centroids. + dataset = hawkeye.load( + ball_feeds=[str(p) for p in BALL_FILES], + player_centroid_feeds=[str(p) for p in CENTROID_FILES], + meta_data=str(HAWKEYE_META), + ) + return dataset + + tracemalloc.start() + # Kloppy is slow, we use pedantic to limit rounds + dataset = benchmark.pedantic(run, rounds=1, iterations=1) + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + + benchmark.extra_info["peak_memory_mb"] = peak / 1024 / 1024 + + assert dataset is not None + + +@pytest.mark.skipif(not FFF_AVAILABLE, reason="fastforward.hawkeye not available") +@pytest.mark.benchmark(group="hawkeye") +def test_hawkeye_fff(benchmark): + def run(): + # Fastforward supports joint_data + dataset = fff.hawkeye.load_tracking( + ball_data=[str(p) for p in BALL_FILES], + player_data=[str(p) for p in CENTROID_FILES], + meta_data=str(HAWKEYE_META), + layout="wide", + ) + # Use .tracking which is typically the Polars DataFrame in fff + return dataset.tracking + + tracemalloc.start() + # Fastforward can be slow, we use pedantic to limit rounds + df = benchmark.pedantic(run, rounds=1, iterations=1) + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + + benchmark.extra_info["peak_memory_mb"] = peak / 1024 / 1024 + + assert isinstance(df, pl.DataFrame) + assert len(df) > 0 diff --git a/python/tests/benchmarks/test_lazy_filtering.py b/python/tests/benchmarks/test_lazy_filtering.py index d2ae791..2081c96 100644 --- a/python/tests/benchmarks/test_lazy_filtering.py +++ b/python/tests/benchmarks/test_lazy_filtering.py @@ -5,7 +5,7 @@ import pytest import tracking_data_container as tdc -STATSPERFORM_FILE = Path(__file__).parents[3] / "data" / "statsperform-ma25.txt" +STATSPERFORM_FILE = Path(__file__).parents[3] / "data" / "statsperform" / "ma25.txt" @pytest.fixture(scope="session") diff --git a/python/tests/benchmarks/test_sportec.py b/python/tests/benchmarks/test_sportec.py index 8841d50..f489309 100644 --- a/python/tests/benchmarks/test_sportec.py +++ b/python/tests/benchmarks/test_sportec.py @@ -21,8 +21,8 @@ except (ImportError, AttributeError): FFF_AVAILABLE = False -SPORTEC_FILE = Path(__file__).parents[3] / "data" / "sportec-tracking.xml" -SPORTEC_META = Path(__file__).parents[3] / "data" / "sportec-meta.xml" +SPORTEC_FILE = Path(__file__).parents[3] / "data" / "sportec" / "tracking.xml" +SPORTEC_META = Path(__file__).parents[3] / "data" / "sportec" / "meta.xml" def tdc_parse_sportec(path: Path) -> pl.DataFrame: diff --git a/python/tests/benchmarks/test_statsperform.py b/python/tests/benchmarks/test_statsperform.py index 11f4c3e..7f38e41 100644 --- a/python/tests/benchmarks/test_statsperform.py +++ b/python/tests/benchmarks/test_statsperform.py @@ -20,8 +20,8 @@ except (ImportError, AttributeError): FFF_AVAILABLE = False -STATSPERFORM_FILE = Path(__file__).parents[3] / "data" / "statsperform-ma25.txt" -STATSPERFORM_META = Path(__file__).parents[3] / "data" / "statsperform-ma1.json" +STATSPERFORM_FILE = Path(__file__).parents[3] / "data" / "statsperform" / "ma25.txt" +STATSPERFORM_META = Path(__file__).parents[3] / "data" / "statsperform" / "ma1.json" def tdc_parse_statsperform(path: Path) -> pl.DataFrame: diff --git a/python/tests/test_select.py b/python/tests/test_select.py index 3d8315a..ec7be6b 100644 --- a/python/tests/test_select.py +++ b/python/tests/test_select.py @@ -35,6 +35,25 @@ def test_container_select_selector_players(self, container) -> None: subset = c.select(c.selector.players().attributes(["x"])) assert subset.schema() == tdc.Fields(["teams/away/players/10/x", "teams/home/players/1/x"]) + def test_container_select_selector_player(self) -> None: + data = { + "teams/home/players/1/x": [2.0], + "teams/home/players/1/y": [3.0], + "teams/home/players/2/x": [4.0], + "teams/away/players/1/x": [5.0], + } + c = tdc.TrackingDataContainer(data) + + # Select specific player by ID and team + subset = c.select(c.selector.player("1", team_id="home")) + assert subset.schema() == tdc.Fields(["teams/home/players/1/x", "teams/home/players/1/y"]) + + # Select specific player by ID only + subset = c.select(c.selector.player("1")) + assert subset.schema() == tdc.Fields( + ["teams/away/players/1/x", "teams/home/players/1/x", "teams/home/players/1/y"] + ) + def test_container_select_wildcard(self) -> None: data = { "ball/x": [1.0], diff --git a/python/tracking_data_container/__init__.py b/python/tracking_data_container/__init__.py index 8a59eb1..aeb2db0 100644 --- a/python/tracking_data_container/__init__.py +++ b/python/tracking_data_container/__init__.py @@ -17,7 +17,7 @@ from .builder import TrackingDataBuilder from .container import LazyTrackingDataContainer, TrackingDataContainer from .coordinate_system import CoordinateSystem -from .io import load_sportec, load_statsperform, scan_parquet +from .io import load_hawkeye, load_sportec, load_statsperform, scan_parquet from .schema import ( Ball, DataField, @@ -46,6 +46,7 @@ "scan_parquet", "load_sportec", "load_statsperform", + "load_hawkeye", "DataField", "TrackedEntity", "Ball", diff --git a/python/tracking_data_container/_tracking_data_container.pyi b/python/tracking_data_container/_tracking_data_container.pyi index 9efdad4..c9a95f3 100644 --- a/python/tracking_data_container/_tracking_data_container.pyi +++ b/python/tracking_data_container/_tracking_data_container.pyi @@ -154,6 +154,7 @@ class ColumnSelector: """A selector for building column expressions based on metadata.""" def ball(self) -> Selection: ... def players(self, team_id: str | None = None) -> Selection: ... + def player(self, player_id: str, team_id: str | None = None) -> Selection: ... def team(self, team_id: str) -> Selection: ... class Frame: @@ -194,6 +195,7 @@ class _TrackingDataContainer: @property def selector(self) -> ColumnSelector: ... def num_rows(self) -> int: ... + def slice(self, offset: int, length: int) -> _TrackingDataContainer: ... def validate(self) -> None: ... def save_parquet(self, path: str) -> None: ... @staticmethod @@ -209,6 +211,7 @@ class _LazyTrackingDataContainer: def select( self, items: str | list[str | _DataField | _Selection] | _DataField | _Selection ) -> _LazyTrackingDataContainer: ... + def slice(self, offset: int, length: int) -> _LazyTrackingDataContainer: ... def filter(self, predicate: Expr) -> _LazyTrackingDataContainer: ... def transform_coordinates(self, to_system: _CoordinateSystem) -> _LazyTrackingDataContainer: ... def transform_orientation(self, mode: OrientationMode | str) -> _LazyTrackingDataContainer: ... @@ -261,8 +264,14 @@ class Expr: def col(name: str) -> Expr: ... def lit(value: Any) -> Expr: ... -def load_sportec(path: str, meta_path: str | None = None) -> _TrackingDataContainer: ... -def load_statsperform(path: str) -> _TrackingDataContainer: ... +def load_sportec(path: str, meta_path: str | None = None) -> Any: ... +def load_statsperform(path: str) -> Any: ... +def load_hawkeye( + ball_paths: list[str], + centroid_paths: list[str], + joint_paths: list[str] | None = None, + meta_path: str | None = None, +) -> Any: ... def scan_parquet(path: str) -> _LazyTrackingDataContainer: ... class _Ops: diff --git a/python/tracking_data_container/container.py b/python/tracking_data_container/container.py index 3311eda..7b3a924 100644 --- a/python/tracking_data_container/container.py +++ b/python/tracking_data_container/container.py @@ -166,6 +166,27 @@ def __getitem__(self, key: int) -> Frame: """Access a specific frame by index.""" return self.materialize_frame(key) + def slice(self, offset: int, length: int) -> TrackingDataContainer: + """ + Extract a slice of frames from the container. + + Parameters + ---------- + offset : int + The starting frame index. + length : int + The number of frames to include. + + Returns + ------- + TrackingDataContainer + A new container containing only the specified frames. + """ + new_inner = self._container.slice(offset, length) + new_container = TrackingDataContainer.__new__(TrackingDataContainer) + new_container._container = new_inner + return new_container + # --- Transformations --- def with_coordinate_system(self, system: CoordinateSystem) -> TrackingDataContainer: @@ -826,6 +847,25 @@ def select(self, items: str | list[str | DataField | Selection]) -> LazyTracking new_inner = self._container.select(processed_items) # type: ignore return LazyTrackingDataContainer(new_inner) + def slice(self, offset: int, length: int) -> LazyTrackingDataContainer: + """ + Lazily extract a slice of frames from the container. + + Parameters + ---------- + offset : int + The starting frame index. + length : int + The number of frames to include. + + Returns + ------- + LazyTrackingDataContainer + A new lazy container with the slice added to the plan. + """ + new_inner = self._container.slice(offset, length) + return LazyTrackingDataContainer(new_inner) + def filter(self, predicate: _Expr) -> LazyTrackingDataContainer: """ Lazily filter rows from the container. diff --git a/python/tracking_data_container/io.py b/python/tracking_data_container/io.py index bff68e0..36aa3e2 100644 --- a/python/tracking_data_container/io.py +++ b/python/tracking_data_container/io.py @@ -6,6 +6,9 @@ from ._tracking_data_container import ( _TrackingDataContainer, ) +from ._tracking_data_container import ( + load_hawkeye as _load_hawkeye, +) from ._tracking_data_container import ( load_sportec as _load_sportec, ) @@ -75,3 +78,37 @@ def load_statsperform(path: str | Path) -> TrackingDataContainer: """ new_inner = _load_statsperform(str(path)) return TrackingDataContainer(_TrackingDataContainer._from_capsule(new_inner)) + + +def load_hawkeye( + ball_paths: list[str | Path], + centroid_paths: list[str | Path], + joint_paths: list[str | Path] | None = None, + meta_path: str | Path | None = None, +) -> TrackingDataContainer: + """ + Loads Hawk-Eye tracking files into an eager container. + + Parameters + ---------- + ball_paths : list of str or Path + The paths to the Hawk-Eye ball tracking data files. + centroid_paths : list of str or Path + The paths to the Hawk-Eye player centroid data files. + joint_paths : list of str or Path, optional + The paths to the Hawk-Eye player joint data files. + meta_path : str or Path, optional + The path to the Hawk-Eye metadata JSON file. + + Returns + ------- + TrackingDataContainer + An eager container containing the loaded data. + """ + new_inner = _load_hawkeye( + [str(p) for p in ball_paths], + [str(p) for p in centroid_paths], + [str(p) for p in joint_paths] if joint_paths else None, + str(meta_path) if meta_path else None, + ) + return TrackingDataContainer(_TrackingDataContainer._from_capsule(new_inner)) diff --git a/python/tracking_data_container/selector.py b/python/tracking_data_container/selector.py index cf5b74c..4f04b0a 100644 --- a/python/tracking_data_container/selector.py +++ b/python/tracking_data_container/selector.py @@ -57,6 +57,10 @@ def players(self, team_id: str | None = None) -> Selection: """Selects all player-related columns.""" return Selection(self._selector.players(team_id)) + def player(self, player_id: str, team_id: str | None = None) -> Selection: + """Selects all columns for a specific player.""" + return Selection(self._selector.player(player_id, team_id)) + def team(self, team_id: str) -> Selection: """Selects all columns for a specific team.""" return Selection(self._selector.team(team_id)) From e2715c466406dfd5e5b323e00031df83adc0b060 Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 13:33:13 +0200 Subject: [PATCH 04/21] feat: implement high-performance Rust pivot for catalog loading --- GEMINI.md | 50 + crates/tdc-core/src/datacontainer/core.rs | 426 ++++- crates/tdc-core/src/schema/attribute.rs | 7 +- crates/tdc-python/src/container.rs | 70 +- crates/tdc-python/src/selector.rs | 13 + debug_warehouse/catalog.db | Bin 0 -> 20480 bytes ...-f000-4b09-aa96-69d2d17151c7.metadata.json | 1 + ...-1069-48a0-9f73-0f5337b4fa63.metadata.json | 1 + ...80fb8d-8d7d-435b-95e1-55f2b9ea7dd3-m0.avro | Bin 0 -> 4474 bytes ...-4f80fb8d-8d7d-435b-95e1-55f2b9ea7dd3.avro | Bin 0 -> 1819 bytes ...-14bf-436f-9ed2-cbbfb2a553b8.metadata.json | 1 + ...-69bc-4280-800d-ab07ceb3c869.metadata.json | 1 + ...25fa0c-bd68-4ba7-bfc2-9a6c8f662a6d-m0.avro | Bin 0 -> 5356 bytes ...-e525fa0c-bd68-4ba7-bfc2-9a6c8f662a6d.avro | Bin 0 -> 1829 bytes examples/07-catalog.ipynb | 320 ++++ examples/08-idsse-warehouse.ipynb | 376 +++++ examples/idsse_warehouse/catalog.db | Bin 0 -> 20480 bytes ...-c746-4f8f-966b-040290fbf4fa.metadata.json | 1 + ...-aa86-4054-844c-b33bbaf9b53b.metadata.json | 1 + ...-e21d-47a5-9d6f-6bc39ac8c361.metadata.json | 1 + ...-c7bf-4e2f-8e10-522378371fe8.metadata.json | 1 + ...defa57-62bd-4a60-87b5-0d6fcafd53ad-m0.avro | Bin 0 -> 4490 bytes ...38ec79-f365-46e1-aa0a-6ff5e030781a-m0.avro | Bin 0 -> 4489 bytes ...ae9c91-7f9c-4bec-b401-856370f90b74-m0.avro | Bin 0 -> 4492 bytes ...-ed38ec79-f365-46e1-aa0a-6ff5e030781a.avro | Bin 0 -> 1900 bytes ...-b3defa57-62bd-4a60-87b5-0d6fcafd53ad.avro | Bin 0 -> 1824 bytes ...-faae9c91-7f9c-4bec-b401-856370f90b74.avro | Bin 0 -> 1949 bytes ...-6439-4604-88e4-272cf35f75ce.metadata.json | 1 + ...-a73e-4da6-8812-af9a7d6b04e9.metadata.json | 1 + ...-fa71-41cb-a109-30c7bf7cadf4.metadata.json | 1 + ...-4237-409f-9d8b-49218b2e1ef4.metadata.json | 1 + ...65052f-9f33-4c4a-b55d-e1fd2bcb223d-m0.avro | Bin 0 -> 6329 bytes ...d1e2c6-167d-47a8-a02b-391e6ef92b65-m0.avro | Bin 0 -> 6347 bytes ...62db46-093c-45e8-be4a-0c6d572dad92-m0.avro | Bin 0 -> 6365 bytes ...-d962db46-093c-45e8-be4a-0c6d572dad92.avro | Bin 0 -> 1851 bytes ...-4665052f-9f33-4c4a-b55d-e1fd2bcb223d.avro | Bin 0 -> 2000 bytes ...-7ad1e2c6-167d-47a8-a02b-391e6ef92b65.avro | Bin 0 -> 1937 bytes inspect_catalog.py | 26 + profile_load.py | 146 ++ profile_warehouse/catalog.db | Bin 0 -> 20480 bytes ...-65e0-450f-9be7-7684faeeac52.metadata.json | 1 + ...-89c7-436e-83d6-51ccb7009828.metadata.json | 1 + ...7f5ba5-48f7-4cb2-ae56-16a5fe7fd851-m0.avro | Bin 0 -> 4470 bytes ...-e27f5ba5-48f7-4cb2-ae56-16a5fe7fd851.avro | Bin 0 -> 1819 bytes ...-7045-4467-ab11-81db388158b2.metadata.json | 1 + ...-eb36-4de8-8147-cabad48bca68.metadata.json | 1 + ...3266b5-4cd0-499d-b61d-580fce440e8d-m0.avro | Bin 0 -> 5402 bytes ...-f03266b5-4cd0-499d-b61d-580fce440e8d.avro | Bin 0 -> 1829 bytes pyproject.toml | 5 + .../benchmarks/test_catalog_performance.py | 68 + python/tests/test_advanced_features.py | 98 ++ python/tests/test_catalog.py | 71 + python/tests/test_ops.py | 2 +- python/tests/test_select.py | 6 +- python/tests/test_transform.py | 32 +- python/tracking_data_container/__init__.py | 12 + .../_tracking_data_container.pyi | 24 +- python/tracking_data_container/catalog.py | 543 ++++++ python/tracking_data_container/config.py | 25 + python/tracking_data_container/container.py | 1479 ++++++----------- python/tracking_data_container/schema.py | 143 +- uv.lock | 996 ++++++++++- 62 files changed, 3873 insertions(+), 1081 deletions(-) create mode 100644 GEMINI.md create mode 100644 debug_warehouse/catalog.db create mode 100644 debug_warehouse/tracking/match_metadata/metadata/00000-140c93eb-f000-4b09-aa96-69d2d17151c7.metadata.json create mode 100644 debug_warehouse/tracking/match_metadata/metadata/00001-702060e8-1069-48a0-9f73-0f5337b4fa63.metadata.json create mode 100644 debug_warehouse/tracking/match_metadata/metadata/4f80fb8d-8d7d-435b-95e1-55f2b9ea7dd3-m0.avro create mode 100644 debug_warehouse/tracking/match_metadata/metadata/snap-8457677481870634211-0-4f80fb8d-8d7d-435b-95e1-55f2b9ea7dd3.avro create mode 100644 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python/tracking_data_container/config.py diff --git a/GEMINI.md b/GEMINI.md new file mode 100644 index 0000000..e37c36d --- /dev/null +++ b/GEMINI.md @@ -0,0 +1,50 @@ +# Gemini Project: tracking-data-container + +This document provides a comprehensive overview of the `tracking-data-container` (TDC) project, designed to facilitate understanding and development. + +## Project Overview + +The `tracking-data-container` is a high-performance, metadata-aware container for sports tracking data. It implements the vision of a shared, standardized interface for the sports analytics ecosystem, backed by Apache Arrow and DataFusion. + +### Motivation & RFC +TDC aims to solve the challenges of fragmentation and duplication in sports tracking pipelines. It acts as the central interface between various tools (e.g., Kloppy, Floodlight, BallRadar), providing: +- **Scalability**: Overcoming the limitations of object-based models for large datasets. +- **Interoperability**: Standardized schemas and zero-copy data exchange. +- **Reproducibility**: Shared logic for derived metrics and transformations. + +### Core Architecture +- **Rust Core (`tdc-core`)**: Leverages Apache Arrow for in-memory data representation and DataFusion for query execution. It handles performance-critical operations like coordinate transformations and orientation normalization. +- **Python Bindings (`tdc-python`)**: Provides a high-level API for Python users, ensuring seamless integration with the data science ecosystem (Pandas, Polars, PyArrow). +- **Schema & CDF**: TDC aligns with the **Common Data Format (CDF)** standard for column naming (e.g., `ball/x`, `teams/{tid}/players/{pid}/x`). + +### Key Components +- **TrackingDataContainer**: The primary eager interface wrapping an Arrow `StructArray` and rich metadata. +- **LazyTrackingDataContainer**: A deferred execution model using DataFusion logical plans for lazy evaluation and filter pushdown. +- **DataField**: A domain-specific representation of a data column, supporting both hierarchical string paths and component-based construction. +- **TrackingDataBuilder**: An optimized ingestor for building containers from raw streams or diverse formats. +- **Frame**: A materialized single frame of data, supporting dual indexing by string name or `DataField`. + +## Building and Running + +The project uses a `Makefile` to streamline common development tasks. + +### Installation +```bash +make setup-dev +``` + +### Building +- **Development**: `make build-dev` +- **Production Wheel**: `make build-wheel` + +### Testing +- **Full Suite**: `make test` +- **Python Only**: `make test-python` +- **Rust Only**: `make test-rust` + +## Development Conventions + +- **Code Style**: `ruff` for Python, `cargo fmt` for Rust. +- **Type Hinting**: Full type hinting required for Python code (`mypy` compliant). +- **Metadata Handling**: Always prefer metadata-aware selectors (`container.selector`) over hardcoded strings. +- **Schema Compliance**: All changes should maintain compatibility with the CDF v0.2.2 specification. diff --git a/crates/tdc-core/src/datacontainer/core.rs b/crates/tdc-core/src/datacontainer/core.rs index 90cb2c1..45e166f 100644 --- a/crates/tdc-core/src/datacontainer/core.rs +++ b/crates/tdc-core/src/datacontainer/core.rs @@ -1,14 +1,34 @@ use crate::error::{TrackingDataContainerErrorKind, TrackingDataContainerResult}; -use crate::schema::{CoordinateSystem, DataField, Fields, OrientationMode}; +use crate::schema::{CoordinateSystem, DataField, Fields, OrientationMode, TrackedEntity}; use arrow_arith::numeric::{add, mul}; use arrow_array::array::{Array, ArrayRef, Float32Array, Float64Array, StructArray}; +use arrow_array::cast::AsArray; use arrow_array::{RecordBatch, Scalar}; use arrow_ord::cmp::eq; -use arrow_schema::{DataType::*, Field, Fields as ArrowFields}; +use arrow_schema::{DataType, DataType::*, Field, Fields as ArrowFields}; use arrow_select::zip::zip; use datafusion::prelude::*; use std::sync::Arc; +#[derive(Debug)] +pub struct TrackingDataContainer { + pub data: StructArray, + pub schema: Fields, + pub coordinate_system: Option, + pub orientation: Option, +} + +impl Clone for TrackingDataContainer { + fn clone(&self) -> Self { + Self { + data: self.data.clone(), + schema: self.schema.clone(), + coordinate_system: self.coordinate_system.clone(), + orientation: self.orientation, + } + } +} + impl TrackingDataContainer { pub fn new(data: StructArray, schema: Fields) -> Self { Self { @@ -59,42 +79,7 @@ impl TrackingDataContainer { self.orientation, )) } -} - -impl crate::selector::Select for TrackingDataContainer { - fn select(&self, fields: Vec) -> TrackingDataContainerResult { - let indices = self.schema.get_column_indices(&fields); - - let mut new_arrays = Vec::with_capacity(fields.len()); - let mut new_arrow_fields = Vec::with_capacity(fields.len()); - let mut new_schema_fields = Vec::with_capacity(fields.len()); - - for (field, maybe_idx) in fields.into_iter().zip(indices.into_iter()) { - if let Some(idx) = maybe_idx { - new_arrays.push(self.data.column(idx).clone()); - new_arrow_fields.push(self.data.fields()[idx].clone()); - new_schema_fields.push(field); - } - } - - let new_data = if new_arrays.is_empty() { - StructArray::new_empty_fields(self.num_rows(), None) - } else { - StructArray::try_new(ArrowFields::from(new_arrow_fields), new_arrays, None)? - }; - - Ok(Self { - data: new_data, - schema: Fields { - fields: new_schema_fields, - }, - coordinate_system: self.coordinate_system.clone(), - orientation: self.orientation, - }) - } -} -impl TrackingDataContainer { pub fn with_columns(&self, fields: Vec, arrays: Vec) -> Self { let mut new_fields: Vec> = self.data.fields().iter().cloned().collect(); let mut new_columns: Vec = self.data.columns().to_vec(); @@ -102,7 +87,7 @@ impl TrackingDataContainer { let mut field_map: std::collections::HashMap = new_fields .iter() .enumerate() - .map(|(i, f)| (f.name().clone(), i)) + .map(|(i, f): (usize, &Arc)| (f.name().clone(), i)) .collect(); for (field, array) in fields.into_iter().zip(arrays.into_iter()) { @@ -172,10 +157,8 @@ impl TrackingDataContainer { let mut new_cols = self.data.columns().to_vec(); let mut new_fields = self.data.fields().iter().cloned().collect::>(); - // Pre-calculate transformation coefficients and hoist scalars let (m_x, o_x) = from.get_transform_coeffs(&to_system, true)?; let (m_y, o_y) = from.get_transform_coeffs(&to_system, false)?; - // For Z, we use the same scale as Y for now, or X. Most sports tracking Z is meters if X/Y are meters. let (m_z, o_z) = (m_y, o_y); let m_x_f64 = Scalar::new(Float64Array::from(vec![m_x])); @@ -287,16 +270,15 @@ impl TrackingDataContainer { &arrow_arith::boolean::and_not(&target_mask, ¤t_mask)?, )?; - // Hoist flip scalars let (m_x, o_x) = self .coordinate_system .as_ref() - .map(|cs| cs.get_flip_coeffs(true)) + .map(|cs: &CoordinateSystem| cs.get_flip_coeffs(true)) .unwrap_or((-1.0, 0.0)); let (m_y, o_y) = self .coordinate_system .as_ref() - .map(|cs| cs.get_flip_coeffs(false)) + .map(|cs: &CoordinateSystem| cs.get_flip_coeffs(false)) .unwrap_or((-1.0, 0.0)); let m_x_f64 = Scalar::new(Float64Array::from(vec![m_x])); @@ -387,25 +369,357 @@ impl TrackingDataContainer { _ => arrow_array::BooleanArray::new_null(num_rows), } } -} -#[derive(Debug)] -pub struct TrackingDataContainer { - pub data: StructArray, - pub schema: Fields, - pub coordinate_system: Option, - pub orientation: Option, + pub fn pivot(data: StructArray) -> TrackingDataContainerResult { + use crate::datacontainer::builder::TrackingDataBuilder; + use ahash::AHashSet; + use arrow_array::types::{ + Float32Type, Float64Type, Int64Type, TimestampMicrosecondType, UInt64Type, + }; + use std::str::FromStr; + + if data.is_empty() { + return Ok(Self::new(data, Fields { fields: vec![] })); + } + + let fields = data.fields(); + let columns = data.columns(); + + // 1. Identify key columns + let mut frame_id_idx = None; + let mut timestamp_idx = None; + let mut player_id_idx = None; + let mut team_id_idx = None; + let mut attr_indices = Vec::new(); + let mut core_indices = Vec::new(); + + let core_names: AHashSet<&str> = [ + "match/id", + "competition/id", + "season/id", + "timestamp", + "frame_id", + "period", + "period_id", + "ball_status", + "ball_poss_team_id", + "ball_poss_status", + ] + .into_iter() + .collect(); + + for (i, field) in fields.iter().enumerate() { + let name = field.name().as_str(); + if name == "frame_id" { + frame_id_idx = Some(i); + core_indices.push(i); + } else if name == "timestamp" { + timestamp_idx = Some(i); + core_indices.push(i); + } else if name == "player/id" { + player_id_idx = Some(i); + } else if name == "team/id" { + team_id_idx = Some(i); + } else if core_names.contains(name) { + core_indices.push(i); + } else { + attr_indices.push(i); + } + } + + // 2. Select the best frame index column + let frame_idx_col = if let Some(fi) = frame_id_idx + && columns[fi].null_count() < columns[fi].len() + { + fi + } else if let Some(ti) = timestamp_idx { + ti + } else { + frame_id_idx.or(timestamp_idx).ok_or_else(|| { + TrackingDataContainerErrorKind::InvalidParameters( + "Missing frame_id or timestamp column".to_string(), + ) + })? + }; + + let frame_col = &columns[frame_idx_col]; + let is_frame_id = fields[frame_idx_col].name() == "frame_id"; + + let player_id_idx = player_id_idx.ok_or_else(|| { + TrackingDataContainerErrorKind::InvalidParameters( + "Missing player/id column".to_string(), + ) + })?; + let team_id_idx = team_id_idx.ok_or_else(|| { + TrackingDataContainerErrorKind::InvalidParameters("Missing team/id column".to_string()) + })?; + + // 3. Determine min/max frame index for fixed builder if possible + let mut min_f = i64::MAX; + let mut max_f = i64::MIN; + + let get_frame_val = |i: usize| -> Option { + if frame_col.is_null(i) { + return None; + } + match frame_col.data_type() { + Int64 => Some(frame_col.as_primitive::().value(i)), + UInt64 => Some(frame_col.as_primitive::().value(i) as i64), + Timestamp(_, _) => Some( + frame_col + .as_primitive::() + .value(i), + ), + _ => Some(i as i64), + } + }; + + for i in 0..data.len() { + if let Some(f) = get_frame_val(i) { + if f < min_f { + min_f = f; + } + if f > max_f { + max_f = f; + } + } + } + + // Only use fixed builder if it's a frame_id and the range is reasonable + let range = if min_f <= max_f { + (max_f - min_f) as usize + } else { + 0 + }; + let mut builder = if is_frame_id && min_f <= max_f && range < 1_000_000 { + TrackingDataBuilder::new_fixed(min_f, max_f) + } else { + TrackingDataBuilder::new() + }; + + // 4. Pre-create columns for core fields + struct CoreColumn { + idx: usize, + builder_idx: usize, + dtype: DataType, + } + let core_cols: Vec = core_indices + .iter() + .map(|&idx| { + let col_name = fields[idx].name(); + let dtype = fields[idx].data_type().clone(); + let builder_idx = builder.get_or_create_column_id(col_name, dtype.clone()); + CoreColumn { + idx, + builder_idx, + dtype, + } + }) + .collect(); + + // 5. Pre-calculate entity attribute mappings + let mut entity_map = ahash::AHashMap::new(); + + // 6. Pivot data + let mut last_frame_val = None; + let mut last_row_idx = 0; + + for i in 0..data.len() { + let f = match get_frame_val(i) { + Some(val) => val, + None => continue, + }; + + let row_idx = if Some(f) == last_frame_val { + last_row_idx + } else { + let ridx = builder.get_row_index(f); + last_frame_val = Some(f); + last_row_idx = ridx; + ridx + }; + + // Insert core columns (always check for NULLs, even if same frame) + for c in &core_cols { + let col = &columns[c.idx]; + if !col.is_null(i) { + match &c.dtype { + Int64 => builder.insert_int_at_idx( + row_idx, + c.builder_idx, + col.as_primitive::().value(i), + ), + UInt64 => builder.insert_int_at_idx( + row_idx, + c.builder_idx, + col.as_primitive::().value(i) as i64, + ), + Float32 => builder.insert_float_at_idx( + row_idx, + c.builder_idx, + col.as_primitive::().value(i) as f64, + ), + Float64 => builder.insert_float_at_idx( + row_idx, + c.builder_idx, + col.as_primitive::().value(i), + ), + Utf8 => builder.insert_string_at_idx( + row_idx, + c.builder_idx, + col.as_string::().value(i), + ), + LargeUtf8 => builder.insert_string_at_idx( + row_idx, + c.builder_idx, + col.as_string::().value(i), + ), + Timestamp(_, _) => builder.insert_int_at_idx( + row_idx, + c.builder_idx, + col.as_primitive::().value(i), + ), + _ => {} + } + } + } + + // Insert attribute columns + let player_col = &columns[player_id_idx]; + if player_col.is_null(i) { + continue; + } + let p_id = match player_col.data_type() { + Utf8 => player_col.as_string::().value(i), + LargeUtf8 => player_col.as_string::().value(i), + _ => continue, + }; + + let team_col = &columns[team_id_idx]; + let t_id = if team_col.is_null(i) { + "none" + } else { + match team_col.data_type() { + Utf8 => team_col.as_string::().value(i), + LargeUtf8 => team_col.as_string::().value(i), + _ => "none", + } + }; + + let attr_builder_indices = entity_map.entry((p_id, t_id)).or_insert_with(|| { + attr_indices + .iter() + .map(|&attr_idx| { + let attr_name = fields[attr_idx].name(); + let entity = if p_id == "ball" { + TrackedEntity::Ball + } else if let Some(team_id) = p_id.strip_prefix("team:") { + TrackedEntity::Team { + team_id: team_id.to_string(), + } + } else if let Some(referee_id) = p_id.strip_prefix("referee:") { + TrackedEntity::Referee { + referee_id: referee_id.to_string(), + } + } else { + TrackedEntity::Player { + team_id: t_id.to_string(), + player_id: p_id.to_string(), + } + }; + + let attribute = crate::schema::TrackedEntityAttribute::from_str(attr_name) + .unwrap_or_else(|_| { + crate::schema::TrackedEntityAttribute::Other(attr_name.to_string()) + }); + + let field = DataField::TrackedEntity { entity, attribute }; + builder.get_or_create_column_id( + &field.name(), + fields[attr_idx].data_type().clone(), + ) + }) + .collect::>() + }); + + for (attr_pos, &builder_col_idx) in attr_builder_indices.iter().enumerate() { + let attr_idx = attr_indices[attr_pos]; + let col = &columns[attr_idx]; + if !col.is_null(i) { + match col.data_type() { + Float32 => builder.insert_float_at_idx( + row_idx, + builder_col_idx, + col.as_primitive::().value(i) as f64, + ), + Float64 => builder.insert_float_at_idx( + row_idx, + builder_col_idx, + col.as_primitive::().value(i), + ), + Int64 => builder.insert_int_at_idx( + row_idx, + builder_col_idx, + col.as_primitive::().value(i), + ), + UInt64 => builder.insert_int_at_idx( + row_idx, + builder_col_idx, + col.as_primitive::().value(i) as i64, + ), + Utf8 => builder.insert_string_at_idx( + row_idx, + builder_col_idx, + col.as_string::().value(i), + ), + LargeUtf8 => builder.insert_string_at_idx( + row_idx, + builder_col_idx, + col.as_string::().value(i), + ), + _ => {} + } + } + } + } + + builder.build() + } } -impl Clone for TrackingDataContainer { - fn clone(&self) -> Self { - Self { - data: self.data.clone(), - schema: self.schema.clone(), + +impl crate::selector::Select for TrackingDataContainer { + fn select(&self, fields: Vec) -> TrackingDataContainerResult { + let indices = self.schema.get_column_indices(&fields); + + let mut new_arrays = Vec::with_capacity(fields.len()); + let mut new_arrow_fields = Vec::with_capacity(fields.len()); + let mut new_schema_fields = Vec::with_capacity(fields.len()); + + for (field, maybe_idx) in fields.into_iter().zip(indices.into_iter()) { + if let Some(idx) = maybe_idx { + new_arrays.push(self.data.column(idx).clone()); + new_arrow_fields.push(self.data.fields()[idx].clone()); + new_schema_fields.push(field); + } + } + + let new_data = if new_arrays.is_empty() { + StructArray::new_empty_fields(self.num_rows(), None) + } else { + StructArray::try_new(ArrowFields::from(new_arrow_fields), new_arrays, None)? + }; + + Ok(Self { + data: new_data, + schema: Fields { + fields: new_schema_fields, + }, coordinate_system: self.coordinate_system.clone(), orientation: self.orientation, - } + }) } } + impl crate::selector::expr::HasSchema for TrackingDataContainer { fn schema(&self) -> &Fields { &self.schema diff --git a/crates/tdc-core/src/schema/attribute.rs b/crates/tdc-core/src/schema/attribute.rs index f3fa88d..8317a54 100644 --- a/crates/tdc-core/src/schema/attribute.rs +++ b/crates/tdc-core/src/schema/attribute.rs @@ -168,8 +168,11 @@ impl Attribute for FrameAttribute { Period => DataType::Utf8, Timestamp => DataType::Timestamp(TimeUnit::Microsecond, None), BallStatus => DataType::Boolean, - MatchId | BallPossTeamId | BallPossStatus | VendorEventName | VendorTrackingName - | Other(_) => DataType::Utf8, + MatchId | BallPossTeamId | BallPossStatus | VendorEventName | VendorTrackingName => { + DataType::Utf8 + } + Other(name) if name == "x" || name == "y" || name == "z" => DataType::Float32, + Other(_) => DataType::Utf8, } } diff --git a/crates/tdc-python/src/container.rs b/crates/tdc-python/src/container.rs index 8ab2f72..93d9a29 100644 --- a/crates/tdc-python/src/container.rs +++ b/crates/tdc-python/src/container.rs @@ -134,6 +134,49 @@ impl PyTrackingDataContainer { }) } + #[staticmethod] + fn from_arrow( + _py: Python<'_>, + array_capsule: Bound<'_, PyCapsule>, + schema_capsule: Bound<'_, PyCapsule>, + coordinate_system: Option, + orientation: Option, + ) -> PyResult { + let array = pyo3_arrow::PyArray::from_arrow_pycapsule(&array_capsule, &schema_capsule)?; + let struct_array = array + .into_inner() + .0 + .as_any() + .downcast_ref::() + .ok_or_else(|| PyTypeError::new_err("Expected StructArray"))? + .clone(); + + let fields: Vec = struct_array + .fields() + .iter() + .map(|f| DataField::from_name(f.name())) + .collect(); + + let orientation = if let Some(o) = orientation { + use std::str::FromStr; + Some( + tdc_core::schema::OrientationMode::from_str(&o) + .map_err(|e| PyValueError::new_err(e.to_string()))?, + ) + } else { + None + }; + + Ok(PyTrackingDataContainer { + inner: TrackingDataContainer { + data: struct_array, + schema: Fields { fields }, + coordinate_system: coordinate_system.map(|cs| cs.inner), + orientation, + }, + }) + } + fn lazy(&self) -> PyResult { Ok(PyLazyTrackingDataContainer { inner: self.inner.lazy().into_pyresult()?, @@ -304,12 +347,27 @@ impl PyTrackingDataContainer { fn selector(&self) -> PyColumnSelector { PyColumnSelector } - fn __repr__(&self) -> String { - format!( - "", - self.inner.num_rows(), - self.inner.schema.len() - ) + + #[staticmethod] + fn pivot(data: Bound<'_, PyAny>) -> PyResult { + let capsules = data.call_method0("__arrow_c_array__")?; + let tuple = capsules.downcast::()?; + let array_item = tuple.get_item(0)?; + let array_capsule = array_item.downcast::()?; + let schema_item = tuple.get_item(1)?; + let schema_capsule = schema_item.downcast::()?; + + let array = pyo3_arrow::PyArray::from_arrow_pycapsule(array_capsule, schema_capsule)?; + let struct_array = array + .into_inner() + .0 + .as_any() + .downcast_ref::() + .ok_or_else(|| PyTypeError::new_err("Expected StructArray"))? + .clone(); + + let inner = TrackingDataContainer::pivot(struct_array).into_pyresult()?; + Ok(PyTrackingDataContainer { inner }) } fn materialize_frame<'py>(&self, py: Python<'py>, frame: usize) -> PyResult { diff --git a/crates/tdc-python/src/selector.rs b/crates/tdc-python/src/selector.rs index 37a64e5..42cc6bb 100644 --- a/crates/tdc-python/src/selector.rs +++ b/crates/tdc-python/src/selector.rs @@ -107,6 +107,19 @@ impl PyExpr { inner: self.inner.clone() / other.inner, } } + + fn __and__(&self, other: PyExpr) -> PyExpr { + PyExpr { + inner: self.inner.clone().and(other.inner), + } + } + + fn __or__(&self, other: PyExpr) -> PyExpr { + PyExpr { + inner: self.inner.clone().or(other.inner), + } + } + fn __pow__(&self, exponent: f64, _modulo: Option>) -> PyExpr { PyExpr { inner: power(self.inner.clone(), df::lit(exponent)), diff --git a/debug_warehouse/catalog.db b/debug_warehouse/catalog.db new file mode 100644 index 0000000000000000000000000000000000000000..36ea378b9bb8d1cf269b7325a0c2db34e631e5f1 GIT binary patch literal 20480 zcmeI&T~E_c7zglnybPAXywDIuOpPY6gu~9(y;U!C;u4JrIzh6l=CnJ7%2=W85N;Q) zjGqX<2^Yp2KLcKRwz*f`BqSJ!|7Och+ow6teouRGp6%V+72D^E<+!`dS5)bSB+JrM zMUf;aDBfe@-P!_TFxuJ?pK{lD&@d=1eY!an`5}#lPo?Q<~q+0J2b$gkv% z^c{Y5j;I4xuJU$VoKo65gTmh4K2 zAA0*`u~5`m)pr{E`Y;MTKJ4sRZCMV^M5FR?uBSAOopv#rdx1alH_x3Pyb<3ieH-ex z%5(w;$Lw;Sl|-HOvLhsIr_v5>xYrB3=kf#Fse1Z3VuzHblTG7}T~s;;M0p+u#%I=| ze=ASp60cK`=KhFyrY7po)ztL16bPS5;j`(_7kkcd6$Bsv0SG_<0uX=z1Rwx`D=ko~ z$P=@3^R>;|W{F#@TK0XHnLBo6tM#eZmw1+4=8s~r*oMblFD919hUg)1bKCb~&-no_ z*Bc12o-1_IsrbyUh~p)0RJZhx%;no+DahM#?LlLTSZj$G6N`2lCu+jTQZ=cPv}Twj znaEO-rL;ttK{KL-p_4WD~{&|v=7-?>)X-gyVq+yYy7!r+_62vqN%Sf

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\n", + "shape: (5, 7)
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"match_A"1969-12-31 20:00:01nullnull0.00.00.5
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" + ], + "text/plain": [ + "shape: (5, 7)\n", + "┌──────────┬─────────────────────────┬──────────┬────────┬────────┬────────┬────────┐\n", + "│ match/id ┆ timestamp ┆ frame_id ┆ period ┆ ball/x ┆ ball/y ┆ ball/z │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ datetime[μs] ┆ u64 ┆ str ┆ f32 ┆ f32 ┆ f32 │\n", + "╞══════════╪═════════════════════════╪══════════╪════════╪════════╪════════╪════════╡\n", + "│ match_A ┆ 1969-12-31 20:00:01 ┆ null ┆ null ┆ 0.0 ┆ 0.0 ┆ 0.5 │\n", + "│ match_A ┆ 1969-12-31 20:00:01.040 ┆ null ┆ null ┆ 1.0 ┆ 0.5 ┆ 0.5 │\n", + "│ match_A ┆ 1969-12-31 20:00:01.080 ┆ null ┆ null ┆ 2.0 ┆ 1.0 ┆ 0.5 │\n", + "│ match_A ┆ 1969-12-31 20:00:01.120 ┆ null ┆ null ┆ 3.0 ┆ 1.5 ┆ 0.5 │\n", + "│ match_A ┆ 1969-12-31 20:00:01.160 ┆ null ┆ null ┆ 4.0 ┆ 2.0 ┆ 0.5 │\n", + "└──────────┴─────────────────────────┴──────────┴────────┴────────┴────────┴────────┘" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final = catalog.load_container(match_id=\"match_A\").collect()\n", + "final.to_polars().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Cleanup\n", + "\n", + "Remove the example warehouse." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warehouse cleaned up.\n" + ] + } + ], + "source": [ + "if Path(warehouse_path).exists():\n", + " shutil.rmtree(warehouse_path)\n", + "print(\"Warehouse cleaned up.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "/Users/probberechts/Developer/tracking_data_container", + "language": "python", + "name": "tracking_data_container" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/08-idsse-warehouse.ipynb b/examples/08-idsse-warehouse.ipynb new file mode 100644 index 0000000..477b6a2 --- /dev/null +++ b/examples/08-idsse-warehouse.ipynb @@ -0,0 +1,376 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-Match Warehouse with IDSSE Dataset\n", + "\n", + "This notebook demonstrates building a multi-match tracking data warehouse using the **IDSSE dataset**. \n", + "\n", + "We will:\n", + "1. Discover multiple matches in the local `data` directory.\n", + "2. Parse the Sportec (DFL) XML files using TDC's high-performance Rust parser.\n", + "3. Ingest all matches into a single Iceberg table using a scalable hybrid format.\n", + "4. Demonstrate efficient cross-match lazy querying." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import tracking_data_container as tdc\n", + "import pyarrow as pa\n", + "import polars as pl\n", + "from pathlib import Path\n", + "import shutil\n", + "import time\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Discover Data\n", + "\n", + "We use the `examples/data` directory which contains professional matches in Sportec format." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 7 matches to ingest.\n", + " - DFL-MAT-J03WMX\n", + " - DFL-MAT-J03WN1\n", + " - DFL-MAT-J03WOH\n", + " - DFL-MAT-J03WOY\n", + " - DFL-MAT-J03WPY\n", + " - DFL-MAT-J03WQQ\n", + " - DFL-MAT-J03WR9\n" + ] + } + ], + "source": [ + "data_dir = Path(\"data\")\n", + "if not data_dir.exists():\n", + " # Fallback for if we are running from the root instead of examples/\n", + " data_dir = Path(\"examples/data\")\n", + "\n", + "all_files = os.listdir(data_dir)\n", + "\n", + "# Pair position files with metadata files\n", + "pos_files = sorted([f for f in all_files if \"positions_raw_observed\" in f])\n", + "meta_files = sorted([f for f in all_files if \"matchinformation\" in f])\n", + "\n", + "pairs = []\n", + "for pos in pos_files:\n", + " # Extract match ID (the last part of the filename)\n", + " match_id = pos.split(\"_\")[-1].replace(\".xml\", \"\")\n", + " meta = next((m for m in meta_files if match_id in m), None)\n", + " if meta:\n", + " pairs.append({\"match_id\": match_id, \"pos\": pos, \"meta\": meta})\n", + "\n", + "print(f\"Found {len(pairs)} matches to ingest.\")\n", + "for p in pairs:\n", + " print(f\" - {p['match_id']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Setup Warehouse\n", + "\n", + "We initialize our `TrackingDataCatalog`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warehouse initialized at ./idsse_warehouse\n" + ] + } + ], + "source": [ + "warehouse_path = \"./idsse_warehouse\"\n", + "if Path(warehouse_path).exists():\n", + " shutil.rmtree(warehouse_path)\n", + "\n", + "catalog = tdc.TrackingDataCatalog(warehouse_path=warehouse_path)\n", + "catalog.initialize()\n", + "print(f\"Warehouse initialized at {warehouse_path}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Ingest Matches\n", + "\n", + "For each match, we parse the raw XML using the optimized Rust backend and save it to the Iceberg table. \n", + "\n", + "**Hybrid Approach**: The container is wide-format (one column per player), but the catalog automatically \"melts\" it into a fixed long-format schema for storage." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Processing match DFL-MAT-J03WMX...\n", + " ✅ Parsed in 1.76s, Saved in 2.21s\n", + " ✅ Rows: 145,967\n", + "\n", + "Processing match DFL-MAT-J03WN1...\n", + " ✅ Parsed in 1.91s, Saved in 1.93s\n", + " ✅ Rows: 141,561\n", + "\n", + "Processing match DFL-MAT-J03WOH...\n", + " ✅ Parsed in 1.30s, Saved in 1.73s\n", + " ✅ Rows: 137,214\n" + ] + } + ], + "source": [ + "for pair in pairs[:3]: # Let's just do 3 for speed in this example\n", + " match_id = pair['match_id']\n", + " print(f\"\\nProcessing match {match_id}...\")\n", + " \n", + " pos_path = data_dir / pair['pos']\n", + " meta_path = data_dir / pair['meta']\n", + " \n", + " # Parse (High-Performance Rust)\n", + " start = time.perf_counter()\n", + " container = tdc.load_sportec(str(pos_path), str(meta_path))\n", + " parse_time = time.perf_counter() - start\n", + " \n", + " # Add match ID column (required for partitioning)\n", + " container = container.add_column(\"match/id\", [match_id] * container.num_rows())\n", + " \n", + " # Save to Warehouse (melts to long format internally)\n", + " start = time.perf_counter()\n", + " catalog.save_container(container)\n", + " save_time = time.perf_counter() - start\n", + " \n", + " print(f\" ✅ Parsed in {parse_time:.2f}s, Saved in {save_time:.2f}s\")\n", + " print(f\" ✅ Rows: {container.num_rows():,}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Querying the Warehouse\n", + "\n", + "Now that our data is stored in Iceberg, we can perform efficient queries. \n", + "\n", + "### Count rows per match\n", + "Loading is instantaneous because it is **Lazy**. The data is only pivoted back to the wide format when you call `.collect()`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warehouse Summary:\n", + " - Match DFL-MAT-J03WMX: 165,259 rows\n", + " - Match DFL-MAT-J03WN1: 162,273 rows\n", + " - Match DFL-MAT-J03WOH: 157,790 rows\n", + "\n", + "Total rows in warehouse: 485,322\n" + ] + } + ], + "source": [ + "print(\"Warehouse Summary:\")\n", + "total_rows = 0\n", + "for pair in pairs[:3]:\n", + " m_id = pair['match_id']\n", + " # returns a LazyTrackingDataContainer\n", + " lazy_container = catalog.load_container(match_id=m_id)\n", + " \n", + " # Execute plan and pivot to wide format\n", + " loaded = lazy_container.collect()\n", + " \n", + " rows = loaded.num_rows()\n", + " total_rows += rows\n", + " print(f\" - Match {m_id}: {rows:,} rows\")\n", + "\n", + "print(f\"\\nTotal rows in warehouse: {total_rows:,}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load specific player\n", + "We can load data for a specific match and see the wide-format columns automatically reconstructed." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for DFL-MAT-J03WMX...\n", + " ✅ Loaded and pivoted 165259 rows in 1.3019s\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (5, 348)\n", + "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n", + "│ match/id ┆ competiti ┆ season/id ┆ timestamp ┆ … ┆ teams/awa ┆ teams/awa ┆ teams/awa ┆ teams/aw │\n", + "│ --- ┆ on/id ┆ --- ┆ --- ┆ ┆ y/players ┆ y/players ┆ y/players ┆ ay/playe │\n", + "│ str ┆ --- ┆ str ┆ datetime[ ┆ ┆ /DFL-OBJ- ┆ /DFL-OBJ- ┆ /DFL-OBJ- ┆ rs/DFL-O │\n", + "│ ┆ str ┆ ┆ μs] ┆ ┆ 000… ┆ 000… ┆ 000… ┆ BJ-000… │\n", + "│ ┆ ┆ ┆ ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ str ┆ str ┆ str ┆ str │\n", + "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n", + "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", + "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ 000IA|awa ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ 2.600 ┆ ┆ y ┆ ┆ ┆ │\n", + "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", + "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ 000IA|awa ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ 2.640 ┆ ┆ y ┆ ┆ ┆ │\n", + "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", + "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ 000IA|awa ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ 2.680 ┆ ┆ y ┆ ┆ ┆ │\n", + "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", + "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ 000IA|awa ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ 2.720 ┆ ┆ y ┆ ┆ ┆ │\n", + "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", + "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ 000IA|awa ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ 2.760 ┆ ┆ y ┆ ┆ ┆ │\n", + "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "target_match = pairs[0]['match_id']\n", + "\n", + "print(f\"Loading data for {target_match}...\")\n", + "start = time.perf_counter()\n", + "\n", + "# Load specific match lazily and collect\n", + "container = catalog.load_container(match_id=target_match).collect()\n", + "\n", + "query_time = time.perf_counter() - start\n", + "print(f\" ✅ Loaded and pivoted {container.num_rows()} rows in {query_time:.4f}s\")\n", + "container.to_polars().head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "No such file or directory (os error 2): ...rehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet (set POLARS_VERBOSE=1 to see full path)\n\nThis error occurred with the following context stack:\n\t[1] 'parquet scan'\n\t[2] 'sink'\n", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m start = time.perf_counter()\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m df = \u001b[43mpl\u001b[49m\u001b[43m.\u001b[49m\u001b[43mscan_parquet\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43midsse_warehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m query_time = time.perf_counter() - start\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m ✅ Loaded and pivoted \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(df)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m rows in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquery_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33ms\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/_utils/deprecation.py:97\u001b[39m, in \u001b[36mdeprecate_streaming_parameter..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33mengine\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33min-memory\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m kwargs[\u001b[33m\"\u001b[39m\u001b[33mstreaming\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m---> \u001b[39m\u001b[32m97\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/lazyframe/opt_flags.py:328\u001b[39m, in \u001b[36mforward_old_opt_flags..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 325\u001b[39m optflags = cb(optflags, kwargs.pop(key)) \u001b[38;5;66;03m# type: ignore[no-untyped-call,unused-ignore]\u001b[39;00m\n\u001b[32m 327\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33moptimizations\u001b[39m\u001b[33m\"\u001b[39m] = optflags\n\u001b[32m--> \u001b[39m\u001b[32m328\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/lazyframe/frame.py:2429\u001b[39m, in \u001b[36mLazyFrame.collect\u001b[39m\u001b[34m(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, cluster_with_columns, collapse_joins, no_optimization, engine, background, optimizations, **_kwargs)\u001b[39m\n\u001b[32m 2427\u001b[39m \u001b[38;5;66;03m# Only for testing purposes\u001b[39;00m\n\u001b[32m 2428\u001b[39m callback = _kwargs.get(\u001b[33m\"\u001b[39m\u001b[33mpost_opt_callback\u001b[39m\u001b[33m\"\u001b[39m, callback)\n\u001b[32m-> \u001b[39m\u001b[32m2429\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m wrap_df(\u001b[43mldf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[43m)\u001b[49m)\n", + "\u001b[31mFileNotFoundError\u001b[39m: No such file or directory (os error 2): ...rehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet (set POLARS_VERBOSE=1 to see full path)\n\nThis error occurred with the following context stack:\n\t[1] 'parquet scan'\n\t[2] 'sink'\n" + ] + } + ], + "source": [ + "start = time.perf_counter()\n", + "\n", + "df = pl.scan_parquet(\"idsse_warehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet\").collect()\n", + "query_time = time.perf_counter() - start\n", + "print(f\" ✅ Loaded and pivoted {len(df)} rows in {query_time:.4f}s\")\n", + "container.to_polars().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Cleanup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if Path(warehouse_path).exists():\n", + " shutil.rmtree(warehouse_path)\n", + "print(\"Warehouse cleaned up.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "/Users/probberechts/Developer/tracking_data_container", + "language": "python", + "name": "tracking_data_container" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/idsse_warehouse/catalog.db b/examples/idsse_warehouse/catalog.db new file mode 100644 index 0000000000000000000000000000000000000000..de0809ea32fad02b8cfb3182ee67b1bed7a67ceb GIT binary patch literal 20480 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import TrackingDataContainer, TrackingDataCatalog +from tracking_data_container.schema import TrackedEntity, DataField + +def generate_synthetic_data(n_frames=146000, n_entities=25): + print(f"Generating synthetic data: {n_frames} frames, {n_entities} entities...") + + # Core columns + frames = np.arange(n_frames) + timestamps = frames * 40000 # 25 fps + + data = { + "match/id": ["m1"] * n_frames, + "frame_id": frames, + "timestamp": timestamps, + "period": ["1"] * n_frames, + } + + # Entities + # 1 ball, 11 home, 11 away, 2 referees = 25 + entity_keys = [] + # Ball + entity_keys.append("ball|none") + # Home team + for i in range(1, 12): + entity_keys.append(f"h{i}|home") + # Away team + for i in range(1, 12): + entity_keys.append(f"a{i}|away") + # Referees + for i in range(1, 3): + entity_keys.append(f"r{i}|referee") + + entity_keys = entity_keys[:n_entities] + + # To make it easier and faster to generate, we'll create the long format directly + print("Creating long-format dataframe...") + + # Repeat core columns for each entity + long_data = { + "match/id": np.tile(["m1"], n_frames * n_entities), + "frame_id": np.tile(frames, n_entities), + "timestamp": np.tile(timestamps, n_entities), + "period": np.tile(["1"], n_frames * n_entities), + } + + # Entity columns + p_ids = [] + t_ids = [] + e_keys = [] + for ek in entity_keys: + p_id, t_id = ek.split("|") + p_ids.extend([p_id] * n_frames) + t_ids.extend([t_id] * n_frames) + e_keys.extend([ek] * n_frames) + + long_data["player/id"] = p_ids + long_data["team/id"] = t_ids + long_data["entity_key"] = e_keys + + # Attributes: x, y, z, name + long_data["x"] = np.random.randn(n_frames * n_entities).astype(np.float32) + long_data["y"] = np.random.randn(n_frames * n_entities).astype(np.float32) + long_data["z"] = np.random.randn(n_frames * n_entities).astype(np.float32) + long_data["name"] = np.tile(["player_name"], n_frames * n_entities) + + df_long = pl.DataFrame(long_data) + + # We need to return a TrackingDataContainer. + # TDC is usually wide, so we pivot it first. + print("Pivoting to wide format for TrackingDataContainer...") + t0 = time.perf_counter() + container = TrackingDataContainer.pivot(df_long) + t1 = time.perf_counter() + print(f"Initial pivot took {t1 - t0:.2f}s") + + return container + +def profile_load(): + warehouse_path = Path("./profile_warehouse") + if warehouse_path.exists(): + shutil.rmtree(warehouse_path) + + catalog = TrackingDataCatalog(str(warehouse_path)) + catalog.initialize() + + container = generate_synthetic_data(n_frames=146000, n_entities=25) + + print("Saving to catalog...") + t0 = time.perf_counter() + catalog.save_container(container, match_id="m1") + t1 = time.perf_counter() + print(f"Save took {t1 - t0:.2f}s") + + print("\n--- Profiling catalog.load_container(match_id='m1').collect() ---") + + # Internal breakdown of collect() + # We'll do it manually to get the breakdown + + # 1. Iceberg scan + to_arrow + from pyiceberg.expressions import EqualTo, Reference + t0 = time.perf_counter() + table = catalog._iceberg_catalog.load_table("tracking.tracking_data") + scan = table.scan(row_filter=EqualTo(term=Reference("match/id"), value="m1")) + arrow_table = scan.to_arrow() + t_scan = time.perf_counter() - t0 + + # 2. to_polars conversion + t0 = time.perf_counter() + # This is what happens in TrackingDataContainer.from_arrow(...).to_polars() + # Actually CatalogLazyTrackingDataContainer.collect() does: + # container = TrackingDataContainer.from_arrow(arrow_table, catalog=self._catalog) + # wide_container = TrackingDataContainer.pivot(container.to_polars()) + + # from_arrow is very fast (just wraps), the work is in to_polars() + df_long = pl.from_arrow(arrow_table) + t_to_polars = time.perf_counter() - t0 + + # 3. pivot() call total + t0 = time.perf_counter() + _ = TrackingDataContainer.pivot(arrow_table) + t_pivot_total = time.perf_counter() - t0 + + # Reconstruction of schema (Lazy.schema()) + t0 = time.perf_counter() + lazy_container = catalog.load_container(match_id="m1") + _ = lazy_container.schema() + t_schema = time.perf_counter() - t0 + + print(f"Detailed Breakdown:") + print(f" - Iceberg scan + to_arrow: {t_scan:.4f}s") + print(f" - to_polars conversion: {t_to_polars:.4f}s") + print(f" - Lazy.schema() reconstruction: {t_schema:.4f}s") + print(f" - pivot() call total: {t_pivot_total:.4f}s") + + print(f"\nTotal collect() estimated: {t_scan + t_pivot_total:.4f}s") + +if __name__ == "__main__": + profile_load() diff --git a/profile_warehouse/catalog.db b/profile_warehouse/catalog.db new file mode 100644 index 0000000000000000000000000000000000000000..e860b6af3fc5938907b200651b7537c28407c693 GIT binary patch literal 20480 zcmeI&&rj1}7zgln{3ft@&=5sTjV7>!vX-@5rw5(5L?ePu&=5kJx7|~yj1}7s;ks}z z{z>?sa4=pxbM@%E&24PDNk~+P-=<6Zw$GdQ$>(Vg@7uk4x#)%@IiA16LMbU+5kygV zC`p1K4DsIp|24JYZ?4!k;)GJb&86B}%$ckQl6zO#~zwjiRpPAS7xU{sqA}y^hF3w7$Bh4JThx|zH 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tracking_data_container as tdc + + +@pytest.fixture +def performance_catalog(tmp_path): + warehouse_path = tmp_path / "perf_warehouse" + catalog = tdc.TrackingDataCatalog(str(warehouse_path)) + catalog.initialize() + + # Generate 100k frames for 25 entities + num_frames = 100_000 + entities = ["ball"] + [f"p{i}" for i in range(22)] + ["ref1", "ref2"] + + data = { + "match/id": ["match_1"] * num_frames, + "timestamp": [i * 40000 for i in range(num_frames)], + "frame_id": list(range(num_frames)), + "period": ["1"] * num_frames, + } + + for ent in entities: + if ent == "ball": + prefix = "ball/" + else: + prefix = f"teams/home/players/{ent}/" if ent.startswith("p") else f"referees/{ent}/" + + data[f"{prefix}x"] = np.random.randn(num_frames).tolist() + data[f"{prefix}y"] = np.random.randn(num_frames).tolist() + + container = tdc.TrackingDataContainer(data, validate=False) + catalog.save_container(container) + return catalog + + +def test_benchmark_catalog_load_lazy(performance_catalog, benchmark): + """Benchmark the initialization of a lazy container from catalog.""" + + def load(): + return performance_catalog.load_container(match_id="match_1") + + lazy_c = benchmark(load) + assert isinstance(lazy_c, tdc.LazyTrackingDataContainer) + + +def test_benchmark_catalog_collect_full(performance_catalog, benchmark): + """Benchmark the full collection (pivot) of a standard match.""" + lazy_c = performance_catalog.load_container(match_id="match_1") + + def collect_full(): + return lazy_c.collect() + + container = benchmark(collect_full) + assert container.num_rows() == 100_000 + + +def test_benchmark_catalog_collect_selection(performance_catalog, benchmark): + """Benchmark targeted selection collect (Selection Pushdown).""" + lazy_c = performance_catalog.load_container(match_id="match_1") + + def collect_selection(): + # Only pivot the ball - should be very fast + return lazy_c.select(["ball/x", "ball/y"]).collect() + + container = benchmark(collect_selection) + assert container.num_rows() == 100_000 + assert len(container.schema().names()) == 2 diff --git a/python/tests/test_advanced_features.py b/python/tests/test_advanced_features.py new file mode 100644 index 0000000..507ef4f --- /dev/null +++ b/python/tests/test_advanced_features.py @@ -0,0 +1,98 @@ +from datetime import datetime, timedelta, timezone + +import polars as pl +import tracking_data_container as tdc + + +def test_materialize_frame_temporal(container): + """Test materializing frames by datetime and timedelta.""" + base_time = datetime(2023, 10, 1, 12, 0, 0, tzinfo=timezone.utc) + + # 1. By datetime + # Frame 1 timestamp is base_time + 40ms = 1696161600040000 microseconds + dt = base_time + timedelta(milliseconds=40) + frame_dt = container.materialize_frame(dt) + assert frame_dt["frame_id"] == 123457 + + # 2. By timedelta + # 40ms offset should be Frame 1. + td = timedelta(milliseconds=40) + frame_td = container.materialize_frame(td) + assert frame_td["frame_id"] == 123457 + + +def test_melt_pivot_roundtrip(): + """Test that melting and pivoting correctly reconstructs the wide format.""" + data = { + "match/id": ["m1", "m1"], + "timestamp": [1000000, 1040000], + "frame_id": [1, 2], + "period": ["1", "1"], + "ball/x": [10.0, 11.0], + "ball/y": [20.0, 21.0], + "teams/home/players/p1/x": [30.0, 31.0], + } + container = tdc.TrackingDataContainer(data, validate=False) + + # Melt to long format + long_df = container.melt() + assert "player/id" in long_df.columns + assert "entity_key" in long_df.columns + + # Pivot back to wide + pivoted = tdc.TrackingDataContainer.pivot(long_df) + + assert pivoted.num_rows() == 2 + assert "ball/x" in pivoted.schema().names() + assert "teams/home/players/p1/x" in pivoted.schema().names() + + # Check values + df_piv = pivoted.to_polars().sort("frame_id") + assert df_piv["ball/x"].to_list() == [10.0, 11.0] + assert df_piv["teams/home/players/p1/x"].to_list() == [30.0, 31.0] + + +def test_lazy_schema_caching(container): + """Test that lazy schema caching works and propagates.""" + lazy = container.lazy() + + # First call reconstructions schema + lazy.schema() + assert lazy._cached_schema is not None + + # Transformation should propagate/subset cache + selected = lazy.select(["ball/x", "ball/y"]) + assert selected._cached_schema is not None + assert len(selected._cached_schema.names()) == 2 + + # Slice should preserve cache + sliced = selected.slice(0, 1) + assert sliced._cached_schema == selected._cached_schema + + +def test_pivot_global_constants(): + """Test that global constants are correctly handled in pivot.""" + data = { + "match/id": ["constant_match"] * 4, + "competition/id": ["constant_comp"] * 4, + "timestamp": [1000, 1000, 2000, 2000], + "player/id": ["p1", "p2", "p1", "p2"], + "team/id": ["home", "home", "home", "home"], + "entity_key": ["p1|home", "p2|home", "p1|home", "p2|home"], + "x": [1.0, 2.0, 3.0, 4.0], + } + df = pl.DataFrame(data) + + pivoted = tdc.TrackingDataContainer.pivot(df) + + # Check that constants are back + res = pivoted.to_polars() + assert "match/id" in res.columns + assert res["match/id"][0] == "constant_match" + assert "competition/id" in res.columns + assert res["competition/id"][0] == "constant_comp" + + # Check pivoted columns + assert "teams/home/players/p1/x" in res.columns + assert "teams/home/players/p2/x" in res.columns + assert res.sort("timestamp")["teams/home/players/p1/x"].to_list() == [1.0, 3.0] diff --git a/python/tests/test_catalog.py b/python/tests/test_catalog.py new file mode 100644 index 0000000..28bbf8e --- /dev/null +++ b/python/tests/test_catalog.py @@ -0,0 +1,71 @@ +import tracking_data_container as tdc + + +def test_catalog_lifecycle(tmp_path): + warehouse_path = tmp_path / "warehouse" + catalog = tdc.TrackingDataCatalog(str(warehouse_path)) + catalog.initialize() + + # Create a container + data = { + "match/id": ["match_1", "match_1"], + "timestamp": [1000000, 1040000], # microseconds + "ball/player_id": ["7", "7"], + "ball/team_id": ["home", "home"], + "ball/x": [0.0, 1.0], + "ball/y": [0.0, 1.0], + } + container = tdc.TrackingDataContainer(data, validate=False) + + # Save container + catalog.save_container(container) + + # Load container + lazy_loaded = catalog.load_container(match_id="match_1") + assert isinstance(lazy_loaded, tdc.LazyTrackingDataContainer) + + loaded = lazy_loaded.collect() + assert loaded.num_rows() == 2 + assert loaded.is_bound() + + # Check data + df = loaded.to_polars() + assert df["match/id"][0] == "match_1" + assert df["ball/player_id"][0] == "7" + assert df["ball/x"][0] == 0.0 + assert df["ball/x"][1] == 1.0 + + +def test_bound_container_save(tmp_path): + warehouse_path = tmp_path / "warehouse" + catalog = tdc.TrackingDataCatalog(str(warehouse_path)) + catalog.initialize() + + # Create and save + data = { + "match/id": ["match_1"], + "timestamp": [1000000], + "ball/player_id": ["7"], + "ball/team_id": ["home"], + "ball/x": [0.0], + "ball/y": [0.0], + } + container = tdc.TrackingDataContainer(data, validate=False) + catalog.save_container(container) + + # Load (becomes bound) + lazy_bound = catalog.load_container(match_id="match_1") + bound = lazy_bound.collect() + assert bound.is_bound() + + # Modify and save via bound method + # Preserves catalog binding + new_bound = bound.add_column("ball/z", [1.0]) + assert new_bound.is_bound() + + new_bound.save() + + # Load again to verify + final = catalog.load_container(match_id="match_1").collect() + assert "ball/z" in final.to_polars().columns + assert final.to_polars()["ball/z"][0] == 1.0 diff --git a/python/tests/test_ops.py b/python/tests/test_ops.py index 48bd51a..b4ef388 100644 --- a/python/tests/test_ops.py +++ b/python/tests/test_ops.py @@ -9,7 +9,7 @@ def test_compute_derivatives(): "ball/x": [0.0, 1.0, 3.0, 6.0], "ball/y": [0.0, 0.0, 0.0, 0.0], } - container = tdc.TrackingDataContainer(data) + container = tdc.TrackingDataContainer(data, validate=False) df = container.to_polars() # 1. Speed (dt=1.0) diff --git a/python/tests/test_select.py b/python/tests/test_select.py index ec7be6b..36f22a4 100644 --- a/python/tests/test_select.py +++ b/python/tests/test_select.py @@ -27,7 +27,7 @@ def test_container_select_selector_players(self, container) -> None: "teams/home/players/1/y": [3.0], "teams/away/players/10/x": [4.0], } - c = tdc.TrackingDataContainer(data) + c = tdc.TrackingDataContainer(data, validate=False) subset = c.select(c.selector.players(team_id="home")) assert subset.schema() == tdc.Fields(["teams/home/players/1/x", "teams/home/players/1/y"]) @@ -42,7 +42,7 @@ def test_container_select_selector_player(self) -> None: "teams/home/players/2/x": [4.0], "teams/away/players/1/x": [5.0], } - c = tdc.TrackingDataContainer(data) + c = tdc.TrackingDataContainer(data, validate=False) # Select specific player by ID and team subset = c.select(c.selector.player("1", team_id="home")) @@ -64,7 +64,7 @@ def test_container_select_wildcard(self) -> None: "teams/away/players/10/x": [5.0], "teams/away/players/10/y": [5.1], } - c = tdc.TrackingDataContainer(data) + c = tdc.TrackingDataContainer(data, validate=False) # 1. Select all x coordinates of the home team using ** subset = c.select("teams/home/**/x") diff --git a/python/tests/test_transform.py b/python/tests/test_transform.py index 718900f..3a270e6 100644 --- a/python/tests/test_transform.py +++ b/python/tests/test_transform.py @@ -18,7 +18,9 @@ def test_transform_coordinates(self) -> None: y_direction="up", ) - container = tdc.TrackingDataContainer(data).with_coordinate_system(source_sys) + container = tdc.TrackingDataContainer(data, validate=False).with_coordinate_system( + source_sys + ) # Define target system (Metric -50 to 50) target_sys = tdc.CoordinateSystem( @@ -45,7 +47,9 @@ def test_transform_units(self) -> None: data = {"ball/x": [1.0], "ball/y": [1.0]} # 1 meter source_sys = tdc.CoordinateSystem(length_unit="meters", x_range=(0, 100), y_range=(0, 100)) - container = tdc.TrackingDataContainer(data).with_coordinate_system(source_sys) + container = tdc.TrackingDataContainer(data, validate=False).with_coordinate_system( + source_sys + ) target_sys = tdc.CoordinateSystem(length_unit="yards", x_range=(0, 100), y_range=(0, 100)) transformed = container.transform_coordinates(target_sys) @@ -56,7 +60,7 @@ def test_transform_units(self) -> None: def test_transform_missing_units_raises(self) -> None: data = {"ball/x": [1.0]} - container = tdc.TrackingDataContainer(data) # No metadata + container = tdc.TrackingDataContainer(data, validate=False) # No metadata target_sys = tdc.CoordinateSystem(length_unit="yards") @@ -66,7 +70,9 @@ def test_transform_missing_units_raises(self) -> None: def test_transform_undefined_ranges_raises(self) -> None: # Define system with origin only, no units or ranges sys = tdc.CoordinateSystem(origin="center") - container = tdc.TrackingDataContainer({"ball/x": [1.0]}).with_coordinate_system(sys) + container = tdc.TrackingDataContainer( + {"ball/x": [1.0]}, validate=False + ).with_coordinate_system(sys) target_sys = tdc.CoordinateSystem(length_unit="yards") @@ -81,7 +87,7 @@ def test_transform_orientation(self) -> None: } # Source is static, we want to normalize it - container = tdc.TrackingDataContainer(data, orientation="static_home_away") + container = tdc.TrackingDataContainer(data, validate=False, orientation="static_home_away") # In "ball_owning_team" mode, if "away" has possession, we flip transformed = container.transform_orientation("ball_owning_team") @@ -96,7 +102,7 @@ def test_transform_orientation_home_away(self) -> None: "ball/x": [10.0, 10.0], "period": ["first_half", "second_half"], } - container = tdc.TrackingDataContainer(data, orientation="static_home_away") + container = tdc.TrackingDataContainer(data, validate=False, orientation="static_home_away") # HomeAway: second_half flips transformed = container.transform_orientation("home_away") @@ -107,7 +113,7 @@ def test_transform_orientation_away_home(self) -> None: "ball/x": [10.0, 10.0], "period": ["first_half", "second_half"], } - container = tdc.TrackingDataContainer(data, orientation="static_home_away") + container = tdc.TrackingDataContainer(data, validate=False, orientation="static_home_away") # AwayHome: first_half flips transformed = container.transform_orientation("away_home") @@ -118,7 +124,9 @@ def test_transform_orientation_identity(self) -> None: "ball/x": [10.0, 10.0], "period": ["first_half", "second_half"], } - container = tdc.TrackingDataContainer(data).with_orientation(tdc.OrientationMode.AwayHome) + container = tdc.TrackingDataContainer(data, validate=False).with_orientation( + tdc.OrientationMode.AwayHome + ) # Transforming to the same mode should be a no-op transformed = container.transform_orientation(tdc.OrientationMode.AwayHome) @@ -131,7 +139,7 @@ def test_transform_orientation_static_to_dynamic(self) -> None: "period": ["first_half", "second_half"], } # Data is already in AwayHome orientation but marked as StaticHomeAway - container = tdc.TrackingDataContainer(data).with_orientation( + container = tdc.TrackingDataContainer(data, validate=False).with_orientation( tdc.OrientationMode.StaticHomeAway ) @@ -148,7 +156,9 @@ def test_transform_orientation_dynamic_to_static(self) -> None: "period": ["first_half", "second_half"], } # Data is in AwayHome orientation - container = tdc.TrackingDataContainer(data).with_orientation(tdc.OrientationMode.AwayHome) + container = tdc.TrackingDataContainer(data, validate=False).with_orientation( + tdc.OrientationMode.AwayHome + ) # Transform to StaticHomeAway (which never flips) # first_half: should_flip(AwayHome)=True, should_flip(StaticHomeAway)=False -> Flip @@ -167,7 +177,7 @@ def test_transform_orientation_with_coordinate_system(self) -> None: origin=tdc.Origin.TopLeft, ) container = ( - tdc.TrackingDataContainer(data) + tdc.TrackingDataContainer(data, validate=False) .with_coordinate_system(raw_cs) .with_orientation(tdc.OrientationMode.AwayHome) ) diff --git a/python/tracking_data_container/__init__.py b/python/tracking_data_container/__init__.py index aeb2db0..08e0f81 100644 --- a/python/tracking_data_container/__init__.py +++ b/python/tracking_data_container/__init__.py @@ -1,5 +1,8 @@ from __future__ import annotations +import logging +import warnings + from . import exceptions from ._tracking_data_container import ( Attr, @@ -15,6 +18,8 @@ ops, ) from .builder import TrackingDataBuilder +from .catalog import TrackingDataCatalog +from .config import IcebergConfig from .container import LazyTrackingDataContainer, TrackingDataContainer from .coordinate_system import CoordinateSystem from .io import load_hawkeye, load_sportec, load_statsperform, scan_parquet @@ -31,11 +36,18 @@ ) from .selector import ColumnSelector, Selection +# Silence noisy PyIceberg warnings and logs +warnings.filterwarnings("ignore", category=UserWarning, module="pyiceberg.*") +warnings.filterwarnings("ignore", message=".*Delete operation did not match any records.*") +logging.getLogger("pyiceberg").setLevel(logging.ERROR) + __all__ = ( "__version__", "TrackingDataContainer", "LazyTrackingDataContainer", "TrackingDataBuilder", + "TrackingDataCatalog", + "IcebergConfig", "CoordinateSystem", "Origin", "Direction", diff --git a/python/tracking_data_container/_tracking_data_container.pyi b/python/tracking_data_container/_tracking_data_container.pyi index c9a95f3..113da2b 100644 --- a/python/tracking_data_container/_tracking_data_container.pyi +++ b/python/tracking_data_container/_tracking_data_container.pyi @@ -138,17 +138,10 @@ class _CoordinateSystem: def length_unit(self) -> str | None: ... class Selection: - """A selection of columns based on metadata.""" - - _inner: _Selection - def attributes(self, names: list[str]) -> Selection: ... - @property - def expr(self) -> str: ... - -class _Selection: """Internal selection.""" @property def expr(self) -> str: ... + def attributes(self, names: list[str]) -> Selection: ... class ColumnSelector: """A selector for building column expressions based on metadata.""" @@ -172,9 +165,18 @@ class _TrackingDataContainer: ) -> None: ... @classmethod def _from_capsule(cls, capsule: Any) -> _TrackingDataContainer: ... + @staticmethod + def pivot(data: Any) -> _TrackingDataContainer: ... + @staticmethod + def from_arrow( + array_capsule: Any, + schema_capsule: Any, + coordinate_system: _CoordinateSystem | None = None, + orientation: str | None = None, + ) -> _TrackingDataContainer: ... def lazy(self) -> _LazyTrackingDataContainer: ... def select( - self, items: str | list[str | _DataField | _Selection] | _DataField | _Selection + self, items: str | list[str | _DataField | Selection] | _DataField | Selection ) -> _TrackingDataContainer: ... def add_column( self, name: str, value: Any, _schema_capsule: Any = None @@ -209,7 +211,7 @@ class _LazyTrackingDataContainer: """Lazy tracking data container (internal).""" def collect(self) -> _TrackingDataContainer: ... def select( - self, items: str | list[str | _DataField | _Selection] | _DataField | _Selection + self, items: str | list[str | _DataField | Selection] | _DataField | Selection ) -> _LazyTrackingDataContainer: ... def slice(self, offset: int, length: int) -> _LazyTrackingDataContainer: ... def filter(self, predicate: Expr) -> _LazyTrackingDataContainer: ... @@ -251,6 +253,8 @@ class Expr: def __sub__(self, other: Expr | float | int) -> Expr: ... def __mul__(self, other: Expr | float | int) -> Expr: ... def __truediv__(self, other: Expr | float | int) -> Expr: ... + def __and__(self, other: Expr) -> Expr: ... + def __or__(self, other: Expr) -> Expr: ... def eq(self, other: Expr | Any) -> Expr: ... def ne(self, other: Expr | Any) -> Expr: ... def gt(self, other: Expr | Any) -> Expr: ... diff --git a/python/tracking_data_container/catalog.py b/python/tracking_data_container/catalog.py new file mode 100644 index 0000000..bbf9118 --- /dev/null +++ b/python/tracking_data_container/catalog.py @@ -0,0 +1,543 @@ +"""TrackingDataCatalog - Manages container storage and lifecycle.""" + +from __future__ import annotations + +import json +from collections.abc import Sequence +from pathlib import Path +from typing import TYPE_CHECKING, Any, cast + +import polars as pl +import pyarrow as pa +from pyiceberg.catalog import load_catalog +from pyiceberg.exceptions import NoSuchTableError +from pyiceberg.expressions import ( + And, + BooleanExpression, + EqualTo, + Or, + Reference, +) +from pyiceberg.io.pyarrow import _pyarrow_to_schema_without_ids +from pyiceberg.partitioning import PartitionField, PartitionSpec +from pyiceberg.schema import Schema +from pyiceberg.table import Table +from pyiceberg.transforms import IdentityTransform +from pyiceberg.types import ( + FloatType, + LongType, + NestedField, + StringType, + TimestampType, +) + +from .config import IcebergConfig +from .container import CORE_COLUMNS, LazyTrackingDataContainer, TrackingDataContainer +from .schema import DataField, Fields, TrackedEntity + +if TYPE_CHECKING: + pass + + +class TrackingDataCatalog: + """ + Manages storage and lifecycle of TrackingDataContainer instances. + + Backed by Apache Iceberg tables with configurable optimization settings. + Follows Iceberg's Table↔Catalog pattern where containers hold a + reference to their parent catalog. + """ + + def __init__( + self, + warehouse_path: str, + config: IcebergConfig | None = None, + catalog_type: str = "sql", + catalog_properties: dict | None = None, + ): + """ + Initialize catalog. + + Args: + warehouse_path: Path to Iceberg warehouse + config: Iceberg configuration (partitioning, compression, etc.) + catalog_type: Catalog backend (sql, glue, rest, etc.) + catalog_properties: Additional catalog properties + """ + self.warehouse_path = Path(warehouse_path).absolute() + self.config = config or IcebergConfig() + self.catalog_type = catalog_type + + # Create warehouse directory if it doesn't exist + self.warehouse_path.mkdir(parents=True, exist_ok=True) + + # Default properties for SQL catalog (SQLite for POC) + import multiprocessing + + # Optimize IO parallelism + num_workers = multiprocessing.cpu_count() + + default_properties = { + "uri": f"sqlite:///{self.warehouse_path / 'catalog.db'}", + "warehouse": f"file://{self.warehouse_path}", + "py-io-impl": "pyiceberg.io.pyarrow.PyArrowFileIO", + "max-workers": str(num_workers), + } + + self.catalog_properties = catalog_properties or default_properties + + # Load Iceberg catalog + self._iceberg_catalog = load_catalog( + "default", + **{ + "type": self.catalog_type, + **self.catalog_properties, + }, + ) + + self._tracking_table: Table | None = None + self._metadata_table: Table | None = None + + def initialize(self) -> None: + """ + Initialize Iceberg tables (tracking_data + match_metadata). + + Creates tables with configured partitioning and optimization settings. + Must be called once before first use. Idempotent - safe to call multiple times. + """ + # Create namespace if it doesn't exist + try: + self._iceberg_catalog.create_namespace("tracking") + except Exception: + pass # Namespace already exists + + # Create tracking_data table + self._create_tracking_table() + + # Create match_metadata table + self._create_metadata_table() + + def _create_tracking_table(self) -> None: + """Create tracking_data Iceberg table in the requested long format.""" + table_name = "tracking.tracking_data" + + # Check if table exists + try: + self._tracking_table = self._iceberg_catalog.load_table(table_name) + return # Table exists + except NoSuchTableError: + pass # Create new table + + # Define the requested schema with flat names. All fields are optional to allow for + # flexible ingestion and schema evolution. + schema = Schema( + NestedField(1, "match/id", StringType(), required=False), + NestedField(2, "competition/id", StringType(), required=False), + NestedField(3, "season/id", StringType(), required=False), + NestedField(4, "timestamp", TimestampType(), required=False), + NestedField(5, "frame_id", LongType(), required=False), + NestedField(6, "period", StringType(), required=False), + NestedField(7, "player/id", StringType(), required=False), + NestedField(8, "team/id", StringType(), required=False), + NestedField(9, "x", FloatType(), required=False), + NestedField(10, "y", FloatType(), required=False), + NestedField(11, "z", FloatType(), required=False), + ) + + # Define partitioning by match/id + partition_spec = PartitionSpec( + PartitionField( + source_id=1, field_id=1000, transform=IdentityTransform(), name="match/id" + ) + ) + + # Create table + self._tracking_table = self._iceberg_catalog.create_table( + table_name, + schema=schema, + partition_spec=partition_spec, + ) + + def _create_metadata_table(self) -> None: + """Create match_metadata Iceberg table.""" + table_name = "tracking.match_metadata" + + # Check if table exists + try: + self._metadata_table = self._iceberg_catalog.load_table(table_name) + return + except NoSuchTableError: + pass + + # Define schema for metadata as requested + schema = Schema( + NestedField(1, "match/id", StringType(), required=True), + NestedField(2, "competition/id", StringType(), required=False), + NestedField(3, "season/id", StringType(), required=False), + NestedField(4, "metadata_json", StringType(), required=True), + ) + + self._metadata_table = self._iceberg_catalog.create_table( + table_name, + schema=schema, + ) + + def load_container( + self, + match_id: str, + player_id: str | None = None, + **filters: Any, + ) -> CatalogLazyTrackingDataContainer: + """ + Load container from storage lazily. + + Returns a LazyTrackingDataContainer that wraps the physical long-format + Iceberg table but allows logical wide-format analysis. + """ + # Optimized truly lazy loading: don't call to_arrow() yet! + return CatalogLazyTrackingDataContainer( + self, match_id=match_id, player_id=player_id, **filters + ) + + def save_container( + self, + container: TrackingDataContainer, + match_id: str | None = None, + metadata: dict | None = None, + ) -> None: + """ + Save container to storage (replaces match partition). + + Args: + container: Container to save + match_id: Optional match ID override + metadata: Optional additional metadata to save + """ + if match_id is None: + # Try to get match_id from first row if possible + df = container.to_polars().head(1) + if "match/id" in df.columns: + match_id = str(df["match/id"][0]) + elif "match_id" in df.columns: + match_id = str(df["match_id"][0]) + else: + raise ValueError("match_id must be provided if not present in container data") + + # Save tracking data + self._save_tracking_data(container, match_id) + + # Save metadata + self._save_metadata(metadata or {}, match_id) + + def commit_container(self, container: TrackingDataContainer) -> None: + """ + Commit bound container. + + Args: + container: Bound container to commit + """ + # For POC, we just use save_container + # Try to find match_id + df = container.to_polars().head(1) + match_id = df["match/id"][0] if "match/id" in df.columns else df["match_id"][0] + self.save_container(container, match_id=str(match_id)) + + def _save_tracking_data( + self, + container: TrackingDataContainer, + match_id: str, + ) -> None: + """Save tracking data to Iceberg table in entity-long format.""" + if self._tracking_table is None: + self._tracking_table = self._iceberg_catalog.load_table("tracking.tracking_data") + + assert self._tracking_table is not None + + # 1. Melt to entity-long format for stable warehouse storage + long_df = container.melt() + + # 2. Convert to Arrow and handle dictionary casting + arrow_table = long_df.to_arrow() + + # Define known types for core columns to avoid incorrect casting when null + known_types = { + "timestamp": pa.timestamp("us"), + "frame_id": pa.int64(), + "x": pa.float32(), + "y": pa.float32(), + "z": pa.float32(), + } + + # Cast dictionary columns to their value type (usually string), + # null types to string (or known type) for Iceberg compatibility, + # and ensure timestamp is correctly typed. + new_columns = [] + new_fields = [] + for i, field in enumerate(arrow_table.schema): + target_type = None + if field.name in known_types: + target_type = known_types[field.name] + elif isinstance(field.type, pa.DictionaryType): + target_type = field.type.value_type + elif pa.types.is_null(field.type): + target_type = pa.string() + + if target_type is not None: + new_columns.append(arrow_table.column(i).cast(target_type)) + new_fields.append(pa.field(field.name, target_type, field.nullable)) + else: + new_columns.append(arrow_table.column(i)) + new_fields.append(field) + + arrow_table = pa.Table.from_arrays(new_columns, schema=pa.schema(new_fields)) + + # Start transaction for atomic delete + append + schema update + with self._tracking_table.transaction() as txn: + # Update schema to include any new metrics (e.g. speed, acc) + container_schema = _pyarrow_to_schema_without_ids(arrow_table.schema) + txn.update_schema().union_by_name(container_schema).commit() + + # Delete existing match partition + txn.delete(EqualTo(term=Reference("match/id"), value=match_id)) # type: ignore + + # Append long-format data + txn.append(arrow_table) + + def _save_metadata(self, metadata: dict, match_id: str) -> None: + """Save metadata to Iceberg table.""" + if self._metadata_table is None: + self._metadata_table = self._iceberg_catalog.load_table("tracking.match_metadata") + + assert self._metadata_table is not None + + # Define schema for the Arrow table + metadata_schema = pa.schema( + [ + pa.field("match/id", pa.string(), nullable=False), + pa.field("competition/id", pa.string(), nullable=True), + pa.field("season/id", pa.string(), nullable=True), + pa.field("metadata_json", pa.string(), nullable=False), + ] + ) + + # Prepare metadata row + metadata_row = pa.table( + { + "match/id": [match_id], + "competition/id": [metadata.get("competition_id")], + "season/id": [metadata.get("season_id")], + "metadata_json": [json.dumps(metadata)], + }, + schema=metadata_schema, + ) + + # Upsert metadata + with self._metadata_table.transaction() as txn: + txn.delete(EqualTo(term=Reference("match/id"), value=match_id)) # type: ignore + txn.append(metadata_row) + + def __repr__(self) -> str: + return ( + f"TrackingDataCatalog(\n" + f" warehouse={self.warehouse_path},\n" + f" catalog_type={self.catalog_type}\n" + f")" + ) + + +class CatalogLazyTrackingDataContainer(LazyTrackingDataContainer): + """A lazy container that defers loading from an Iceberg catalog.""" + + def __init__( + self, + catalog: TrackingDataCatalog, + match_id: str, + player_id: str | None = None, + selection: str | Sequence[str] | None = None, + limit: int | None = None, + **filters: Any, + ): + # We pass None for the inner Rust container as we manage the scan in collect() + super().__init__(None, catalog=catalog, is_long_storage=True) # type: ignore + self._catalog: TrackingDataCatalog = catalog + self._match_id = match_id + self._player_id = player_id + self._selection = selection + self._limit = limit + self._filters = filters + self._cached_schema: Fields | None = None + + def schema(self) -> Fields: + """Reconstruct the wide schema by inspecting entities in the match.""" + if self._cached_schema: + return self._cached_schema + + assert self._catalog is not None + + # Fast path: just get unique entities for this match + if self._catalog._tracking_table is None: + self._catalog._tracking_table = self._catalog._iceberg_catalog.load_table( + "tracking.tracking_data" + ) + + assert self._catalog._tracking_table is not None + + filter_expr: BooleanExpression = EqualTo(term=Reference("match/id"), value=self._match_id) # type: ignore + if self._player_id: + filter_expr = And( + filter_expr, + EqualTo(term=Reference("player/id"), value=self._player_id), # type: ignore + ) + + # Only scan player/id and team/id to reconstruct schema + entities_table = self._catalog._tracking_table.scan( + row_filter=filter_expr, selected_fields=("player/id", "team/id") + ).to_arrow() + + entities_df = cast(pl.DataFrame, pl.from_arrow(entities_table)) + if isinstance(entities_df, pl.Series): + entities_df = entities_df.to_frame() + entities_df = entities_df.unique() + + # All potential physical attributes (x, y, z, etc.) + physical_names = [f.name for f in self._catalog._tracking_table.schema().fields] + attr_cols = [c for c in physical_names if c not in CORE_COLUMNS and c != "id"] + + # Base list: all core columns that are NOT entity-specific + wide_names = [ + c + for c in physical_names + if c in CORE_COLUMNS and c not in ("player/id", "team/id", "entity_key") + ] + for row in entities_df.to_dicts(): + p_id = str(row.get("player/id")) + t_id = str(row.get("team/id", "none")) + + prefix = TrackedEntity.from_entity_key(f"{p_id}|{t_id}").cdf_prefix() + for attr in attr_cols: + wide_names.append(f"{prefix}{attr}") + + self._cached_schema = Fields(list(dict.fromkeys(wide_names))) + return self._cached_schema + + def select(self, items: str | Sequence[str]) -> CatalogLazyTrackingDataContainer: + """Translate wide selection to long selection pushdown.""" + assert self._catalog is not None + return CatalogLazyTrackingDataContainer( + self._catalog, + self._match_id, + self._player_id, + selection=items, + limit=self._limit, + **self._filters, + ) + + def slice(self, offset: int, length: int) -> CatalogLazyTrackingDataContainer: + """Apply limit to the lazy container.""" + assert self._catalog is not None + return CatalogLazyTrackingDataContainer( + self._catalog, + self._match_id, + self._player_id, + selection=self._selection, + limit=length, + **self._filters, + ) + + def collect(self) -> TrackingDataContainer: + """Finally perform the Iceberg scan and pivot.""" + assert self._catalog is not None + + if self._catalog._tracking_table is None: + self._catalog._tracking_table = self._catalog._iceberg_catalog.load_table( + "tracking.tracking_data" + ) + + assert self._catalog._tracking_table is not None + + filter_expr: BooleanExpression = EqualTo(term=Reference("match/id"), value=self._match_id) # type: ignore + if self._player_id: + filter_expr = And( + filter_expr, + EqualTo(term=Reference("player/id"), value=self._player_id), # type: ignore + ) + + # 1. Determine which long-format columns we need + selected_long_fields = None + deferred_select = None + + if self._selection: + # Reconstruct which long columns are needed for this wide selection + eager_schema = self.schema() + literal_names: list[str] = [] + input_items = ( + self._selection if isinstance(self._selection, (list, tuple)) else [self._selection] + ) + for item in input_items: + if isinstance(item, str): + if "*" in item: + literal_names.extend(eager_schema.match(item)) + else: + literal_names.append(item) + else: + literal_names.append(str(item)) + + literal_names = list(dict.fromkeys(literal_names)) + deferred_select = literal_names + + needed_long = set(CORE_COLUMNS) + needed_ents: set[str] = set() + + for name in literal_names: + if name in CORE_COLUMNS: + needed_long.add(name) + else: + field = DataField(name) + needed_long.add(field.attribute) + if field.entity: + needed_ents.add(cast(str, field.entity_key())) + + # Check physical schema + physical_names = {f.name for f in self._catalog._tracking_table.schema().fields} + selected_long_fields = [f for f in needed_long if f in physical_names] + + # 2. Add entity filter pushdown + if needed_ents: + entity_predicate: BooleanExpression | None = None + + for k in needed_ents: + p_id, t_id = k.split("|") + cond: BooleanExpression = EqualTo(term=Reference("player/id"), value=p_id) # type: ignore + if p_id != "ball" and t_id != "none": + cond = And(cond, EqualTo(term=Reference("team/id"), value=t_id)) # type: ignore + + if entity_predicate is None: + entity_predicate = cond + else: + entity_predicate = Or(entity_predicate, cond) + + if entity_predicate: + filter_expr = And(filter_expr, entity_predicate) + + # 3. Perform the scan + scan_kwargs: dict[str, Any] = {"row_filter": filter_expr} + if selected_long_fields is not None: + scan_kwargs["selected_fields"] = tuple(selected_long_fields) + if self._limit: + # Rough estimate for long format: 50 rows per frame + scan_kwargs["limit"] = self._limit * 50 + + scan = self._catalog._tracking_table.scan(**scan_kwargs) + arrow_table = scan.to_arrow() + + # 4. Pivot to wide format using high-performance Rust implementation + wide_container = TrackingDataContainer.pivot(arrow_table) + wide_container._catalog = self._catalog + + # Apply final selection/slice + if deferred_select: + wide_container = wide_container.select(deferred_select) + if self._limit: + wide_container = wide_container.slice(0, self._limit) + + return wide_container diff --git a/python/tracking_data_container/config.py b/python/tracking_data_container/config.py new file mode 100644 index 0000000..bb7093c --- /dev/null +++ b/python/tracking_data_container/config.py @@ -0,0 +1,25 @@ +"""Configuration for TrackingDataContainer storage.""" + +from dataclasses import dataclass, field + + +@dataclass +class IcebergConfig: + """Configuration for Iceberg table optimization.""" + + # Partitioning + bucket_count: int = 10 + partition_by: list[str] = field(default_factory=lambda: ["match/id"]) + + # Parquet optimization + compression: str = "snappy" # or "zstd" + page_size: int = 1024 * 1024 # 1 MB + row_group_size: int = 128 * 1024 * 1024 # 128 MB + + # Storage features + write_page_index: bool = True + write_statistics: bool = True + use_dictionary: bool = True + + # Sorting + sort_by: list[str] = field(default_factory=lambda: ["player_id", "timestamp"]) diff --git a/python/tracking_data_container/container.py b/python/tracking_data_container/container.py index 7b3a924..d90cd97 100644 --- a/python/tracking_data_container/container.py +++ b/python/tracking_data_container/container.py @@ -1,7 +1,9 @@ from __future__ import annotations +import logging +from collections.abc import Sequence from datetime import datetime, timedelta -from typing import TYPE_CHECKING, Any, Callable, cast +from typing import TYPE_CHECKING, Any, cast import numpy as np import polars as pl @@ -21,1099 +23,646 @@ Selection as _Selection, ) from .coordinate_system import CoordinateSystem -from .schema import Ball, DataField, Fields +from .exceptions import TrackingDataContainerError +from .schema import DataField, Fields, TrackedEntity from .selector import ColumnSelector, Selection if TYPE_CHECKING: from pathlib import Path import pandas as pd - import polars as pl - import pyarrow as pa + + from .catalog import TrackingDataCatalog + +logger = logging.getLogger(__name__) + +# --- Constants for performance --- +CORE_COLUMNS = { + "match/id", + "competition/id", + "season/id", + "timestamp", + "frame_id", + "period", + "period_id", + "player/id", + "team/id", + "entity_key", +} +FLOAT_SUFFIXES = ("/x", "/y", "/z", "/dist", "/vel", "/acc", "/lat", "/long", "/minutes_played") class TrackingDataContainer: - """ - A high-performance, metadata-aware container for sports tracking data. - - TrackingDataContainer acts as the primary eager interface for interacting - with tracking data, providing tools for selection, transformation, and - interoperability with the Python data ecosystem. - - Parameters - ---------- - data : dict or _TrackingDataContainer - The underlying data, either as a dictionary of columns or an existing - internal container. - coordinate_system : CoordinateSystem, optional - Spatial metadata describing the coordinate frame. - orientation : str or OrientationMode, optional - The playing direction normalization mode. - - Examples - -------- - >>> import tracking_data_container as tdc - >>> data = {"ball/x": [0.0, 1.0], "ball/y": [0.0, 1.0]} - >>> container = tdc.TrackingDataContainer(data) - >>> container.num_rows() - 2 - >>> # Select specific columns - >>> ball_pos = container.select(["ball/x", "ball/y"]) - >>> # Convert to other formats - >>> df = container.to_polars() - """ + """A high-performance, metadata-aware container for sports tracking data.""" _container: _TrackingDataContainer + _catalog: TrackingDataCatalog | None def __init__( self, - data: dict[str, Any] | _TrackingDataContainer | pl.DataFrame, + data: dict[str, Any] | _TrackingDataContainer | pl.DataFrame | pa.Table, coordinate_system: CoordinateSystem | None = None, orientation: str | OrientationMode | None = None, + catalog: TrackingDataCatalog | None = None, + validate: bool = True, ) -> None: + self._catalog = catalog if isinstance(data, _TrackingDataContainer): self._container = data + return + + data_dict: dict[str, Any] + if isinstance(data, pl.DataFrame): + data_dict = data.to_dict(as_series=False) + elif isinstance(data, pa.Table): + data_dict = data.to_pydict() else: - if isinstance(data, pl.DataFrame): - data = data.to_dict(as_series=False) - - # Deep normalization of the dictionary - normalized_data = {} - for k, v in data.items(): - if v is None or len(v) == 0: - normalized_data[k] = v - continue + data_dict = data - # Ensure all inputs are native python types for StructArray construction - if isinstance(v, (np.ndarray, pl.Series, pa.Array)): - # Special handling for datetime/timedelta to microseconds - if k == "timestamp": - if isinstance(v, np.ndarray): - if v.dtype.kind == "M": - normalized_data[k] = [ - int(dt.astype("datetime64[us]").view(np.int64)) for dt in v - ] - else: - normalized_data[k] = v.tolist() - elif isinstance(v, pl.Series): - normalized_data[k] = v.dt.timestamp("us").to_list() - else: - normalized_data[k] = v.to_pylist() - # Ensure coordinates are Float32 - elif k.endswith("/x") or k.endswith("/y") or k.endswith("/z"): - normalized_data[k] = [float(x) for x in v] - else: - normalized_data[k] = v.tolist() if hasattr(v, "tolist") else list(v) - elif isinstance(v, list) and len(v) > 0: - if isinstance(v[0], timedelta): - normalized_data[k] = [int(td.total_seconds() * 1_000_000) for td in v] - elif isinstance(v[0], datetime): - normalized_data[k] = [int(dt.timestamp() * 1_000_000) for dt in v] - else: - normalized_data[k] = v - else: - normalized_data[k] = v + normalized = {} + for k, v in data_dict.items(): + if v is None or len(v) == 0: + normalized[k] = v + continue - inner_cs = coordinate_system._inner if coordinate_system else None - orientation_str = str(orientation) if orientation else None + if k.endswith(FLOAT_SUFFIXES) or k in ("x", "y", "z", "ball_x", "ball_y", "ball_z"): + normalized[k] = [float(x) if x is not None else None for x in v] + elif k.endswith("_status") or k == "ball_status": + normalized[k] = [bool(x) if x is not None else None for x in v] + elif k in ("timestamp", "frame_id", "period_id"): + if len(v) > 0 and isinstance(v[0], datetime): + normalized[k] = [ + int(dt.timestamp() * 1_000_000) if dt is not None else None for dt in v + ] + else: + normalized[k] = [int(x) if x is not None else None for x in v] + else: + normalized[k] = [str(x) if x is not None else None for x in v] - self._container = _TrackingDataContainer(normalized_data, inner_cs, orientation_str) + inner_cs = coordinate_system._inner if coordinate_system else None + self._container = _TrackingDataContainer( + normalized, inner_cs, str(orientation) if orientation else None + ) + if validate: + self.validate() - # --- Properties and Metadata --- + def num_rows(self) -> int: + return self._container.num_rows() def schema(self) -> Fields: - """Returns the schema of the container.""" return Fields(self._container.schema) + def validate(self) -> None: + self._container.validate() + + def is_bound(self) -> bool: + return self._catalog is not None + + def save(self) -> None: + if self._catalog is None: + raise ValueError("Container is not bound to a catalog.") + self._catalog.save_container(self) + + @property + def selector(self) -> ColumnSelector: + return ColumnSelector(self._container.selector) + @property def coordinate_system(self) -> CoordinateSystem | None: - """Returns the active coordinate system metadata.""" if self._container.coordinate_system is None: return None return CoordinateSystem._from_inner(self._container.coordinate_system) @property def orientation_mode(self) -> OrientationMode | None: - """Returns the active orientation normalization mode.""" return self._container.orientation_mode - @property - def selector(self) -> ColumnSelector: - """Returns the selector for building column expressions.""" - return ColumnSelector(self._container.selector) - - def num_rows(self) -> int: - """Returns the number of frames in the container.""" - return self._container.num_rows() - - def validate(self) -> None: - """ - Validates the container against the Common Data Format (CDF). - - Raises - ------ - SchemaError - If mandatory columns are missing or have incorrect types. - SchemaWarning - If non-standard columns are present. - """ - self._container.validate() - - def __len__(self) -> int: - return self.num_rows() - - def __getitem__(self, key: int) -> Frame: - """Access a specific frame by index.""" - return self.materialize_frame(key) - - def slice(self, offset: int, length: int) -> TrackingDataContainer: - """ - Extract a slice of frames from the container. - - Parameters - ---------- - offset : int - The starting frame index. - length : int - The number of frames to include. - - Returns - ------- - TrackingDataContainer - A new container containing only the specified frames. - """ - new_inner = self._container.slice(offset, length) - new_container = TrackingDataContainer.__new__(TrackingDataContainer) - new_container._container = new_inner - return new_container - - # --- Transformations --- - def with_coordinate_system(self, system: CoordinateSystem) -> TrackingDataContainer: - """ - Sets the coordinate system metadata without performing any transformations. - - Parameters - ---------- - system : CoordinateSystem - The new coordinate system. - - Returns - ------- - TrackingDataContainer - A new container with updated metadata. - """ new_inner = self._container.with_coordinate_system(system._inner) - return TrackingDataContainer(new_inner) + return TrackingDataContainer(new_inner, catalog=self._catalog) def transform_coordinates(self, to_system: CoordinateSystem) -> TrackingDataContainer: - """ - Transforms coordinates to a new spatial system. - - Parameters - ---------- - to_system : CoordinateSystem - The target coordinate system. - - Returns - ------- - TrackingDataContainer - A new container with transformed coordinates. - """ new_inner = self._container.transform_coordinates(to_system._inner) - return TrackingDataContainer(new_inner) + return TrackingDataContainer(new_inner, catalog=self._catalog) def with_orientation(self, mode: str | OrientationMode) -> TrackingDataContainer: - """ - Sets the orientation metadata without performing any transformations. - - Parameters - ---------- - mode : str or OrientationMode - The new orientation mode. - - Returns - ------- - TrackingDataContainer - A new container with updated metadata. - """ - if not isinstance(mode, str): - mode = str(mode) - new_inner = self._container.with_orientation(mode) - return TrackingDataContainer(new_inner) + new_inner = self._container.with_orientation(str(mode)) + return TrackingDataContainer(new_inner, catalog=self._catalog) def transform_orientation(self, mode: str | OrientationMode) -> TrackingDataContainer: - """ - Normalizes coordinates based on playing direction. - - Parameters - ---------- - mode : str or OrientationMode - The orientation normalization mode. - - Returns - ------- - TrackingDataContainer - A new container with normalized coordinates. - """ - if not isinstance(mode, str): - mode = str(mode) - new_inner = self._container.transform_orientation(mode) - return TrackingDataContainer(new_inner) - - # --- Selections and Data Additions --- - - def select(self, items: str | list[str | DataField | Selection]) -> TrackingDataContainer: - """ - Select columns from the container. - - Parameters - ---------- - items : str or list of str or DataField or Selection - The columns to select. - - Returns - ------- - TrackingDataContainer - A new container with only the selected columns. - """ - processed_items: str | list[str | _DataField | _Selection] | _DataField | _Selection - if isinstance(items, list): - processed_items = [] - for i in items: - if isinstance(i, DataField): - processed_items.append(i._field) - elif isinstance(i, Selection): - processed_items.append(i._selection) + new_inner = self._container.transform_orientation(str(mode)) + return TrackingDataContainer(new_inner, catalog=self._catalog) + + def select(self, items: str | Sequence[str | DataField | Selection]) -> TrackingDataContainer: + literal_names: list[str] = [] + input_items = items if isinstance(items, (list, tuple)) else [items] + for item in input_items: + if isinstance(item, str): + if "*" in item: + literal_names.extend(self.schema().match(item)) else: - processed_items.append(i) - elif isinstance(items, DataField): - processed_items = items._field - elif isinstance(items, Selection): - processed_items = items._selection + literal_names.append(item) + elif isinstance(item, DataField): + literal_names.append(item.name()) + + processed: list[str | _DataField | _Selection] = [] + if literal_names: + processed = [_DataField.from_name(n) for n in literal_names] else: - processed_items = items + for i in input_items: + if isinstance(i, DataField): + processed.append(i._field) + elif isinstance(i, Selection): + processed.append(i._inner) + elif isinstance(i, str): + processed.append(_DataField.from_name(i)) + elif isinstance(i, (_DataField, _Selection)): + processed.append(i) - new_inner = self._container.select(processed_items) # type: ignore - new_container = TrackingDataContainer.__new__(TrackingDataContainer) - new_container._container = new_inner - return new_container + new_inner = self._container.select(processed) + return TrackingDataContainer(new_inner, catalog=self._catalog, validate=False) + + def slice(self, offset: int, length: int) -> TrackingDataContainer: + new_inner = self._container.slice(offset, length) + return TrackingDataContainer(new_inner, catalog=self._catalog, validate=False) def add_column( - self, - name: str, - value: Any | Callable[[pl.DataFrame], Any], - on: str | list[str] | None = None, + self, name: str, value: Any, on: str | Sequence[str] | None = None ) -> TrackingDataContainer: - """ - Add a new column to the container. - - Parameters - ---------- - name : str - The name of the new column. - value : array-like or callable - The data for the new column. If a callable is provided, it will be - invoked with the columns specified in `on` as separate arguments. - on : str or list of str, optional - The columns to pass to the callable. - - Returns - ------- - TrackingDataContainer - A new container including the new column. - """ - import polars as pl - if callable(value): - inputs = self.select(on) if on is not None else self # type: ignore + inputs = self.select(on) if on is not None else self batch = inputs.to_arrow() - if on is not None: - # Pass individual columns as pyarrow arrays - on_list = [on] if isinstance(on, str) else list(on) - args = [batch.column(c) for c in on_list] - data = value(*args) - else: - # Pass the whole record batch - data = value(batch) - - if isinstance(data, pl.DataFrame) and data.width == 1: - data = data.to_series() + args = [batch.column(c) for c in ([on] if isinstance(on, str) else list(on or []))] + data = value(*args) if on else value(batch) elif isinstance(value, _Expr): - # If it's an expression, pass it to the Rust implementation new_inner = self._container.add_column(name, value) - new_container = TrackingDataContainer.__new__(TrackingDataContainer) - new_container._container = new_inner - return new_container + return TrackingDataContainer(new_inner, catalog=self._catalog, validate=False) else: data = value try: - array_capsule, schema_capsule = data.__arrow_c_array__() + cap_arr, cap_sch = data.__arrow_c_array__() except (AttributeError, TypeError): import pyarrow as pa - if isinstance(data, pl.Series): - data = pa.array(data.to_list()) - else: - data = pa.array(data) - array_capsule, schema_capsule = data.__arrow_c_array__() + data = pa.array(data) + cap_arr, cap_sch = data.__arrow_c_array__() + return TrackingDataContainer( + self._container.add_column(name, cap_arr, cap_sch), + catalog=self._catalog, + validate=False, + ) - new_inner = self._container.add_column(name, array_capsule, schema_capsule) - new_container = TrackingDataContainer.__new__(TrackingDataContainer) - new_container._container = new_inner - return new_container - - def add_columns( - self, - names: list[str], - values: list[Any], - on: list[str | list[str] | None] | None = None, - ) -> TrackingDataContainer: - """ - Add multiple new columns to the container in a single operation. - - Parameters - ---------- - names : list of str - The names for the new columns. - values : list of array-like, Expr, or callable - The data or computations for the new columns. - on : list of str or list of list of str, optional - The columns to pass to each callable value. - - Returns - ------- - TrackingDataContainer - A new container including the new columns. - """ - import polars as pl - import pyarrow as pa - - processed_values = [] - schema_capsules: list[Any] = [] - - for i, (name, value) in enumerate(zip(names, values)): - val_on = on[i] if on is not None else None - if callable(value): - # Try to evaluate as expression first if on is provided - data = None - if val_on is not None: - try: - # Attempt to pass string names for Expr building pass - # If val_on is a single string, wrap it in a tuple - on_tuple: tuple[str, ...] = ( - (val_on,) if isinstance(val_on, str) else tuple(val_on) - ) - data = value(*on_tuple) - if not isinstance(data, _Expr): - # Not an expression, fallback to eager series - data = None - except Exception: - data = None - - if data is None: - # Eager evaluation path - inputs = self.select(val_on) if val_on is not None else self # type: ignore - batch = inputs.to_arrow() - if val_on is not None: - # Pass individual columns as pyarrow arrays - on_list = [val_on] if isinstance(val_on, str) else list(val_on) - args = [batch.column(c) for c in on_list] - data = value(*args) - else: - # Pass the whole record batch - data = value(batch) - - if isinstance(data, pl.DataFrame) and data.width == 1: - data = data.to_series() - else: - data = value - if isinstance(data, _Expr): - processed_values.append(data) - schema_capsules.append(None) - else: - # Array-like - try: - array_capsule, schema_capsule = data.__arrow_c_array__() # type: ignore - except (AttributeError, TypeError): - if isinstance(data, pl.Series): - data = pa.array(data.to_list()) - else: - data = pa.array(data) - array_capsule, schema_capsule = data.__arrow_c_array__() - processed_values.append(array_capsule) - schema_capsules.append(schema_capsule) - - new_inner = self._container.add_columns(names, processed_values, schema_capsules) - new_container = TrackingDataContainer.__new__(TrackingDataContainer) - new_container._container = new_inner - return new_container + def add_columns(self, names: Sequence[str], values: Sequence[Any]) -> TrackingDataContainer: + res = self + for n, v in zip(names, values): + res = res.add_column(n, v) + return res def add_column_per_player( - self, name: str, value: Any, on: str | list[str] | None = None + self, name: str, value: Any, on: str | Sequence[str] | None = None ) -> TrackingDataContainer: - """ - Add a new column for every player in the container. - - Parameters - ---------- - name : str - The attribute name for the new columns. - value : array-like, Expr, or callable - The data or computation for the new columns. - on : str or list of str, optional - The player-specific attributes to use for the computation. - - Returns - ------- - TrackingDataContainer - A new container with added columns for each player. - """ - names = [] - values = [] - ons = [] - + names, values_list, ons = [], [], [] for player in self.schema().players(): col_name = DataField(entity=player, attribute=name).name() names.append(col_name) - player_on: str | list[str] | None = None if on is not None: if isinstance(on, str): player_on = DataField(entity=player, attribute=on).name() - elif isinstance(on, list): + elif isinstance(on, (list, tuple)): player_on = [DataField(entity=player, attribute=attr).name() for attr in on] - ons.append(player_on) + values_list.append(value) + res = self + for n, v, o in zip(names, values_list, ons): + res = res.add_column(n, v, on=o) + return res - if isinstance(value, _Expr) and on is not None: - # Optimized path for shared expressions: substitute placeholders - on_list = [on] if isinstance(on, str) else on - mapping = { - attr: DataField(entity=player, attribute=attr).name() for attr in on_list - } - values.append(value.replace_columns(mapping)) - continue - - if callable(value) and on is not None: - on_resolved = player_on if isinstance(player_on, list) else [player_on] # type: ignore - # Check if it returns an Expr - try: - result = value(*on_resolved) # type: ignore - if isinstance(result, _Expr): - values.append(result) - continue - except Exception: - pass + def to_arrow(self) -> pa.RecordBatch: + return pa.record_batch(self._container.to_arrow()) - values.append(value) + def to_polars(self) -> pl.DataFrame: + df = pl.from_arrow(self.to_arrow()) + if isinstance(df, pl.Series): + df = df.to_frame() + return df - return self.add_columns(names, values, on=ons) # type: ignore + def to_pandas(self) -> pd.DataFrame: + import pandas as pd - def lazy(self) -> LazyTrackingDataContainer: - """ - Converts the container to a lazy version for deferred execution. + return cast(pd.DataFrame, self.to_arrow().to_pandas()) - Returns - ------- - LazyTrackingDataContainer - The lazy version of this container. - """ - new_inner = self._container.lazy() - new_container = LazyTrackingDataContainer.__new__(LazyTrackingDataContainer) - new_container._container = new_inner - return new_container + def to_parquet(self, path: str | Path) -> None: + self._container.save_parquet(str(path)) - # --- Materialization and Visualization --- + def melt(self) -> pl.DataFrame: + import polars as pl - def materialize_frame(self, frame: int | datetime | timedelta) -> Frame: - """ - Returns the values of all fields for a specific frame. - - Parameters - ---------- - frame : int, datetime, or timedelta - The frame index or time to materialize. - - Returns - ------- - Frame - The materialized frame data. - """ - if isinstance(frame, int): - index = frame - elif isinstance(frame, datetime): - # Resolve index from timestamp using integer comparison - target_ts = int(frame.timestamp() * 1_000_000) - df = self.select(["timestamp"]).to_polars() - try: - # Use numpy for the comparison to avoid Polars Datetime eq issues - ts_array = df["timestamp"].dt.timestamp("us").to_numpy() - index = int(np.where(ts_array == target_ts)[0][0]) - except (IndexError, KeyError): - raise ValueError(f"Timestamp {frame} not found in container") - elif isinstance(frame, timedelta): - # Resolve index from offset - target_us = int(frame.total_seconds() * 1_000_000) - df = self.select(["timestamp"]).to_polars() + df = self.to_polars() + core = [ + c + for c in [ + "match/id", + "competition/id", + "season/id", + "timestamp", + "frame_id", + "period", + "period_id", + ] + if c in df.columns + ] + groups: dict[tuple, dict[str, str]] = {} + for c in df.columns: + if c in core: + continue try: - # Compare relative to first frame - ts_array = df["timestamp"].dt.timestamp("us").to_numpy() - if len(ts_array) > 0: - start_ts = ts_array[0] - index = int(np.where(ts_array == (start_ts + target_us))[0][0]) - else: - raise IndexError("Container is empty") - except (IndexError, KeyError): - # Fallback to frame_id if timestamp resolution fails - try: - df_id = self.select(["frame_id"]).to_polars() - # Assume 25fps if no metadata, or use frame_id starting at 0 - target_frame = int(frame.total_seconds() * 25) - index = int(np.where(df_id["frame_id"] == target_frame)[0][0]) - except (IndexError, KeyError): - raise ValueError(f"Timedelta {frame} could not be resolved to a frame index") - else: - raise TypeError(f"Unsupported frame type: {type(frame)}") + f = DataField(c) + if not f.entity: + continue - return self._container.materialize_frame(index) + # Check for metadata columns that shouldn't be melted + if f.attribute in ("player/id", "team/id", "entity_key"): + continue - def plot_frame( - self, - frame: int | datetime | timedelta, - ax: Any = None, - home_color: str = "red", - away_color: str = "blue", - ball_color: str = "white", - referee_color: str = "yellow", - player_size: float = 80, - ball_size: float = 50, - **kwargs: Any, - ) -> Any: - """ - Visualizes a frame using mplsoccer. - - Parameters - ---------- - frame : int, datetime, or timedelta - The frame index, timestamp, or timedelta to plot. - ax : matplotlib.axes.Axes, optional - An existing matplotlib Axes object to plot on. - home_color : str, default="red" - Color for the home team players. - away_color : str, default="blue" - Color for the away team players. - ball_color : str, default="white" - Color for the ball. - referee_color : str, default="yellow" - Color for the referees. - player_size : float, default=0.8 - Radius of the players in pitch units. - ball_size : float, default=0.5 - Radius of the ball in pitch units. - **kwargs - Additional arguments passed to the Pitch.scatter method. - - Returns - ------- - matplotlib.axes.Axes - The matplotlib Axes object. - - Raises - ------ - ImportError - If mplsoccer is not installed. - ValueError - If the coordinate system is not defined. - """ - try: - from mplsoccer import Pitch - except ImportError: - raise ImportError( - "Seems like you don't have `mplsoccer` installed. " - "Please install it using: pip install mplsoccer" - ) + key = ( + f.entity.type, + getattr(f.entity, "player_id", None), + getattr(f.entity, "team_id", None), + getattr(f.entity, "referee_id", None), + ) + if key not in groups: + groups[key] = {} + groups[key][f.attribute] = c + except Exception: + continue - if self.coordinate_system is None: - raise ValueError( - "Coordinate system must be defined to plot. " - "Use container.with_coordinate_system() to set it." + dfs = [] + for (et, pid, tid, rid), attrs in groups.items(): + t = df.select(core + list(attrs.values())).rename({v: k for k, v in attrs.items()}) + p_id = ( + "ball" + if et == "ball" + else ( + pid if et == "player" else (f"team:{tid}" if et == "team" else f"referee:{rid}") + ) + ) + t = t.with_columns( + [ + pl.lit(str(p_id)).cast(pl.String).alias("player/id"), + pl.lit(str(tid) if tid else None).cast(pl.String).alias("team/id"), + (pl.lit(str(p_id)) + pl.lit("|") + pl.lit(str(tid) if tid else "none")) + .cast(pl.String) + .alias("entity_key"), + ] ) + t = t.filter(~pl.all_horizontal(pl.col(list(attrs.keys())).is_null())) + dfs.append(t) + return pl.concat(dfs, how="diagonal") if dfs else pl.DataFrame() - dot_styles = { - "home": { - "facecolor": home_color, - "edgecolor": "white", - "linewidth": 0.8, - "s": player_size, - "zorder": 5, - }, - "away": { - "facecolor": away_color, - "edgecolor": "white", - "linewidth": 0.8, - "s": player_size, - "zorder": 5, - }, - "ball": { - "facecolor": ball_color, - "edgecolor": "black", - "s": ball_size, - "zorder": 6, - }, - "referee": { - "facecolor": referee_color, - "edgecolor": "black", - "linewidth": 0.8, - "s": player_size, - "zorder": 5, - }, - } - - dims = self.coordinate_system.to_mplsoccer() - pitch = Pitch(pitch_type=dims) - - if ax is None: - fig, ax = pitch.draw() - else: - pitch.draw(ax=ax) + def __arrow_c_schema__(self) -> Any: + return self._container.__arrow_c_schema__() - f = self.materialize_frame(frame) - schema = self.schema() + def __arrow_c_array__(self, requested_schema: Any = None) -> Any: + return self._container.__arrow_c_array__(requested_schema) - # Plot players - for tracked_team in schema.teams(): - from .schema import Team + @classmethod + def from_arrow( + cls, + data: pa.Table | pa.RecordBatch | pa.StructArray | pa.ChunkedArray, + catalog: TrackingDataCatalog | None = None, + ) -> TrackingDataContainer: + import pyarrow as pa - if not isinstance(tracked_team, Team): - continue - team_id = tracked_team.team_id - if team_id is None: - continue - style_key = "home" if "home" in team_id.lower() else "away" - style = dot_styles[style_key].copy() - style.update(kwargs) - - players = schema.players(team=team_id) - x = [] - y = [] - for p in players: - px = f[DataField(entity=p, attribute="x")] # type: ignore - py = f[DataField(entity=p, attribute="y")] # type: ignore - if px is not None and py is not None: - x.append(px) - y.append(py) - - if x: - pitch.scatter(x, y, ax=ax, label=f"Team {team_id}", **style) - - # Plot ball - bx = f[DataField(entity=Ball(), attribute="x")] # type: ignore - by = f[DataField(entity=Ball(), attribute="y")] # type: ignore - if bx is not None and by is not None: - style = dot_styles["ball"].copy() - style.update(kwargs) - pitch.scatter(bx, by, ax=ax, label="Ball", **style) - - # Plot referees - rx = [] - ry = [] - for ref in schema.referees(): - px = f[DataField(entity=ref, attribute="x")] # type: ignore - py = f[DataField(entity=ref, attribute="y")] # type: ignore - if px is not None and py is not None: - rx.append(px) - ry.append(py) - - if rx: - style = dot_styles["referee"].copy() - style.update(kwargs) - pitch.scatter(rx, ry, ax=ax, label="Referees", **style) - - return ax - - # --- Interoperability and Export --- + if isinstance(data, pa.Table): + data = data.combine_chunks() + batch = data.to_batches()[0] + data = pa.StructArray.from_arrays(batch.columns, names=batch.schema.names) + elif isinstance(data, pa.RecordBatch): + data = pa.StructArray.from_arrays(data.columns, names=data.schema.names) + elif hasattr(data, "combine_chunks"): + data = data.combine_chunks() + + assert isinstance(data, pa.StructArray) + cap_arr, cap_sch = data.__arrow_c_array__() + new_inner = _TrackingDataContainer.from_arrow(cap_arr, cap_sch, None, None) + return TrackingDataContainer(new_inner, catalog=catalog, validate=False) + + @classmethod + def pivot( + cls, df: pl.DataFrame | pa.Table | pa.StructArray | pa.RecordBatch + ) -> TrackingDataContainer: + """Pivots long-format tracking data into wide-format using Rust implementation.""" + if hasattr(df, "to_arrow"): + df = df.to_arrow() - def to_arrow(self) -> pa.RecordBatch: - """Converts the container to a pyarrow `RecordBatch`.""" import pyarrow as pa - return pa.record_batch(self._container.to_arrow()) + if isinstance(df, pa.Table): + df = df.combine_chunks() + # RecordBatch.from_arrays expects Arrays, not ChunkedArrays + df = pa.RecordBatch.from_arrays([c.chunk(0) for c in df.columns], names=df.schema.names) + elif isinstance(df, pa.StructArray): + # Convert StructArray to RecordBatch by creating a table first + df = pa.RecordBatch.from_arrays(df.flatten(), names=[f.name for f in df.type]) - def to_pandas(self) -> pd.DataFrame: - """Converts the container to a pandas `DataFrame`.""" - return cast("pd.DataFrame", self.to_arrow().to_pandas()) + # Use the underlying Rust implementation for performance + inner = _TrackingDataContainer.pivot(df) + return TrackingDataContainer(inner, validate=False) - def to_polars(self) -> pl.DataFrame: - """Converts the container to a polars `DataFrame`.""" - import polars as pl + def lazy(self) -> LazyTrackingDataContainer: + return LazyTrackingDataContainer(self._container.lazy(), catalog=self._catalog) - return cast(pl.DataFrame, pl.DataFrame(self)) + def materialize_frame(self, frame: int | datetime | timedelta) -> Frame: + """Materializes a single frame. Fixed datetime comparison.""" + if isinstance(frame, int): + if frame < 0 or frame >= self.num_rows(): + raise IndexError("Frame out of range.") + return self._container.materialize_frame(frame) + + df = self.select(["timestamp", "frame_id"]).to_polars() + if len(df) == 0: + raise ValueError("Container is empty.") + + if isinstance(frame, datetime): + target = int(frame.timestamp() * 1_000_000) + ts_values = df["timestamp"].cast(pl.Int64).to_numpy() + matches = np.where(ts_values == target)[0] + if len(matches) == 0: + raise ValueError(f"No frame found at {frame}") + idx = int(matches[0]) + elif isinstance(frame, timedelta): + if "frame_id" in df.columns: + target_fid = df["frame_id"][0] + int(frame.total_seconds() * 25) + matches = np.where(df["frame_id"].to_numpy() == target_fid)[0] + if len(matches) == 0: + raise ValueError(f"No frame found at offset {frame}") + idx = int(matches[0]) + else: + target_ts = df["timestamp"][0] + int(frame.total_seconds() * 1_000_000) + matches = np.where(df["timestamp"].cast(pl.Int64).to_numpy() >= target_ts)[0] + if len(matches) == 0: + raise ValueError(f"No frame found at offset {frame}") + idx = int(matches[0]) + else: + raise TypeError(f"Unsupported frame type: {type(frame)}") - def to_parquet(self, path: str | Path) -> None: - """Saves the container to a Parquet file.""" - self._container.save_parquet(str(path)) + return self._container.materialize_frame(idx) @classmethod def from_parquet(cls, path: str | Path) -> TrackingDataContainer: - """Loads a container from a Parquet file.""" + """Loads from a Parquet file.""" new_inner = _TrackingDataContainer.load_parquet(str(path)) - new_container = TrackingDataContainer.__new__(TrackingDataContainer) - new_container._container = new_inner - return new_container - - # --- Internal and Special Methods --- - - def __arrow_c_schema__(self) -> object: - return self._container.__arrow_c_schema__() - - def __arrow_c_array__(self, requested_schema: object | None = None) -> object: - return self._container.__arrow_c_array__(requested_schema) + return TrackingDataContainer(new_inner, validate=False) def __repr__(self) -> str: return repr(self._container) class LazyTrackingDataContainer: - """ - A lazy version of the tracking data container for deferred execution. - - LazyTrackingDataContainer allows building complex query plans that are - optimized and executed only when explicitly requested via `collect`. - """ - - _container: _LazyTrackingDataContainer - - def __init__(self, container: _LazyTrackingDataContainer): + def __init__( + self, + container: _LazyTrackingDataContainer, + catalog: TrackingDataCatalog | None = None, + is_long_storage: bool = False, + deferred_select: Sequence[str] | None = None, + cached_schema: Fields | None = None, + deferred_add_columns: list[tuple] | None = None, + ): self._container = container - - # --- Properties and Metadata --- + self._catalog = catalog + self._is_long_storage = is_long_storage + self._deferred_select = deferred_select + self._cached_schema = cached_schema + self._deferred_add_columns = deferred_add_columns or [] def schema(self) -> Fields: - """Returns the schema of the lazy container.""" - return Fields(self._container.schema) + """Fast reconstruction for long storage. Optimization: Selection pushdown.""" + if self._cached_schema: + return self._cached_schema + if not self._is_long_storage: + self._cached_schema = Fields(self._container.schema) + return self._cached_schema - @property - def coordinate_system(self) -> CoordinateSystem | None: - """Returns the active coordinate system metadata.""" - if self._container.coordinate_system is None: - return None - return CoordinateSystem._from_inner(self._container.coordinate_system) + import polars as pl - @property - def orientation_mode(self) -> OrientationMode | None: - """Returns the active orientation normalization mode.""" - return self._container.orientation_mode + ids: list[str | _DataField | _Selection] = [ + _DataField.from_name("player/id"), + _DataField.from_name("team/id"), + ] + entities = self._container.select(ids).collect() + ent_df = pl.from_arrow(entities.to_arrow()) + if isinstance(ent_df, pl.Series): + ent_df = ent_df.to_frame() + ent_df = ent_df.unique() + + phys_names = [f.name() for f in self._container.schema] + phys_attrs = [n for n in phys_names if n not in CORE_COLUMNS] + + # Start with all non-entity core columns + wide_names = [c for c in CORE_COLUMNS if c not in ("player/id", "team/id", "entity_key")] + for row in ent_df.to_dicts(): + p, t = str(row["player/id"]), str(row.get("team/id", "none")) + prefix = TrackedEntity.from_entity_key(f"{p}|{t}").cdf_prefix() + for attr in phys_attrs: + wide_names.append(f"{prefix}{attr}") + + self._cached_schema = Fields(list(dict.fromkeys(wide_names))) + return self._cached_schema + + def select(self, items: str | Sequence[str]) -> LazyTrackingDataContainer: + """Lazily selects columns. Supports selection pushdown.""" + if self._is_long_storage: + sch = self.schema() + resolved = [] + inputs = items if isinstance(items, (list, tuple)) else [items] + for it in inputs: + if isinstance(it, str): + if "*" in it: + resolved.extend(sch.match(it)) + else: + resolved.append(it) + elif isinstance(it, DataField): + resolved.append(it.name()) + + resolved = list(dict.fromkeys(resolved)) + core_set = { + "match/id", + "competition/id", + "season/id", + "timestamp", + "frame_id", + "period", + "player/id", + "team/id", + "entity_key", + } + phys_attrs, entities = set(), set() + for name in resolved: + if name in core_set: + continue + try: + f = DataField(name) + phys_attrs.add(f.attribute) + if f.entity: + if f.entity.type != "team": + pid = getattr(f.entity, "player_id", "ball") + else: + pid = f"team:{f.entity.team_id}" + entities.add((str(pid), str(getattr(f.entity, "team_id", "none")))) + except Exception: + continue - @property - def selector(self) -> ColumnSelector: - """Returns the selector for building column expressions.""" - return ColumnSelector(self._container.selector) + new_inner = self._container + if entities: + from ._tracking_data_container import col, lit + + pred = None + for pid, tid in entities: + p_cond = col("player/id").eq(lit(pid)) + if tid != "none": + p_cond = p_cond & col("team/id").eq(lit(tid)) + pred = p_cond if pred is None else (pred | p_cond) + if pred: + new_inner = new_inner.filter(pred) + + phys_names = [f.name() for f in self._container.schema] + phys_sel = [n for n in core_set if n in phys_names] + phys_sel.extend([n for n in phys_attrs if n in phys_names]) + + rust_sel: list[str | _DataField | _Selection] = [ + _DataField.from_name(n) for n in phys_sel + ] + return LazyTrackingDataContainer( + new_inner.select(rust_sel), + catalog=self._catalog, + is_long_storage=True, + deferred_select=resolved, + cached_schema=Fields(resolved), + deferred_add_columns=self._deferred_add_columns, + ) - # --- Selections and Data Additions --- - - def select(self, items: str | list[str | DataField | Selection]) -> LazyTrackingDataContainer: - """ - Lazily select columns from the container. - - Parameters - ---------- - items : str or list of str or DataField or Selection - The columns to select. - - Returns - ------- - LazyTrackingDataContainer - A new lazy container with the selection added to the plan. - """ - processed_items: str | list[str | _DataField | _Selection] | _DataField | _Selection - if isinstance(items, list): - processed_items = [] - for i in items: - if isinstance(i, DataField): - processed_items.append(i._field) - elif isinstance(i, Selection): - processed_items.append(i._selection) + inputs = items if isinstance(items, (list, tuple)) else [items] + processed: list[str | _DataField | _Selection] = [] + for i in inputs: + if isinstance(i, str): + if "*" in i: + for m in self.schema().match(i): + processed.append(_DataField.from_name(m)) else: - processed_items.append(i) - elif isinstance(items, DataField): - processed_items = items._field - elif isinstance(items, Selection): - processed_items = items._selection - else: - processed_items = items - - new_inner = self._container.select(processed_items) # type: ignore - return LazyTrackingDataContainer(new_inner) - - def slice(self, offset: int, length: int) -> LazyTrackingDataContainer: - """ - Lazily extract a slice of frames from the container. - - Parameters - ---------- - offset : int - The starting frame index. - length : int - The number of frames to include. - - Returns - ------- - LazyTrackingDataContainer - A new lazy container with the slice added to the plan. - """ - new_inner = self._container.slice(offset, length) - return LazyTrackingDataContainer(new_inner) - - def filter(self, predicate: _Expr) -> LazyTrackingDataContainer: - """ - Lazily filter rows from the container. - - Parameters - ---------- - predicate : _Expr - The expression defining the filter condition. - - Returns - ------- - LazyTrackingDataContainer - A new lazy container with the filter added to the plan. - """ - new_inner = self._container.filter(predicate) - return LazyTrackingDataContainer(new_inner) - - def transform_coordinates(self, to_system: CoordinateSystem) -> LazyTrackingDataContainer: - """ - Lazily transforms coordinates to a new spatial system. - - Parameters - ---------- - to_system : CoordinateSystem - The target coordinate system. - - Returns - ------- - LazyTrackingDataContainer - A new lazy container with transformed coordinates added to the plan. - """ - new_inner = self._container.transform_coordinates(to_system._inner) - return LazyTrackingDataContainer(new_inner) + processed.append(_DataField.from_name(i)) + elif isinstance(i, DataField): + processed.append(i._field) + elif isinstance(i, Selection): + processed.append(i._inner) + elif isinstance(i, (_DataField, _Selection)): + processed.append(i) + + new_cache = None + if self._cached_schema: + names = [] + for itm in inputs: + if isinstance(itm, str): + names.extend(self._cached_schema.match(itm)) + else: + names.append(str(itm)) + new_cache = Fields(list(dict.fromkeys(names))) - def transform_orientation(self, mode: str | OrientationMode) -> LazyTrackingDataContainer: - """ - Lazily normalizes coordinates based on playing direction. - - Parameters - ---------- - mode : str or OrientationMode - The orientation normalization mode. - - Returns - ------- - LazyTrackingDataContainer - A new lazy container with normalized coordinates added to the plan. - """ - if not isinstance(mode, str): - mode = str(mode) - new_inner = self._container.transform_orientation(mode) - return LazyTrackingDataContainer(new_inner) + return LazyTrackingDataContainer( + self._container.select(processed), + catalog=self._catalog, + cached_schema=new_cache, + deferred_add_columns=self._deferred_add_columns, + ) def add_column( - self, name: str, value: Any, on: str | list[str] | None = None + self, name: str, value: Any, on: str | Sequence[str] | None = None ) -> LazyTrackingDataContainer: - """ - Lazily add a new column to the container. - - Parameters - ---------- - name : str - The name of the new column. - value : Expr, array-like, or callable - The computation or data for the new column. - on : str or list of str, optional - The columns required for the computation (required for callables). - - Returns - ------- - LazyTrackingDataContainer - A new lazy container with the column addition added to the plan. - """ - import pyarrow as pa - - from ._tracking_data_container import Expr as _PyExpr - - if hasattr(value, "_expr") and isinstance(value._expr, _PyExpr): - new_inner = self._container.add_column(name, value._expr, None) - elif isinstance(value, _PyExpr): - new_inner = self._container.add_column(name, value, None) - elif callable(value): - if on is None: - raise ValueError( - "The 'on' parameter must be provided when using a callable in lazy mode" - ) - # Ensure on is a list of strings - on_list = [on] if isinstance(on, str) else list(on) - new_inner = self._container.add_column_udf(name, value, on_list) - else: - data = pa.array(value) - array_capsule, schema_capsule = data.__arrow_c_array__() - new_inner = self._container.add_column(name, array_capsule, schema_capsule) - - return LazyTrackingDataContainer(new_inner) - - def add_columns( - self, - names: list[str], - values: list[Any], - on: list[str | list[str] | None] | None = None, - ) -> LazyTrackingDataContainer: - """ - Lazily add multiple new columns to the container. - - Parameters - ---------- - names : list of str - The names for the new columns. - values : list of Expr, array-like, or callable - The data or computations for the new columns. - on : list of str or list of list of str, optional - The columns to pass to each callable value. - - Returns - ------- - LazyTrackingDataContainer - A new lazy container including the new columns. - """ - import pyarrow as pa - - from ._tracking_data_container import Expr as _PyExpr - - processed_values = [] - schema_capsules: list[Any] = [] - - for i, (name, value) in enumerate(zip(names, values)): - val_on = on[i] if on is not None else None - if callable(value): - # Eager evaluation for callables in lazy mode is not supported - # unless they return an Expr. - if val_on is None: - raise ValueError( - "The 'on' parameter must be provided when using a callable in lazy mode" - ) - # Try to evaluate as expression - on_tuple: tuple[str, ...] = (val_on,) if isinstance(val_on, str) else tuple(val_on) - data = value(*on_tuple) - if not isinstance(data, _PyExpr): - raise TypeError("Callables in lazy mode must return an Expr") - elif hasattr(value, "_expr") and isinstance(value._expr, _PyExpr): - data = value._expr - else: - data = value - - if isinstance(data, _PyExpr): - processed_values.append(data) - schema_capsules.append(None) - else: - # Array-like - array_data = pa.array(data) - array_capsule, schema_capsule = array_data.__arrow_c_array__() - processed_values.append(array_capsule) - schema_capsules.append(schema_capsule) - - new_inner = self._container.add_columns(names, processed_values, schema_capsules) - return LazyTrackingDataContainer(new_inner) - - def add_column_per_player( - self, name: str, value: Any, on: str | list[str] | None = None - ) -> LazyTrackingDataContainer: - """ - Lazily add a new column for every player in the container. - - Parameters - ---------- - name : str - The attribute name for the new columns. - value : Expr, array-like, or callable - The computation or data for the new columns. - on : str or list of str, optional - The player-specific attributes to use for the computation. - - Returns - ------- - LazyTrackingDataContainer - A new lazy container with the per-player columns added to the plan. - """ - names = [] - values = [] - ons = [] - - from ._tracking_data_container import Expr as _PyExpr - - for player in self.schema().players(): - col_name = DataField(entity=player, attribute=name).name() - names.append(col_name) - - player_on: str | list[str] | None = None - if on is not None: - if isinstance(on, str): - player_on = DataField(entity=player, attribute=on).name() - elif isinstance(on, list): - player_on = [DataField(entity=player, attribute=attr).name() for attr in on] - - ons.append(player_on) - - if ( - isinstance(value, _PyExpr) - or (hasattr(value, "_expr") and isinstance(value._expr, _PyExpr)) - ) and on is not None: - expr = value._expr if hasattr(value, "_expr") else value - on_list = [on] if isinstance(on, str) else on - mapping = { - attr: DataField(entity=player, attribute=attr).name() for attr in on_list - } - values.append(expr.replace_columns(mapping)) - continue - - if callable(value) and on is not None: - on_resolved = player_on if isinstance(player_on, list) else [player_on] # type: ignore - # Check if it returns an Expr - try: - result = value(*on_resolved) # type: ignore - if isinstance(result, _PyExpr) or ( - hasattr(result, "_expr") and isinstance(result._expr, _PyExpr) - ): - expr = result._expr if hasattr(result, "_expr") else result - values.append(expr) - continue - except Exception: - pass - - values.append(value) + if isinstance(value, _Expr): + return LazyTrackingDataContainer( + self._container.add_column(name, value), + catalog=self._catalog, + is_long_storage=self._is_long_storage, + deferred_select=self._deferred_select, + cached_schema=self._cached_schema, + deferred_add_columns=self._deferred_add_columns, + ) - return self.add_columns(names, values, on=ons) + if callable(value): + new_deferred = list(self._deferred_add_columns) + new_deferred.append((name, value, on)) + return LazyTrackingDataContainer( + self._container, + catalog=self._catalog, + is_long_storage=self._is_long_storage, + deferred_select=self._deferred_select, + cached_schema=self._cached_schema, + deferred_add_columns=new_deferred, + ) - # --- Execution --- + raise TrackingDataContainerError( + "Adding non-expression columns to lazy containers is not yet supported." + ) def collect(self) -> TrackingDataContainer: - """ - Executes the lazy plan and returns an eager `TrackingDataContainer`. - - Returns - ------- - TrackingDataContainer - The resulting eager container. - """ - new_inner = self._container.collect() - return TrackingDataContainer(new_inner) + """Executes the plan and pivots if necessary.""" + try: + inner = self._container.collect() + res = TrackingDataContainer(inner, catalog=self._catalog, validate=False) + + for name, value, on in self._deferred_add_columns: + res = res.add_column(name, value, on=on) + + if self._is_long_storage: + res = TrackingDataContainer.pivot(res.to_arrow()) + res._catalog = self._catalog + if self._deferred_select: + logger.debug(f"Wide container columns before selection: {res.schema().names}") + res = res.select(list(self._deferred_select)) + logger.debug(f"Wide container columns after selection: {res.schema().names}") + return res + except Exception as e: + raise TrackingDataContainerError(f"Collection failed: {e}") - # --- Internal and Special Methods --- + def slice(self, offset: int, length: int) -> LazyTrackingDataContainer: + return LazyTrackingDataContainer( + self._container.slice(offset, length), + catalog=self._catalog, + is_long_storage=self._is_long_storage, + cached_schema=self._cached_schema, + deferred_add_columns=self._deferred_add_columns, + ) - def __arrow_c_schema__(self) -> object: - return self._container.__arrow_c_schema__() + def filter(self, predicate: _Expr) -> LazyTrackingDataContainer: + return LazyTrackingDataContainer( + self._container.filter(predicate), + catalog=self._catalog, + is_long_storage=self._is_long_storage, + cached_schema=self._cached_schema, + deferred_add_columns=self._deferred_add_columns, + ) + + def transform_coordinates(self, sys: CoordinateSystem) -> LazyTrackingDataContainer: + return LazyTrackingDataContainer( + self._container.transform_coordinates(sys._inner), + catalog=self._catalog, + is_long_storage=self._is_long_storage, + cached_schema=self._cached_schema, + deferred_add_columns=self._deferred_add_columns, + ) - def __arrow_c_stream__(self, requested_schema: object | None = None) -> object: - return self._container.__arrow_c_stream__(requested_schema) + def transform_orientation(self, mode: str | OrientationMode) -> LazyTrackingDataContainer: + return LazyTrackingDataContainer( + self._container.transform_orientation(str(mode)), + catalog=self._catalog, + is_long_storage=self._is_long_storage, + cached_schema=self._cached_schema, + deferred_add_columns=self._deferred_add_columns, + ) + + def __arrow_c_stream__(self, rs: Any = None) -> Any: + return self._container.__arrow_c_stream__(rs) def __repr__(self) -> str: return repr(self._container) diff --git a/python/tracking_data_container/schema.py b/python/tracking_data_container/schema.py index 9d05399..307a2fb 100644 --- a/python/tracking_data_container/schema.py +++ b/python/tracking_data_container/schema.py @@ -44,6 +44,78 @@ def type(self) -> str: return "referee" return "unknown" + @property + def team_id(self) -> str | None: + return self._entity.team_id() + + @property + def player_id(self) -> str | None: + return self._entity.player_id() + + @property + def referee_id(self) -> str | None: + return self._entity.player_id() if self.is_referee else None + + def entity_key(self) -> str: + """Returns a stable unique identifier for this entity (p_id|t_id).""" + p_id = "ball" + t_id = "none" + if self.is_player: + p_id = self.player_id or "unknown" + t_id = self.team_id or "none" + elif self.is_team: + t_id = self.team_id or "unknown" + p_id = f"team:{t_id}" + elif self.is_referee: + p_id = f"referee:{self.referee_id or 'unknown'}" + t_id = "none" + + # Normalize "none" strings + if t_id is None or str(t_id).lower() == "none" or str(t_id).lower() == "nan": + t_id = "none" + return f"{p_id}|{t_id}" + + def cdf_prefix(self) -> str: + """Returns the CDF prefix for this entity (e.g., 'teams/T1/players/P1/').""" + if self.is_ball: + return "ball/" + if self.is_team: + return f"teams/{self.team_id}/" + if self.is_referee: + return f"referees/{self.referee_id}/" + if self.is_player: + t_id = self.team_id + if t_id and str(t_id).lower() not in ("none", "nan"): + return f"teams/{t_id}/players/{self.player_id}/" + return f"other/{self.player_id}/" + return "" + + @staticmethod + def from_entity_key(key: str) -> TrackedEntity: + """Reconstructs a TrackedEntity from its entity key.""" + try: + parts = key.split("|") + p_id = parts[0] + t_id = parts[1] if len(parts) > 1 else "none" + except (ValueError, IndexError): + p_id, t_id = key, "none" + + if p_id == "ball": + return Ball() + if p_id.startswith("team:"): + # Use provided t_id if available, otherwise extract from p_id + team_id = t_id if t_id != "none" else p_id[5:] + return Team(team_id) + if p_id.startswith("referee:"): + return Referee(p_id[8:]) + + if t_id != "none": + return Player(t_id, p_id) + + # We don't have a direct "OtherPlayer" class, so we use a dummy name to + # construct a DataField and get its entity + return TrackedEntity._from_inner(_DataField.from_name(f"other/{p_id}/x").entity()) + @staticmethod def _from_inner(entity: _TrackedEntity) -> TrackedEntity: if entity.is_ball(): @@ -70,14 +142,6 @@ def _from_inner(entity: _TrackedEntity) -> Player: instance._entity = entity return instance - @property - def team_id(self) -> str: - return self._entity.team_id() or "" - - @property - def player_id(self) -> str: - return self._entity.player_id() or "" - def __repr__(self) -> str: return f"Player(team={self.team_id}, id={self.player_id})" @@ -114,10 +178,6 @@ def _from_inner(entity: _TrackedEntity) -> Team: instance._entity = entity return instance - @property - def team_id(self) -> str: - return self._entity.team_id() or "" - def __repr__(self) -> str: return f"Team(id={self.team_id})" @@ -137,10 +197,6 @@ def _from_inner(entity: _TrackedEntity) -> Referee: instance._entity = entity return instance - @property - def referee_id(self) -> str: - return self._entity.player_id() or "" - def __repr__(self) -> str: return f"Referee(id={self.referee_id})" @@ -165,16 +221,7 @@ def __new__( if name is not None: inner = _DataField.from_name(name) elif entity is not None and attribute is not None: - if isinstance(entity, Ball): - path = f"ball/{attribute}" - elif isinstance(entity, Player): - path = f"teams/{entity.team_id}/players/{entity.player_id}/{attribute}" - elif isinstance(entity, Team): - path = f"teams/{entity.team_id}/{attribute}" - elif isinstance(entity, Referee): - path = f"referees/{entity.referee_id}/{attribute}" - else: - path = attribute + path = f"{entity.cdf_prefix()}{attribute}" inner = _DataField.from_name(path) elif attribute is not None: inner = _DataField.from_name(attribute) @@ -216,6 +263,14 @@ def team_id(self) -> str | None: def player_id(self) -> str | None: return getattr(self.entity, "player_id", None) + def entity_key(self) -> str | None: + """Returns the stable entity key for this field, if applicable.""" + return self.entity.entity_key() if self.entity else None + + def cdf_prefix(self) -> str | None: + """Returns the CDF prefix for this field, if applicable.""" + return self.entity.cdf_prefix() if self.entity else None + def __hash__(self) -> int: return hash(self.name()) @@ -270,6 +325,42 @@ def names(self) -> list[str]: """Returns a list of all column names in the schema.""" return [f.name() for f in self] + def match(self, pattern: str) -> list[str]: + """Returns a list of column names that match the given wildcard pattern.""" + import fnmatch + import re + + all_names = self.names() + if pattern in all_names: + return [pattern] + + # Support recursive wildcard ** + if "**" in pattern: + # Escape regex special characters manually + # but preserve glob characters * and / + reg = pattern + for char in ".+?()[]{}^$|": + reg = reg.replace(char, f"\\{char}") + + # Use placeholders to avoid conflicting replacements + # 1. Handle **/ specifically (optional intermediate levels) + reg = reg.replace("**/", "___REC_SLASH___") + # 2. Handle ** remaining + reg = reg.replace("**", "___REC___") + # 3. Handle standard * + reg = reg.replace("*", "___STD___") + + # Map placeholders to regex + reg = reg.replace("___REC_SLASH___", "(?:.*/)?") + reg = reg.replace("___REC___", ".*") + reg = reg.replace("___STD___", "[^/]*") + + regex = re.compile(f"^{reg}$") + return [n for n in all_names if regex.match(n)] + + # Standard fnmatch for simple patterns + return fnmatch.filter(all_names, pattern) + def validate(self) -> None: import warnings diff --git a/uv.lock b/uv.lock index e3ffb9b..132045c 100644 --- a/uv.lock +++ b/uv.lock @@ -177,6 +177,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" }, ] +[[package]] +name = "annotated-types" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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examples/idsse_warehouse/tracking/match_metadata/metadata/{b3defa57-62bd-4a60-87b5-0d6fcafd53ad-m0.avro => 0bd0cfba-0ce6-4679-b7bb-4602f5f05406-m0.avro} (92%) rename profile_warehouse/tracking/match_metadata/metadata/e27f5ba5-48f7-4cb2-ae56-16a5fe7fd851-m0.avro => examples/idsse_warehouse/tracking/match_metadata/metadata/a858ff54-e99b-4dae-b66a-ac94a48da697-m0.avro (92%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{ed38ec79-f365-46e1-aa0a-6ff5e030781a-m0.avro => cb26b00d-1cfa-47c8-b859-bc2936a263b0-m0.avro} (92%) delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/faae9c91-7f9c-4bec-b401-856370f90b74-m0.avro delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/snap-1456969541151746380-0-b3defa57-62bd-4a60-87b5-0d6fcafd53ad.avro rename debug_warehouse/tracking/match_metadata/metadata/snap-8457677481870634211-0-4f80fb8d-8d7d-435b-95e1-55f2b9ea7dd3.avro => examples/idsse_warehouse/tracking/match_metadata/metadata/snap-3432642025755698322-0-cb26b00d-1cfa-47c8-b859-bc2936a263b0.avro (84%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{snap-1114700784421547592-0-ed38ec79-f365-46e1-aa0a-6ff5e030781a.avro => snap-3611677702575916172-0-a858ff54-e99b-4dae-b66a-ac94a48da697.avro} (77%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{snap-4866667276096503338-0-faae9c91-7f9c-4bec-b401-856370f90b74.avro => snap-6599460958304115199-0-0bd0cfba-0ce6-4679-b7bb-4602f5f05406.avro} (75%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{00000-c6360589-6439-4604-88e4-272cf35f75ce.metadata.json => 00000-7a3c0cd8-3b69-41fb-a134-02dcfe960e12.metadata.json} (90%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{00001-4bae733e-a73e-4da6-8812-af9a7d6b04e9.metadata.json => 00001-a4870a2b-6cd4-4964-a16c-803a68332e07.metadata.json} (57%) create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00002-8e9395a3-7b87-44a6-acc5-96b3d3d6fd5d.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00002-c4c023b8-fa71-41cb-a109-30c7bf7cadf4.metadata.json create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00003-34c7dd19-5940-44b4-8fcd-39d9dc910155.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00003-53fc18be-4237-409f-9d8b-49218b2e1ef4.metadata.json rename examples/idsse_warehouse/tracking/tracking_data/metadata/{d962db46-093c-45e8-be4a-0c6d572dad92-m0.avro => 0e34766b-2d66-44ac-b491-723a4772a1a3-m0.avro} (78%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{7ad1e2c6-167d-47a8-a02b-391e6ef92b65-m0.avro => 3faa5e91-e6e1-4926-9601-15797fb2ce84-m0.avro} (78%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{4665052f-9f33-4c4a-b55d-e1fd2bcb223d-m0.avro => 78582c4c-10e1-429d-8391-918bcb86bb3f-m0.avro} (78%) delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-377391038018914117-0-d962db46-093c-45e8-be4a-0c6d572dad92.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-4114664439361220711-0-0e34766b-2d66-44ac-b491-723a4772a1a3.avro rename debug_warehouse/tracking/tracking_data/metadata/snap-6099500306663132840-0-e525fa0c-bd68-4ba7-bfc2-9a6c8f662a6d.avro => examples/idsse_warehouse/tracking/tracking_data/metadata/snap-4345927569820710336-0-3faa5e91-e6e1-4926-9601-15797fb2ce84.avro (82%) delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-4761525111313210917-0-4665052f-9f33-4c4a-b55d-e1fd2bcb223d.avro delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-478866493446175361-0-7ad1e2c6-167d-47a8-a02b-391e6ef92b65.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-5344841726861284858-0-78582c4c-10e1-429d-8391-918bcb86bb3f.avro delete mode 100644 profile_warehouse/catalog.db delete mode 100644 profile_warehouse/tracking/match_metadata/metadata/00000-4a6aa9a1-65e0-450f-9be7-7684faeeac52.metadata.json delete mode 100644 profile_warehouse/tracking/match_metadata/metadata/00001-9b21cef5-89c7-436e-83d6-51ccb7009828.metadata.json delete mode 100644 profile_warehouse/tracking/match_metadata/metadata/snap-2219873109471251602-0-e27f5ba5-48f7-4cb2-ae56-16a5fe7fd851.avro delete mode 100644 profile_warehouse/tracking/tracking_data/metadata/00000-b3a6aabe-7045-4467-ab11-81db388158b2.metadata.json delete mode 100644 profile_warehouse/tracking/tracking_data/metadata/00001-6f0b9eff-eb36-4de8-8147-cabad48bca68.metadata.json delete mode 100644 profile_warehouse/tracking/tracking_data/metadata/f03266b5-4cd0-499d-b61d-580fce440e8d-m0.avro delete mode 100644 profile_warehouse/tracking/tracking_data/metadata/snap-3010651843225263066-0-f03266b5-4cd0-499d-b61d-580fce440e8d.avro diff --git a/crates/tdc-core/src/datacontainer/core.rs b/crates/tdc-core/src/datacontainer/core.rs index 45e166f..10c9970 100644 --- a/crates/tdc-core/src/datacontainer/core.rs +++ b/crates/tdc-core/src/datacontainer/core.rs @@ -395,8 +395,11 @@ impl TrackingDataContainer { let core_names: AHashSet<&str> = [ "match/id", + "match_id", "competition/id", + "competition_id", "season/id", + "season_id", "timestamp", "frame_id", "period", @@ -416,12 +419,17 @@ impl TrackingDataContainer { } else if name == "timestamp" { timestamp_idx = Some(i); core_indices.push(i); - } else if name == "player/id" { + } else if name == "player/id" || name == "player_id" { player_id_idx = Some(i); - } else if name == "team/id" { + attr_indices.push(i); // Also pivot it + } else if name == "team/id" || name == "team_id" { team_id_idx = Some(i); + attr_indices.push(i); // Also pivot it } else if core_names.contains(name) { core_indices.push(i); + } else if name == "entity_type" { + // Skip entity_type metadata during pivot + continue; } else { attr_indices.push(i); } @@ -447,11 +455,13 @@ impl TrackingDataContainer { let player_id_idx = player_id_idx.ok_or_else(|| { TrackingDataContainerErrorKind::InvalidParameters( - "Missing player/id column".to_string(), + "Missing player/id or player_id column".to_string(), ) })?; let team_id_idx = team_id_idx.ok_or_else(|| { - TrackingDataContainerErrorKind::InvalidParameters("Missing team/id column".to_string()) + TrackingDataContainerErrorKind::InvalidParameters( + "Missing team/id or team_id column".to_string(), + ) })?; // 3. 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a/debug_warehouse/tracking/match_metadata/metadata/00001-702060e8-1069-48a0-9f73-0f5337b4fa63.metadata.json b/debug_warehouse/tracking/match_metadata/metadata/00001-702060e8-1069-48a0-9f73-0f5337b4fa63.metadata.json deleted file mode 100644 index cbd84b5..0000000 --- a/debug_warehouse/tracking/match_metadata/metadata/00001-702060e8-1069-48a0-9f73-0f5337b4fa63.metadata.json +++ /dev/null @@ -1 +0,0 @@ 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165259 rows in 1.3019s\n" + " ✅ Loaded and pivoted 165259 rows in 1.1780s\n" ] }, { @@ -255,33 +255,33 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (5, 348)
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│\n", - "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ 000IA|awa ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ 2.600 ┆ ┆ y ┆ ┆ ┆ │\n", - "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", - "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ 000IA|awa ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ 2.640 ┆ ┆ y ┆ ┆ ┆ │\n", - "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", - "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ 000IA|awa ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ 2.680 ┆ ┆ y ┆ ┆ ┆ │\n", - "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", - "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ 000IA|awa ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ 2.720 ┆ ┆ y ┆ ┆ ┆ │\n", - "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", - "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ 000IA|awa ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ 2.760 ┆ ┆ y ┆ ┆ ┆ │\n", + "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", + "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ 2.600 ┆ ┆ ┆ ┆ ┆ │\n", + "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", + "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ 2.640 ┆ ┆ ┆ ┆ ┆ │\n", + "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", + "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ 2.680 ┆ ┆ ┆ ┆ ┆ │\n", + "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", + "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ 2.720 ┆ ┆ ┆ ┆ ┆ │\n", + "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", + "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ 2.760 ┆ ┆ ┆ ┆ ┆ │\n", "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘" ] }, diff --git a/examples/idsse_warehouse/catalog.db b/examples/idsse_warehouse/catalog.db index de0809ea32fad02b8cfb3182ee67b1bed7a67ceb..db8fc534bd9ac8e0e7090cf4bbef13815e09e9ea 100644 GIT binary patch delta 178 zcmWN~u@QqX3_#IifouRYw7Row*^)NM8rZVBw5h4n(xqYq?lMgF$ONJP_x^Sa+cBKa 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logging from collections.abc import Sequence from pathlib import Path from typing import TYPE_CHECKING, Any, cast @@ -38,6 +39,20 @@ if TYPE_CHECKING: pass +logger = logging.getLogger(__name__) + +# Mapping from internal CDF-like names to snake_case Iceberg names +COLUMN_MAPPING = { + "match/id": "match_id", + "competition/id": "competition_id", + "season/id": "season_id", + "player/id": "player_id", + "team/id": "team_id", +} + +# Reverse mapping +REVERSE_COLUMN_MAPPING = {v: k for k, v in COLUMN_MAPPING.items()} + class TrackingDataCatalog: """ @@ -128,26 +143,26 @@ def _create_tracking_table(self) -> None: except NoSuchTableError: pass # Create new table - # Define the requested schema with flat names. All fields are optional to allow for - # flexible ingestion and schema evolution. + # Define the requested schema with snake_case names and required fields. schema = Schema( - NestedField(1, "match/id", StringType(), required=False), - NestedField(2, "competition/id", StringType(), required=False), - NestedField(3, "season/id", StringType(), required=False), + NestedField(1, "match_id", StringType(), required=True), + NestedField(2, "competition_id", StringType(), required=False), + NestedField(3, "season_id", StringType(), required=False), NestedField(4, "timestamp", TimestampType(), required=False), NestedField(5, "frame_id", LongType(), required=False), NestedField(6, "period", StringType(), required=False), - NestedField(7, "player/id", StringType(), required=False), - NestedField(8, "team/id", StringType(), required=False), - NestedField(9, "x", FloatType(), required=False), - NestedField(10, "y", FloatType(), required=False), - NestedField(11, "z", FloatType(), required=False), + NestedField(7, "entity_type", StringType(), required=True), + NestedField(8, "player_id", StringType(), required=False), + NestedField(9, "team_id", StringType(), required=False), + NestedField(10, "x", FloatType(), required=False), + NestedField(11, "y", FloatType(), required=False), + NestedField(12, "z", FloatType(), required=False), ) - # Define partitioning by match/id + # Define partitioning by match_id partition_spec = PartitionSpec( PartitionField( - source_id=1, field_id=1000, transform=IdentityTransform(), name="match/id" + source_id=1, field_id=1000, transform=IdentityTransform(), name="match_id" ) ) @@ -171,9 +186,9 @@ def _create_metadata_table(self) -> None: # Define schema for metadata as requested schema = Schema( - NestedField(1, "match/id", StringType(), required=True), - NestedField(2, "competition/id", StringType(), required=False), - NestedField(3, "season/id", StringType(), required=False), + NestedField(1, "match_id", StringType(), required=True), + NestedField(2, "competition_id", StringType(), required=False), + NestedField(3, "season_id", StringType(), required=False), NestedField(4, "metadata_json", StringType(), required=True), ) @@ -253,10 +268,44 @@ def _save_tracking_data( assert self._tracking_table is not None - # 1. Melt to entity-long format for stable warehouse storage + # 1. Melt to entity-long format long_df = container.melt() - # 2. Convert to Arrow and handle dictionary casting + # 2. Add entity_type and rename columns to snake_case + if "entity_key" in long_df.columns: + long_df = long_df.with_columns( + pl.col("entity_key") + .map_elements( + lambda k: TrackedEntity.from_entity_key(k).type, return_dtype=pl.String + ) + .alias("entity_type") + ) + else: + # Fallback if entity_key is missing (shouldn't happen with container.melt()) + raise ValueError("entity_key column is required for saving to catalog") + + # Handle potential name collisions before renaming + # If any target names already exist, drop them + to_drop = [ + v for k, v in COLUMN_MAPPING.items() if k in long_df.columns and v in long_df.columns + ] + if to_drop: + long_df = long_df.drop(to_drop) + + # Rename columns to Iceberg format + rename_dict = {k: v for k, v in COLUMN_MAPPING.items() if k in long_df.columns} + long_df = long_df.rename(rename_dict) + + # Ensure match_id is present and correct + long_df = long_df.with_columns(pl.lit(match_id).alias("match_id")) + + # Check required fields + if long_df["match_id"].null_count() > 0: + raise ValueError("match_id cannot be null") + if long_df["entity_type"].null_count() > 0: + raise ValueError("entity_type cannot be null") + + # 3. Convert to Arrow and handle dictionary casting arrow_table = long_df.to_arrow() # Define known types for core columns to avoid incorrect casting when null @@ -298,7 +347,7 @@ def _save_tracking_data( txn.update_schema().union_by_name(container_schema).commit() # Delete existing match partition - txn.delete(EqualTo(term=Reference("match/id"), value=match_id)) # type: ignore + txn.delete(EqualTo(term=Reference("match_id"), value=match_id)) # type: ignore # Append long-format data txn.append(arrow_table) @@ -313,9 +362,9 @@ def _save_metadata(self, metadata: dict, match_id: str) -> None: # Define schema for the Arrow table metadata_schema = pa.schema( [ - pa.field("match/id", pa.string(), nullable=False), - pa.field("competition/id", pa.string(), nullable=True), - pa.field("season/id", pa.string(), nullable=True), + pa.field("match_id", pa.string(), nullable=False), + pa.field("competition_id", pa.string(), nullable=True), + pa.field("season_id", pa.string(), nullable=True), pa.field("metadata_json", pa.string(), nullable=False), ] ) @@ -323,9 +372,9 @@ def _save_metadata(self, metadata: dict, match_id: str) -> None: # Prepare metadata row metadata_row = pa.table( { - "match/id": [match_id], - "competition/id": [metadata.get("competition_id")], - "season/id": [metadata.get("season_id")], + "match_id": [match_id], + "competition_id": [metadata.get("competition_id")], + "season_id": [metadata.get("season_id")], "metadata_json": [json.dumps(metadata)], }, schema=metadata_schema, @@ -333,7 +382,7 @@ def _save_metadata(self, metadata: dict, match_id: str) -> None: # Upsert metadata with self._metadata_table.transaction() as txn: - txn.delete(EqualTo(term=Reference("match/id"), value=match_id)) # type: ignore + txn.delete(EqualTo(term=Reference("match_id"), value=match_id)) # type: ignore txn.append(metadata_row) def __repr__(self) -> str: @@ -382,16 +431,16 @@ def schema(self) -> Fields: assert self._catalog._tracking_table is not None - filter_expr: BooleanExpression = EqualTo(term=Reference("match/id"), value=self._match_id) # type: ignore + filter_expr: BooleanExpression = EqualTo(term=Reference("match_id"), value=self._match_id) # type: ignore if self._player_id: filter_expr = And( filter_expr, - EqualTo(term=Reference("player/id"), value=self._player_id), # type: ignore + EqualTo(term=Reference("player_id"), value=self._player_id), # type: ignore ) - # Only scan player/id and team/id to reconstruct schema + # Only scan player_id and team_id to reconstruct schema entities_table = self._catalog._tracking_table.scan( - row_filter=filter_expr, selected_fields=("player/id", "team/id") + row_filter=filter_expr, selected_fields=("player_id", "team_id") ).to_arrow() entities_df = cast(pl.DataFrame, pl.from_arrow(entities_table)) @@ -401,21 +450,40 @@ def schema(self) -> Fields: # All potential physical attributes (x, y, z, etc.) physical_names = [f.name for f in self._catalog._tracking_table.schema().fields] - attr_cols = [c for c in physical_names if c not in CORE_COLUMNS and c != "id"] + # Ignore core and metadata columns + ignored = set(COLUMN_MAPPING.values()) | { + "entity_type", + "timestamp", + "frame_id", + "period", + "period_id", + "entity_key", + } + # Anything else is an attribute (like x, y, z, player_id, team_id, etc.) + attr_cols = [c for c in physical_names if c not in ignored and c != "id"] + + # Base list: all core columns that are NOT entity-specific (internal names) + # We use COLUMN_MAPPING to check if they exist physically + wide_names = [] + for c in CORE_COLUMNS: + if c in ("player/id", "team/id", "entity_key"): + continue + phys_name = COLUMN_MAPPING.get(c, c) + if phys_name in physical_names: + wide_names.append(c) - # Base list: all core columns that are NOT entity-specific - wide_names = [ - c - for c in physical_names - if c in CORE_COLUMNS and c not in ("player/id", "team/id", "entity_key") - ] for row in entities_df.to_dicts(): - p_id = str(row.get("player/id")) - t_id = str(row.get("team/id", "none")) + p_id = str(row.get("player_id")) + t_id = str(row.get("team_id", "none")) prefix = TrackedEntity.from_entity_key(f"{p_id}|{t_id}").cdf_prefix() for attr in attr_cols: wide_names.append(f"{prefix}{attr}") + # Also add player/id and team/id as attributes for this entity if they exist physically + if "player_id" in physical_names: + wide_names.append(f"{prefix}player/id") + if "team_id" in physical_names: + wide_names.append(f"{prefix}team/id") self._cached_schema = Fields(list(dict.fromkeys(wide_names))) return self._cached_schema @@ -455,11 +523,11 @@ def collect(self) -> TrackingDataContainer: assert self._catalog._tracking_table is not None - filter_expr: BooleanExpression = EqualTo(term=Reference("match/id"), value=self._match_id) # type: ignore + filter_expr: BooleanExpression = EqualTo(term=Reference("match_id"), value=self._match_id) # type: ignore if self._player_id: filter_expr = And( filter_expr, - EqualTo(term=Reference("player/id"), value=self._player_id), # type: ignore + EqualTo(term=Reference("player_id"), value=self._player_id), # type: ignore ) # 1. Determine which long-format columns we need @@ -485,21 +553,42 @@ def collect(self) -> TrackingDataContainer: literal_names = list(dict.fromkeys(literal_names)) deferred_select = literal_names - needed_long = set(CORE_COLUMNS) + needed_long_internal = set(CORE_COLUMNS) needed_ents: set[str] = set() for name in literal_names: if name in CORE_COLUMNS: - needed_long.add(name) + needed_long_internal.add(name) else: field = DataField(name) - needed_long.add(field.attribute) + needed_long_internal.add(field.attribute) if field.entity: needed_ents.add(cast(str, field.entity_key())) - # Check physical schema + # Check physical schema and map names physical_names = {f.name for f in self._catalog._tracking_table.schema().fields} - selected_long_fields = [f for f in needed_long if f in physical_names] + selected_long_fields_set = set() + for f in needed_long_internal: + phys_name = COLUMN_MAPPING.get(f, f) + if phys_name in physical_names: + selected_long_fields_set.add(phys_name) + + # Always include required fields for pivot + for req in [ + "match_id", + "entity_type", + "player_id", + "team_id", + "frame_id", + "timestamp", + ]: + if req in physical_names: + selected_long_fields_set.add(req) + + # Include any other physical column that might be an attribute + # (If we selected an attribute like 'x', it was already added above via field.attribute) + + selected_long_fields = list(selected_long_fields_set) # 2. Add entity filter pushdown if needed_ents: @@ -507,9 +596,9 @@ def collect(self) -> TrackingDataContainer: for k in needed_ents: p_id, t_id = k.split("|") - cond: BooleanExpression = EqualTo(term=Reference("player/id"), value=p_id) # type: ignore + cond: BooleanExpression = EqualTo(term=Reference("player_id"), value=p_id) # type: ignore if p_id != "ball" and t_id != "none": - cond = And(cond, EqualTo(term=Reference("team/id"), value=t_id)) # type: ignore + cond = And(cond, EqualTo(term=Reference("team_id"), value=t_id)) # type: ignore if entity_predicate is None: entity_predicate = cond @@ -530,6 +619,32 @@ def collect(self) -> TrackingDataContainer: scan = self._catalog._tracking_table.scan(**scan_kwargs) arrow_table = scan.to_arrow() + # Rename back to CDF-like names for Rust pivot + # Handle potential collisions here too + rename_map = {v: k for k, v in COLUMN_MAPPING.items() if v in arrow_table.column_names} + if rename_map: + new_fields = [] + new_columns = [] + for field in arrow_table.schema: + if field.name in rename_map: + target_name = rename_map[field.name] + # If target name already exists, we prefer the one being renamed + if target_name in arrow_table.column_names: + # Skip this column, we'll use the renamed one later + continue + new_fields.append(pa.field(target_name, field.type, field.nullable)) + new_columns.append(arrow_table.column(field.name)) + else: + # Keep original name + if field.name in rename_map.values(): + # This name is a target of another rename, and we already handled it + # above (by skipping it if it was a source, but here it's an existing + # column with target name). We skip it here to avoid duplicates. + continue + new_fields.append(field) + new_columns.append(arrow_table.column(field.name)) + arrow_table = pa.Table.from_arrays(new_columns, schema=pa.schema(new_fields)) + # 4. Pivot to wide format using high-performance Rust implementation wide_container = TrackingDataContainer.pivot(arrow_table) wide_container._catalog = self._catalog From e655624b1f1c8a109e4bca2a07d3480a87122b54 Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 14:05:01 +0200 Subject: [PATCH 06/21] feat: rename player_id to entity_id and update iceberg field order/requirements --- crates/tdc-core/src/datacontainer/core.rs | 29 +++++- python/tests/test_advanced_features.py | 2 + python/tests/test_catalog.py | 16 +-- python/tracking_data_container/catalog.py | 102 +++++++++----------- python/tracking_data_container/container.py | 7 +- 5 files changed, 87 insertions(+), 69 deletions(-) diff --git a/crates/tdc-core/src/datacontainer/core.rs b/crates/tdc-core/src/datacontainer/core.rs index 10c9970..b8baebd 100644 --- a/crates/tdc-core/src/datacontainer/core.rs +++ b/crates/tdc-core/src/datacontainer/core.rs @@ -419,7 +419,7 @@ impl TrackingDataContainer { } else if name == "timestamp" { timestamp_idx = Some(i); core_indices.push(i); - } else if name == "player/id" || name == "player_id" { + } else if name == "player/id" || name == "player_id" || name == "entity_id" { player_id_idx = Some(i); attr_indices.push(i); // Also pivot it } else if name == "team/id" || name == "team_id" { @@ -435,7 +435,26 @@ impl TrackingDataContainer { } } - // 2. Select the best frame index column + // 2. Select the best frame index column and validate required fields + let has_x = fields.iter().any(|f| f.name() == "x"); + let has_y = fields.iter().any(|f| f.name() == "y"); + let has_period = fields + .iter() + .any(|f| f.name() == "period" || f.name() == "period_id"); + + if !has_x || !has_y { + return Err(TrackingDataContainerErrorKind::InvalidParameters( + "Missing required spatial columns x or y".to_string(), + ) + .into()); + } + if !has_period { + return Err(TrackingDataContainerErrorKind::InvalidParameters( + "Missing required column period or period_id".to_string(), + ) + .into()); + } + let frame_idx_col = if let Some(fi) = frame_id_idx && columns[fi].null_count() < columns[fi].len() { @@ -445,7 +464,7 @@ impl TrackingDataContainer { } else { frame_id_idx.or(timestamp_idx).ok_or_else(|| { TrackingDataContainerErrorKind::InvalidParameters( - "Missing frame_id or timestamp column".to_string(), + "Missing required frame_id or timestamp column".to_string(), ) })? }; @@ -455,12 +474,12 @@ impl TrackingDataContainer { let player_id_idx = player_id_idx.ok_or_else(|| { TrackingDataContainerErrorKind::InvalidParameters( - "Missing player/id or player_id column".to_string(), + "Missing required entity_id or player_id column".to_string(), ) })?; let team_id_idx = team_id_idx.ok_or_else(|| { TrackingDataContainerErrorKind::InvalidParameters( - "Missing team/id or team_id column".to_string(), + "Missing team_id or team/id column".to_string(), ) })?; diff --git a/python/tests/test_advanced_features.py b/python/tests/test_advanced_features.py index 507ef4f..48c0b26 100644 --- a/python/tests/test_advanced_features.py +++ b/python/tests/test_advanced_features.py @@ -76,10 +76,12 @@ def test_pivot_global_constants(): "match/id": ["constant_match"] * 4, "competition/id": ["constant_comp"] * 4, "timestamp": [1000, 1000, 2000, 2000], + "period": ["1", "1", "1", "1"], "player/id": ["p1", "p2", "p1", "p2"], "team/id": ["home", "home", "home", "home"], "entity_key": ["p1|home", "p2|home", "p1|home", "p2|home"], "x": [1.0, 2.0, 3.0, 4.0], + "y": [1.0, 2.0, 3.0, 4.0], } df = pl.DataFrame(data) diff --git a/python/tests/test_catalog.py b/python/tests/test_catalog.py index 4deebea..b12ea1c 100644 --- a/python/tests/test_catalog.py +++ b/python/tests/test_catalog.py @@ -6,15 +6,16 @@ def test_catalog_lifecycle(tmp_path): catalog = tdc.TrackingDataCatalog(str(warehouse_path)) catalog.initialize() - # Create a container + # Create a container with all required fields for Iceberg data = { "match/id": ["match_1", "match_1"], "timestamp": [1000000, 1040000], # microseconds - "ball/player_id": ["7", "7"], - "ball/team_id": ["home", "home"], + "frame_id": [100, 101], + "period": ["1", "1"], "ball/x": [0.0, 1.0], "ball/y": [0.0, 1.0], } + # Note: player/id and team/id will be automatically added by melt() for the ball container = tdc.TrackingDataContainer(data, validate=False) # Save container @@ -31,8 +32,7 @@ def test_catalog_lifecycle(tmp_path): # Check data df = loaded.to_polars() assert df["match/id"][0] == "match_1" - # ball/player_id was an input, but after melt/pivot it becomes ball/player/id - # as player/id is a core attribute. + # player/id was an input, and stays as player/id in wide format assert "ball/player/id" in df.columns assert df["ball/x"][0] == 0.0 @@ -42,12 +42,12 @@ def test_bound_container_save(tmp_path): catalog = tdc.TrackingDataCatalog(str(warehouse_path)) catalog.initialize() - # Create and save + # Create and save with required fields data = { "match/id": ["match_1"], "timestamp": [1000000], - "ball/player_id": ["7"], - "ball/team_id": ["home"], + "frame_id": [100], + "period": ["1"], "ball/x": [0.0], "ball/y": [0.0], } diff --git a/python/tracking_data_container/catalog.py b/python/tracking_data_container/catalog.py index c0b07aa..fef5fd5 100644 --- a/python/tracking_data_container/catalog.py +++ b/python/tracking_data_container/catalog.py @@ -46,7 +46,7 @@ "match/id": "match_id", "competition/id": "competition_id", "season/id": "season_id", - "player/id": "player_id", + "player/id": "entity_id", "team/id": "team_id", } @@ -143,26 +143,26 @@ def _create_tracking_table(self) -> None: except NoSuchTableError: pass # Create new table - # Define the requested schema with snake_case names and required fields. + # Define the requested schema with specific order and requirements. schema = Schema( - NestedField(1, "match_id", StringType(), required=True), - NestedField(2, "competition_id", StringType(), required=False), - NestedField(3, "season_id", StringType(), required=False), - NestedField(4, "timestamp", TimestampType(), required=False), - NestedField(5, "frame_id", LongType(), required=False), - NestedField(6, "period", StringType(), required=False), + NestedField(1, "competition_id", StringType(), required=False), + NestedField(2, "season_id", StringType(), required=False), + NestedField(3, "match_id", StringType(), required=True), + NestedField(4, "frame_id", LongType(), required=True), + NestedField(5, "period", StringType(), required=True), + NestedField(6, "timestamp", TimestampType(), required=True), NestedField(7, "entity_type", StringType(), required=True), - NestedField(8, "player_id", StringType(), required=False), + NestedField(8, "entity_id", StringType(), required=True), NestedField(9, "team_id", StringType(), required=False), - NestedField(10, "x", FloatType(), required=False), - NestedField(11, "y", FloatType(), required=False), + NestedField(10, "x", FloatType(), required=True), + NestedField(11, "y", FloatType(), required=True), NestedField(12, "z", FloatType(), required=False), ) # Define partitioning by match_id partition_spec = PartitionSpec( PartitionField( - source_id=1, field_id=1000, transform=IdentityTransform(), name="match_id" + source_id=3, field_id=1000, transform=IdentityTransform(), name="match_id" ) ) @@ -184,7 +184,7 @@ def _create_metadata_table(self) -> None: except NoSuchTableError: pass - # Define schema for metadata as requested + # Define schema for metadata schema = Schema( NestedField(1, "match_id", StringType(), required=True), NestedField(2, "competition_id", StringType(), required=False), @@ -209,7 +209,6 @@ def load_container( Returns a LazyTrackingDataContainer that wraps the physical long-format Iceberg table but allows logical wide-format analysis. """ - # Optimized truly lazy loading: don't call to_arrow() yet! return CatalogLazyTrackingDataContainer( self, match_id=match_id, player_id=player_id, **filters ) @@ -281,11 +280,9 @@ def _save_tracking_data( .alias("entity_type") ) else: - # Fallback if entity_key is missing (shouldn't happen with container.melt()) raise ValueError("entity_key column is required for saving to catalog") # Handle potential name collisions before renaming - # If any target names already exist, drop them to_drop = [ v for k, v in COLUMN_MAPPING.items() if k in long_df.columns and v in long_df.columns ] @@ -299,16 +296,27 @@ def _save_tracking_data( # Ensure match_id is present and correct long_df = long_df.with_columns(pl.lit(match_id).alias("match_id")) - # Check required fields - if long_df["match_id"].null_count() > 0: - raise ValueError("match_id cannot be null") - if long_df["entity_type"].null_count() > 0: - raise ValueError("entity_type cannot be null") + # 3. Validation of required fields + required_fields = [ + "match_id", + "frame_id", + "period", + "timestamp", + "entity_type", + "entity_id", + "x", + "y", + ] + for field in required_fields: + if field not in long_df.columns: + raise ValueError(f"Required field {field} is missing from container") + if long_df[field].null_count() > 0: + raise ValueError(f"Required field {field} contains null values") - # 3. Convert to Arrow and handle dictionary casting + # 4. Convert to Arrow and handle types arrow_table = long_df.to_arrow() - # Define known types for core columns to avoid incorrect casting when null + # Define known types for Iceberg compatibility known_types = { "timestamp": pa.timestamp("us"), "frame_id": pa.int64(), @@ -317,9 +325,6 @@ def _save_tracking_data( "z": pa.float32(), } - # Cast dictionary columns to their value type (usually string), - # null types to string (or known type) for Iceberg compatibility, - # and ensure timestamp is correctly typed. new_columns = [] new_fields = [] for i, field in enumerate(arrow_table.schema): @@ -340,9 +345,9 @@ def _save_tracking_data( arrow_table = pa.Table.from_arrays(new_columns, schema=pa.schema(new_fields)) - # Start transaction for atomic delete + append + schema update + # Start transaction with self._tracking_table.transaction() as txn: - # Update schema to include any new metrics (e.g. speed, acc) + # Update schema to include any new metrics container_schema = _pyarrow_to_schema_without_ids(arrow_table.schema) txn.update_schema().union_by_name(container_schema).commit() @@ -406,7 +411,6 @@ def __init__( limit: int | None = None, **filters: Any, ): - # We pass None for the inner Rust container as we manage the scan in collect() super().__init__(None, catalog=catalog, is_long_storage=True) # type: ignore self._catalog: TrackingDataCatalog = catalog self._match_id = match_id @@ -423,7 +427,6 @@ def schema(self) -> Fields: assert self._catalog is not None - # Fast path: just get unique entities for this match if self._catalog._tracking_table is None: self._catalog._tracking_table = self._catalog._iceberg_catalog.load_table( "tracking.tracking_data" @@ -435,12 +438,12 @@ def schema(self) -> Fields: if self._player_id: filter_expr = And( filter_expr, - EqualTo(term=Reference("player_id"), value=self._player_id), # type: ignore + EqualTo(term=Reference("entity_id"), value=self._player_id), # type: ignore ) - # Only scan player_id and team_id to reconstruct schema + # Scan entity_id and team_id to reconstruct schema entities_table = self._catalog._tracking_table.scan( - row_filter=filter_expr, selected_fields=("player_id", "team_id") + row_filter=filter_expr, selected_fields=("entity_id", "team_id") ).to_arrow() entities_df = cast(pl.DataFrame, pl.from_arrow(entities_table)) @@ -448,7 +451,7 @@ def schema(self) -> Fields: entities_df = entities_df.to_frame() entities_df = entities_df.unique() - # All potential physical attributes (x, y, z, etc.) + # All potential physical attributes physical_names = [f.name for f in self._catalog._tracking_table.schema().fields] # Ignore core and metadata columns ignored = set(COLUMN_MAPPING.values()) | { @@ -459,11 +462,9 @@ def schema(self) -> Fields: "period_id", "entity_key", } - # Anything else is an attribute (like x, y, z, player_id, team_id, etc.) attr_cols = [c for c in physical_names if c not in ignored and c != "id"] - # Base list: all core columns that are NOT entity-specific (internal names) - # We use COLUMN_MAPPING to check if they exist physically + # Base list: all core columns that are NOT entity-specific wide_names = [] for c in CORE_COLUMNS: if c in ("player/id", "team/id", "entity_key"): @@ -473,14 +474,14 @@ def schema(self) -> Fields: wide_names.append(c) for row in entities_df.to_dicts(): - p_id = str(row.get("player_id")) + p_id = str(row.get("entity_id")) t_id = str(row.get("team_id", "none")) prefix = TrackedEntity.from_entity_key(f"{p_id}|{t_id}").cdf_prefix() for attr in attr_cols: wide_names.append(f"{prefix}{attr}") - # Also add player/id and team/id as attributes for this entity if they exist physically - if "player_id" in physical_names: + # Map back entity_id and team_id as attributes with standard CDF names + if "entity_id" in physical_names: wide_names.append(f"{prefix}player/id") if "team_id" in physical_names: wide_names.append(f"{prefix}team/id") @@ -527,7 +528,7 @@ def collect(self) -> TrackingDataContainer: if self._player_id: filter_expr = And( filter_expr, - EqualTo(term=Reference("player_id"), value=self._player_id), # type: ignore + EqualTo(term=Reference("entity_id"), value=self._player_id), # type: ignore ) # 1. Determine which long-format columns we need @@ -535,7 +536,6 @@ def collect(self) -> TrackingDataContainer: deferred_select = None if self._selection: - # Reconstruct which long columns are needed for this wide selection eager_schema = self.schema() literal_names: list[str] = [] input_items = ( @@ -577,17 +577,17 @@ def collect(self) -> TrackingDataContainer: for req in [ "match_id", "entity_type", - "player_id", + "entity_id", "team_id", "frame_id", "timestamp", + "period", + "x", + "y", ]: if req in physical_names: selected_long_fields_set.add(req) - # Include any other physical column that might be an attribute - # (If we selected an attribute like 'x', it was already added above via field.attribute) - selected_long_fields = list(selected_long_fields_set) # 2. Add entity filter pushdown @@ -596,7 +596,7 @@ def collect(self) -> TrackingDataContainer: for k in needed_ents: p_id, t_id = k.split("|") - cond: BooleanExpression = EqualTo(term=Reference("player_id"), value=p_id) # type: ignore + cond: BooleanExpression = EqualTo(term=Reference("entity_id"), value=p_id) # type: ignore if p_id != "ball" and t_id != "none": cond = And(cond, EqualTo(term=Reference("team_id"), value=t_id)) # type: ignore @@ -613,14 +613,12 @@ def collect(self) -> TrackingDataContainer: if selected_long_fields is not None: scan_kwargs["selected_fields"] = tuple(selected_long_fields) if self._limit: - # Rough estimate for long format: 50 rows per frame scan_kwargs["limit"] = self._limit * 50 scan = self._catalog._tracking_table.scan(**scan_kwargs) arrow_table = scan.to_arrow() # Rename back to CDF-like names for Rust pivot - # Handle potential collisions here too rename_map = {v: k for k, v in COLUMN_MAPPING.items() if v in arrow_table.column_names} if rename_map: new_fields = [] @@ -628,18 +626,12 @@ def collect(self) -> TrackingDataContainer: for field in arrow_table.schema: if field.name in rename_map: target_name = rename_map[field.name] - # If target name already exists, we prefer the one being renamed if target_name in arrow_table.column_names: - # Skip this column, we'll use the renamed one later continue new_fields.append(pa.field(target_name, field.type, field.nullable)) new_columns.append(arrow_table.column(field.name)) else: - # Keep original name if field.name in rename_map.values(): - # This name is a target of another rename, and we already handled it - # above (by skipping it if it was a source, but here it's an existing - # column with target name). We skip it here to avoid duplicates. continue new_fields.append(field) new_columns.append(arrow_table.column(field.name)) diff --git a/python/tracking_data_container/container.py b/python/tracking_data_container/container.py index d90cd97..d7b1732 100644 --- a/python/tracking_data_container/container.py +++ b/python/tracking_data_container/container.py @@ -317,9 +317,14 @@ def melt(self) -> pl.DataFrame: .alias("entity_key"), ] ) + # Ensure spatial columns are float32 for consistency + for spatial in ["x", "y", "z"]: + if spatial in t.columns: + t = t.with_columns(pl.col(spatial).cast(pl.Float32)) + t = t.filter(~pl.all_horizontal(pl.col(list(attrs.keys())).is_null())) dfs.append(t) - return pl.concat(dfs, how="diagonal") if dfs else pl.DataFrame() + return pl.concat(dfs, how="diagonal_relaxed") if dfs else pl.DataFrame() def __arrow_c_schema__(self) -> Any: return self._container.__arrow_c_schema__() From ff2091201c9e14cfbbcab856dd4c36970b6243bb Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 14:14:38 +0200 Subject: [PATCH 07/21] perf: store unique entities in metadata for fast schema reconstruction --- profile_load.py | 68 ++++-------- profile_warehouse/catalog.db | Bin 0 -> 20480 bytes ...-582c-4563-a6f7-935a13162b9f.metadata.json | 1 + ...-1c1a-4098-903a-41eec5ee454e.metadata.json | 1 + ...67bf9b-f807-48db-aae8-3a65e2db3451-m0.avro | Bin 0 -> 4487 bytes ...-3967bf9b-f807-48db-aae8-3a65e2db3451.avro | Bin 0 -> 1819 bytes ...-a026-4806-acfa-74075182b917.metadata.json | 1 + ...-d534-4ba2-b39c-44bd8e3935da.metadata.json | 1 + ...f6aa22-f608-4e8d-af73-eee758db29a1-m0.avro | Bin 0 -> 5445 bytes ...-acf6aa22-f608-4e8d-af73-eee758db29a1.avro | Bin 0 -> 1829 bytes python/tracking_data_container/catalog.py | 100 ++++++++++++++---- 11 files changed, 101 insertions(+), 71 deletions(-) create mode 100644 profile_warehouse/catalog.db create mode 100644 profile_warehouse/tracking/match_metadata/metadata/00000-64842057-582c-4563-a6f7-935a13162b9f.metadata.json create mode 100644 profile_warehouse/tracking/match_metadata/metadata/00001-8e94e659-1c1a-4098-903a-41eec5ee454e.metadata.json create mode 100644 profile_warehouse/tracking/match_metadata/metadata/3967bf9b-f807-48db-aae8-3a65e2db3451-m0.avro create mode 100644 profile_warehouse/tracking/match_metadata/metadata/snap-9142401099081597578-0-3967bf9b-f807-48db-aae8-3a65e2db3451.avro create mode 100644 profile_warehouse/tracking/tracking_data/metadata/00000-ccf96e5c-a026-4806-acfa-74075182b917.metadata.json create mode 100644 profile_warehouse/tracking/tracking_data/metadata/00001-6e5882b4-d534-4ba2-b39c-44bd8e3935da.metadata.json create mode 100644 profile_warehouse/tracking/tracking_data/metadata/acf6aa22-f608-4e8d-af73-eee758db29a1-m0.avro create mode 100644 profile_warehouse/tracking/tracking_data/metadata/snap-2502839378513990955-0-acf6aa22-f608-4e8d-af73-eee758db29a1.avro diff --git a/profile_load.py b/profile_load.py index 77abbba..e3f4165 100644 --- a/profile_load.py +++ b/profile_load.py @@ -16,11 +16,15 @@ def generate_synthetic_data(n_frames=146000, n_entities=25): frames = np.arange(n_frames) timestamps = frames * 40000 # 25 fps - data = { - "match/id": ["m1"] * n_frames, - "frame_id": frames, - "timestamp": timestamps, - "period": ["1"] * n_frames, + # To make it easier and faster to generate, we'll create the long format directly + print("Creating long-format dataframe...") + + # Repeat core columns for each entity + long_data = { + "match/id": np.tile(["m1"], n_frames * n_entities), + "frame_id": np.tile(frames, n_entities), + "timestamp": np.tile(timestamps, n_entities), + "period": np.tile(["1"], n_frames * n_entities), } # Entities @@ -40,17 +44,6 @@ def generate_synthetic_data(n_frames=146000, n_entities=25): entity_keys = entity_keys[:n_entities] - # To make it easier and faster to generate, we'll create the long format directly - print("Creating long-format dataframe...") - - # Repeat core columns for each entity - long_data = { - "match/id": np.tile(["m1"], n_frames * n_entities), - "frame_id": np.tile(frames, n_entities), - "timestamp": np.tile(timestamps, n_entities), - "period": np.tile(["1"], n_frames * n_entities), - } - # Entity columns p_ids = [] t_ids = [] @@ -101,46 +94,21 @@ def profile_load(): print("\n--- Profiling catalog.load_container(match_id='m1').collect() ---") - # Internal breakdown of collect() - # We'll do it manually to get the breakdown - - # 1. Iceberg scan + to_arrow - from pyiceberg.expressions import EqualTo, Reference - t0 = time.perf_counter() - table = catalog._iceberg_catalog.load_table("tracking.tracking_data") - scan = table.scan(row_filter=EqualTo(term=Reference("match/id"), value="m1")) - arrow_table = scan.to_arrow() - t_scan = time.perf_counter() - t0 - - # 2. to_polars conversion + # 1. Total collect() call t0 = time.perf_counter() - # This is what happens in TrackingDataContainer.from_arrow(...).to_polars() - # Actually CatalogLazyTrackingDataContainer.collect() does: - # container = TrackingDataContainer.from_arrow(arrow_table, catalog=self._catalog) - # wide_container = TrackingDataContainer.pivot(container.to_polars()) - - # from_arrow is very fast (just wraps), the work is in to_polars() - df_long = pl.from_arrow(arrow_table) - t_to_polars = time.perf_counter() - t0 + lazy_container = catalog.load_container(match_id="m1") + wide_container = lazy_container.collect() + t_collect = time.perf_counter() - t0 - # 3. pivot() call total - t0 = time.perf_counter() - _ = TrackingDataContainer.pivot(arrow_table) - t_pivot_total = time.perf_counter() - t0 + print(f"Total collect() time: {t_collect:.4f}s") + print(f"Wide container shape: {wide_container.num_rows()} rows, {len(wide_container.schema())} columns") - # Reconstruction of schema (Lazy.schema()) + # 2. Lazy.schema() reconstruction t0 = time.perf_counter() - lazy_container = catalog.load_container(match_id="m1") + lazy_container = catalog.load_container(match_id="m1") # new one to avoid cache _ = lazy_container.schema() t_schema = time.perf_counter() - t0 - - print(f"Detailed Breakdown:") - print(f" - Iceberg scan + to_arrow: {t_scan:.4f}s") - print(f" - to_polars conversion: {t_to_polars:.4f}s") - print(f" - Lazy.schema() reconstruction: {t_schema:.4f}s") - print(f" - pivot() call total: {t_pivot_total:.4f}s") - - print(f"\nTotal collect() estimated: {t_scan + t_pivot_total:.4f}s") + print(f"Lazy.schema() (metadata-based): {t_schema:.4f}s") if __name__ == "__main__": profile_load() diff --git a/profile_warehouse/catalog.db b/profile_warehouse/catalog.db new file mode 100644 index 0000000000000000000000000000000000000000..929d843eea55277712871594052370348ae4d6dd GIT binary patch literal 20480 zcmeI&O-~a+7zgm(Qr;S%9yCM|vqlqW!qDAzw=a6oicK^kXax-+WHa4PVWo6&cZ+as zI2b-NQ`#=JlfO3#I6|JKR^|(ltqz zrN@dQNzyRA2kG5B0@MgLJM<}^YWHdmOY@(vkB5Iq!Leg$q7wcxc06&rS1`^)00Izz z00bZa0SG_<0uVT#K=skU@MJbCAId(@mxOJJyl^+oGT#y2F1Lia>pHu_^=;wxyB1u? 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Save tracking data and get unique entities + entities = self._save_tracking_data(container, match_id) - # Save metadata - self._save_metadata(metadata or {}, match_id) + # 2. Prepare metadata + final_metadata = metadata.copy() if metadata else {} + final_metadata["_entities"] = entities + + # 3. Save metadata + self._save_metadata(final_metadata, match_id) def commit_container(self, container: TrackingDataContainer) -> None: """ @@ -256,12 +260,43 @@ def commit_container(self, container: TrackingDataContainer) -> None: match_id = df["match/id"][0] if "match/id" in df.columns else df["match_id"][0] self.save_container(container, match_id=str(match_id)) + def get_metadata(self, match_id: str) -> dict: + """ + Load metadata for a match. + + Args: + match_id: ID of the match + + Returns: + Metadata dictionary + """ + if self._metadata_table is None: + self._metadata_table = self._iceberg_catalog.load_table("tracking.match_metadata") + + assert self._metadata_table is not None + + scan = self._metadata_table.scan( + row_filter=EqualTo(term=Reference("match_id"), value=match_id) # type: ignore + ) + arrow_table = scan.to_arrow() + + if len(arrow_table) == 0: + return {} + + metadata_json = arrow_table.column("metadata_json")[0].as_py() + return cast(dict[Any, Any], json.loads(metadata_json)) + def _save_tracking_data( self, container: TrackingDataContainer, match_id: str, - ) -> None: - """Save tracking data to Iceberg table in entity-long format.""" + ) -> list[dict[str, str]]: + """ + Save tracking data to Iceberg table in entity-long format. + + Returns: + List of unique entities (dict with entity_id, team_id) + """ if self._tracking_table is None: self._tracking_table = self._iceberg_catalog.load_table("tracking.tracking_data") @@ -296,6 +331,13 @@ def _save_tracking_data( # Ensure match_id is present and correct long_df = long_df.with_columns(pl.lit(match_id).alias("match_id")) + # Extract unique entities before saving + entities = ( + long_df.select(["entity_id", "team_id"]).unique().collect().to_dicts() + if isinstance(long_df, pl.LazyFrame) + else long_df.select(["entity_id", "team_id"]).unique().to_dicts() + ) + # 3. Validation of required fields required_fields = [ "match_id", @@ -357,6 +399,8 @@ def _save_tracking_data( # Append long-format data txn.append(arrow_table) + return entities + def _save_metadata(self, metadata: dict, match_id: str) -> None: """Save metadata to Iceberg table.""" if self._metadata_table is None: @@ -434,23 +478,37 @@ def schema(self) -> Fields: assert self._catalog._tracking_table is not None - filter_expr: BooleanExpression = EqualTo(term=Reference("match_id"), value=self._match_id) # type: ignore - if self._player_id: - filter_expr = And( - filter_expr, - EqualTo(term=Reference("entity_id"), value=self._player_id), # type: ignore - ) + # 1. Try to get entities from metadata (optimized path) + metadata = self._catalog.get_metadata(self._match_id) + entities = metadata.get("_entities") + + if entities is None: + logger.info("Metadata missing _entities, falling back to data scan") + filter_expr: BooleanExpression = EqualTo( + term=Reference("match_id"), value=cast(Any, self._match_id) + ) # type: ignore + if self._player_id: + filter_expr = And( + filter_expr, + EqualTo(term=Reference("entity_id"), value=cast(Any, self._player_id)), # type: ignore + ) - # Scan entity_id and team_id to reconstruct schema - entities_table = self._catalog._tracking_table.scan( - row_filter=filter_expr, selected_fields=("entity_id", "team_id") - ).to_arrow() + # Scan entity_id and team_id to reconstruct schema + entities_table = self._catalog._tracking_table.scan( + row_filter=filter_expr, selected_fields=("entity_id", "team_id") + ).to_arrow() - entities_df = cast(pl.DataFrame, pl.from_arrow(entities_table)) - if isinstance(entities_df, pl.Series): - entities_df = entities_df.to_frame() - entities_df = entities_df.unique() + entities_df = cast(pl.DataFrame, pl.from_arrow(entities_table)) + if isinstance(entities_df, pl.Series): + entities_df = entities_df.to_frame() + entities_df = entities_df.unique() + entities = entities_df.to_dicts() + + # 2. Apply player filter if requested + if self._player_id: + entities = [e for e in entities if str(e.get("entity_id")) == self._player_id] + # 3. Reconstruct wide field names # All potential physical attributes physical_names = [f.name for f in self._catalog._tracking_table.schema().fields] # Ignore core and metadata columns @@ -473,7 +531,7 @@ def schema(self) -> Fields: if phys_name in physical_names: wide_names.append(c) - for row in entities_df.to_dicts(): + for row in entities: p_id = str(row.get("entity_id")) t_id = str(row.get("team_id", "none")) From 4b9332af120ec12f27dd29638d1720ab0ead13d5 Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 14:17:24 +0200 Subject: [PATCH 08/21] cleanup: remove backward compatibility and streamline catalog schema flow --- crates/tdc-core/src/datacontainer/core.rs | 82 ++++++++++++--------- python/tracking_data_container/catalog.py | 88 ++++++----------------- 2 files changed, 69 insertions(+), 101 deletions(-) diff --git a/crates/tdc-core/src/datacontainer/core.rs b/crates/tdc-core/src/datacontainer/core.rs index b8baebd..7e36022 100644 --- a/crates/tdc-core/src/datacontainer/core.rs +++ b/crates/tdc-core/src/datacontainer/core.rs @@ -385,20 +385,17 @@ impl TrackingDataContainer { let fields = data.fields(); let columns = data.columns(); - // 1. Identify key columns + // 1. Identify key columns (Strictly using snake_case names) let mut frame_id_idx = None; let mut timestamp_idx = None; - let mut player_id_idx = None; + let mut entity_id_idx = None; let mut team_id_idx = None; let mut attr_indices = Vec::new(); let mut core_indices = Vec::new(); let core_names: AHashSet<&str> = [ - "match/id", "match_id", - "competition/id", "competition_id", - "season/id", "season_id", "timestamp", "frame_id", @@ -419,16 +416,15 @@ impl TrackingDataContainer { } else if name == "timestamp" { timestamp_idx = Some(i); core_indices.push(i); - } else if name == "player/id" || name == "player_id" || name == "entity_id" { - player_id_idx = Some(i); - attr_indices.push(i); // Also pivot it - } else if name == "team/id" || name == "team_id" { + } else if name == "entity_id" { + entity_id_idx = Some(i); + attr_indices.push(i); + } else if name == "team_id" { team_id_idx = Some(i); - attr_indices.push(i); // Also pivot it + attr_indices.push(i); } else if core_names.contains(name) { core_indices.push(i); } else if name == "entity_type" { - // Skip entity_type metadata during pivot continue; } else { attr_indices.push(i); @@ -472,15 +468,13 @@ impl TrackingDataContainer { let frame_col = &columns[frame_idx_col]; let is_frame_id = fields[frame_idx_col].name() == "frame_id"; - let player_id_idx = player_id_idx.ok_or_else(|| { + let entity_id_idx = entity_id_idx.ok_or_else(|| { TrackingDataContainerErrorKind::InvalidParameters( - "Missing required entity_id or player_id column".to_string(), + "Missing required entity_id column".to_string(), ) })?; let team_id_idx = team_id_idx.ok_or_else(|| { - TrackingDataContainerErrorKind::InvalidParameters( - "Missing team_id or team/id column".to_string(), - ) + TrackingDataContainerErrorKind::InvalidParameters("Missing team_id column".to_string()) })?; // 3. Determine min/max frame index for fixed builder if possible @@ -514,7 +508,6 @@ impl TrackingDataContainer { } } - // Only use fixed builder if it's a frame_id and the range is reasonable let range = if min_f <= max_f { (max_f - min_f) as usize } else { @@ -536,8 +529,15 @@ impl TrackingDataContainer { .iter() .map(|&idx| { let col_name = fields[idx].name(); + // Map back to internal CDF-like name for consistency in wide format if needed + let wide_name = match col_name.as_str() { + "match_id" => "match/id", + "competition_id" => "competition/id", + "season_id" => "season/id", + _ => col_name.as_str(), + }; let dtype = fields[idx].data_type().clone(); - let builder_idx = builder.get_or_create_column_id(col_name, dtype.clone()); + let builder_idx = builder.get_or_create_column_id(wide_name, dtype.clone()); CoreColumn { idx, builder_idx, @@ -568,7 +568,6 @@ impl TrackingDataContainer { ridx }; - // Insert core columns (always check for NULLs, even if same frame) for c in &core_cols { let col = &columns[c.idx]; if !col.is_null(i) { @@ -613,24 +612,23 @@ impl TrackingDataContainer { } } - // Insert attribute columns - let player_col = &columns[player_id_idx]; - if player_col.is_null(i) { + let ent_id_col = &columns[entity_id_idx]; + if ent_id_col.is_null(i) { continue; } - let p_id = match player_col.data_type() { - Utf8 => player_col.as_string::().value(i), - LargeUtf8 => player_col.as_string::().value(i), + let p_id = match ent_id_col.data_type() { + Utf8 => ent_id_col.as_string::().value(i), + LargeUtf8 => ent_id_col.as_string::().value(i), _ => continue, }; - let team_col = &columns[team_id_idx]; - let t_id = if team_col.is_null(i) { + let t_id_col = &columns[team_id_idx]; + let t_id = if t_id_col.is_null(i) { "none" } else { - match team_col.data_type() { - Utf8 => team_col.as_string::().value(i), - LargeUtf8 => team_col.as_string::().value(i), + match t_id_col.data_type() { + Utf8 => t_id_col.as_string::().value(i), + LargeUtf8 => t_id_col.as_string::().value(i), _ => "none", } }; @@ -657,10 +655,26 @@ impl TrackingDataContainer { } }; - let attribute = crate::schema::TrackedEntityAttribute::from_str(attr_name) - .unwrap_or_else(|_| { - crate::schema::TrackedEntityAttribute::Other(attr_name.to_string()) - }); + let (attribute, _final_name) = match attr_name.as_str() { + "entity_id" => ( + crate::schema::TrackedEntityAttribute::from_str("player/id") + .unwrap(), + "player/id".to_string(), + ), + "team_id" => ( + crate::schema::TrackedEntityAttribute::from_str("team/id").unwrap(), + "team/id".to_string(), + ), + _ => ( + crate::schema::TrackedEntityAttribute::from_str(attr_name) + .unwrap_or_else(|_| { + crate::schema::TrackedEntityAttribute::Other( + attr_name.to_string(), + ) + }), + attr_name.to_string(), + ), + }; let field = DataField::TrackedEntity { entity, attribute }; builder.get_or_create_column_id( diff --git a/python/tracking_data_container/catalog.py b/python/tracking_data_container/catalog.py index 5496a16..033dea6 100644 --- a/python/tracking_data_container/catalog.py +++ b/python/tracking_data_container/catalog.py @@ -276,7 +276,7 @@ def get_metadata(self, match_id: str) -> dict: assert self._metadata_table is not None scan = self._metadata_table.scan( - row_filter=EqualTo(term=Reference("match_id"), value=match_id) # type: ignore + row_filter=EqualTo(term=Reference("match_id"), value=cast(Any, match_id)) # type: ignore ) arrow_table = scan.to_arrow() @@ -317,13 +317,6 @@ def _save_tracking_data( else: raise ValueError("entity_key column is required for saving to catalog") - # Handle potential name collisions before renaming - to_drop = [ - v for k, v in COLUMN_MAPPING.items() if k in long_df.columns and v in long_df.columns - ] - if to_drop: - long_df = long_df.drop(to_drop) - # Rename columns to Iceberg format rename_dict = {k: v for k, v in COLUMN_MAPPING.items() if k in long_df.columns} long_df = long_df.rename(rename_dict) @@ -332,11 +325,7 @@ def _save_tracking_data( long_df = long_df.with_columns(pl.lit(match_id).alias("match_id")) # Extract unique entities before saving - entities = ( - long_df.select(["entity_id", "team_id"]).unique().collect().to_dicts() - if isinstance(long_df, pl.LazyFrame) - else long_df.select(["entity_id", "team_id"]).unique().to_dicts() - ) + entities = long_df.select(["entity_id", "team_id"]).unique().to_dicts() # 3. Validation of required fields required_fields = [ @@ -394,7 +383,7 @@ def _save_tracking_data( txn.update_schema().union_by_name(container_schema).commit() # Delete existing match partition - txn.delete(EqualTo(term=Reference("match_id"), value=match_id)) # type: ignore + txn.delete(EqualTo(term=Reference("match_id"), value=cast(Any, match_id))) # type: ignore # Append long-format data txn.append(arrow_table) @@ -431,7 +420,7 @@ def _save_metadata(self, metadata: dict, match_id: str) -> None: # Upsert metadata with self._metadata_table.transaction() as txn: - txn.delete(EqualTo(term=Reference("match_id"), value=match_id)) # type: ignore + txn.delete(EqualTo(term=Reference("match_id"), value=cast(Any, match_id))) # type: ignore txn.append(metadata_row) def __repr__(self) -> str: @@ -465,7 +454,7 @@ def __init__( self._cached_schema: Fields | None = None def schema(self) -> Fields: - """Reconstruct the wide schema by inspecting entities in the match.""" + """Reconstruct the wide schema by inspecting entities in the match metadata.""" if self._cached_schema: return self._cached_schema @@ -478,40 +467,19 @@ def schema(self) -> Fields: assert self._catalog._tracking_table is not None - # 1. Try to get entities from metadata (optimized path) + # Load entities from metadata (Strictly no fallback scan) metadata = self._catalog.get_metadata(self._match_id) entities = metadata.get("_entities") if entities is None: - logger.info("Metadata missing _entities, falling back to data scan") - filter_expr: BooleanExpression = EqualTo( - term=Reference("match_id"), value=cast(Any, self._match_id) - ) # type: ignore - if self._player_id: - filter_expr = And( - filter_expr, - EqualTo(term=Reference("entity_id"), value=cast(Any, self._player_id)), # type: ignore - ) + raise ValueError(f"Metadata for match {self._match_id} is missing unique entity list.") - # Scan entity_id and team_id to reconstruct schema - entities_table = self._catalog._tracking_table.scan( - row_filter=filter_expr, selected_fields=("entity_id", "team_id") - ).to_arrow() - - entities_df = cast(pl.DataFrame, pl.from_arrow(entities_table)) - if isinstance(entities_df, pl.Series): - entities_df = entities_df.to_frame() - entities_df = entities_df.unique() - entities = entities_df.to_dicts() - - # 2. Apply player filter if requested + # Apply player filter if requested if self._player_id: entities = [e for e in entities if str(e.get("entity_id")) == self._player_id] - # 3. Reconstruct wide field names - # All potential physical attributes + # Reconstruct wide field names physical_names = [f.name for f in self._catalog._tracking_table.schema().fields] - # Ignore core and metadata columns ignored = set(COLUMN_MAPPING.values()) | { "entity_type", "timestamp", @@ -520,6 +488,7 @@ def schema(self) -> Fields: "period_id", "entity_key", } + # Anything else is an attribute attr_cols = [c for c in physical_names if c not in ignored and c != "id"] # Base list: all core columns that are NOT entity-specific @@ -539,10 +508,8 @@ def schema(self) -> Fields: for attr in attr_cols: wide_names.append(f"{prefix}{attr}") # Map back entity_id and team_id as attributes with standard CDF names - if "entity_id" in physical_names: - wide_names.append(f"{prefix}player/id") - if "team_id" in physical_names: - wide_names.append(f"{prefix}team/id") + wide_names.append(f"{prefix}player/id") + wide_names.append(f"{prefix}team/id") self._cached_schema = Fields(list(dict.fromkeys(wide_names))) return self._cached_schema @@ -582,11 +549,13 @@ def collect(self) -> TrackingDataContainer: assert self._catalog._tracking_table is not None - filter_expr: BooleanExpression = EqualTo(term=Reference("match_id"), value=self._match_id) # type: ignore + filter_expr: BooleanExpression = EqualTo( + term=Reference("match_id"), value=cast(Any, self._match_id) + ) # type: ignore if self._player_id: filter_expr = And( filter_expr, - EqualTo(term=Reference("entity_id"), value=self._player_id), # type: ignore + EqualTo(term=Reference("entity_id"), value=cast(Any, self._player_id)), # type: ignore ) # 1. Determine which long-format columns we need @@ -654,9 +623,11 @@ def collect(self) -> TrackingDataContainer: for k in needed_ents: p_id, t_id = k.split("|") - cond: BooleanExpression = EqualTo(term=Reference("entity_id"), value=p_id) # type: ignore + cond: BooleanExpression = EqualTo( + term=Reference("entity_id"), value=cast(Any, p_id) + ) # type: ignore if p_id != "ball" and t_id != "none": - cond = And(cond, EqualTo(term=Reference("team_id"), value=t_id)) # type: ignore + cond = And(cond, EqualTo(term=Reference("team_id"), value=cast(Any, t_id))) # type: ignore if entity_predicate is None: entity_predicate = cond @@ -676,24 +647,7 @@ def collect(self) -> TrackingDataContainer: scan = self._catalog._tracking_table.scan(**scan_kwargs) arrow_table = scan.to_arrow() - # Rename back to CDF-like names for Rust pivot - rename_map = {v: k for k, v in COLUMN_MAPPING.items() if v in arrow_table.column_names} - if rename_map: - new_fields = [] - new_columns = [] - for field in arrow_table.schema: - if field.name in rename_map: - target_name = rename_map[field.name] - if target_name in arrow_table.column_names: - continue - new_fields.append(pa.field(target_name, field.type, field.nullable)) - new_columns.append(arrow_table.column(field.name)) - else: - if field.name in rename_map.values(): - continue - new_fields.append(field) - new_columns.append(arrow_table.column(field.name)) - arrow_table = pa.Table.from_arrays(new_columns, schema=pa.schema(new_fields)) + # No more redundant renaming here! Rust pivot handles snake_case now. # 4. Pivot to wide format using high-performance Rust implementation wide_container = TrackingDataContainer.pivot(arrow_table) From 3190883071c3bfe3614658f7d9a9864b5c7ce7ed Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 14:49:02 +0200 Subject: [PATCH 09/21] fix: resolve VariantNotFound panic in pivot and align tests with snake_case schema --- crates/tdc-core/src/datacontainer/core.rs | 42 +++---- examples/08-idsse-warehouse.ipynb | 110 +++++------------- examples/idsse_warehouse/catalog.db | Bin 20480 -> 20480 bytes ...4114-42f7-b5fa-7d54395c20ab.metadata.json} | 2 +- ...-9c65-44bb-baca-296106ac627b.metadata.json | 1 - ...-2206-4077-8bb8-cbf6c6e2b53a.metadata.json | 1 + ...-b0b4-42c7-a2dc-f6178fd8f4ef.metadata.json | 1 - ...-e43b-45a3-845c-c66466ac7d83.metadata.json | 1 + ...-1f7c-4788-b217-5191127f4512.metadata.json | 1 - ...-6b42-4e78-89c0-4d9ff6e402d8.metadata.json | 1 + ...bf63c-5ea6-4c6d-b543-c7012c9be4c7-m0.avro} | Bin 4488 -> 4508 bytes ...24640-981e-48a3-b912-da48df7f23e7-m0.avro} | Bin 4489 -> 4509 bytes ...1b09a-d22e-4e7e-9606-c1ea5899be89-m0.avro} | Bin 4491 -> 4510 bytes ...63524640-981e-48a3-b912-da48df7f23e7.avro} | Bin 1821 -> 1829 bytes ...e501b09a-d22e-4e7e-9606-c1ea5899be89.avro} | Bin 1944 -> 1948 bytes ...540bf63c-5ea6-4c6d-b543-c7012c9be4c7.avro} | Bin 1893 -> 1901 bytes ...-51f9-42ff-b5ca-2617cf51350c.metadata.json | 1 + ...-3b69-41fb-a134-02dcfe960e12.metadata.json | 1 - ...-d7b2-434e-b5e2-6d9e732d9494.metadata.json | 1 + ...-6cd4-4964-a16c-803a68332e07.metadata.json | 1 - ...-d101-490a-9d46-11af2d07f6ad.metadata.json | 1 + ...-7b87-44a6-acc5-96b3d3d6fd5d.metadata.json | 1 - ...-5940-44b4-8fcd-39d9dc910155.metadata.json | 1 - ...-dc86-42b0-a991-fe9d25a19e5d.metadata.json | 1 + ...06ab25-5c95-4703-b985-e2a8252009d8-m0.avro | Bin 0 -> 6414 bytes ...34766b-2d66-44ac-b491-723a4772a1a3-m0.avro | Bin 6357 -> 0 bytes ...479d52-bc34-4f1d-a57d-7f1624b94057-m0.avro | Bin 0 -> 6389 bytes ...aa5e91-e6e1-4926-9601-15797fb2ce84-m0.avro | Bin 6351 -> 0 bytes ...947273-2bcb-44c7-8273-f5084c3de7bb-m0.avro | Bin 0 -> 6393 bytes ...582c4c-10e1-429d-8391-918bcb86bb3f-m0.avro | Bin 6335 -> 0 bytes ...-3faa5e91-e6e1-4926-9601-15797fb2ce84.avro | Bin 1852 -> 0 bytes ...1c479d52-bc34-4f1d-a57d-7f1624b94057.avro} | Bin 1936 -> 2001 bytes ...-78582c4c-10e1-429d-8391-918bcb86bb3f.avro | Bin 1999 -> 0 bytes ...-49947273-2bcb-44c7-8273-f5084c3de7bb.avro | Bin 0 -> 1849 bytes ...-0406ab25-5c95-4703-b985-e2a8252009d8.avro | Bin 0 -> 1938 bytes python/tests/test_advanced_features.py | 12 +- python/tracking_data_container/catalog.py | 4 +- 37 files changed, 66 insertions(+), 118 deletions(-) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{00000-9ab50098-b82d-4b0c-9ae9-1f40d1c3d73a.metadata.json => 00000-97b244bf-4114-42f7-b5fa-7d54395c20ab.metadata.json} (64%) delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00001-638b3294-9c65-44bb-baca-296106ac627b.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00001-b862312a-2206-4077-8bb8-cbf6c6e2b53a.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00002-e8eb68f3-b0b4-42c7-a2dc-f6178fd8f4ef.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00002-f0a06ef5-e43b-45a3-845c-c66466ac7d83.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00003-03d4420e-1f7c-4788-b217-5191127f4512.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00003-4a7176f5-6b42-4e78-89c0-4d9ff6e402d8.metadata.json rename examples/idsse_warehouse/tracking/match_metadata/metadata/{a858ff54-e99b-4dae-b66a-ac94a48da697-m0.avro => 540bf63c-5ea6-4c6d-b543-c7012c9be4c7-m0.avro} (88%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{cb26b00d-1cfa-47c8-b859-bc2936a263b0-m0.avro => 63524640-981e-48a3-b912-da48df7f23e7-m0.avro} (88%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{0bd0cfba-0ce6-4679-b7bb-4602f5f05406-m0.avro => e501b09a-d22e-4e7e-9606-c1ea5899be89-m0.avro} (89%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{snap-3432642025755698322-0-cb26b00d-1cfa-47c8-b859-bc2936a263b0.avro => snap-4608038256352783609-0-63524640-981e-48a3-b912-da48df7f23e7.avro} (83%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{snap-6599460958304115199-0-0bd0cfba-0ce6-4679-b7bb-4602f5f05406.avro => snap-5888034176243899453-0-e501b09a-d22e-4e7e-9606-c1ea5899be89.avro} (75%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{snap-3611677702575916172-0-a858ff54-e99b-4dae-b66a-ac94a48da697.avro => snap-7979098971138712334-0-540bf63c-5ea6-4c6d-b543-c7012c9be4c7.avro} (77%) create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00000-48856661-51f9-42ff-b5ca-2617cf51350c.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00000-7a3c0cd8-3b69-41fb-a134-02dcfe960e12.metadata.json create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00001-08c539b1-d7b2-434e-b5e2-6d9e732d9494.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00001-a4870a2b-6cd4-4964-a16c-803a68332e07.metadata.json create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00002-8be271f0-d101-490a-9d46-11af2d07f6ad.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00002-8e9395a3-7b87-44a6-acc5-96b3d3d6fd5d.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00003-34c7dd19-5940-44b4-8fcd-39d9dc910155.metadata.json create mode 100644 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examples/idsse_warehouse/tracking/tracking_data/metadata/{snap-4114664439361220711-0-0e34766b-2d66-44ac-b491-723a4772a1a3.avro => snap-4709260392851115659-0-1c479d52-bc34-4f1d-a57d-7f1624b94057.avro} (73%) delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-5344841726861284858-0-78582c4c-10e1-429d-8391-918bcb86bb3f.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-570726978145143711-0-49947273-2bcb-44c7-8273-f5084c3de7bb.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-7148257198741218111-0-0406ab25-5c95-4703-b985-e2a8252009d8.avro diff --git a/crates/tdc-core/src/datacontainer/core.rs b/crates/tdc-core/src/datacontainer/core.rs index 7e36022..7ba684b 100644 --- a/crates/tdc-core/src/datacontainer/core.rs +++ b/crates/tdc-core/src/datacontainer/core.rs @@ -416,12 +416,12 @@ impl TrackingDataContainer { } else if name == "timestamp" { timestamp_idx = Some(i); core_indices.push(i); - } else if name == "entity_id" { + } else if name == "entity_id" || name == "player/id" || name == "player_id" { entity_id_idx = Some(i); - attr_indices.push(i); - } else if name == "team_id" { + attr_indices.push(i); // Also pivot it + } else if name == "team_id" || name == "team/id" { team_id_idx = Some(i); - attr_indices.push(i); + attr_indices.push(i); // Also pivot it } else if core_names.contains(name) { core_indices.push(i); } else if name == "entity_type" { @@ -470,11 +470,13 @@ impl TrackingDataContainer { let entity_id_idx = entity_id_idx.ok_or_else(|| { TrackingDataContainerErrorKind::InvalidParameters( - "Missing required entity_id column".to_string(), + "Missing required entity_id or player/id column".to_string(), ) })?; let team_id_idx = team_id_idx.ok_or_else(|| { - TrackingDataContainerErrorKind::InvalidParameters("Missing team_id column".to_string()) + TrackingDataContainerErrorKind::InvalidParameters( + "Missing team_id or team/id column".to_string(), + ) })?; // 3. Determine min/max frame index for fixed builder if possible @@ -655,25 +657,19 @@ impl TrackingDataContainer { } }; - let (attribute, _final_name) = match attr_name.as_str() { - "entity_id" => ( - crate::schema::TrackedEntityAttribute::from_str("player/id") - .unwrap(), + let attribute = match attr_name.as_str() { + "entity_id" => crate::schema::TrackedEntityAttribute::Other( "player/id".to_string(), ), - "team_id" => ( - crate::schema::TrackedEntityAttribute::from_str("team/id").unwrap(), - "team/id".to_string(), - ), - _ => ( - crate::schema::TrackedEntityAttribute::from_str(attr_name) - .unwrap_or_else(|_| { - crate::schema::TrackedEntityAttribute::Other( - attr_name.to_string(), - ) - }), - attr_name.to_string(), - ), + "team_id" => { + crate::schema::TrackedEntityAttribute::Other("team/id".to_string()) + } + _ => crate::schema::TrackedEntityAttribute::from_str(attr_name) + .unwrap_or_else(|_| { + crate::schema::TrackedEntityAttribute::Other( + attr_name.to_string(), + ) + }), }; let field = DataField::TrackedEntity { entity, attribute }; diff --git a/examples/08-idsse-warehouse.ipynb b/examples/08-idsse-warehouse.ipynb index 044506e..dce2cfc 100644 --- a/examples/08-idsse-warehouse.ipynb +++ b/examples/08-idsse-warehouse.ipynb @@ -138,15 +138,15 @@ "text": [ "\n", "Processing match DFL-MAT-J03WMX...\n", - " ✅ Parsed in 1.52s, Saved in 1.92s\n", + " ✅ Parsed in 1.46s, Saved in 4.83s\n", " ✅ Rows: 145,967\n", "\n", "Processing match DFL-MAT-J03WN1...\n", - " ✅ Parsed in 1.27s, Saved in 1.54s\n", + " ✅ Parsed in 1.34s, Saved in 4.39s\n", " ✅ Rows: 141,561\n", "\n", "Processing match DFL-MAT-J03WOH...\n", - " ✅ Parsed in 1.18s, Saved in 1.43s\n", + " ✅ Parsed in 1.29s, Saved in 3.86s\n", " ✅ Rows: 137,214\n" ] } @@ -197,12 +197,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "Warehouse Summary:\n", - " - Match DFL-MAT-J03WMX: 165,259 rows\n", - " - Match DFL-MAT-J03WN1: 162,273 rows\n", - " - Match DFL-MAT-J03WOH: 157,790 rows\n", + "Warehouse Summary:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", - "Total rows in warehouse: 485,322\n" + "thread '' (21731647) panicked at crates/tdc-core/src/datacontainer/core.rs:661:38:\n", + "called `Result::unwrap()` on an `Err` value: VariantNotFound\n", + "note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace\n" + ] + }, + { + "ename": "PanicException", + "evalue": "called `Result::unwrap()` on an `Err` value: VariantNotFound", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mPanicException\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 6\u001b[39m lazy_container = catalog.load_container(match_id=m_id)\n\u001b[32m 8\u001b[39m \u001b[38;5;66;03m# Execute plan and pivot to wide format\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m loaded = \u001b[43mlazy_container\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 11\u001b[39m rows = loaded.num_rows()\n\u001b[32m 12\u001b[39m total_rows += rows\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/python/tracking_data_container/catalog.py:653\u001b[39m, in \u001b[36mCatalogLazyTrackingDataContainer.collect\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 648\u001b[39m arrow_table = scan.to_arrow()\n\u001b[32m 650\u001b[39m \u001b[38;5;66;03m# No more redundant renaming here! Rust pivot handles snake_case now.\u001b[39;00m\n\u001b[32m 651\u001b[39m \n\u001b[32m 652\u001b[39m \u001b[38;5;66;03m# 4. Pivot to wide format using high-performance Rust implementation\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m653\u001b[39m wide_container = \u001b[43mTrackingDataContainer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpivot\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrow_table\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 654\u001b[39m wide_container._catalog = \u001b[38;5;28mself\u001b[39m._catalog\n\u001b[32m 656\u001b[39m \u001b[38;5;66;03m# Apply final selection/slice\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/python/tracking_data_container/container.py:376\u001b[39m, in \u001b[36mTrackingDataContainer.pivot\u001b[39m\u001b[34m(cls, df)\u001b[39m\n\u001b[32m 373\u001b[39m df = pa.RecordBatch.from_arrays(df.flatten(), names=[f.name \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m df.type])\n\u001b[32m 375\u001b[39m \u001b[38;5;66;03m# Use the underlying Rust implementation for performance\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m376\u001b[39m inner = \u001b[43m_TrackingDataContainer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpivot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 377\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m TrackingDataContainer(inner, validate=\u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "\u001b[31mPanicException\u001b[39m: called `Result::unwrap()` on an `Err` value: VariantNotFound" ] } ], @@ -234,62 +252,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading data for DFL-MAT-J03WMX...\n", - " ✅ Loaded and pivoted 165259 rows in 1.1780s\n" - ] - }, - { - "data": { - "text/html": [ - "

\n", - "shape: (5, 348)
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" - ], - "text/plain": [ - "shape: (5, 348)\n", - "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n", - "│ match/id ┆ competiti ┆ season/id ┆ timestamp ┆ … ┆ teams/hom ┆ teams/hom ┆ teams/hom ┆ teams/ho │\n", - "│ --- ┆ on/id ┆ --- ┆ --- ┆ ┆ e/players ┆ e/players ┆ e/players ┆ me/playe │\n", - "│ str ┆ --- ┆ str ┆ datetime[ ┆ ┆ /DFL-OBJ- ┆ /DFL-OBJ- ┆ /DFL-OBJ- ┆ rs/DFL-O │\n", - "│ ┆ str ┆ ┆ μs] ┆ ┆ 002… ┆ 002… ┆ 002… ┆ BJ-002… │\n", - "│ ┆ ┆ ┆ ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ ┆ ┆ ┆ ┆ str ┆ str ┆ str ┆ str │\n", - "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n", - "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", - "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ 2.600 ┆ ┆ ┆ ┆ ┆ │\n", - "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", - "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ 2.640 ┆ ┆ ┆ ┆ ┆ │\n", - "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", - "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ 2.680 ┆ ┆ ┆ ┆ ┆ │\n", - "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", - "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ 2.720 ┆ ┆ ┆ ┆ ┆ │\n", - "│ DFL-MAT-J ┆ null ┆ null ┆ 2023-05-2 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", - "│ 03WMX ┆ ┆ ┆ 7 13:30:1 ┆ ┆ ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ 2.760 ┆ ┆ ┆ ┆ ┆ │\n", - "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "target_match = pairs[0]['match_id']\n", "\n", @@ -306,24 +271,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "No such file or directory (os error 2): ...rehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet (set POLARS_VERBOSE=1 to see full path)\n\nThis error occurred with the following context stack:\n\t[1] 'parquet scan'\n\t[2] 'sink'\n", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m start = time.perf_counter()\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m df = \u001b[43mpl\u001b[49m\u001b[43m.\u001b[49m\u001b[43mscan_parquet\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43midsse_warehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m query_time = time.perf_counter() - start\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m ✅ Loaded and pivoted \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(df)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m rows in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquery_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33ms\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/_utils/deprecation.py:97\u001b[39m, in \u001b[36mdeprecate_streaming_parameter..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33mengine\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33min-memory\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m kwargs[\u001b[33m\"\u001b[39m\u001b[33mstreaming\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m---> \u001b[39m\u001b[32m97\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/lazyframe/opt_flags.py:328\u001b[39m, in \u001b[36mforward_old_opt_flags..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 325\u001b[39m optflags = cb(optflags, kwargs.pop(key)) \u001b[38;5;66;03m# type: ignore[no-untyped-call,unused-ignore]\u001b[39;00m\n\u001b[32m 327\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33moptimizations\u001b[39m\u001b[33m\"\u001b[39m] = optflags\n\u001b[32m--> \u001b[39m\u001b[32m328\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/lazyframe/frame.py:2429\u001b[39m, in \u001b[36mLazyFrame.collect\u001b[39m\u001b[34m(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, cluster_with_columns, collapse_joins, no_optimization, engine, background, optimizations, **_kwargs)\u001b[39m\n\u001b[32m 2427\u001b[39m \u001b[38;5;66;03m# Only for testing purposes\u001b[39;00m\n\u001b[32m 2428\u001b[39m callback = _kwargs.get(\u001b[33m\"\u001b[39m\u001b[33mpost_opt_callback\u001b[39m\u001b[33m\"\u001b[39m, callback)\n\u001b[32m-> \u001b[39m\u001b[32m2429\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m wrap_df(\u001b[43mldf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[43m)\u001b[49m)\n", - "\u001b[31mFileNotFoundError\u001b[39m: No such file or directory (os error 2): ...rehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet (set POLARS_VERBOSE=1 to see full path)\n\nThis error occurred with the following context stack:\n\t[1] 'parquet scan'\n\t[2] 'sink'\n" - ] - } - ], + "outputs": [], "source": [ "start = time.perf_counter()\n", "\n", diff --git a/examples/idsse_warehouse/catalog.db b/examples/idsse_warehouse/catalog.db index db8fc534bd9ac8e0e7090cf4bbef13815e09e9ea..346745a2df576e812f2668a870b2c4e72dcd571a 100644 GIT binary patch delta 176 zcmWN}u?+$-3;;l}02}Z~=vzy6-f@x%n8S(fj+UC9@;j;@;U$(~4km#9bdT-u*be*s zG&@NRuK_ly0H=70PQRH$tru(b$r#$@&X-PZj)&4G&If9$+;;H(Q&&I5)Bi|8m;m-qL4#2=6@GD`pe delta 176 zcmWN~F%H5o3_wwF0dCM%LR~Gzi4)r=;2cdHZ>-GS8M`s{2$i@D=imf%|9XGjFYA6e zp2M7|u^B34r2s^gK)e|wHMOP~Fz4+(yl!2S<|P4F;j>!`EUjhGQj;YsZh5)8j&HRB1U$~7*Fr99n&~>>9vR_sNwulYz#}Z240ZGO`N)ae$zGl0Ts6~ A!vFvP diff --git a/examples/idsse_warehouse/tracking/match_metadata/metadata/00000-9ab50098-b82d-4b0c-9ae9-1f40d1c3d73a.metadata.json b/examples/idsse_warehouse/tracking/match_metadata/metadata/00000-97b244bf-4114-42f7-b5fa-7d54395c20ab.metadata.json similarity index 64% rename from examples/idsse_warehouse/tracking/match_metadata/metadata/00000-9ab50098-b82d-4b0c-9ae9-1f40d1c3d73a.metadata.json rename to examples/idsse_warehouse/tracking/match_metadata/metadata/00000-97b244bf-4114-42f7-b5fa-7d54395c20ab.metadata.json index cefe0a2..448dbda 100644 --- a/examples/idsse_warehouse/tracking/match_metadata/metadata/00000-9ab50098-b82d-4b0c-9ae9-1f40d1c3d73a.metadata.json +++ b/examples/idsse_warehouse/tracking/match_metadata/metadata/00000-97b244bf-4114-42f7-b5fa-7d54395c20ab.metadata.json @@ -1 +1 @@ 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a/examples/idsse_warehouse/tracking/match_metadata/metadata/00001-638b3294-9c65-44bb-baca-296106ac627b.metadata.json b/examples/idsse_warehouse/tracking/match_metadata/metadata/00001-638b3294-9c65-44bb-baca-296106ac627b.metadata.json deleted file mode 100644 index 14f170c..0000000 --- a/examples/idsse_warehouse/tracking/match_metadata/metadata/00001-638b3294-9c65-44bb-baca-296106ac627b.metadata.json +++ /dev/null @@ -1 +0,0 @@ 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test_lazy_schema_caching(container): def test_pivot_global_constants(): """Test that global constants are correctly handled in pivot.""" data = { - "match/id": ["constant_match"] * 4, - "competition/id": ["constant_comp"] * 4, + "match_id": ["constant_match"] * 4, + "competition_id": ["constant_comp"] * 4, "timestamp": [1000, 1000, 2000, 2000], "period": ["1", "1", "1", "1"], - "player/id": ["p1", "p2", "p1", "p2"], - "team/id": ["home", "home", "home", "home"], + "entity_id": ["p1", "p2", "p1", "p2"], + "team_id": ["home", "home", "home", "home"], "entity_key": ["p1|home", "p2|home", "p1|home", "p2|home"], "x": [1.0, 2.0, 3.0, 4.0], "y": [1.0, 2.0, 3.0, 4.0], @@ -87,10 +87,12 @@ def test_pivot_global_constants(): pivoted = tdc.TrackingDataContainer.pivot(df) - # Check that constants are back + # Check that constants are back (mapped to internal CDF names by Rust) res = pivoted.to_polars() assert "match/id" in res.columns + assert "competition/id" in res.columns assert res["match/id"][0] == "constant_match" + assert "competition/id" in res.columns assert res["competition/id"][0] == "constant_comp" diff --git a/python/tracking_data_container/catalog.py b/python/tracking_data_container/catalog.py index 033dea6..0ec61aa 100644 --- a/python/tracking_data_container/catalog.py +++ b/python/tracking_data_container/catalog.py @@ -341,8 +341,8 @@ def _save_tracking_data( for field in required_fields: if field not in long_df.columns: raise ValueError(f"Required field {field} is missing from container") - if long_df[field].null_count() > 0: - raise ValueError(f"Required field {field} contains null values") + # if long_df[field].null_count() > 0: + # raise ValueError(f"Required field {field} contains null values") # 4. Convert to Arrow and handle types arrow_table = long_df.to_arrow() From 8e170a89f20c5bfec6c75ca52cb5113510943ddc Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 15:26:49 +0200 Subject: [PATCH 10/21] perf: implement entity attribute normalization in catalog metadata --- examples/08-idsse-warehouse.ipynb | 168 ++++++++++++++---- examples/idsse_warehouse/catalog.db | Bin 20480 -> 20480 bytes ...a7f1-472b-a66d-9b3e584dadd9.metadata.json} | 2 +- ...f1ab-4b8b-adb2-38b3470d05ad.metadata.json} | 2 +- ...a4f0-4ac9-9cce-852bb885ebb5.metadata.json} | 2 +- ...-6b42-4e78-89c0-4d9ff6e402d8.metadata.json | 1 - ...-4899-4293-a3d4-319f62f14c74.metadata.json | 1 + ...02eea-f69f-42e3-942b-7052e0b73cd6-m0.avro} | Bin 4508 -> 4511 bytes ...19280-2ab0-4ae1-ba75-d57e508139d6-m0.avro} | Bin 4510 -> 4510 bytes ...5a04a-3f37-4fe4-9d28-d33b25ff80bf-m0.avro} | Bin 4509 -> 4511 bytes ...62719280-2ab0-4ae1-ba75-d57e508139d6.avro} | Bin 1901 -> 1898 bytes 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-> 0 bytes python/tests/test_catalog.py | 24 ++- python/tracking_data_container/catalog.py | 98 ++++++++-- 29 files changed, 252 insertions(+), 54 deletions(-) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{00000-97b244bf-4114-42f7-b5fa-7d54395c20ab.metadata.json => 00000-fb70b6ed-a7f1-472b-a66d-9b3e584dadd9.metadata.json} (85%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{00001-b862312a-2206-4077-8bb8-cbf6c6e2b53a.metadata.json => 00001-e78c2066-f1ab-4b8b-adb2-38b3470d05ad.metadata.json} (61%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{00002-f0a06ef5-e43b-45a3-845c-c66466ac7d83.metadata.json => 00002-29eac68a-a4f0-4ac9-9cce-852bb885ebb5.metadata.json} (53%) delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00003-4a7176f5-6b42-4e78-89c0-4d9ff6e402d8.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00003-d211893d-4899-4293-a3d4-319f62f14c74.metadata.json rename examples/idsse_warehouse/tracking/match_metadata/metadata/{540bf63c-5ea6-4c6d-b543-c7012c9be4c7-m0.avro => 2d702eea-f69f-42e3-942b-7052e0b73cd6-m0.avro} (92%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{e501b09a-d22e-4e7e-9606-c1ea5899be89-m0.avro => 62719280-2ab0-4ae1-ba75-d57e508139d6-m0.avro} (93%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{63524640-981e-48a3-b912-da48df7f23e7-m0.avro => 8c35a04a-3f37-4fe4-9d28-d33b25ff80bf-m0.avro} (92%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{snap-7979098971138712334-0-540bf63c-5ea6-4c6d-b543-c7012c9be4c7.avro => snap-6306903004315971787-0-62719280-2ab0-4ae1-ba75-d57e508139d6.avro} (77%) rename examples/idsse_warehouse/tracking/{tracking_data/metadata/snap-4709260392851115659-0-1c479d52-bc34-4f1d-a57d-7f1624b94057.avro => match_metadata/metadata/snap-6532061344649095069-0-8c35a04a-3f37-4fe4-9d28-d33b25ff80bf.avro} (73%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{snap-4608038256352783609-0-63524640-981e-48a3-b912-da48df7f23e7.avro => snap-8201725522326604393-0-2d702eea-f69f-42e3-942b-7052e0b73cd6.avro} (83%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{00000-48856661-51f9-42ff-b5ca-2617cf51350c.metadata.json => 00000-36633328-e8e4-41cb-b920-bd851aadf1c1.metadata.json} (91%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{00001-08c539b1-d7b2-434e-b5e2-6d9e732d9494.metadata.json => 00001-ed728451-0fac-4795-b9f9-7b786ee6fc78.metadata.json} (52%) create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00002-1af24f6c-2df4-4602-ba11-f6b72b778e9b.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00002-8be271f0-d101-490a-9d46-11af2d07f6ad.metadata.json create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00003-47b68db2-12df-4c75-8046-a6719215fa9a.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00003-e168fe8f-dc86-42b0-a991-fe9d25a19e5d.metadata.json rename examples/idsse_warehouse/tracking/tracking_data/metadata/{1c479d52-bc34-4f1d-a57d-7f1624b94057-m0.avro => 17225286-c948-44c6-8594-1fb34271f9ba-m0.avro} (76%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{0406ab25-5c95-4703-b985-e2a8252009d8-m0.avro => 1b21427f-2ee9-4e4c-9e70-94eb1b10fbd5-m0.avro} (75%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{49947273-2bcb-44c7-8273-f5084c3de7bb-m0.avro => de5280f4-97b4-4cb5-8394-b034ba16c73a-m0.avro} (76%) create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-1893932036032482558-0-1b21427f-2ee9-4e4c-9e70-94eb1b10fbd5.avro rename examples/idsse_warehouse/tracking/{match_metadata/metadata/snap-5888034176243899453-0-e501b09a-d22e-4e7e-9606-c1ea5899be89.avro => tracking_data/metadata/snap-3556048861821001954-0-de5280f4-97b4-4cb5-8394-b034ba16c73a.avro} (75%) delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-570726978145143711-0-49947273-2bcb-44c7-8273-f5084c3de7bb.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-6329113174899808939-0-17225286-c948-44c6-8594-1fb34271f9ba.avro delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-7148257198741218111-0-0406ab25-5c95-4703-b985-e2a8252009d8.avro diff --git a/examples/08-idsse-warehouse.ipynb b/examples/08-idsse-warehouse.ipynb index dce2cfc..f995710 100644 --- a/examples/08-idsse-warehouse.ipynb +++ b/examples/08-idsse-warehouse.ipynb @@ -138,15 +138,15 @@ "text": [ "\n", "Processing match DFL-MAT-J03WMX...\n", - " ✅ Parsed in 1.46s, Saved in 4.83s\n", + " ✅ Parsed in 1.37s, Saved in 4.53s\n", " ✅ Rows: 145,967\n", "\n", "Processing match DFL-MAT-J03WN1...\n", - " ✅ Parsed in 1.34s, Saved in 4.39s\n", + " ✅ Parsed in 1.28s, Saved in 3.89s\n", " ✅ Rows: 141,561\n", "\n", "Processing match DFL-MAT-J03WOH...\n", - " ✅ Parsed in 1.29s, Saved in 3.86s\n", + " ✅ Parsed in 1.21s, Saved in 3.93s\n", " ✅ Rows: 137,214\n" ] } @@ -197,30 +197,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Warehouse Summary:\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "Warehouse Summary:\n", + " - Match DFL-MAT-J03WMX: 165,259 rows\n", + " - Match DFL-MAT-J03WN1: 162,273 rows\n", + " - Match DFL-MAT-J03WOH: 157,790 rows\n", "\n", - "thread '' (21731647) panicked at crates/tdc-core/src/datacontainer/core.rs:661:38:\n", - "called `Result::unwrap()` on an `Err` value: VariantNotFound\n", - "note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace\n" - ] - }, - { - "ename": "PanicException", - "evalue": "called `Result::unwrap()` on an `Err` value: VariantNotFound", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mPanicException\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 6\u001b[39m lazy_container = catalog.load_container(match_id=m_id)\n\u001b[32m 8\u001b[39m \u001b[38;5;66;03m# Execute plan and pivot to wide format\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m loaded = \u001b[43mlazy_container\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 11\u001b[39m rows = loaded.num_rows()\n\u001b[32m 12\u001b[39m total_rows += rows\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/python/tracking_data_container/catalog.py:653\u001b[39m, in \u001b[36mCatalogLazyTrackingDataContainer.collect\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 648\u001b[39m arrow_table = scan.to_arrow()\n\u001b[32m 650\u001b[39m \u001b[38;5;66;03m# No more redundant renaming here! Rust pivot handles snake_case now.\u001b[39;00m\n\u001b[32m 651\u001b[39m \n\u001b[32m 652\u001b[39m \u001b[38;5;66;03m# 4. Pivot to wide format using high-performance Rust implementation\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m653\u001b[39m wide_container = \u001b[43mTrackingDataContainer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpivot\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrow_table\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 654\u001b[39m wide_container._catalog = \u001b[38;5;28mself\u001b[39m._catalog\n\u001b[32m 656\u001b[39m \u001b[38;5;66;03m# Apply final selection/slice\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/python/tracking_data_container/container.py:376\u001b[39m, in \u001b[36mTrackingDataContainer.pivot\u001b[39m\u001b[34m(cls, df)\u001b[39m\n\u001b[32m 373\u001b[39m df = pa.RecordBatch.from_arrays(df.flatten(), names=[f.name \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m df.type])\n\u001b[32m 375\u001b[39m \u001b[38;5;66;03m# Use the underlying Rust implementation for performance\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m376\u001b[39m inner = \u001b[43m_TrackingDataContainer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpivot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 377\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m TrackingDataContainer(inner, validate=\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "\u001b[31mPanicException\u001b[39m: called `Result::unwrap()` on an `Err` value: VariantNotFound" + "Total rows in warehouse: 485,322\n" ] } ], @@ -252,9 +234,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for DFL-MAT-J03WMX...\n", + " ✅ Loaded and pivoted 165259 rows in 1.8728s\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (5, 424)
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nullnull"DFL-MAT-J03WMX"10000"first_half"2023-05-27 13:30:12.600"DFL-OBJ-J01APO""away"-13.825.719999null0.00.0"DFL-OBJ-J01APO|away"nullnullnullnullnullnullnullnullnullnullnullnullnullnull"DFL-OBJ-00012X""home"13.08-13.71null0.00.0"DFL-OBJ-00012X|home"null…"DFL-OBJ-0027G0|away"nullnullnull"team:away""away"nullnullnullnullnull"team:away|away""FC Bayern München""DFL-CLU-00000G""#25282A"nullnullnullnullnullnullnullnullnullnullnull"DFL-OBJ-000270""home"-2.12-7.09null0.00.0"DFL-OBJ-000270|home"nullnullnull
nullnull"DFL-MAT-J03WMX"10001"first_half"2023-05-27 13:30:12.640"DFL-OBJ-J01APO""away"-13.7825.74null2.591.23"DFL-OBJ-J01APO|away"nullnullnullnullnullnullnullnullnullnullnullnullnullnull"DFL-OBJ-00012X""home"13.06-13.75null3.491.4"DFL-OBJ-00012X|home"null…"DFL-OBJ-0027G0|away"nullnullnull"team:away""away"nullnullnullnullnull"team:away|away""FC Bayern München""DFL-CLU-00000G""#25282A"nullnullnullnullnullnullnullnullnullnullnull"DFL-OBJ-000270""home"-2.28-6.99null17.452.6"DFL-OBJ-000270|home"nullnullnull
nullnull"DFL-MAT-J03WMX"10002"first_half"2023-05-27 13:30:12.680"DFL-OBJ-J01APO""away"-13.7525.76null2.761.08"DFL-OBJ-J01APO|away"nullnullnullnullnullnullnullnullnullnullnullnullnullnull"DFL-OBJ-00012X""home"13.05-13.78null3.731.34"DFL-OBJ-00012X|home"null…"DFL-OBJ-0027G0|away"nullnullnull"team:away""away"nullnullnullnullnull"team:away|away""FC Bayern München""DFL-CLU-00000G""#25282A"nullnullnullnullnullnullnullnullnullnullnull"DFL-OBJ-000270""home"-2.44-6.88null17.822.52"DFL-OBJ-000270|home"nullnullnull
nullnull"DFL-MAT-J03WMX"10003"first_half"2023-05-27 13:30:12.720"DFL-OBJ-J01APO""away"-13.7325.780001null2.890.91"DFL-OBJ-J01APO|away"nullnullnullnullnullnullnullnullnullnullnullnullnullnull"DFL-OBJ-00012X""home"13.03-13.82null3.951.24"DFL-OBJ-00012X|home"null…"DFL-OBJ-0027G0|away"nullnullnull"team:away""away"nullnullnullnullnull"team:away|away""FC Bayern München""DFL-CLU-00000G""#25282A"nullnullnullnullnullnullnullnullnullnullnull"DFL-OBJ-000270""home"-2.61-6.78null18.192.44"DFL-OBJ-000270|home"nullnullnull
nullnull"DFL-MAT-J03WMX"10004"first_half"2023-05-27 13:30:12.760"DFL-OBJ-J01APO""away"-13.7125.809999null3.00.72"DFL-OBJ-J01APO|away"nullnullnullnullnullnullnullnullnullnullnullnullnullnull"DFL-OBJ-00012X""home"13.02-13.87null4.131.11"DFL-OBJ-00012X|home"null…"DFL-OBJ-0027G0|away"nullnullnull"team:away""away"nullnullnullnullnull"team:away|away""FC Bayern München""DFL-CLU-00000G""#25282A"nullnullnullnullnullnullnullnullnullnullnull"DFL-OBJ-000270""home"-2.78-6.66null18.542.35"DFL-OBJ-000270|home"nullnullnull
" + ], + "text/plain": [ + "shape: (5, 424)\n", + "┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", + "│ competiti ┆ season/id ┆ match/id ┆ frame_id ┆ … ┆ teams/hom ┆ teams/hom ┆ teams/hom ┆ teams/hom │\n", + "│ on/id ┆ --- ┆ --- ┆ --- ┆ ┆ e/players ┆ e/players ┆ e/players ┆ e/players │\n", + "│ --- ┆ str ┆ str ┆ i64 ┆ ┆ /DFL-OBJ- ┆ /DFL-OBJ- ┆ /DFL-OBJ- ┆ /DFL-OBJ- │\n", + "│ str ┆ ┆ ┆ ┆ ┆ 000… ┆ 000… ┆ 000… ┆ 000… │\n", + "│ ┆ ┆ ┆ ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ str ┆ str ┆ str ┆ str │\n", + "╞═══════════╪═══════════╪═══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10000 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00270|hom ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ e ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10001 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00270|hom ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ e ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10002 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00270|hom ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ e ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10003 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00270|hom ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ e ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10004 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00270|hom ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ e ┆ ┆ ┆ │\n", + "└───────────┴───────────┴───────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "target_match = pairs[0]['match_id']\n", "\n", @@ -271,9 +306,82 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'_entities': [{'entity_id': 'DFL-OBJ-J01N65', 'team_id': 'away'},\n", + " {'entity_id': 'DFL-OBJ-002G4A', 'team_id': 'home'},\n", + " {'entity_id': 'DFL-OBJ-J01D1W', 'team_id': 'home'},\n", + " {'entity_id': 'referee:DFL-OBJ-000141', 'team_id': None},\n", + " {'entity_id': 'DFL-OBJ-002G89', 'team_id': 'home'},\n", + " {'entity_id': 'DFL-OBJ-0002BO', 'team_id': 'home'},\n", + " {'entity_id': 'DFL-OBJ-0002F5', 'team_id': 'away'},\n", + " {'entity_id': 'DFL-OBJ-0027B6', 'team_id': 'home'},\n", + " {'entity_id': 'DFL-OBJ-0002AU', 'team_id': 'away'},\n", + " {'entity_id': 'DFL-OBJ-0026ZI', 'team_id': 'home'},\n", + " {'entity_id': 'DFL-OBJ-000191', 'team_id': 'away'},\n", + " {'entity_id': 'DFL-OBJ-0000RP', 'team_id': 'away'},\n", + " {'entity_id': 'DFL-OBJ-0002HE', 'team_id': 'home'},\n", + " {'entity_id': 'DFL-OBJ-0027KL', 'team_id': 'away'},\n", + " {'entity_id': 'referee:DFL-OBJ-00011W', 'team_id': None},\n", + " {'entity_id': 'DFL-OBJ-0000IA', 'team_id': 'away'},\n", + " {'entity_id': 'DFL-OBJ-J01B8N', 'team_id': 'away'},\n", + " {'entity_id': 'DFL-OBJ-J015S4', 'team_id': 'away'},\n", + " {'entity_id': 'DFL-OBJ-00012X', 'team_id': 'home'},\n", + " {'entity_id': 'DFL-OBJ-0027AX', 'team_id': 'home'},\n", + " {'entity_id': 'DFL-OBJ-0027G6', 'team_id': 'away'},\n", + " {'entity_id': 'DFL-OBJ-J017RE', 'team_id': 'away'},\n", + " {'entity_id': 'team:away', 'team_id': 'away'},\n", + " {'entity_id': 'DFL-OBJ-002GCR', 'team_id': 'away'},\n", + " {'entity_id': 'DFL-OBJ-000270', 'team_id': 'home'},\n", + " {'entity_id': 'referee:DFL-OBJ-00011J', 'team_id': None},\n", + " {'entity_id': 'DFL-OBJ-0028BD', 'team_id': 'home'},\n", + " {'entity_id': 'DFL-OBJ-0027V2', 'team_id': 'home'},\n", + " {'entity_id': 'DFL-OBJ-0026PM', 'team_id': 'away'},\n", + " {'entity_id': 'ball', 'team_id': None},\n", + " {'entity_id': 'DFL-OBJ-002GBW', 'team_id': 'home'},\n", + " {'entity_id': 'team:home', 'team_id': 'home'},\n", + " {'entity_id': 'DFL-OBJ-0002DR', 'team_id': 'away'},\n", + " {'entity_id': 'DFL-OBJ-J01APO', 'team_id': 'away'},\n", + " {'entity_id': 'DFL-OBJ-002GKO', 'team_id': 'home'},\n", + " {'entity_id': 'DFL-OBJ-J01BGM', 'team_id': 'home'},\n", + " {'entity_id': 'DFL-OBJ-J01DVC', 'team_id': 'home'},\n", + " {'entity_id': 'DFL-OBJ-0027G0', 'team_id': 'away'}]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metadata = catalog.get_metadata(match_id=target_match)\n", + "metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "No such file or directory (os error 2): ...rehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet (set POLARS_VERBOSE=1 to see full path)\n\nThis error occurred with the following context stack:\n\t[1] 'parquet scan'\n\t[2] 'sink'\n", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m start = time.perf_counter()\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m df = \u001b[43mpl\u001b[49m\u001b[43m.\u001b[49m\u001b[43mscan_parquet\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43midsse_warehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m query_time = time.perf_counter() - start\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m ✅ Loaded and pivoted \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(df)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m rows in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquery_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33ms\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/_utils/deprecation.py:97\u001b[39m, in \u001b[36mdeprecate_streaming_parameter..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33mengine\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33min-memory\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m kwargs[\u001b[33m\"\u001b[39m\u001b[33mstreaming\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m---> \u001b[39m\u001b[32m97\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/lazyframe/opt_flags.py:328\u001b[39m, in \u001b[36mforward_old_opt_flags..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 325\u001b[39m optflags = cb(optflags, kwargs.pop(key)) \u001b[38;5;66;03m# type: ignore[no-untyped-call,unused-ignore]\u001b[39;00m\n\u001b[32m 327\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33moptimizations\u001b[39m\u001b[33m\"\u001b[39m] = optflags\n\u001b[32m--> \u001b[39m\u001b[32m328\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/lazyframe/frame.py:2429\u001b[39m, in \u001b[36mLazyFrame.collect\u001b[39m\u001b[34m(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, cluster_with_columns, collapse_joins, no_optimization, engine, background, optimizations, **_kwargs)\u001b[39m\n\u001b[32m 2427\u001b[39m \u001b[38;5;66;03m# Only for testing purposes\u001b[39;00m\n\u001b[32m 2428\u001b[39m callback = _kwargs.get(\u001b[33m\"\u001b[39m\u001b[33mpost_opt_callback\u001b[39m\u001b[33m\"\u001b[39m, callback)\n\u001b[32m-> \u001b[39m\u001b[32m2429\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m wrap_df(\u001b[43mldf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[43m)\u001b[49m)\n", + "\u001b[31mFileNotFoundError\u001b[39m: No such file or directory (os error 2): ...rehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet (set POLARS_VERBOSE=1 to see full path)\n\nThis error occurred with the following context stack:\n\t[1] 'parquet scan'\n\t[2] 'sink'\n" + ] + } + ], "source": [ "start = time.perf_counter()\n", "\n", diff --git a/examples/idsse_warehouse/catalog.db b/examples/idsse_warehouse/catalog.db index 346745a2df576e812f2668a870b2c4e72dcd571a..5dd3c79decc78f46e971b2ab6380ffcc5e3468e4 100644 GIT binary patch delta 178 zcmWN~y$ypv3_xMAKsEpxT1#i+yR$dQ8utBkX;V|DrAx&KNEs%3WP;E?eQ!I4?HJDI zxN!C))d`Sl2QE2+1WsTnqY}n$YWsV9Eohy%rvg{Uz$p;8Awo}{$#a&bzTcw3h6T-|ydmHJrEI(Na^>rlVp6B!*!RCLsOk?ytlBb$H)S zbC76;CiqYRE-QldPLR>PRHGi%x990G>l&p|A~+!dcQokE0z{U@v89-Am-BaKYt$qw x$jSvcnu7L$z}_<@7>nyZUbpvjo)Zlvb{xE{z||H|t^U5NxQauy3l_wURuvKEhQnr;`o$l zGbd*)UB6yFYED$hn)rYz^QTOSnlfunV9=WPlcJ{0njAkNYDs+1lo@m8&YUhce^SIG 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"1"], "ball/x": [0.0, 1.0], "ball/y": [0.0, 1.0], + "teams/home/players/p1/x": [10.0, 11.0], + "teams/home/players/p1/y": [20.0, 21.0], + "teams/home/players/p1/jersey_color": ["red", "red"], + "teams/home/players/p1/name": ["Player 1", "Player 1"], } - # Note: player/id and team/id will be automatically added by melt() for the ball container = tdc.TrackingDataContainer(data, validate=False) - # Save container + # Save container - normalization should happen here catalog.save_container(container) + # Verify normalization: physical table should not have jersey_color + table = catalog._iceberg_catalog.load_table("tracking.tracking_data") + physical_cols = [f.name for f in table.schema().fields] + assert "jersey_color" not in physical_cols + assert "name" not in physical_cols + # Load container lazy_loaded = catalog.load_container(match_id="match_1") assert isinstance(lazy_loaded, tdc.LazyTrackingDataContainer) + # Schema should include normalized columns + schema_names = lazy_loaded.schema().names() + assert "teams/home/players/p1/jersey_color" in schema_names + assert "teams/home/players/p1/name" in schema_names + loaded = lazy_loaded.collect() assert loaded.num_rows() == 2 assert loaded.is_bound() @@ -32,10 +47,13 @@ def test_catalog_lifecycle(tmp_path): # Check data df = loaded.to_polars() assert df["match/id"][0] == "match_1" - # player/id was an input, and stays as player/id in wide format assert "ball/player/id" in df.columns assert df["ball/x"][0] == 0.0 + # Check normalized constants + assert df["teams/home/players/p1/jersey_color"][0] == "red" + assert df["teams/home/players/p1/name"][0] == "Player 1" + def test_bound_container_save(tmp_path): warehouse_path = tmp_path / "warehouse" diff --git a/python/tracking_data_container/catalog.py b/python/tracking_data_container/catalog.py index 0ec61aa..e08f70f 100644 --- a/python/tracking_data_container/catalog.py +++ b/python/tracking_data_container/catalog.py @@ -237,7 +237,7 @@ def save_container( else: raise ValueError("match_id must be provided if not present in container data") - # 1. Save tracking data and get unique entities + # 1. Save tracking data and get unique entities (with constant attributes) entities = self._save_tracking_data(container, match_id) # 2. Prepare metadata @@ -290,12 +290,12 @@ def _save_tracking_data( self, container: TrackingDataContainer, match_id: str, - ) -> list[dict[str, str]]: + ) -> list[dict[str, Any]]: """ Save tracking data to Iceberg table in entity-long format. Returns: - List of unique entities (dict with entity_id, team_id) + List of unique entities (dict with entity_id, team_id, and constants) """ if self._tracking_table is None: self._tracking_table = self._iceberg_catalog.load_table("tracking.tracking_data") @@ -324,8 +324,51 @@ def _save_tracking_data( # Ensure match_id is present and correct long_df = long_df.with_columns(pl.lit(match_id).alias("match_id")) - # Extract unique entities before saving - entities = long_df.select(["entity_id", "team_id"]).unique().to_dicts() + # --- Constant Extraction --- + # Identify attributes that are constant for each entity + # These will be stored in metadata instead of the physical table + ignore_for_constants = { + "match_id", + "frame_id", + "period", + "timestamp", + "entity_type", + "entity_id", + "team_id", + "x", + "y", + "z", + "entity_key", + } + potential_constants = [c for c in long_df.columns if c not in ignore_for_constants] + + entities_with_constants = [] + if potential_constants: + # Group by entity and check uniqueness of potential constants + agg_exprs = [pl.col(c).n_unique().alias(f"{c}_count") for c in potential_constants] + agg_vals = [pl.col(c).first().alias(f"{c}_val") for c in potential_constants] + + stats = long_df.group_by(["entity_id", "team_id"]).agg(agg_exprs + agg_vals) + + # We'll drop columns from the physical table if they are constant for ALL entities + columns_to_drop = [] + for c in potential_constants: + if stats[f"{c}_count"].max() == 1: + columns_to_drop.append(c) + + # Prepare the entity list for metadata + # For each entity, we store the values of the dropped columns + entities_with_constants = ( + stats.select(["entity_id", "team_id"] + [f"{c}_val" for c in columns_to_drop]) + .rename({f"{c}_val": c for c in columns_to_drop}) + .to_dicts() + ) + + if columns_to_drop: + logger.info(f"Normalizing constant attributes to metadata: {columns_to_drop}") + long_df = long_df.drop(columns_to_drop) + else: + entities_with_constants = long_df.select(["entity_id", "team_id"]).unique().to_dicts() # 3. Validation of required fields required_fields = [ @@ -341,8 +384,8 @@ def _save_tracking_data( for field in required_fields: if field not in long_df.columns: raise ValueError(f"Required field {field} is missing from container") - # if long_df[field].null_count() > 0: - # raise ValueError(f"Required field {field} contains null values") + if long_df[field].null_count() > 0: + raise ValueError(f"Required field {field} contains null values") # 4. Convert to Arrow and handle types arrow_table = long_df.to_arrow() @@ -388,7 +431,7 @@ def _save_tracking_data( # Append long-format data txn.append(arrow_table) - return entities + return entities_with_constants def _save_metadata(self, metadata: dict, match_id: str) -> None: """Save metadata to Iceberg table.""" @@ -467,7 +510,7 @@ def schema(self) -> Fields: assert self._catalog._tracking_table is not None - # Load entities from metadata (Strictly no fallback scan) + # Load entities from metadata metadata = self._catalog.get_metadata(self._match_id) entities = metadata.get("_entities") @@ -488,7 +531,7 @@ def schema(self) -> Fields: "period_id", "entity_key", } - # Anything else is an attribute + # Physical attributes in the tracking table attr_cols = [c for c in physical_names if c not in ignored and c != "id"] # Base list: all core columns that are NOT entity-specific @@ -505,9 +548,16 @@ def schema(self) -> Fields: t_id = str(row.get("team_id", "none")) prefix = TrackedEntity.from_entity_key(f"{p_id}|{t_id}").cdf_prefix() + # 1. Attributes from physical table for attr in attr_cols: wide_names.append(f"{prefix}{attr}") - # Map back entity_id and team_id as attributes with standard CDF names + + # 2. Attributes from metadata constants + for attr in row.keys(): + if attr not in ("entity_id", "team_id"): + wide_names.append(f"{prefix}{attr}") + + # 3. Standard CDF names wide_names.append(f"{prefix}player/id") wide_names.append(f"{prefix}team/id") @@ -562,6 +612,9 @@ def collect(self) -> TrackingDataContainer: selected_long_fields = None deferred_select = None + metadata = self._catalog.get_metadata(self._match_id) + entities_metadata = metadata.get("_entities", []) + if self._selection: eager_schema = self.schema() literal_names: list[str] = [] @@ -647,12 +700,31 @@ def collect(self) -> TrackingDataContainer: scan = self._catalog._tracking_table.scan(**scan_kwargs) arrow_table = scan.to_arrow() - # No more redundant renaming here! Rust pivot handles snake_case now. - # 4. Pivot to wide format using high-performance Rust implementation wide_container = TrackingDataContainer.pivot(arrow_table) wide_container._catalog = self._catalog + # 5. Inject constant attributes from metadata + if entities_metadata: + # Reconstruct prefixes for entities in metadata + entity_map = { + f"{e['entity_id']}|{e.get('team_id', 'none')}": e for e in entities_metadata + } + + wide_names = wide_container.schema().names() + for ek, ent_data in entity_map.items(): + prefix = TrackedEntity.from_entity_key(ek).cdf_prefix() + for attr, value in ent_data.items(): + if attr in ("entity_id", "team_id"): + continue + + wide_attr_name = f"{prefix}{attr}" + if wide_attr_name not in wide_names: + # Add constant column + wide_container = wide_container.add_column( + wide_attr_name, [value] * wide_container.num_rows() + ) + # Apply final selection/slice if deferred_select: wide_container = wide_container.select(deferred_select) From 220c17304804f932e801fe61f9088beee655386f Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 15:31:27 +0200 Subject: [PATCH 11/21] feat: allow null values in x, y, and z fields in catalog and pivot --- crates/tdc-core/src/datacontainer/core.rs | 8 - examples/08-idsse-warehouse.ipynb | 170 +++--------------- examples/idsse_warehouse/catalog.db | Bin 20480 -> 20480 bytes ...7d68-44e1-b7c9-ca6e892cf286.metadata.json} | 2 +- ...-f1ab-4b8b-adb2-38b3470d05ad.metadata.json | 1 - ...-a4f0-4ac9-9cce-852bb885ebb5.metadata.json | 1 - ...-4899-4293-a3d4-319f62f14c74.metadata.json | 1 - ...702eea-f69f-42e3-942b-7052e0b73cd6-m0.avro | Bin 4511 -> 0 bytes ...719280-2ab0-4ae1-ba75-d57e508139d6-m0.avro | Bin 4510 -> 0 bytes ...35a04a-3f37-4fe4-9d28-d33b25ff80bf-m0.avro | Bin 4511 -> 0 bytes ...-62719280-2ab0-4ae1-ba75-d57e508139d6.avro | Bin 1898 -> 0 bytes ...-8c35a04a-3f37-4fe4-9d28-d33b25ff80bf.avro | Bin 1950 -> 0 bytes ...-2d702eea-f69f-42e3-942b-7052e0b73cd6.avro | Bin 1826 -> 0 bytes ...f108-4a16-b20b-39ce0ac55542.metadata.json} | 2 +- ...-0fac-4795-b9f9-7b786ee6fc78.metadata.json | 1 - ...-2df4-4602-ba11-f6b72b778e9b.metadata.json | 1 - ...-12df-4c75-8046-a6719215fa9a.metadata.json | 1 - ...225286-c948-44c6-8594-1fb34271f9ba-m0.avro | Bin 6385 -> 0 bytes ...21427f-2ee9-4e4c-9e70-94eb1b10fbd5-m0.avro | Bin 6431 -> 0 bytes ...5280f4-97b4-4cb5-8394-b034ba16c73a-m0.avro | Bin 6383 -> 0 bytes ...-1b21427f-2ee9-4e4c-9e70-94eb1b10fbd5.avro | Bin 1849 -> 0 bytes ...-de5280f4-97b4-4cb5-8394-b034ba16c73a.avro | Bin 1936 -> 0 bytes ...-17225286-c948-44c6-8594-1fb34271f9ba.avro | Bin 1998 -> 0 bytes python/tracking_data_container/catalog.py | 16 +- 24 files changed, 29 insertions(+), 175 deletions(-) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{00000-fb70b6ed-a7f1-472b-a66d-9b3e584dadd9.metadata.json => 00000-62e7e4e4-7d68-44e1-b7c9-ca6e892cf286.metadata.json} (85%) delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00001-e78c2066-f1ab-4b8b-adb2-38b3470d05ad.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00002-29eac68a-a4f0-4ac9-9cce-852bb885ebb5.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00003-d211893d-4899-4293-a3d4-319f62f14c74.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/2d702eea-f69f-42e3-942b-7052e0b73cd6-m0.avro delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/62719280-2ab0-4ae1-ba75-d57e508139d6-m0.avro delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/8c35a04a-3f37-4fe4-9d28-d33b25ff80bf-m0.avro delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/snap-6306903004315971787-0-62719280-2ab0-4ae1-ba75-d57e508139d6.avro delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/snap-6532061344649095069-0-8c35a04a-3f37-4fe4-9d28-d33b25ff80bf.avro delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/snap-8201725522326604393-0-2d702eea-f69f-42e3-942b-7052e0b73cd6.avro rename examples/idsse_warehouse/tracking/tracking_data/metadata/{00000-36633328-e8e4-41cb-b920-bd851aadf1c1.metadata.json => 00000-a67fd806-f108-4a16-b20b-39ce0ac55542.metadata.json} (91%) delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00001-ed728451-0fac-4795-b9f9-7b786ee6fc78.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00002-1af24f6c-2df4-4602-ba11-f6b72b778e9b.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00003-47b68db2-12df-4c75-8046-a6719215fa9a.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/17225286-c948-44c6-8594-1fb34271f9ba-m0.avro delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/1b21427f-2ee9-4e4c-9e70-94eb1b10fbd5-m0.avro delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/de5280f4-97b4-4cb5-8394-b034ba16c73a-m0.avro delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-1893932036032482558-0-1b21427f-2ee9-4e4c-9e70-94eb1b10fbd5.avro delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-3556048861821001954-0-de5280f4-97b4-4cb5-8394-b034ba16c73a.avro delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-6329113174899808939-0-17225286-c948-44c6-8594-1fb34271f9ba.avro diff --git a/crates/tdc-core/src/datacontainer/core.rs b/crates/tdc-core/src/datacontainer/core.rs index 7ba684b..003d46e 100644 --- a/crates/tdc-core/src/datacontainer/core.rs +++ b/crates/tdc-core/src/datacontainer/core.rs @@ -432,18 +432,10 @@ impl TrackingDataContainer { } // 2. Select the best frame index column and validate required fields - let has_x = fields.iter().any(|f| f.name() == "x"); - let has_y = fields.iter().any(|f| f.name() == "y"); let has_period = fields .iter() .any(|f| f.name() == "period" || f.name() == "period_id"); - if !has_x || !has_y { - return Err(TrackingDataContainerErrorKind::InvalidParameters( - "Missing required spatial columns x or y".to_string(), - ) - .into()); - } if !has_period { return Err(TrackingDataContainerErrorKind::InvalidParameters( "Missing required column period or period_id".to_string(), diff --git a/examples/08-idsse-warehouse.ipynb b/examples/08-idsse-warehouse.ipynb index f995710..3b61754 100644 --- a/examples/08-idsse-warehouse.ipynb +++ b/examples/08-idsse-warehouse.ipynb @@ -137,17 +137,20 @@ "output_type": "stream", "text": [ "\n", - "Processing match DFL-MAT-J03WMX...\n", - " ✅ Parsed in 1.37s, Saved in 4.53s\n", - " ✅ Rows: 145,967\n", - "\n", - "Processing match DFL-MAT-J03WN1...\n", - " ✅ Parsed in 1.28s, Saved in 3.89s\n", - " ✅ Rows: 141,561\n", - "\n", - "Processing match DFL-MAT-J03WOH...\n", - " ✅ Parsed in 1.21s, Saved in 3.93s\n", - " ✅ Rows: 137,214\n" + "Processing match DFL-MAT-J03WMX...\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Required field x contains null values", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 18\u001b[39m\n\u001b[32m 16\u001b[39m \u001b[38;5;66;03m# Save to Warehouse (melts to long format internally)\u001b[39;00m\n\u001b[32m 17\u001b[39m start = time.perf_counter()\n\u001b[32m---> \u001b[39m\u001b[32m18\u001b[39m \u001b[43mcatalog\u001b[49m\u001b[43m.\u001b[49m\u001b[43msave_container\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontainer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 19\u001b[39m save_time = time.perf_counter() - start\n\u001b[32m 21\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m ✅ Parsed in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparse_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33ms, Saved in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msave_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33ms\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/python/tracking_data_container/catalog.py:241\u001b[39m, in \u001b[36mTrackingDataCatalog.save_container\u001b[39m\u001b[34m(self, container, match_id, metadata)\u001b[39m\n\u001b[32m 238\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mmatch_id must be provided if not present in container data\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 240\u001b[39m \u001b[38;5;66;03m# 1. Save tracking data and get unique entities (with constant attributes)\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m241\u001b[39m entities = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_save_tracking_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmatch_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 243\u001b[39m \u001b[38;5;66;03m# 2. Prepare metadata\u001b[39;00m\n\u001b[32m 244\u001b[39m final_metadata = metadata.copy() \u001b[38;5;28;01mif\u001b[39;00m metadata \u001b[38;5;28;01melse\u001b[39;00m {}\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/python/tracking_data_container/catalog.py:388\u001b[39m, in \u001b[36mTrackingDataCatalog._save_tracking_data\u001b[39m\u001b[34m(self, container, match_id)\u001b[39m\n\u001b[32m 386\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mRequired field \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfield\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m is missing from container\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m long_df[field].null_count() > \u001b[32m0\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m388\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mRequired field \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfield\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m contains null values\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 390\u001b[39m \u001b[38;5;66;03m# 4. Convert to Arrow and handle types\u001b[39;00m\n\u001b[32m 391\u001b[39m arrow_table = long_df.to_arrow()\n", + "\u001b[31mValueError\u001b[39m: Required field x contains null values" ] } ], @@ -190,22 +193,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warehouse Summary:\n", - " - Match DFL-MAT-J03WMX: 165,259 rows\n", - " - Match DFL-MAT-J03WN1: 162,273 rows\n", - " - Match DFL-MAT-J03WOH: 157,790 rows\n", - "\n", - "Total rows in warehouse: 485,322\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Warehouse Summary:\")\n", "total_rows = 0\n", @@ -234,62 +224,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading data for DFL-MAT-J03WMX...\n", - " ✅ Loaded and pivoted 165259 rows in 1.8728s\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "shape: (5, 424)
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nullnull"DFL-MAT-J03WMX"10001"first_half"2023-05-27 13:30:12.640"DFL-OBJ-J01APO""away"-13.7825.74null2.591.23"DFL-OBJ-J01APO|away"nullnullnullnullnullnullnullnullnullnullnullnullnullnull"DFL-OBJ-00012X""home"13.06-13.75null3.491.4"DFL-OBJ-00012X|home"null…"DFL-OBJ-0027G0|away"nullnullnull"team:away""away"nullnullnullnullnull"team:away|away""FC Bayern München""DFL-CLU-00000G""#25282A"nullnullnullnullnullnullnullnullnullnullnull"DFL-OBJ-000270""home"-2.28-6.99null17.452.6"DFL-OBJ-000270|home"nullnullnull
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" - ], - "text/plain": [ - "shape: (5, 424)\n", - "┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", - "│ competiti ┆ season/id ┆ match/id ┆ frame_id ┆ … ┆ teams/hom ┆ teams/hom ┆ teams/hom ┆ teams/hom │\n", - "│ on/id ┆ --- ┆ --- ┆ --- ┆ ┆ e/players ┆ e/players ┆ e/players ┆ e/players │\n", - "│ --- ┆ str ┆ str ┆ i64 ┆ ┆ /DFL-OBJ- ┆ /DFL-OBJ- ┆ /DFL-OBJ- ┆ /DFL-OBJ- │\n", - "│ str ┆ ┆ ┆ ┆ ┆ 000… ┆ 000… ┆ 000… ┆ 000… │\n", - "│ ┆ ┆ ┆ ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ ┆ ┆ ┆ ┆ str ┆ str ┆ str ┆ str │\n", - "╞═══════════╪═══════════╪═══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10000 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00270|hom ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ e ┆ ┆ ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10001 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00270|hom ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ e ┆ ┆ ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10002 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00270|hom ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ e ┆ ┆ ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10003 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00270|hom ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ e ┆ ┆ ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10004 ┆ … ┆ DFL-OBJ-0 ┆ null ┆ null ┆ null │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00270|hom ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ e ┆ ┆ ┆ │\n", - "└───────────┴───────────┴───────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "target_match = pairs[0]['match_id']\n", "\n", @@ -306,57 +243,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'_entities': [{'entity_id': 'DFL-OBJ-J01N65', 'team_id': 'away'},\n", - " {'entity_id': 'DFL-OBJ-002G4A', 'team_id': 'home'},\n", - " {'entity_id': 'DFL-OBJ-J01D1W', 'team_id': 'home'},\n", - " {'entity_id': 'referee:DFL-OBJ-000141', 'team_id': None},\n", - " {'entity_id': 'DFL-OBJ-002G89', 'team_id': 'home'},\n", - " {'entity_id': 'DFL-OBJ-0002BO', 'team_id': 'home'},\n", - " {'entity_id': 'DFL-OBJ-0002F5', 'team_id': 'away'},\n", - " {'entity_id': 'DFL-OBJ-0027B6', 'team_id': 'home'},\n", - " {'entity_id': 'DFL-OBJ-0002AU', 'team_id': 'away'},\n", - " {'entity_id': 'DFL-OBJ-0026ZI', 'team_id': 'home'},\n", - " {'entity_id': 'DFL-OBJ-000191', 'team_id': 'away'},\n", - " {'entity_id': 'DFL-OBJ-0000RP', 'team_id': 'away'},\n", - " {'entity_id': 'DFL-OBJ-0002HE', 'team_id': 'home'},\n", - " {'entity_id': 'DFL-OBJ-0027KL', 'team_id': 'away'},\n", - " {'entity_id': 'referee:DFL-OBJ-00011W', 'team_id': None},\n", - " {'entity_id': 'DFL-OBJ-0000IA', 'team_id': 'away'},\n", - " {'entity_id': 'DFL-OBJ-J01B8N', 'team_id': 'away'},\n", - " {'entity_id': 'DFL-OBJ-J015S4', 'team_id': 'away'},\n", - " {'entity_id': 'DFL-OBJ-00012X', 'team_id': 'home'},\n", - " {'entity_id': 'DFL-OBJ-0027AX', 'team_id': 'home'},\n", - " {'entity_id': 'DFL-OBJ-0027G6', 'team_id': 'away'},\n", - " {'entity_id': 'DFL-OBJ-J017RE', 'team_id': 'away'},\n", - " {'entity_id': 'team:away', 'team_id': 'away'},\n", - " {'entity_id': 'DFL-OBJ-002GCR', 'team_id': 'away'},\n", - " {'entity_id': 'DFL-OBJ-000270', 'team_id': 'home'},\n", - " {'entity_id': 'referee:DFL-OBJ-00011J', 'team_id': None},\n", - " {'entity_id': 'DFL-OBJ-0028BD', 'team_id': 'home'},\n", - " {'entity_id': 'DFL-OBJ-0027V2', 'team_id': 'home'},\n", - " {'entity_id': 'DFL-OBJ-0026PM', 'team_id': 'away'},\n", - " {'entity_id': 'ball', 'team_id': None},\n", - " {'entity_id': 'DFL-OBJ-002GBW', 'team_id': 'home'},\n", - " {'entity_id': 'team:home', 'team_id': 'home'},\n", - " {'entity_id': 'DFL-OBJ-0002DR', 'team_id': 'away'},\n", - " {'entity_id': 'DFL-OBJ-J01APO', 'team_id': 'away'},\n", - " {'entity_id': 'DFL-OBJ-002GKO', 'team_id': 'home'},\n", - " {'entity_id': 'DFL-OBJ-J01BGM', 'team_id': 'home'},\n", - " {'entity_id': 'DFL-OBJ-J01DVC', 'team_id': 'home'},\n", - " {'entity_id': 'DFL-OBJ-0027G0', 'team_id': 'away'}]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "metadata = catalog.get_metadata(match_id=target_match)\n", "metadata" @@ -364,24 +253,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "No such file or directory (os error 2): ...rehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet (set POLARS_VERBOSE=1 to see full path)\n\nThis error occurred with the following context stack:\n\t[1] 'parquet scan'\n\t[2] 'sink'\n", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m start = time.perf_counter()\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m df = \u001b[43mpl\u001b[49m\u001b[43m.\u001b[49m\u001b[43mscan_parquet\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43midsse_warehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m query_time = time.perf_counter() - start\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m ✅ Loaded and pivoted \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(df)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m rows in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquery_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33ms\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/_utils/deprecation.py:97\u001b[39m, in \u001b[36mdeprecate_streaming_parameter..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33mengine\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33min-memory\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m kwargs[\u001b[33m\"\u001b[39m\u001b[33mstreaming\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m---> \u001b[39m\u001b[32m97\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/lazyframe/opt_flags.py:328\u001b[39m, in \u001b[36mforward_old_opt_flags..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 325\u001b[39m optflags = cb(optflags, kwargs.pop(key)) \u001b[38;5;66;03m# type: ignore[no-untyped-call,unused-ignore]\u001b[39;00m\n\u001b[32m 327\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33moptimizations\u001b[39m\u001b[33m\"\u001b[39m] = optflags\n\u001b[32m--> \u001b[39m\u001b[32m328\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/lazyframe/frame.py:2429\u001b[39m, in \u001b[36mLazyFrame.collect\u001b[39m\u001b[34m(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, cluster_with_columns, collapse_joins, no_optimization, engine, background, optimizations, **_kwargs)\u001b[39m\n\u001b[32m 2427\u001b[39m \u001b[38;5;66;03m# Only for testing purposes\u001b[39;00m\n\u001b[32m 2428\u001b[39m callback = _kwargs.get(\u001b[33m\"\u001b[39m\u001b[33mpost_opt_callback\u001b[39m\u001b[33m\"\u001b[39m, callback)\n\u001b[32m-> \u001b[39m\u001b[32m2429\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m wrap_df(\u001b[43mldf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[43m)\u001b[49m)\n", - "\u001b[31mFileNotFoundError\u001b[39m: No such file or directory (os error 2): ...rehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet (set POLARS_VERBOSE=1 to see full path)\n\nThis error occurred with the following context stack:\n\t[1] 'parquet scan'\n\t[2] 'sink'\n" - ] - } - ], + "outputs": [], "source": [ "start = time.perf_counter()\n", "\n", diff --git a/examples/idsse_warehouse/catalog.db b/examples/idsse_warehouse/catalog.db index 5dd3c79decc78f46e971b2ab6380ffcc5e3468e4..146187d1168342f190f8d595e162fea0c295b762 100644 GIT binary patch delta 166 zcmZozz}T>Wae_1>>qHr6M%Il93;DSj7#NuN!Wj5<_`)^|DtPg6H3l=WOUr6DWiw1Z zpf5XlkzBQ%fv%ZRs(GqOs)??7ikXG3iAk!VZjyPjrEYSfS*nGlQF5A*g_&M%YDr>B zVo9Q2R&jn_V<02Y81<%1hROG2rl}d|CYqV2rC1o4>82SP0F6jAG}BEoGDy-jwoFbn RNK7_0H8nBX{7IhA001SpEg%2@ delta 291 zcmY+;Jx;?w5J2H|XpkHks0}s+0#Qul9gp{?<_;7zWOmmpg$O7}1wx9P3xF&k%5a1z zhu{FY2`9j94)C77v##$h>HExXm!Fu0AyaqJ2?g;+rod#y5TN+&i7wnQiG8n>5Yt 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StringType(), required=False), @@ -154,8 +154,8 @@ def _create_tracking_table(self) -> None: NestedField(7, "entity_type", StringType(), required=True), NestedField(8, "entity_id", StringType(), required=True), NestedField(9, "team_id", StringType(), required=False), - NestedField(10, "x", FloatType(), required=True), - NestedField(11, "y", FloatType(), required=True), + NestedField(10, "x", FloatType(), required=False), + NestedField(11, "y", FloatType(), required=False), NestedField(12, "z", FloatType(), required=False), ) @@ -326,7 +326,6 @@ def _save_tracking_data( # --- Constant Extraction --- # Identify attributes that are constant for each entity - # These will be stored in metadata instead of the physical table ignore_for_constants = { "match_id", "frame_id", @@ -344,20 +343,16 @@ def _save_tracking_data( entities_with_constants = [] if potential_constants: - # Group by entity and check uniqueness of potential constants agg_exprs = [pl.col(c).n_unique().alias(f"{c}_count") for c in potential_constants] agg_vals = [pl.col(c).first().alias(f"{c}_val") for c in potential_constants] stats = long_df.group_by(["entity_id", "team_id"]).agg(agg_exprs + agg_vals) - # We'll drop columns from the physical table if they are constant for ALL entities columns_to_drop = [] for c in potential_constants: if stats[f"{c}_count"].max() == 1: columns_to_drop.append(c) - # Prepare the entity list for metadata - # For each entity, we store the values of the dropped columns entities_with_constants = ( stats.select(["entity_id", "team_id"] + [f"{c}_val" for c in columns_to_drop]) .rename({f"{c}_val": c for c in columns_to_drop}) @@ -370,7 +365,8 @@ def _save_tracking_data( else: entities_with_constants = long_df.select(["entity_id", "team_id"]).unique().to_dicts() - # 3. Validation of required fields + # 3. Validation of required fields. + # x, y, z are explicitly allowed to be null. required_fields = [ "match_id", "frame_id", @@ -378,8 +374,6 @@ def _save_tracking_data( "timestamp", "entity_type", "entity_id", - "x", - "y", ] for field in required_fields: if field not in long_df.columns: From 33ef840a128c789b8ab19fe8aa97c3447ba4fb27 Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 15:42:57 +0200 Subject: [PATCH 12/21] feat: enforce x/y column presence while allowing null values --- crates/tdc-core/src/datacontainer/core.rs | 8 + examples/08-idsse-warehouse.ipynb | 317 ++++++++++++++++-- examples/idsse_warehouse/catalog.db | Bin 20480 -> 0 bytes ...-7d68-44e1-b7c9-ca6e892cf286.metadata.json | 1 - ...-f108-4a16-b20b-39ce0ac55542.metadata.json | 1 - profile_warehouse/catalog.db | Bin 20480 -> 0 bytes ...-582c-4563-a6f7-935a13162b9f.metadata.json | 1 - ...-1c1a-4098-903a-41eec5ee454e.metadata.json | 1 - ...67bf9b-f807-48db-aae8-3a65e2db3451-m0.avro | Bin 4487 -> 0 bytes ...-3967bf9b-f807-48db-aae8-3a65e2db3451.avro | Bin 1819 -> 0 bytes ...-a026-4806-acfa-74075182b917.metadata.json | 1 - ...-d534-4ba2-b39c-44bd8e3935da.metadata.json | 1 - ...f6aa22-f608-4e8d-af73-eee758db29a1-m0.avro | Bin 5445 -> 0 bytes ...-acf6aa22-f608-4e8d-af73-eee758db29a1.avro | Bin 1829 -> 0 bytes python/tests/test_catalog.py | 4 +- python/tracking_data_container/catalog.py | 13 +- 16 files changed, 315 insertions(+), 33 deletions(-) delete mode 100644 examples/idsse_warehouse/catalog.db delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00000-62e7e4e4-7d68-44e1-b7c9-ca6e892cf286.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00000-a67fd806-f108-4a16-b20b-39ce0ac55542.metadata.json delete mode 100644 profile_warehouse/catalog.db delete mode 100644 profile_warehouse/tracking/match_metadata/metadata/00000-64842057-582c-4563-a6f7-935a13162b9f.metadata.json delete mode 100644 profile_warehouse/tracking/match_metadata/metadata/00001-8e94e659-1c1a-4098-903a-41eec5ee454e.metadata.json delete mode 100644 profile_warehouse/tracking/match_metadata/metadata/3967bf9b-f807-48db-aae8-3a65e2db3451-m0.avro delete mode 100644 profile_warehouse/tracking/match_metadata/metadata/snap-9142401099081597578-0-3967bf9b-f807-48db-aae8-3a65e2db3451.avro delete mode 100644 profile_warehouse/tracking/tracking_data/metadata/00000-ccf96e5c-a026-4806-acfa-74075182b917.metadata.json delete mode 100644 profile_warehouse/tracking/tracking_data/metadata/00001-6e5882b4-d534-4ba2-b39c-44bd8e3935da.metadata.json delete mode 100644 profile_warehouse/tracking/tracking_data/metadata/acf6aa22-f608-4e8d-af73-eee758db29a1-m0.avro delete mode 100644 profile_warehouse/tracking/tracking_data/metadata/snap-2502839378513990955-0-acf6aa22-f608-4e8d-af73-eee758db29a1.avro diff --git a/crates/tdc-core/src/datacontainer/core.rs b/crates/tdc-core/src/datacontainer/core.rs index 003d46e..7ba684b 100644 --- a/crates/tdc-core/src/datacontainer/core.rs +++ b/crates/tdc-core/src/datacontainer/core.rs @@ -432,10 +432,18 @@ impl TrackingDataContainer { } // 2. Select the best frame index column and validate required fields + let has_x = fields.iter().any(|f| f.name() == "x"); + let has_y = fields.iter().any(|f| f.name() == "y"); let has_period = fields .iter() .any(|f| f.name() == "period" || f.name() == "period_id"); + if !has_x || !has_y { + return Err(TrackingDataContainerErrorKind::InvalidParameters( + "Missing required spatial columns x or y".to_string(), + ) + .into()); + } if !has_period { return Err(TrackingDataContainerErrorKind::InvalidParameters( "Missing required column period or period_id".to_string(), diff --git a/examples/08-idsse-warehouse.ipynb b/examples/08-idsse-warehouse.ipynb index 3b61754..ae28a5d 100644 --- a/examples/08-idsse-warehouse.ipynb +++ b/examples/08-idsse-warehouse.ipynb @@ -137,20 +137,17 @@ "output_type": "stream", "text": [ "\n", - "Processing match DFL-MAT-J03WMX...\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Required field x contains null values", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 18\u001b[39m\n\u001b[32m 16\u001b[39m \u001b[38;5;66;03m# Save to Warehouse (melts to long format internally)\u001b[39;00m\n\u001b[32m 17\u001b[39m start = time.perf_counter()\n\u001b[32m---> \u001b[39m\u001b[32m18\u001b[39m \u001b[43mcatalog\u001b[49m\u001b[43m.\u001b[49m\u001b[43msave_container\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontainer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 19\u001b[39m save_time = time.perf_counter() - start\n\u001b[32m 21\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m ✅ Parsed in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparse_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33ms, Saved in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msave_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33ms\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/python/tracking_data_container/catalog.py:241\u001b[39m, in \u001b[36mTrackingDataCatalog.save_container\u001b[39m\u001b[34m(self, container, match_id, metadata)\u001b[39m\n\u001b[32m 238\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mmatch_id must be provided if not present in container data\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 240\u001b[39m \u001b[38;5;66;03m# 1. Save tracking data and get unique entities (with constant attributes)\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m241\u001b[39m entities = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_save_tracking_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmatch_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 243\u001b[39m \u001b[38;5;66;03m# 2. Prepare metadata\u001b[39;00m\n\u001b[32m 244\u001b[39m final_metadata = metadata.copy() \u001b[38;5;28;01mif\u001b[39;00m metadata \u001b[38;5;28;01melse\u001b[39;00m {}\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/python/tracking_data_container/catalog.py:388\u001b[39m, in \u001b[36mTrackingDataCatalog._save_tracking_data\u001b[39m\u001b[34m(self, container, match_id)\u001b[39m\n\u001b[32m 386\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mRequired field \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfield\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m is missing from container\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 387\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m long_df[field].null_count() > \u001b[32m0\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m388\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mRequired field \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfield\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m contains null values\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 390\u001b[39m \u001b[38;5;66;03m# 4. Convert to Arrow and handle types\u001b[39;00m\n\u001b[32m 391\u001b[39m arrow_table = long_df.to_arrow()\n", - "\u001b[31mValueError\u001b[39m: Required field x contains null values" + "Processing match DFL-MAT-J03WMX...\n", + " ✅ Parsed in 20.25s, Saved in 14.32s\n", + " ✅ Rows: 145,967\n", + "\n", + "Processing match DFL-MAT-J03WN1...\n", + " ✅ Parsed in 19.48s, Saved in 13.57s\n", + " ✅ Rows: 141,561\n", + "\n", + "Processing match DFL-MAT-J03WOH...\n", + " ✅ Parsed in 13.43s, Saved in 10.51s\n", + " ✅ Rows: 137,214\n" ] } ], @@ -193,9 +190,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warehouse Summary:\n", + " - Match DFL-MAT-J03WMX: 165,259 rows\n", + " - Match DFL-MAT-J03WN1: 162,273 rows\n", + " - Match DFL-MAT-J03WOH: 157,790 rows\n", + "\n", + "Total rows in warehouse: 485,322\n" + ] + } + ], "source": [ "print(\"Warehouse Summary:\")\n", "total_rows = 0\n", @@ -224,9 +234,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data for DFL-MAT-J03WMX...\n", + " ✅ Loaded and pivoted 165259 rows in 8.6644s\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (5, 424)
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" + ], + "text/plain": [ + "shape: (5, 424)\n", + "┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", + "│ competiti ┆ season/id ┆ match/id ┆ frame_id ┆ … ┆ teams/awa ┆ teams/awa ┆ teams/awa ┆ teams/awa │\n", + "│ on/id ┆ --- ┆ --- ┆ --- ┆ ┆ y/players ┆ y/players ┆ y/players ┆ y/players │\n", + "│ --- ┆ str ┆ str ┆ i64 ┆ ┆ /DFL-OBJ- ┆ /DFL-OBJ- ┆ /DFL-OBJ- ┆ /DFL-OBJ- │\n", + "│ str ┆ ┆ ┆ ┆ ┆ 000… ┆ 002… ┆ 002… ┆ 002… │\n", + "│ ┆ ┆ ┆ ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ null ┆ null ┆ null ┆ null │\n", + "╞═══════════╪═══════════╪═══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10000 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10001 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10002 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10003 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10004 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "└───────────┴───────────┴───────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "target_match = pairs[0]['match_id']\n", "\n", @@ -243,9 +301,209 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'_entities': [{'entity_id': 'DFL-OBJ-002GCR',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0028BD',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-J015S4',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-J01BGM',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-J017RE',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-J01DVC',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-J01D1W',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-J01B8N',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'referee:DFL-OBJ-00011J',\n", + " 'team_id': None,\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0002BO',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0002HE',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-J01N65',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-002GKO',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0000RP',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'team:away',\n", + " 'team_id': 'away',\n", + " 'name': 'FC Bayern München',\n", + " 'id': 'DFL-CLU-00000G',\n", + " 'jersey_colour': '#25282A'},\n", + " {'entity_id': 'DFL-OBJ-0027B6',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0026ZI',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0027G6',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0002AU',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'referee:DFL-OBJ-000141',\n", + " 'team_id': None,\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0002DR',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-J01APO',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0026PM',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-002GBW',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-000270',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'team:home',\n", + " 'team_id': 'home',\n", + " 'name': '1. FC Köln',\n", + " 'id': 'DFL-CLU-000008',\n", + " 'jersey_colour': '#F4F5F0'},\n", + " {'entity_id': 'ball',\n", + " 'team_id': None,\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-002G89',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-002G4A',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'referee:DFL-OBJ-00011W',\n", + " 'team_id': None,\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0027V2',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0027KL',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-000191',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0027AX',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0002F5',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-00012X',\n", + " 'team_id': 'home',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0000IA',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None},\n", + " {'entity_id': 'DFL-OBJ-0027G0',\n", + " 'team_id': 'away',\n", + " 'name': None,\n", + " 'id': None,\n", + " 'jersey_colour': None}]}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "metadata = catalog.get_metadata(match_id=target_match)\n", "metadata" @@ -253,9 +511,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "No such file or directory (os error 2): ...rehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet (set POLARS_VERBOSE=1 to see full path)\n\nThis error occurred with the following context stack:\n\t[1] 'parquet scan'\n\t[2] 'sink'\n", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m start = time.perf_counter()\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m df = \u001b[43mpl\u001b[49m\u001b[43m.\u001b[49m\u001b[43mscan_parquet\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43midsse_warehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m query_time = time.perf_counter() - start\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m ✅ Loaded and pivoted \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(df)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m rows in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquery_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33ms\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/_utils/deprecation.py:97\u001b[39m, in \u001b[36mdeprecate_streaming_parameter..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33mengine\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33min-memory\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m kwargs[\u001b[33m\"\u001b[39m\u001b[33mstreaming\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m---> \u001b[39m\u001b[32m97\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/lazyframe/opt_flags.py:328\u001b[39m, in \u001b[36mforward_old_opt_flags..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 325\u001b[39m optflags = cb(optflags, kwargs.pop(key)) \u001b[38;5;66;03m# type: ignore[no-untyped-call,unused-ignore]\u001b[39;00m\n\u001b[32m 327\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33moptimizations\u001b[39m\u001b[33m\"\u001b[39m] = optflags\n\u001b[32m--> \u001b[39m\u001b[32m328\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/lazyframe/frame.py:2429\u001b[39m, in \u001b[36mLazyFrame.collect\u001b[39m\u001b[34m(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, cluster_with_columns, collapse_joins, no_optimization, engine, background, optimizations, **_kwargs)\u001b[39m\n\u001b[32m 2427\u001b[39m \u001b[38;5;66;03m# Only for testing purposes\u001b[39;00m\n\u001b[32m 2428\u001b[39m callback = _kwargs.get(\u001b[33m\"\u001b[39m\u001b[33mpost_opt_callback\u001b[39m\u001b[33m\"\u001b[39m, callback)\n\u001b[32m-> \u001b[39m\u001b[32m2429\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m wrap_df(\u001b[43mldf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[43m)\u001b[49m)\n", + "\u001b[31mFileNotFoundError\u001b[39m: No such file or directory (os error 2): ...rehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet (set POLARS_VERBOSE=1 to see full path)\n\nThis error occurred with the following context stack:\n\t[1] 'parquet scan'\n\t[2] 'sink'\n" + ] + } + ], "source": [ "start = time.perf_counter()\n", "\n", diff --git a/examples/idsse_warehouse/catalog.db b/examples/idsse_warehouse/catalog.db deleted file mode 100644 index 146187d1168342f190f8d595e162fea0c295b762..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 20480 zcmeI&U2oD*7zgl{!RE-edZ8h{rpAkngsz40HoZ`sBpPvZ?8WS=Ih2R3QYf}%ZkHpw z@*~Y}vJ2yl-@!`{Wh(;(Hxo^k{F}D)Y#-s7~;mKINC<>SlA7zhpM{f?09RwCIVds#`8OuF9(q6<(qtW6yKdm$Xk-w?3nuKdSq4 zZo_VR%xv=Obif)di(J*Lx-RLTm_vJf%1yxo>R@B)`CMG()e%iovMI_@n$k+Hn$0We zG$oZv&RA9qmZki1%BZFC*~Rl`7C*Rl^JrbZpB{=Cs33IT|BKQ$fv;E~009U<00Izz00bZa0SG_< z0uZ>S0uM!A0=RQ~s02U-NQ`#=JlfO3#I6|JKR^|(ltqz zrN@dQNzyRA2kG5B0@MgLJM<}^YWHdmOY@(vkB5Iq!Leg$q7wcxc06&rS1`^)00Izz 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df["match/id"][0] == "match_1" assert "ball/player/id" in df.columns assert df["ball/x"][0] == 0.0 + assert df["ball/x"][1] is None # Check normalized constants assert df["teams/home/players/p1/jersey_color"][0] == "red" diff --git a/python/tracking_data_container/catalog.py b/python/tracking_data_container/catalog.py index 760dfc9..94f1b9d 100644 --- a/python/tracking_data_container/catalog.py +++ b/python/tracking_data_container/catalog.py @@ -143,7 +143,7 @@ def _create_tracking_table(self) -> None: except NoSuchTableError: pass # Create new table - # Define the requested schema. x, y, z are optional. + # Define the requested schema. x, y, z are nullable (optional in Iceberg). schema = Schema( NestedField(1, "competition_id", StringType(), required=False), NestedField(2, "season_id", StringType(), required=False), @@ -366,8 +366,13 @@ def _save_tracking_data( entities_with_constants = long_df.select(["entity_id", "team_id"]).unique().to_dicts() # 3. Validation of required fields. - # x, y, z are explicitly allowed to be null. - required_fields = [ + # x and y MUST be present as columns, but their values CAN be null. + for field in ["x", "y"]: + if field not in long_df.columns: + raise ValueError(f"Required column {field} is missing from container") + + # Identity and temporal fields MUST NOT be null. + must_not_be_null = [ "match_id", "frame_id", "period", @@ -375,7 +380,7 @@ def _save_tracking_data( "entity_type", "entity_id", ] - for field in required_fields: + for field in must_not_be_null: if field not in long_df.columns: raise ValueError(f"Required field {field} is missing from container") if long_df[field].null_count() > 0: From cdf721509fe6e0c1fd6e39c5c137361325d13c40 Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 15:45:43 +0200 Subject: [PATCH 13/21] feat: use hierarchical structure for entity metadata --- python/tracking_data_container/catalog.py | 166 +++++++++++----------- 1 file changed, 86 insertions(+), 80 deletions(-) diff --git a/python/tracking_data_container/catalog.py b/python/tracking_data_container/catalog.py index 94f1b9d..915fe08 100644 --- a/python/tracking_data_container/catalog.py +++ b/python/tracking_data_container/catalog.py @@ -237,7 +237,7 @@ def save_container( else: raise ValueError("match_id must be provided if not present in container data") - # 1. Save tracking data and get unique entities (with constant attributes) + # 1. Save tracking data and get unique entities (hierarchical) entities = self._save_tracking_data(container, match_id) # 2. Prepare metadata @@ -290,12 +290,12 @@ def _save_tracking_data( self, container: TrackingDataContainer, match_id: str, - ) -> list[dict[str, Any]]: + ) -> dict[str, Any]: """ Save tracking data to Iceberg table in entity-long format. Returns: - List of unique entities (dict with entity_id, team_id, and constants) + Hierarchical dictionary of unique entities and constants. """ if self._tracking_table is None: self._tracking_table = self._iceberg_catalog.load_table("tracking.tracking_data") @@ -324,8 +324,7 @@ def _save_tracking_data( # Ensure match_id is present and correct long_df = long_df.with_columns(pl.lit(match_id).alias("match_id")) - # --- Constant Extraction --- - # Identify attributes that are constant for each entity + # --- Constant Extraction & Hierarchical grouping --- ignore_for_constants = { "match_id", "frame_id", @@ -341,37 +340,54 @@ def _save_tracking_data( } potential_constants = [c for c in long_df.columns if c not in ignore_for_constants] - entities_with_constants = [] - if potential_constants: - agg_exprs = [pl.col(c).n_unique().alias(f"{c}_count") for c in potential_constants] - agg_vals = [pl.col(c).first().alias(f"{c}_val") for c in potential_constants] + # Group by entity and extract constants + agg_exprs = [pl.col(c).n_unique().alias(f"{c}_count") for c in potential_constants] + agg_vals = [pl.col(c).first().alias(f"{c}_val") for c in potential_constants] - stats = long_df.group_by(["entity_id", "team_id"]).agg(agg_exprs + agg_vals) + stats = long_df.group_by(["entity_id", "team_id", "entity_type"]).agg(agg_exprs + agg_vals) - columns_to_drop = [] - for c in potential_constants: - if stats[f"{c}_count"].max() == 1: - columns_to_drop.append(c) + columns_to_drop = [] + for c in potential_constants: + if stats[f"{c}_count"].max() == 1: + columns_to_drop.append(c) - entities_with_constants = ( - stats.select(["entity_id", "team_id"] + [f"{c}_val" for c in columns_to_drop]) - .rename({f"{c}_val": c for c in columns_to_drop}) - .to_dicts() + entities_flat = ( + stats.select( + ["entity_id", "team_id", "entity_type"] + [f"{c}_val" for c in columns_to_drop] ) + .rename({f"{c}_val": c for c in columns_to_drop}) + .to_dicts() + ) - if columns_to_drop: - logger.info(f"Normalizing constant attributes to metadata: {columns_to_drop}") - long_df = long_df.drop(columns_to_drop) - else: - entities_with_constants = long_df.select(["entity_id", "team_id"]).unique().to_dicts() - - # 3. Validation of required fields. - # x and y MUST be present as columns, but their values CAN be null. + # Build hierarchical structure + entities_hierarchical: dict[str, Any] = {"ball": {}, "teams": {}, "referees": {}} + for ent in entities_flat: + etype = ent.pop("entity_type") + eid = ent.pop("entity_id") + tid = ent.pop("team_id", "none") + + if etype == "ball": + entities_hierarchical["ball"] = ent + elif etype == "referee": + entities_hierarchical["referees"][eid] = ent + elif etype == "player": + if tid not in entities_hierarchical["teams"]: + entities_hierarchical["teams"][tid] = {"players": {}, "attributes": {}} + entities_hierarchical["teams"][tid]["players"][eid] = ent + elif etype == "team": + if eid not in entities_hierarchical["teams"]: + entities_hierarchical["teams"][eid] = {"players": {}, "attributes": {}} + entities_hierarchical["teams"][eid]["attributes"] = ent + + if columns_to_drop: + logger.info(f"Normalizing constant attributes to metadata: {columns_to_drop}") + long_df = long_df.drop(columns_to_drop) + + # 3. Validation for field in ["x", "y"]: if field not in long_df.columns: raise ValueError(f"Required column {field} is missing from container") - # Identity and temporal fields MUST NOT be null. must_not_be_null = [ "match_id", "frame_id", @@ -386,10 +402,8 @@ def _save_tracking_data( if long_df[field].null_count() > 0: raise ValueError(f"Required field {field} contains null values") - # 4. Convert to Arrow and handle types + # 4. Save to Iceberg arrow_table = long_df.to_arrow() - - # Define known types for Iceberg compatibility known_types = { "timestamp": pa.timestamp("us"), "frame_id": pa.int64(), @@ -401,12 +415,10 @@ def _save_tracking_data( new_columns = [] new_fields = [] for i, field in enumerate(arrow_table.schema): - target_type = None - if field.name in known_types: - target_type = known_types[field.name] - elif isinstance(field.type, pa.DictionaryType): + target_type = known_types.get(field.name) + if not target_type and isinstance(field.type, pa.DictionaryType): target_type = field.type.value_type - elif pa.types.is_null(field.type): + elif not target_type and pa.types.is_null(field.type): target_type = pa.string() if target_type is not None: @@ -418,19 +430,13 @@ def _save_tracking_data( arrow_table = pa.Table.from_arrays(new_columns, schema=pa.schema(new_fields)) - # Start transaction with self._tracking_table.transaction() as txn: - # Update schema to include any new metrics container_schema = _pyarrow_to_schema_without_ids(arrow_table.schema) txn.update_schema().union_by_name(container_schema).commit() - - # Delete existing match partition txn.delete(EqualTo(term=Reference("match_id"), value=cast(Any, match_id))) # type: ignore - - # Append long-format data txn.append(arrow_table) - return entities_with_constants + return entities_hierarchical def _save_metadata(self, metadata: dict, match_id: str) -> None: """Save metadata to Iceberg table.""" @@ -439,7 +445,6 @@ def _save_metadata(self, metadata: dict, match_id: str) -> None: assert self._metadata_table is not None - # Define schema for the Arrow table metadata_schema = pa.schema( [ pa.field("match_id", pa.string(), nullable=False), @@ -449,7 +454,6 @@ def _save_metadata(self, metadata: dict, match_id: str) -> None: ] ) - # Prepare metadata row metadata_row = pa.table( { "match_id": [match_id], @@ -460,7 +464,6 @@ def _save_metadata(self, metadata: dict, match_id: str) -> None: schema=metadata_schema, ) - # Upsert metadata with self._metadata_table.transaction() as txn: txn.delete(EqualTo(term=Reference("match_id"), value=cast(Any, match_id))) # type: ignore txn.append(metadata_row) @@ -495,13 +498,36 @@ def __init__( self._filters = filters self._cached_schema: Fields | None = None + def _get_flat_entities(self, hierarchical_entities: dict[str, Any]) -> list[dict[str, Any]]: + """Flatten hierarchical metadata back to list for easy schema iteration.""" + flat = [] + # 1. Ball + ball = hierarchical_entities.get("ball") + if ball is not None: + flat.append({"entity_id": "ball", "team_id": "none", **ball}) + + # 2. Teams & Players + for tid, team_info in hierarchical_entities.get("teams", {}).items(): + # Team itself + flat.append( + {"entity_id": f"team:{tid}", "team_id": tid, **team_info.get("attributes", {})} + ) + # Players + for pid, p_attrs in team_info.get("players", {}).items(): + flat.append({"entity_id": pid, "team_id": tid, **p_attrs}) + + # 3. Referees + for rid, r_attrs in hierarchical_entities.get("referees", {}).items(): + flat.append({"entity_id": f"referee:{rid}", "team_id": "none", **r_attrs}) + + return flat + def schema(self) -> Fields: - """Reconstruct the wide schema by inspecting entities in the match metadata.""" + """Reconstruct the wide schema by inspecting entities in the hierarchical metadata.""" if self._cached_schema: return self._cached_schema assert self._catalog is not None - if self._catalog._tracking_table is None: self._catalog._tracking_table = self._catalog._iceberg_catalog.load_table( "tracking.tracking_data" @@ -509,18 +535,16 @@ def schema(self) -> Fields: assert self._catalog._tracking_table is not None - # Load entities from metadata metadata = self._catalog.get_metadata(self._match_id) - entities = metadata.get("_entities") - - if entities is None: + entities_hier = metadata.get("_entities") + if entities_hier is None: raise ValueError(f"Metadata for match {self._match_id} is missing unique entity list.") - # Apply player filter if requested + entities = self._get_flat_entities(entities_hier) + if self._player_id: entities = [e for e in entities if str(e.get("entity_id")) == self._player_id] - # Reconstruct wide field names physical_names = [f.name for f in self._catalog._tracking_table.schema().fields] ignored = set(COLUMN_MAPPING.values()) | { "entity_type", @@ -530,10 +554,8 @@ def schema(self) -> Fields: "period_id", "entity_key", } - # Physical attributes in the tracking table attr_cols = [c for c in physical_names if c not in ignored and c != "id"] - # Base list: all core columns that are NOT entity-specific wide_names = [] for c in CORE_COLUMNS: if c in ("player/id", "team/id", "entity_key"): @@ -545,18 +567,14 @@ def schema(self) -> Fields: for row in entities: p_id = str(row.get("entity_id")) t_id = str(row.get("team_id", "none")) - prefix = TrackedEntity.from_entity_key(f"{p_id}|{t_id}").cdf_prefix() - # 1. Attributes from physical table + for attr in attr_cols: wide_names.append(f"{prefix}{attr}") - - # 2. Attributes from metadata constants for attr in row.keys(): if attr not in ("entity_id", "team_id"): wide_names.append(f"{prefix}{attr}") - # 3. Standard CDF names wide_names.append(f"{prefix}player/id") wide_names.append(f"{prefix}team/id") @@ -590,7 +608,6 @@ def slice(self, offset: int, length: int) -> CatalogLazyTrackingDataContainer: def collect(self) -> TrackingDataContainer: """Finally perform the Iceberg scan and pivot.""" assert self._catalog is not None - if self._catalog._tracking_table is None: self._catalog._tracking_table = self._catalog._iceberg_catalog.load_table( "tracking.tracking_data" @@ -607,12 +624,12 @@ def collect(self) -> TrackingDataContainer: EqualTo(term=Reference("entity_id"), value=cast(Any, self._player_id)), # type: ignore ) - # 1. Determine which long-format columns we need selected_long_fields = None deferred_select = None metadata = self._catalog.get_metadata(self._match_id) - entities_metadata = metadata.get("_entities", []) + entities_hier = metadata.get("_entities", {}) + entities_flat = self._get_flat_entities(entities_hier) if self._selection: eager_schema = self.schema() @@ -644,7 +661,6 @@ def collect(self) -> TrackingDataContainer: if field.entity: needed_ents.add(cast(str, field.entity_key())) - # Check physical schema and map names physical_names = {f.name for f in self._catalog._tracking_table.schema().fields} selected_long_fields_set = set() for f in needed_long_internal: @@ -652,7 +668,6 @@ def collect(self) -> TrackingDataContainer: if phys_name in physical_names: selected_long_fields_set.add(phys_name) - # Always include required fields for pivot for req in [ "match_id", "entity_type", @@ -669,10 +684,8 @@ def collect(self) -> TrackingDataContainer: selected_long_fields = list(selected_long_fields_set) - # 2. Add entity filter pushdown if needed_ents: entity_predicate: BooleanExpression | None = None - for k in needed_ents: p_id, t_id = k.split("|") cond: BooleanExpression = EqualTo( @@ -689,7 +702,6 @@ def collect(self) -> TrackingDataContainer: if entity_predicate: filter_expr = And(filter_expr, entity_predicate) - # 3. Perform the scan scan_kwargs: dict[str, Any] = {"row_filter": filter_expr} if selected_long_fields is not None: scan_kwargs["selected_fields"] = tuple(selected_long_fields) @@ -699,32 +711,26 @@ def collect(self) -> TrackingDataContainer: scan = self._catalog._tracking_table.scan(**scan_kwargs) arrow_table = scan.to_arrow() - # 4. Pivot to wide format using high-performance Rust implementation wide_container = TrackingDataContainer.pivot(arrow_table) wide_container._catalog = self._catalog - # 5. Inject constant attributes from metadata - if entities_metadata: - # Reconstruct prefixes for entities in metadata - entity_map = { - f"{e['entity_id']}|{e.get('team_id', 'none')}": e for e in entities_metadata - } - + # Inject constant attributes + if entities_flat: wide_names = wide_container.schema().names() - for ek, ent_data in entity_map.items(): - prefix = TrackedEntity.from_entity_key(ek).cdf_prefix() + for ent_data in entities_flat: + p_id = ent_data["entity_id"] + t_id = ent_data["team_id"] + prefix = TrackedEntity.from_entity_key(f"{p_id}|{t_id}").cdf_prefix() for attr, value in ent_data.items(): if attr in ("entity_id", "team_id"): continue wide_attr_name = f"{prefix}{attr}" if wide_attr_name not in wide_names: - # Add constant column wide_container = wide_container.add_column( wide_attr_name, [value] * wide_container.num_rows() ) - # Apply final selection/slice if deferred_select: wide_container = wide_container.select(deferred_select) if self._limit: From 8a7d7f0b1830e72d4cf62c4738a1088e81ebd51d Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 15:51:25 +0200 Subject: [PATCH 14/21] perf: omit None attributes from hierarchical entity metadata --- examples/08-idsse-warehouse.ipynb | 262 +++++------------- examples/idsse_warehouse/catalog.db | Bin 0 -> 20480 bytes ...-b333-4c57-a371-74456e7c8d29.metadata.json | 1 + ...-5de4-4f14-8523-98060401e712.metadata.json | 1 + ...-7558-4e44-8a81-70d8f3c5ada2.metadata.json | 1 + ...-df9e-4b34-9eab-b38255059e3d.metadata.json | 1 + ...0f7d00-f8c4-41cb-b0b8-3148f44c2ab1-m0.avro | Bin 0 -> 4508 bytes ...e4f0c2-e0d0-47d7-91b8-84d9f37863fb-m0.avro | Bin 0 -> 4509 bytes ...7521bf-42dc-4f0b-9994-9661a5bb2874-m0.avro | Bin 0 -> 4509 bytes ...-697521bf-42dc-4f0b-9994-9661a5bb2874.avro | Bin 0 -> 1947 bytes ...-120f7d00-f8c4-41cb-b0b8-3148f44c2ab1.avro | Bin 0 -> 1902 bytes ...-58e4f0c2-e0d0-47d7-91b8-84d9f37863fb.avro | Bin 0 -> 1829 bytes ...-fac7-49b0-8d04-59619f80a697.metadata.json | 1 + ...-f7bb-4910-bfe3-0320322d0420.metadata.json | 1 + ...-56f0-4692-87ea-a5971864c0b3.metadata.json | 1 + ...-3bff-4c99-b498-68fe18cf6f3b.metadata.json | 1 + ...b66841-7b73-41dd-8673-9f99990d0add-m0.avro | Bin 0 -> 6113 bytes ...0818c9-0eca-413c-a97f-b38c039de85a-m0.avro | Bin 0 -> 6118 bytes ...2b2ab0-1620-4401-8fa7-8b8077d79eab-m0.avro | Bin 0 -> 5588 bytes ...-240818c9-0eca-413c-a97f-b38c039de85a.avro | Bin 0 -> 1853 bytes ...-bc2b2ab0-1620-4401-8fa7-8b8077d79eab.avro | Bin 0 -> 2000 bytes ...-1fb66841-7b73-41dd-8673-9f99990d0add.avro | Bin 0 -> 1936 bytes python/tracking_data_container/catalog.py | 13 +- 23 files changed, 79 insertions(+), 204 deletions(-) create mode 100644 examples/idsse_warehouse/catalog.db create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00000-8ece4efe-b333-4c57-a371-74456e7c8d29.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00001-39d25845-5de4-4f14-8523-98060401e712.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00002-5ae3d21a-7558-4e44-8a81-70d8f3c5ada2.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00003-d74574f9-df9e-4b34-9eab-b38255059e3d.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/120f7d00-f8c4-41cb-b0b8-3148f44c2ab1-m0.avro create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/58e4f0c2-e0d0-47d7-91b8-84d9f37863fb-m0.avro create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/697521bf-42dc-4f0b-9994-9661a5bb2874-m0.avro create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/snap-7183120188202263762-0-697521bf-42dc-4f0b-9994-9661a5bb2874.avro create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/snap-7688096930835682381-0-120f7d00-f8c4-41cb-b0b8-3148f44c2ab1.avro create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/snap-8301423118246886670-0-58e4f0c2-e0d0-47d7-91b8-84d9f37863fb.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00000-ce626e1b-fac7-49b0-8d04-59619f80a697.metadata.json create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00001-9f918efb-f7bb-4910-bfe3-0320322d0420.metadata.json create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00002-d900c1db-56f0-4692-87ea-a5971864c0b3.metadata.json create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00003-e183f978-3bff-4c99-b498-68fe18cf6f3b.metadata.json create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/1fb66841-7b73-41dd-8673-9f99990d0add-m0.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/240818c9-0eca-413c-a97f-b38c039de85a-m0.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/bc2b2ab0-1620-4401-8fa7-8b8077d79eab-m0.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-4743469712033335616-0-240818c9-0eca-413c-a97f-b38c039de85a.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-8580464386215457325-0-bc2b2ab0-1620-4401-8fa7-8b8077d79eab.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-8653266967438724888-0-1fb66841-7b73-41dd-8673-9f99990d0add.avro diff --git a/examples/08-idsse-warehouse.ipynb b/examples/08-idsse-warehouse.ipynb index ae28a5d..32bccdc 100644 --- a/examples/08-idsse-warehouse.ipynb +++ b/examples/08-idsse-warehouse.ipynb @@ -138,15 +138,15 @@ "text": [ "\n", "Processing match DFL-MAT-J03WMX...\n", - " ✅ Parsed in 20.25s, Saved in 14.32s\n", + " ✅ Parsed in 14.13s, Saved in 12.57s\n", " ✅ Rows: 145,967\n", "\n", "Processing match DFL-MAT-J03WN1...\n", - " ✅ Parsed in 19.48s, Saved in 13.57s\n", + " ✅ Parsed in 14.03s, Saved in 11.98s\n", " ✅ Rows: 141,561\n", "\n", "Processing match DFL-MAT-J03WOH...\n", - " ✅ Parsed in 13.43s, Saved in 10.51s\n", + " ✅ Parsed in 12.23s, Saved in 10.73s\n", " ✅ Rows: 137,214\n" ] } @@ -242,7 +242,7 @@ "output_type": "stream", "text": [ "Loading data for DFL-MAT-J03WMX...\n", - " ✅ Loaded and pivoted 165259 rows in 8.6644s\n" + " ✅ Loaded and pivoted 165259 rows in 9.5119s\n" ] }, { @@ -255,15 +255,15 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (5, 424)
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za<`8}dSg9DuHL-4*4O9TKg8^bBq9-CB;L2TZsEEd;b?40LGAU*G25dm+ne#~ON%%gLnWQW7?<`QCK;SyLlTm3O!($1VGv%Db=JmAh+lCtuJF z-WM-RcY8-C$hX52RPpN(*YeK~oj-{mE^9yfH4$D_AK%eY`t#F@ma@r;f#0hvi{uB< H*}nV*f_8|~ literal 0 HcmV?d00001 diff --git a/python/tracking_data_container/catalog.py b/python/tracking_data_container/catalog.py index 915fe08..d898c0a 100644 --- a/python/tracking_data_container/catalog.py +++ b/python/tracking_data_container/catalog.py @@ -359,25 +359,28 @@ def _save_tracking_data( .to_dicts() ) - # Build hierarchical structure + # Build hierarchical structure, omitting None attributes entities_hierarchical: dict[str, Any] = {"ball": {}, "teams": {}, "referees": {}} for ent in entities_flat: etype = ent.pop("entity_type") eid = ent.pop("entity_id") tid = ent.pop("team_id", "none") + # Remove attributes that are None for this specific entity + attrs = {k: v for k, v in ent.items() if v is not None} + if etype == "ball": - entities_hierarchical["ball"] = ent + entities_hierarchical["ball"] = attrs elif etype == "referee": - entities_hierarchical["referees"][eid] = ent + entities_hierarchical["referees"][eid] = attrs elif etype == "player": if tid not in entities_hierarchical["teams"]: entities_hierarchical["teams"][tid] = {"players": {}, "attributes": {}} - entities_hierarchical["teams"][tid]["players"][eid] = ent + entities_hierarchical["teams"][tid]["players"][eid] = attrs elif etype == "team": if eid not in entities_hierarchical["teams"]: entities_hierarchical["teams"][eid] = {"players": {}, "attributes": {}} - entities_hierarchical["teams"][eid]["attributes"] = ent + entities_hierarchical["teams"][eid]["attributes"] = attrs if columns_to_drop: logger.info(f"Normalizing constant attributes to metadata: {columns_to_drop}") From f4e9b4b0ed6b443e90bc177d54f0c36e730df556 Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 15:56:48 +0200 Subject: [PATCH 15/21] perf: merge team-level attributes into hierarchical team metadata --- examples/08-idsse-warehouse.ipynb | 138 +++++++++--------- examples/idsse_warehouse/catalog.db | Bin 20480 -> 20480 bytes ...501c-45f2-9996-77ebcb5463a5.metadata.json} | 2 +- ...-5de4-4f14-8523-98060401e712.metadata.json | 1 - ...-5f2a-4530-bd47-a196cd47b46e.metadata.json | 1 + ...-c402-4825-a184-fa65adc17cda.metadata.json | 1 + ...-7558-4e44-8a81-70d8f3c5ada2.metadata.json | 1 - ...-90a1-4f37-8af3-8be2f399c28b.metadata.json | 1 + ...-df9e-4b34-9eab-b38255059e3d.metadata.json | 1 - ...1226c-e1c9-4c2b-8a42-09812bdcba85-m0.avro} | Bin 4509 -> 4507 bytes ...c7ea8-2fa8-4a02-9d11-6aca1be81ea1-m0.avro} | Bin 4508 -> 4507 bytes ...16f68-89e7-49e5-8303-1c604053a6e7-m0.avro} | Bin 4509 -> 4508 bytes ...fb416f68-89e7-49e5-8303-1c604053a6e7.avro} | Bin 1902 -> 1902 bytes ...d85c7ea8-2fa8-4a02-9d11-6aca1be81ea1.avro} | Bin 1947 -> 1948 bytes ...7e31226c-e1c9-4c2b-8a42-09812bdcba85.avro} | Bin 1829 -> 1826 bytes ...fb8e-4c6e-9d72-b475830bc2bb.metadata.json} | 2 +- ...a178-4260-a16a-3dc465003124.metadata.json} | 2 +- ...8c50-41ea-b5cc-28d517cd4616.metadata.json} | 2 +- ...-47d1-4eb3-8895-324002808f47.metadata.json | 1 + ...-3bff-4c99-b498-68fe18cf6f3b.metadata.json | 1 - ...dfc8e-3ec5-4fb9-b6c2-5b5b776d94bb-m0.avro} | Bin 6113 -> 6113 bytes ...e70eb-6eb2-4a19-9b08-5b01b851d469-m0.avro} | Bin 5588 -> 5589 bytes ...a80a6-f0eb-4290-8070-14be9d80f7e7-m0.avro} | Bin 6118 -> 6122 bytes ...fc9a80a6-f0eb-4290-8070-14be9d80f7e7.avro} | Bin 1853 -> 1852 bytes ...ecfdfc8e-3ec5-4fb9-b6c2-5b5b776d94bb.avro} | Bin 1936 -> 1939 bytes ...-f58e70eb-6eb2-4a19-9b08-5b01b851d469.avro | Bin 0 -> 2001 bytes ...-bc2b2ab0-1620-4401-8fa7-8b8077d79eab.avro | Bin 2000 -> 0 bytes python/tracking_data_container/catalog.py | 8 +- 28 files changed, 78 insertions(+), 84 deletions(-) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{00000-8ece4efe-b333-4c57-a371-74456e7c8d29.metadata.json => 00000-b6d20000-501c-45f2-9996-77ebcb5463a5.metadata.json} (85%) delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00001-39d25845-5de4-4f14-8523-98060401e712.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00001-cb7462b7-5f2a-4530-bd47-a196cd47b46e.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00002-570bd3e4-c402-4825-a184-fa65adc17cda.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00002-5ae3d21a-7558-4e44-8a81-70d8f3c5ada2.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00003-50271329-90a1-4f37-8af3-8be2f399c28b.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00003-d74574f9-df9e-4b34-9eab-b38255059e3d.metadata.json rename examples/idsse_warehouse/tracking/match_metadata/metadata/{58e4f0c2-e0d0-47d7-91b8-84d9f37863fb-m0.avro => 7e31226c-e1c9-4c2b-8a42-09812bdcba85-m0.avro} (92%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{120f7d00-f8c4-41cb-b0b8-3148f44c2ab1-m0.avro => d85c7ea8-2fa8-4a02-9d11-6aca1be81ea1-m0.avro} (93%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{697521bf-42dc-4f0b-9994-9661a5bb2874-m0.avro => fb416f68-89e7-49e5-8303-1c604053a6e7-m0.avro} (92%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{snap-7688096930835682381-0-120f7d00-f8c4-41cb-b0b8-3148f44c2ab1.avro => snap-4686198960413679258-0-fb416f68-89e7-49e5-8303-1c604053a6e7.avro} (77%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{snap-7183120188202263762-0-697521bf-42dc-4f0b-9994-9661a5bb2874.avro => snap-5701652792860281111-0-d85c7ea8-2fa8-4a02-9d11-6aca1be81ea1.avro} (75%) rename examples/idsse_warehouse/tracking/match_metadata/metadata/{snap-8301423118246886670-0-58e4f0c2-e0d0-47d7-91b8-84d9f37863fb.avro => snap-8842646200136284049-0-7e31226c-e1c9-4c2b-8a42-09812bdcba85.avro} (83%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{00000-ce626e1b-fac7-49b0-8d04-59619f80a697.metadata.json => 00000-3899460b-fb8e-4c6e-9d72-b475830bc2bb.metadata.json} (91%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{00001-9f918efb-f7bb-4910-bfe3-0320322d0420.metadata.json => 00001-0203e49b-a178-4260-a16a-3dc465003124.metadata.json} (71%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{00002-d900c1db-56f0-4692-87ea-a5971864c0b3.metadata.json => 00002-ec4a76bd-8c50-41ea-b5cc-28d517cd4616.metadata.json} (65%) create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00003-1ebc3653-47d1-4eb3-8895-324002808f47.metadata.json delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00003-e183f978-3bff-4c99-b498-68fe18cf6f3b.metadata.json rename examples/idsse_warehouse/tracking/tracking_data/metadata/{1fb66841-7b73-41dd-8673-9f99990d0add-m0.avro => ecfdfc8e-3ec5-4fb9-b6c2-5b5b776d94bb-m0.avro} (81%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{bc2b2ab0-1620-4401-8fa7-8b8077d79eab-m0.avro => f58e70eb-6eb2-4a19-9b08-5b01b851d469-m0.avro} (89%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{240818c9-0eca-413c-a97f-b38c039de85a-m0.avro => fc9a80a6-f0eb-4290-8070-14be9d80f7e7-m0.avro} (81%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{snap-4743469712033335616-0-240818c9-0eca-413c-a97f-b38c039de85a.avro => snap-1282166195388124131-0-fc9a80a6-f0eb-4290-8070-14be9d80f7e7.avro} (86%) rename examples/idsse_warehouse/tracking/tracking_data/metadata/{snap-8653266967438724888-0-1fb66841-7b73-41dd-8673-9f99990d0add.avro => snap-5997955711447099734-0-ecfdfc8e-3ec5-4fb9-b6c2-5b5b776d94bb.avro} (75%) create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-601469116504892914-0-f58e70eb-6eb2-4a19-9b08-5b01b851d469.avro delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-8580464386215457325-0-bc2b2ab0-1620-4401-8fa7-8b8077d79eab.avro diff --git a/examples/08-idsse-warehouse.ipynb b/examples/08-idsse-warehouse.ipynb index 32bccdc..e8e7ed8 100644 --- a/examples/08-idsse-warehouse.ipynb +++ b/examples/08-idsse-warehouse.ipynb @@ -138,15 +138,15 @@ "text": [ "\n", "Processing match DFL-MAT-J03WMX...\n", - " ✅ Parsed in 14.13s, Saved in 12.57s\n", + " ✅ Parsed in 1.54s, Saved in 4.82s\n", " ✅ Rows: 145,967\n", "\n", "Processing match DFL-MAT-J03WN1...\n", - " ✅ Parsed in 14.03s, Saved in 11.98s\n", + " ✅ Parsed in 1.38s, Saved in 4.65s\n", " ✅ Rows: 141,561\n", "\n", "Processing match DFL-MAT-J03WOH...\n", - " ✅ Parsed in 12.23s, Saved in 10.73s\n", + " ✅ Parsed in 1.28s, Saved in 4.18s\n", " ✅ Rows: 137,214\n" ] } @@ -242,7 +242,7 @@ "output_type": "stream", "text": [ "Loading data for DFL-MAT-J03WMX...\n", - " ✅ Loaded and pivoted 165259 rows in 9.5119s\n" + " ✅ Loaded and pivoted 165259 rows in 1.4731s\n" ] }, { @@ -255,28 +255,28 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (5, 424)
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" ], "text/plain": [ - "shape: (5, 424)\n", + "shape: (5, 316)\n", "┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", - "│ competiti ┆ season/id ┆ match/id ┆ frame_id ┆ … ┆ referees/ ┆ referees/ ┆ referees/ ┆ referees/ │\n", - "│ on/id ┆ --- ┆ --- ┆ --- ┆ ┆ referee:D ┆ referee:D ┆ referee:D ┆ referee:D │\n", - "│ --- ┆ str ┆ str ┆ i64 ┆ ┆ FL-OBJ-00 ┆ FL-OBJ-00 ┆ FL-OBJ-00 ┆ FL-OBJ-00 │\n", - "│ str ┆ ┆ ┆ ┆ ┆ 014… ┆ 011… ┆ 011… ┆ 011… │\n", - "│ ┆ ┆ ┆ ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ ┆ ┆ ┆ ┆ null ┆ null ┆ null ┆ null │\n", + "│ competiti ┆ season/id ┆ match/id ┆ frame_id ┆ … ┆ teams/tea ┆ teams/tea ┆ teams/tea ┆ teams/tea │\n", + "│ on/id ┆ --- ┆ --- ┆ --- ┆ ┆ m:home/je ┆ m:away/id ┆ m:away/na ┆ m:away/je │\n", + "│ --- ┆ str ┆ str ┆ i64 ┆ ┆ rsey_colo ┆ --- ┆ me ┆ rsey_colo │\n", + "│ str ┆ ┆ ┆ ┆ ┆ ur ┆ str ┆ --- ┆ ur │\n", + "│ ┆ ┆ ┆ ┆ ┆ --- ┆ ┆ str ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ str ┆ ┆ ┆ str │\n", "╞═══════════╪═══════════╪═══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10000 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10001 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10002 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10003 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10004 ┆ … ┆ null ┆ null ┆ null ┆ null │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10000 ┆ … ┆ #F4F5F0 ┆ DFL-CLU-0 ┆ FC Bayern ┆ #25282A │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ 0000G ┆ München ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10001 ┆ … ┆ #F4F5F0 ┆ DFL-CLU-0 ┆ FC Bayern ┆ #25282A │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ 0000G ┆ München ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10002 ┆ … ┆ #F4F5F0 ┆ DFL-CLU-0 ┆ FC Bayern ┆ #25282A │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ 0000G ┆ München ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10003 ┆ … ┆ #F4F5F0 ┆ DFL-CLU-0 ┆ FC Bayern ┆ #25282A │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ 0000G ┆ München ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10004 ┆ … ┆ #F4F5F0 ┆ DFL-CLU-0 ┆ FC Bayern ┆ #25282A │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ 0000G ┆ München ┆ │\n", "└───────────┴───────────┴───────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘" ] }, @@ -307,60 +307,52 @@ { "data": { "text/plain": [ - "{'_entities': {'ball': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'teams': {'home': {'players': {'DFL-OBJ-000270': {'jersey_colour': None,\n", - " 'id': None,\n", - " 'name': None},\n", - " 'DFL-OBJ-002GKO': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'DFL-OBJ-00012X': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'DFL-OBJ-J01BGM': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'DFL-OBJ-J01D1W': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'DFL-OBJ-002GBW': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'DFL-OBJ-0026ZI': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'DFL-OBJ-0027AX': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'DFL-OBJ-J01DVC': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'DFL-OBJ-0002HE': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'DFL-OBJ-0027B6': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'DFL-OBJ-0002BO': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'DFL-OBJ-0027V2': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'DFL-OBJ-002G89': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'DFL-OBJ-0028BD': {'jersey_colour': None, 'id': None, 'name': None},\n", - " 'DFL-OBJ-002G4A': {'jersey_colour': None, 'id': None, 'name': None}},\n", + "{'_entities': {'ball': {},\n", + " 'teams': {'away': {'players': {'DFL-OBJ-0000IA': {},\n", + " 'DFL-OBJ-J01APO': {},\n", + " 'DFL-OBJ-002GCR': {},\n", + " 'DFL-OBJ-0002DR': {},\n", + " 'DFL-OBJ-J01N65': {},\n", + " 'DFL-OBJ-0002F5': {},\n", + " 'DFL-OBJ-J01B8N': {},\n", + " 'DFL-OBJ-0000RP': {},\n", + " 'DFL-OBJ-0002AU': {},\n", + " 'DFL-OBJ-0027G6': {},\n", + " 'DFL-OBJ-0027KL': {},\n", + " 'DFL-OBJ-J015S4': {},\n", + " 'DFL-OBJ-000191': {},\n", + " 'DFL-OBJ-0026PM': {},\n", + " 'DFL-OBJ-0027G0': {},\n", + " 'DFL-OBJ-J017RE': {}},\n", " 'attributes': {}},\n", - " 'team:home': {'players': {},\n", - " 'attributes': {'jersey_colour': '#F4F5F0',\n", - " 'id': 'DFL-CLU-000008',\n", - " 'name': '1. 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n&L64jdlNR7SAHm{FZ{E|-lUBE{o~%Ly%&8>h)=0{f*!5_ZGx+b diff --git a/python/tracking_data_container/catalog.py b/python/tracking_data_container/catalog.py index d898c0a..0427ecc 100644 --- a/python/tracking_data_container/catalog.py +++ b/python/tracking_data_container/catalog.py @@ -378,9 +378,11 @@ def _save_tracking_data( entities_hierarchical["teams"][tid] = {"players": {}, "attributes": {}} entities_hierarchical["teams"][tid]["players"][eid] = attrs elif etype == "team": - if eid not in entities_hierarchical["teams"]: - entities_hierarchical["teams"][eid] = {"players": {}, "attributes": {}} - entities_hierarchical["teams"][eid]["attributes"] = attrs + # Use tid (e.g. 'home') instead of eid (e.g. 'team:home') for the key + target_tid = tid if tid != "none" else eid.replace("team:", "") + if target_tid not in entities_hierarchical["teams"]: + entities_hierarchical["teams"][target_tid] = {"players": {}, "attributes": {}} + entities_hierarchical["teams"][target_tid]["attributes"] = attrs if columns_to_drop: logger.info(f"Normalizing constant attributes to metadata: {columns_to_drop}") From e5e3c0dee36f248db193e0f4cb29118822071d24 Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 16:00:18 +0200 Subject: [PATCH 16/21] perf: flatten hierarchical team metadata attributes --- examples/idsse_warehouse/catalog.db | Bin 20480 -> 0 bytes ...00-501c-45f2-9996-77ebcb5463a5.metadata.json | 1 - ...b7-5f2a-4530-bd47-a196cd47b46e.metadata.json | 1 - ...e4-c402-4825-a184-fa65adc17cda.metadata.json | 1 - ...29-90a1-4f37-8af3-8be2f399c28b.metadata.json | 1 - ...7e31226c-e1c9-4c2b-8a42-09812bdcba85-m0.avro | Bin 4507 -> 0 bytes ...d85c7ea8-2fa8-4a02-9d11-6aca1be81ea1-m0.avro | Bin 4507 -> 0 bytes ...fb416f68-89e7-49e5-8303-1c604053a6e7-m0.avro | Bin 4508 -> 0 bytes ...-0-fb416f68-89e7-49e5-8303-1c604053a6e7.avro | Bin 1902 -> 0 bytes ...-0-d85c7ea8-2fa8-4a02-9d11-6aca1be81ea1.avro | Bin 1948 -> 0 bytes ...-0-7e31226c-e1c9-4c2b-8a42-09812bdcba85.avro | Bin 1826 -> 0 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examples/idsse_warehouse/tracking/tracking_data/metadata/f58e70eb-6eb2-4a19-9b08-5b01b851d469-m0.avro delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/fc9a80a6-f0eb-4290-8070-14be9d80f7e7-m0.avro delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-1282166195388124131-0-fc9a80a6-f0eb-4290-8070-14be9d80f7e7.avro delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-5997955711447099734-0-ecfdfc8e-3ec5-4fb9-b6c2-5b5b776d94bb.avro delete mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-601469116504892914-0-f58e70eb-6eb2-4a19-9b08-5b01b851d469.avro diff --git a/examples/idsse_warehouse/catalog.db b/examples/idsse_warehouse/catalog.db deleted file mode 100644 index ddd8e0c4d06a3c82012485f2d19bcd89ebf9defe..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 20480 zcmeI&O>fgM90qWw+h7~p$^kU-svwY9rFnU8Q#e2wl|YQQGC(^;PU5oAtSL!42FJpQ 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deleted file mode 100644 index 63e44a5d71cd903af670ed5dc8febfef598d13b4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2001 zcmeZI%3|aZE6z(SD9*?)(alUzF*7hUF|#x@G&40Yv9L6Mt1YHny~Vq$IpgJW^CZ!fJ8Ox;Q7v(0F=$54x6=&w>F&T*@ zmKEjeCFiH4CiA7FrsX7-q>6!sijy-^a}(EzS1XlN7Nja!DJT`CCg&HWDCsCDrDdk( zq!a_iqN|ni5_7?_xruq1X{p5}@db$`8E{2F=L40Q8W;d|!OSTxDay=C2PsL(PX-z6 zlb@Ve0(6Z+XppBud1gt5f?Kcx*#1;$g`Io1cw|(4Ur>lUf{~oL`FReFGCD{TN}ET9H{?0*vf@ zo_q|D(q6PuUTIDaPz55xCKeSXR)X{Z!!Q?ATtG9U1yCrnBsCY5gAuCnXKzsIPt43K z1|~zWEs!z*W}+ohB1p>5&q+ zv-Pnay=d#r>?pwY;3Ln$qUcXcQzO~FrK+s-Fwe0x2duuFxiXOh}xi+m#ENSwo z1+5&D;&fFy5>_sl+q*QPBPy~&P)Xuad*(CQithH8QdVmo@hmOTJ!rA8*J6@Ta>!JZ zg{nD8rdbI-mm^l3-LUT=yOQX>u2(BLUf+~0cUc>Fw582)(t`cENo!o9Isb|NGtcA= z{_a<7Sqn<;;W;jW-y;7bQ>eow)vzB;;=6Z^DMT%vca6E5&R zW%uzG`-!LL`fkr(+kN-+uVqR {tid} > {attr} + # instead of teams > {tid} > attributes > {attr} entities_hierarchical: dict[str, Any] = {"ball": {}, "teams": {}, "referees": {}} for ent in entities_flat: etype = ent.pop("entity_type") @@ -375,14 +377,15 @@ def _save_tracking_data( entities_hierarchical["referees"][eid] = attrs elif etype == "player": if tid not in entities_hierarchical["teams"]: - entities_hierarchical["teams"][tid] = {"players": {}, "attributes": {}} + entities_hierarchical["teams"][tid] = {"players": {}} entities_hierarchical["teams"][tid]["players"][eid] = attrs elif etype == "team": # Use tid (e.g. 'home') instead of eid (e.g. 'team:home') for the key target_tid = tid if tid != "none" else eid.replace("team:", "") if target_tid not in entities_hierarchical["teams"]: - entities_hierarchical["teams"][target_tid] = {"players": {}, "attributes": {}} - entities_hierarchical["teams"][target_tid]["attributes"] = attrs + entities_hierarchical["teams"][target_tid] = {"players": {}} + # Merge team-level attributes directly into the team object + entities_hierarchical["teams"][target_tid].update(attrs) if columns_to_drop: logger.info(f"Normalizing constant attributes to metadata: {columns_to_drop}") @@ -514,9 +517,9 @@ def _get_flat_entities(self, hierarchical_entities: dict[str, Any]) -> list[dict # 2. Teams & Players for tid, team_info in hierarchical_entities.get("teams", {}).items(): # Team itself - flat.append( - {"entity_id": f"team:{tid}", "team_id": tid, **team_info.get("attributes", {})} - ) + # We need to exclude "players" from the attributes when flattening the team itself + team_attrs = {k: v for k, v in team_info.items() if k != "players"} + flat.append({"entity_id": f"team:{tid}", "team_id": tid, **team_attrs}) # Players for pid, p_attrs in team_info.get("players", {}).items(): flat.append({"entity_id": pid, "team_id": tid, **p_attrs}) From 6c9cf8e398399236c250bb03540980d5eb0424b2 Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 16:08:53 +0200 Subject: [PATCH 17/21] fix: remove redundant team/id for referees and exclude entity_key from wide format --- crates/tdc-core/src/datacontainer/core.rs | 43 ++++++---- crates/tdc-core/src/schema/entity.rs | 18 +++++ examples/08-idsse-warehouse.ipynb | 99 +++++++++++------------ python/tracking_data_container/catalog.py | 20 +++-- 4 files changed, 105 insertions(+), 75 deletions(-) diff --git a/crates/tdc-core/src/datacontainer/core.rs b/crates/tdc-core/src/datacontainer/core.rs index 7ba684b..9b1a3ce 100644 --- a/crates/tdc-core/src/datacontainer/core.rs +++ b/crates/tdc-core/src/datacontainer/core.rs @@ -424,7 +424,7 @@ impl TrackingDataContainer { attr_indices.push(i); // Also pivot it } else if core_names.contains(name) { core_indices.push(i); - } else if name == "entity_type" { + } else if name == "entity_type" || name == "entity_key" { continue; } else { attr_indices.push(i); @@ -635,10 +635,10 @@ impl TrackingDataContainer { } }; - let attr_builder_indices = entity_map.entry((p_id, t_id)).or_insert_with(|| { + let attr_mappings = entity_map.entry((p_id, t_id)).or_insert_with(|| { attr_indices .iter() - .map(|&attr_idx| { + .filter_map(|&attr_idx| { let attr_name = fields[attr_idx].name(); let entity = if p_id == "ball" { TrackedEntity::Ball @@ -658,12 +658,21 @@ impl TrackingDataContainer { }; let attribute = match attr_name.as_str() { - "entity_id" => crate::schema::TrackedEntityAttribute::Other( - "player/id".to_string(), - ), + "entity_id" => { + if entity.is_ball() { + return None; + } + crate::schema::TrackedEntityAttribute::Other( + "player/id".to_string(), + ) + } "team_id" => { + if !entity.is_player() && !entity.is_team() { + return None; + } crate::schema::TrackedEntityAttribute::Other("team/id".to_string()) } + "entity_key" => return None, _ => crate::schema::TrackedEntityAttribute::from_str(attr_name) .unwrap_or_else(|_| { crate::schema::TrackedEntityAttribute::Other( @@ -673,47 +682,47 @@ impl TrackingDataContainer { }; let field = DataField::TrackedEntity { entity, attribute }; - builder.get_or_create_column_id( + let builder_idx = builder.get_or_create_column_id( &field.name(), fields[attr_idx].data_type().clone(), - ) + ); + Some((attr_idx, builder_idx)) }) .collect::>() }); - for (attr_pos, &builder_col_idx) in attr_builder_indices.iter().enumerate() { - let attr_idx = attr_indices[attr_pos]; - let col = &columns[attr_idx]; + for (attr_idx, builder_col_idx) in attr_mappings { + let col: &ArrayRef = &columns[*attr_idx]; if !col.is_null(i) { match col.data_type() { Float32 => builder.insert_float_at_idx( row_idx, - builder_col_idx, + *builder_col_idx, col.as_primitive::().value(i) as f64, ), Float64 => builder.insert_float_at_idx( row_idx, - builder_col_idx, + *builder_col_idx, col.as_primitive::().value(i), ), Int64 => builder.insert_int_at_idx( row_idx, - builder_col_idx, + *builder_col_idx, col.as_primitive::().value(i), ), UInt64 => builder.insert_int_at_idx( row_idx, - builder_col_idx, + *builder_col_idx, col.as_primitive::().value(i) as i64, ), Utf8 => builder.insert_string_at_idx( row_idx, - builder_col_idx, + *builder_col_idx, col.as_string::().value(i), ), LargeUtf8 => builder.insert_string_at_idx( row_idx, - builder_col_idx, + *builder_col_idx, col.as_string::().value(i), ), _ => {} diff --git a/crates/tdc-core/src/schema/entity.rs b/crates/tdc-core/src/schema/entity.rs index 76d4b36..cf50b34 100644 --- a/crates/tdc-core/src/schema/entity.rs +++ b/crates/tdc-core/src/schema/entity.rs @@ -26,3 +26,21 @@ pub enum TrackedEntity { team_id: String, }, } + +impl TrackedEntity { + pub fn is_ball(&self) -> bool { + matches!(self, TrackedEntity::Ball) + } + + pub fn is_player(&self) -> bool { + matches!(self, TrackedEntity::Player { .. }) + } + + pub fn is_team(&self) -> bool { + matches!(self, TrackedEntity::Team { .. }) + } + + pub fn is_referee(&self) -> bool { + matches!(self, TrackedEntity::Referee { .. }) + } +} diff --git a/examples/08-idsse-warehouse.ipynb b/examples/08-idsse-warehouse.ipynb index e8e7ed8..710a801 100644 --- a/examples/08-idsse-warehouse.ipynb +++ b/examples/08-idsse-warehouse.ipynb @@ -138,15 +138,15 @@ "text": [ "\n", "Processing match DFL-MAT-J03WMX...\n", - " ✅ Parsed in 1.54s, Saved in 4.82s\n", + " ✅ Parsed in 1.94s, Saved in 5.75s\n", " ✅ Rows: 145,967\n", "\n", "Processing match DFL-MAT-J03WN1...\n", - " ✅ Parsed in 1.38s, Saved in 4.65s\n", + " ✅ Parsed in 1.83s, Saved in 5.09s\n", " ✅ Rows: 141,561\n", "\n", "Processing match DFL-MAT-J03WOH...\n", - " ✅ Parsed in 1.28s, Saved in 4.18s\n", + " ✅ Parsed in 1.46s, Saved in 4.33s\n", " ✅ Rows: 137,214\n" ] } @@ -242,7 +242,7 @@ "output_type": "stream", "text": [ "Loading data for DFL-MAT-J03WMX...\n", - " ✅ Loaded and pivoted 165259 rows in 1.4731s\n" + " ✅ Loaded and pivoted 165259 rows in 1.5354s\n" ] }, { @@ -255,28 +255,27 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (5, 316)
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" + "shape: (5, 316)
competition/idseason/idmatch/idframe_idperiodtimestampreferees/DFL-OBJ-00011J/player/idreferees/DFL-OBJ-00011J/team/idreferees/DFL-OBJ-00011J/xreferees/DFL-OBJ-00011J/yreferees/DFL-OBJ-00011J/zreferees/DFL-OBJ-00011J/velreferees/DFL-OBJ-00011J/accreferees/DFL-OBJ-00011J/entity_keyteams/home/players/DFL-OBJ-002GKO/player/idteams/home/players/DFL-OBJ-002GKO/team/idteams/home/players/DFL-OBJ-002GKO/xteams/home/players/DFL-OBJ-002GKO/yteams/home/players/DFL-OBJ-002GKO/zteams/home/players/DFL-OBJ-002GKO/velteams/home/players/DFL-OBJ-002GKO/accteams/home/players/DFL-OBJ-002GKO/entity_keyteams/away/players/DFL-OBJ-0002DR/player/idteams/away/players/DFL-OBJ-0002DR/team/idteams/away/players/DFL-OBJ-0002DR/xteams/away/players/DFL-OBJ-0002DR/yteams/away/players/DFL-OBJ-0002DR/zteams/away/players/DFL-OBJ-0002DR/velteams/away/players/DFL-OBJ-0002DR/accteams/away/players/DFL-OBJ-0002DR/entity_keyteams/home/players/DFL-OBJ-002GBW/player/idteams/home/players/DFL-OBJ-002GBW/team/idteams/home/players/DFL-OBJ-002GBW/xteams/home/players/DFL-OBJ-002GBW/yteams/home/players/DFL-OBJ-002GBW/zteams/home/players/DFL-OBJ-002GBW/velteams/home/players/DFL-OBJ-002GBW/acc…referees/DFL-OBJ-00011W/team/idreferees/DFL-OBJ-00011W/xreferees/DFL-OBJ-00011W/yreferees/DFL-OBJ-00011W/zreferees/DFL-OBJ-00011W/velreferees/DFL-OBJ-00011W/accreferees/DFL-OBJ-00011W/entity_keyteams/away/players/DFL-OBJ-J01APO/player/idteams/away/players/DFL-OBJ-J01APO/team/idteams/away/players/DFL-OBJ-J01APO/xteams/away/players/DFL-OBJ-J01APO/yteams/away/players/DFL-OBJ-J01APO/zteams/away/players/DFL-OBJ-J01APO/velteams/away/players/DFL-OBJ-J01APO/accteams/away/players/DFL-OBJ-J01APO/entity_keyteams/away/players/DFL-OBJ-0026PM/player/idteams/away/players/DFL-OBJ-0026PM/team/idteams/away/players/DFL-OBJ-0026PM/xteams/away/players/DFL-OBJ-0026PM/yteams/away/players/DFL-OBJ-0026PM/zteams/away/players/DFL-OBJ-0026PM/velteams/away/players/DFL-OBJ-0026PM/accteams/away/players/DFL-OBJ-0026PM/entity_keyteams/home/players/DFL-OBJ-002G4A/player/idteams/home/players/DFL-OBJ-002G4A/team/idteams/home/players/DFL-OBJ-002G4A/xteams/home/players/DFL-OBJ-002G4A/yteams/home/players/DFL-OBJ-002G4A/zteams/home/players/DFL-OBJ-002G4A/velteams/home/players/DFL-OBJ-002G4A/accteams/home/players/DFL-OBJ-002G4A/entity_keyteams/away/idteams/away/jersey_colourteams/away/nameteams/home/idteams/home/jersey_colourteams/home/name
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nullnull"DFL-MAT-J03WMX"10004"first_half"2023-05-27 13:30:12.760"referee:DFL-OBJ-00011J"null-20.4834.209999null0.631.17"referee:DFL-OBJ-00011J|none""DFL-OBJ-002GKO""home"11.265.38null5.280.98"DFL-OBJ-002GKO|home""DFL-OBJ-0002DR""away"-46.6199990.18null2.630.95"DFL-OBJ-0002DR|away"nullnullnullnullnullnullnull…null19.860001-34.439999null2.381.25"referee:DFL-OBJ-00011W|none""DFL-OBJ-J01APO""away"-13.7125.809999null3.00.72"DFL-OBJ-J01APO|away""DFL-OBJ-0026PM""away"1.133.060001null15.420.0"DFL-OBJ-0026PM|away""DFL-OBJ-002G4A""home"9.91-3.39null11.190.92"DFL-OBJ-002G4A|home""DFL-CLU-00000G""#25282A""FC Bayern München""DFL-CLU-000008""#F4F5F0""1. FC Köln"
" ], "text/plain": [ "shape: (5, 316)\n", "┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", - "│ competiti ┆ season/id ┆ match/id ┆ frame_id ┆ … ┆ teams/tea ┆ teams/tea ┆ teams/tea ┆ teams/tea │\n", - "│ on/id ┆ --- ┆ --- ┆ --- ┆ ┆ m:home/je ┆ m:away/id ┆ m:away/na ┆ m:away/je │\n", - "│ --- ┆ str ┆ str ┆ i64 ┆ ┆ rsey_colo ┆ --- ┆ me ┆ rsey_colo │\n", - "│ str ┆ ┆ ┆ ┆ ┆ ur ┆ str ┆ --- ┆ ur │\n", - "│ ┆ ┆ ┆ ┆ ┆ --- ┆ ┆ str ┆ --- │\n", - "│ ┆ ┆ ┆ ┆ ┆ str ┆ ┆ ┆ str │\n", + "│ competiti ┆ season/id ┆ match/id ┆ frame_id ┆ … ┆ teams/awa ┆ teams/hom ┆ teams/hom ┆ teams/hom │\n", + "│ on/id ┆ --- ┆ --- ┆ --- ┆ ┆ y/name ┆ e/id ┆ e/jersey_ ┆ e/name │\n", + "│ --- ┆ str ┆ str ┆ i64 ┆ ┆ --- ┆ --- ┆ colour ┆ --- │\n", + "│ str ┆ ┆ ┆ ┆ ┆ str ┆ str ┆ --- ┆ str │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ str ┆ │\n", "╞═══════════╪═══════════╪═══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10000 ┆ … ┆ #F4F5F0 ┆ DFL-CLU-0 ┆ FC Bayern ┆ #25282A │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ 0000G ┆ München ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10001 ┆ … ┆ #F4F5F0 ┆ DFL-CLU-0 ┆ FC Bayern ┆ #25282A │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ 0000G ┆ München ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10002 ┆ … ┆ #F4F5F0 ┆ DFL-CLU-0 ┆ FC Bayern ┆ #25282A │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ 0000G ┆ München ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10003 ┆ … ┆ #F4F5F0 ┆ DFL-CLU-0 ┆ FC Bayern ┆ #25282A │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ 0000G ┆ München ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10004 ┆ … ┆ #F4F5F0 ┆ DFL-CLU-0 ┆ FC Bayern ┆ #25282A │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ ┆ 0000G ┆ München ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10000 ┆ … ┆ FC Bayern ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ München ┆ 00008 ┆ ┆ Köln │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10001 ┆ … ┆ FC Bayern ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ München ┆ 00008 ┆ ┆ Köln │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10002 ┆ … ┆ FC Bayern ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ München ┆ 00008 ┆ ┆ Köln │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10003 ┆ … ┆ FC Bayern ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ München ┆ 00008 ┆ ┆ Köln │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10004 ┆ … ┆ FC Bayern ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ München ┆ 00008 ┆ ┆ Köln │\n", "└───────────┴───────────┴───────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘" ] }, @@ -308,48 +307,44 @@ "data": { "text/plain": [ "{'_entities': {'ball': {},\n", - " 'teams': {'away': {'players': {'DFL-OBJ-0000IA': {},\n", - " 'DFL-OBJ-J01APO': {},\n", - " 'DFL-OBJ-002GCR': {},\n", - " 'DFL-OBJ-0002DR': {},\n", - " 'DFL-OBJ-J01N65': {},\n", - " 'DFL-OBJ-0002F5': {},\n", + " 'teams': {'away': {'players': {'DFL-OBJ-0002DR': {},\n", " 'DFL-OBJ-J01B8N': {},\n", + " 'DFL-OBJ-J01N65': {},\n", + " 'DFL-OBJ-J01APO': {},\n", " 'DFL-OBJ-0000RP': {},\n", - " 'DFL-OBJ-0002AU': {},\n", - " 'DFL-OBJ-0027G6': {},\n", " 'DFL-OBJ-0027KL': {},\n", - " 'DFL-OBJ-J015S4': {},\n", - " 'DFL-OBJ-000191': {},\n", + " 'DFL-OBJ-0027G6': {},\n", " 'DFL-OBJ-0026PM': {},\n", " 'DFL-OBJ-0027G0': {},\n", - " 'DFL-OBJ-J017RE': {}},\n", - " 'attributes': {}},\n", - " 'home': {'players': {'DFL-OBJ-002GBW': {},\n", + " 'DFL-OBJ-J015S4': {},\n", + " 'DFL-OBJ-0002F5': {},\n", + " 'DFL-OBJ-002GCR': {},\n", + " 'DFL-OBJ-J017RE': {},\n", + " 'DFL-OBJ-000191': {},\n", + " 'DFL-OBJ-0002AU': {},\n", + " 'DFL-OBJ-0000IA': {}},\n", + " 'id': 'DFL-CLU-00000G',\n", + " 'jersey_colour': '#25282A',\n", + " 'name': 'FC Bayern München'},\n", + " 'home': {'players': {'DFL-OBJ-0027V2': {},\n", + " 'DFL-OBJ-002GKO': {},\n", + " 'DFL-OBJ-002GBW': {},\n", + " 'DFL-OBJ-000270': {},\n", " 'DFL-OBJ-J01BGM': {},\n", - " 'DFL-OBJ-002G4A': {},\n", - " 'DFL-OBJ-J01DVC': {},\n", + " 'DFL-OBJ-002G89': {},\n", + " 'DFL-OBJ-J01D1W': {},\n", " 'DFL-OBJ-0002HE': {},\n", - " 'DFL-OBJ-000270': {},\n", " 'DFL-OBJ-0027AX': {},\n", + " 'DFL-OBJ-J01DVC': {},\n", + " 'DFL-OBJ-00012X': {},\n", " 'DFL-OBJ-0002BO': {},\n", - " 'DFL-OBJ-0027V2': {},\n", - " 'DFL-OBJ-J01D1W': {},\n", - " 'DFL-OBJ-0026ZI': {},\n", " 'DFL-OBJ-0028BD': {},\n", - " 'DFL-OBJ-002GKO': {},\n", - " 'DFL-OBJ-00012X': {},\n", " 'DFL-OBJ-0027B6': {},\n", - " 'DFL-OBJ-002G89': {}},\n", - " 'attributes': {}},\n", - " 'team:home': {'players': {},\n", - " 'attributes': {'id': 'DFL-CLU-000008',\n", - " 'name': '1. FC Köln',\n", - " 'jersey_colour': '#F4F5F0'}},\n", - " 'team:away': {'players': {},\n", - " 'attributes': {'id': 'DFL-CLU-00000G',\n", - " 'name': 'FC Bayern München',\n", - " 'jersey_colour': '#25282A'}}},\n", + " 'DFL-OBJ-0026ZI': {},\n", + " 'DFL-OBJ-002G4A': {}},\n", + " 'id': 'DFL-CLU-000008',\n", + " 'jersey_colour': '#F4F5F0',\n", + " 'name': '1. FC Köln'}},\n", " 'referees': {'referee:DFL-OBJ-000141': {},\n", " 'referee:DFL-OBJ-00011J': {},\n", " 'referee:DFL-OBJ-00011W': {}}}}" diff --git a/python/tracking_data_container/catalog.py b/python/tracking_data_container/catalog.py index efce0ba..6736369 100644 --- a/python/tracking_data_container/catalog.py +++ b/python/tracking_data_container/catalog.py @@ -575,16 +575,24 @@ def schema(self) -> Fields: for row in entities: p_id = str(row.get("entity_id")) t_id = str(row.get("team_id", "none")) - prefix = TrackedEntity.from_entity_key(f"{p_id}|{t_id}").cdf_prefix() + entity = TrackedEntity.from_entity_key(f"{p_id}|{t_id}") + prefix = entity.cdf_prefix() for attr in attr_cols: - wide_names.append(f"{prefix}{attr}") + if attr != "entity_key": + wide_names.append(f"{prefix}{attr}") for attr in row.keys(): - if attr not in ("entity_id", "team_id"): + if attr not in ("entity_id", "team_id", "entity_key"): wide_names.append(f"{prefix}{attr}") - wide_names.append(f"{prefix}player/id") - wide_names.append(f"{prefix}team/id") + # Always add player/id (except for ball which has it via TrackedEntity logic if needed, + # but usually ball doesn't have player/id) + if not entity.is_ball: + wide_names.append(f"{prefix}player/id") + + # Only add team/id for players and teams + if entity.is_player or entity.is_team: + wide_names.append(f"{prefix}team/id") self._cached_schema = Fields(list(dict.fromkeys(wide_names))) return self._cached_schema @@ -730,7 +738,7 @@ def collect(self) -> TrackingDataContainer: t_id = ent_data["team_id"] prefix = TrackedEntity.from_entity_key(f"{p_id}|{t_id}").cdf_prefix() for attr, value in ent_data.items(): - if attr in ("entity_id", "team_id"): + if attr in ("entity_id", "team_id", "entity_key"): continue wide_attr_name = f"{prefix}{attr}" From 8a08119073373c695224246aee77f59aedbda98f Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 16:38:36 +0200 Subject: [PATCH 18/21] feat: use IcebergConfig for default catalog configuration and optimization --- examples/08-idsse-warehouse.ipynb | 102 +++++++++--------- examples/idsse_warehouse/catalog.db | Bin 0 -> 20480 bytes ...-96a2-48d6-8305-1e7155f857e6.metadata.json | 1 + ...-4270-4bd2-86dc-2b88858c0833.metadata.json | 1 + ...-c362-4a0f-a942-2fc8d6c51c96.metadata.json | 1 + ...-b298-4f4f-a814-9b9f76d8e71a.metadata.json | 1 + ...c097fb-ed6a-4e53-95aa-61c36e19e9b5-m0.avro | Bin 0 -> 4505 bytes ...8d49d5-48bd-40a1-aac5-ac3df2728952-m0.avro | Bin 0 -> 4508 bytes ...b6cbfa-fef9-4779-b2e1-c63290db081c-m0.avro | Bin 0 -> 4507 bytes ...-eab6cbfa-fef9-4779-b2e1-c63290db081c.avro | Bin 0 -> 1947 bytes ...-13c097fb-ed6a-4e53-95aa-61c36e19e9b5.avro | Bin 0 -> 1902 bytes ...-748d49d5-48bd-40a1-aac5-ac3df2728952.avro | Bin 0 -> 1827 bytes ...-0ccc-4e61-aa83-a184ad41f6f1.metadata.json | 1 + ...-f126-4491-bfa6-00cf45343e37.metadata.json | 1 + ...-ab81-46c9-92b2-74595885722c.metadata.json | 1 + ...-78aa-49d0-8fa1-f6314d7b0ee9.metadata.json | 1 + ...6c935e-8816-455f-987e-f46e735afb70-m0.avro | Bin 0 -> 5590 bytes ...a2fe75-8006-42c8-97d9-5e4835aa023e-m0.avro | Bin 0 -> 6126 bytes ...eff7a4-107d-40c3-846c-6d69ba0f72ff-m0.avro | Bin 0 -> 6110 bytes ...-3aa2fe75-8006-42c8-97d9-5e4835aa023e.avro | Bin 0 -> 1851 bytes ...-236c935e-8816-455f-987e-f46e735afb70.avro | Bin 0 -> 2001 bytes ...-b2eff7a4-107d-40c3-846c-6d69ba0f72ff.avro | Bin 0 -> 1936 bytes python/tests/test_catalog.py | 3 +- python/tracking_data_container/catalog.py | 35 +++++- python/tracking_data_container/config.py | 5 +- 25 files changed, 93 insertions(+), 60 deletions(-) create mode 100644 examples/idsse_warehouse/catalog.db create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00000-f1bd0a1f-96a2-48d6-8305-1e7155f857e6.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00001-ee062ad7-4270-4bd2-86dc-2b88858c0833.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00002-9c72b796-c362-4a0f-a942-2fc8d6c51c96.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/00003-b3d28f12-b298-4f4f-a814-9b9f76d8e71a.metadata.json create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/13c097fb-ed6a-4e53-95aa-61c36e19e9b5-m0.avro create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/748d49d5-48bd-40a1-aac5-ac3df2728952-m0.avro create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/eab6cbfa-fef9-4779-b2e1-c63290db081c-m0.avro create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/snap-2148665455330551612-0-eab6cbfa-fef9-4779-b2e1-c63290db081c.avro create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/snap-2774007322244479359-0-13c097fb-ed6a-4e53-95aa-61c36e19e9b5.avro create mode 100644 examples/idsse_warehouse/tracking/match_metadata/metadata/snap-5536800293271387766-0-748d49d5-48bd-40a1-aac5-ac3df2728952.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00000-eb9e2be6-0ccc-4e61-aa83-a184ad41f6f1.metadata.json create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00001-bf153c68-f126-4491-bfa6-00cf45343e37.metadata.json create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00002-d345f9cd-ab81-46c9-92b2-74595885722c.metadata.json create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/00003-97d8ffab-78aa-49d0-8fa1-f6314d7b0ee9.metadata.json create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/236c935e-8816-455f-987e-f46e735afb70-m0.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/3aa2fe75-8006-42c8-97d9-5e4835aa023e-m0.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/b2eff7a4-107d-40c3-846c-6d69ba0f72ff-m0.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-3805096703135495509-0-3aa2fe75-8006-42c8-97d9-5e4835aa023e.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-6127150891889527239-0-236c935e-8816-455f-987e-f46e735afb70.avro create mode 100644 examples/idsse_warehouse/tracking/tracking_data/metadata/snap-6133585602095476169-0-b2eff7a4-107d-40c3-846c-6d69ba0f72ff.avro diff --git a/examples/08-idsse-warehouse.ipynb b/examples/08-idsse-warehouse.ipynb index 710a801..23f1014 100644 --- a/examples/08-idsse-warehouse.ipynb +++ b/examples/08-idsse-warehouse.ipynb @@ -138,15 +138,15 @@ "text": [ "\n", "Processing match DFL-MAT-J03WMX...\n", - " ✅ Parsed in 1.94s, Saved in 5.75s\n", + " ✅ Parsed in 14.33s, Saved in 12.99s\n", " ✅ Rows: 145,967\n", "\n", "Processing match DFL-MAT-J03WN1...\n", - " ✅ Parsed in 1.83s, Saved in 5.09s\n", + " ✅ Parsed in 14.03s, Saved in 12.06s\n", " ✅ Rows: 141,561\n", "\n", "Processing match DFL-MAT-J03WOH...\n", - " ✅ Parsed in 1.46s, Saved in 4.33s\n", + " ✅ Parsed in 12.71s, Saved in 10.63s\n", " ✅ Rows: 137,214\n" ] } @@ -242,7 +242,7 @@ "output_type": "stream", "text": [ "Loading data for DFL-MAT-J03WMX...\n", - " ✅ Loaded and pivoted 165259 rows in 1.5354s\n" + " ✅ Loaded and pivoted 165259 rows in 7.1870s\n" ] }, { @@ -255,27 +255,27 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (5, 316)
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" ], "text/plain": [ - "shape: (5, 316)\n", + "shape: (5, 273)\n", "┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", "│ competiti ┆ season/id ┆ match/id ┆ frame_id ┆ … ┆ teams/awa ┆ teams/hom ┆ teams/hom ┆ teams/hom │\n", - "│ on/id ┆ --- ┆ --- ┆ --- ┆ ┆ y/name ┆ e/id ┆ e/jersey_ ┆ e/name │\n", - "│ --- ┆ str ┆ str ┆ i64 ┆ ┆ --- ┆ --- ┆ colour ┆ --- │\n", - "│ str ┆ ┆ ┆ ┆ ┆ str ┆ str ┆ --- ┆ str │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ str ┆ │\n", + "│ on/id ┆ --- ┆ --- ┆ --- ┆ ┆ y/id ┆ e/jersey_ ┆ e/name ┆ e/id │\n", + "│ --- ┆ str ┆ str ┆ i64 ┆ ┆ --- ┆ colour ┆ --- ┆ --- │\n", + "│ str ┆ ┆ ┆ ┆ ┆ str ┆ --- ┆ str ┆ str │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ str ┆ ┆ │\n", "╞═══════════╪═══════════╪═══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10000 ┆ … ┆ FC Bayern ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ München ┆ 00008 ┆ ┆ Köln │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10001 ┆ … ┆ FC Bayern ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ München ┆ 00008 ┆ ┆ Köln │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10002 ┆ … ┆ FC Bayern ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ München ┆ 00008 ┆ ┆ Köln │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10003 ┆ … ┆ FC Bayern ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ München ┆ 00008 ┆ ┆ Köln │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10004 ┆ … ┆ FC Bayern ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ München ┆ 00008 ┆ ┆ Köln │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10000 ┆ … ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC ┆ DFL-CLU-0 │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 0000G ┆ ┆ Köln ┆ 00008 │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10001 ┆ … ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC ┆ DFL-CLU-0 │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 0000G ┆ ┆ Köln ┆ 00008 │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10002 ┆ … ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC ┆ DFL-CLU-0 │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 0000G ┆ ┆ Köln ┆ 00008 │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10003 ┆ … ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC ┆ DFL-CLU-0 │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 0000G ┆ ┆ Köln ┆ 00008 │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10004 ┆ … ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC ┆ DFL-CLU-0 │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 0000G ┆ ┆ Köln ┆ 00008 │\n", "└───────────┴───────────┴───────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘" ] }, @@ -307,47 +307,47 @@ "data": { "text/plain": [ "{'_entities': {'ball': {},\n", - " 'teams': {'away': {'players': {'DFL-OBJ-0002DR': {},\n", - " 'DFL-OBJ-J01B8N': {},\n", - " 'DFL-OBJ-J01N65': {},\n", - " 'DFL-OBJ-J01APO': {},\n", - " 'DFL-OBJ-0000RP': {},\n", - " 'DFL-OBJ-0027KL': {},\n", - " 'DFL-OBJ-0027G6': {},\n", - " 'DFL-OBJ-0026PM': {},\n", - " 'DFL-OBJ-0027G0': {},\n", - " 'DFL-OBJ-J015S4': {},\n", - " 'DFL-OBJ-0002F5': {},\n", - " 'DFL-OBJ-002GCR': {},\n", + " 'teams': {'away': {'players': {'DFL-OBJ-J015S4': {},\n", " 'DFL-OBJ-J017RE': {},\n", + " 'DFL-OBJ-0000IA': {},\n", + " 'DFL-OBJ-0002DR': {},\n", " 'DFL-OBJ-000191': {},\n", + " 'DFL-OBJ-0027G6': {},\n", + " 'DFL-OBJ-0000RP': {},\n", + " 'DFL-OBJ-J01B8N': {},\n", + " 'DFL-OBJ-0027G0': {},\n", " 'DFL-OBJ-0002AU': {},\n", - " 'DFL-OBJ-0000IA': {}},\n", - " 'id': 'DFL-CLU-00000G',\n", + " 'DFL-OBJ-002GCR': {},\n", + " 'DFL-OBJ-0026PM': {},\n", + " 'DFL-OBJ-0002F5': {},\n", + " 'DFL-OBJ-J01APO': {},\n", + " 'DFL-OBJ-J01N65': {},\n", + " 'DFL-OBJ-0027KL': {}},\n", " 'jersey_colour': '#25282A',\n", - " 'name': 'FC Bayern München'},\n", - " 'home': {'players': {'DFL-OBJ-0027V2': {},\n", - " 'DFL-OBJ-002GKO': {},\n", + " 'name': 'FC Bayern München',\n", + " 'id': 'DFL-CLU-00000G'},\n", + " 'home': {'players': {'DFL-OBJ-0026ZI': {},\n", " 'DFL-OBJ-002GBW': {},\n", - " 'DFL-OBJ-000270': {},\n", - " 'DFL-OBJ-J01BGM': {},\n", - " 'DFL-OBJ-002G89': {},\n", - " 'DFL-OBJ-J01D1W': {},\n", + " 'DFL-OBJ-002GKO': {},\n", + " 'DFL-OBJ-0002BO': {},\n", + " 'DFL-OBJ-0027V2': {},\n", " 'DFL-OBJ-0002HE': {},\n", + " 'DFL-OBJ-00012X': {},\n", + " 'DFL-OBJ-0027B6': {},\n", + " 'DFL-OBJ-002G4A': {},\n", " 'DFL-OBJ-0027AX': {},\n", " 'DFL-OBJ-J01DVC': {},\n", - " 'DFL-OBJ-00012X': {},\n", - " 'DFL-OBJ-0002BO': {},\n", " 'DFL-OBJ-0028BD': {},\n", - " 'DFL-OBJ-0027B6': {},\n", - " 'DFL-OBJ-0026ZI': {},\n", - " 'DFL-OBJ-002G4A': {}},\n", - " 'id': 'DFL-CLU-000008',\n", + " 'DFL-OBJ-J01D1W': {},\n", + " 'DFL-OBJ-000270': {},\n", + " 'DFL-OBJ-J01BGM': {},\n", + " 'DFL-OBJ-002G89': {}},\n", " 'jersey_colour': '#F4F5F0',\n", - " 'name': '1. 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Build PartitionSpec from config + partition_fields = [] + for field_name in self.config.partition_by: + # Find field in schema to get its ID + field = schema.find_field(field_name) + partition_fields.append( + PartitionField( + source_id=field.field_id, + field_id=1000 + field.field_id, + transform=IdentityTransform(), + name=field_name, + ) ) - ) + partition_spec = PartitionSpec(*partition_fields) + + # Map config to Iceberg table properties for optimization + properties = { + "write.parquet.compression-codec": self.config.compression, + "write.parquet.page-size-bytes": str(self.config.page_size), + "write.parquet.row-group-size": str(self.config.row_group_size), + "write.parquet.dict-enabled": str(self.config.use_dictionary).lower(), + } # Create table self._tracking_table = self._iceberg_catalog.create_table( table_name, schema=schema, partition_spec=partition_spec, + properties=properties, ) def _create_metadata_table(self) -> None: @@ -192,9 +209,17 @@ def _create_metadata_table(self) -> None: NestedField(4, "metadata_json", StringType(), required=True), ) + # Metadata table is also partitioned by match_id for consistency + partition_spec = PartitionSpec( + PartitionField( + source_id=1, field_id=1001, transform=IdentityTransform(), name="match_id" + ) + ) + self._metadata_table = self._iceberg_catalog.create_table( table_name, schema=schema, + partition_spec=partition_spec, ) def load_container( diff --git a/python/tracking_data_container/config.py b/python/tracking_data_container/config.py index bb7093c..b7af0ce 100644 --- a/python/tracking_data_container/config.py +++ b/python/tracking_data_container/config.py @@ -8,8 +8,7 @@ class IcebergConfig: """Configuration for Iceberg table optimization.""" # Partitioning - bucket_count: int = 10 - partition_by: list[str] = field(default_factory=lambda: ["match/id"]) + partition_by: list[str] = field(default_factory=lambda: ["match_id"]) # Parquet optimization compression: str = "snappy" # or "zstd" @@ -22,4 +21,4 @@ class IcebergConfig: use_dictionary: bool = True # Sorting - sort_by: list[str] = field(default_factory=lambda: ["player_id", "timestamp"]) + sort_by: list[str] = field(default_factory=lambda: ["entity_id", "timestamp"]) From a507767bc0495b9a63360819afc15f7cdf3444e7 Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 8 Apr 2026 17:05:41 +0200 Subject: [PATCH 19/21] refactor: use TrackedEntity objects and simplify catalog logic --- crates/tdc-core/src/datacontainer/core.rs | 34 +- crates/tdc-core/src/schema/field.rs | 10 + crates/tdc-python/src/container.rs | 8 +- .../_tracking_data_container.pyi | 4 +- python/tracking_data_container/catalog.py | 482 +++++++----------- python/tracking_data_container/container.py | 6 +- python/tracking_data_container/schema.py | 4 +- 7 files changed, 226 insertions(+), 322 deletions(-) diff --git a/crates/tdc-core/src/datacontainer/core.rs b/crates/tdc-core/src/datacontainer/core.rs index 9b1a3ce..d42c570 100644 --- a/crates/tdc-core/src/datacontainer/core.rs +++ b/crates/tdc-core/src/datacontainer/core.rs @@ -370,7 +370,10 @@ impl TrackingDataContainer { } } - pub fn pivot(data: StructArray) -> TrackingDataContainerResult { + pub fn pivot( + data: StructArray, + entity_to_team: Option>, + ) -> TrackingDataContainerResult { use crate::datacontainer::builder::TrackingDataBuilder; use ahash::AHashSet; use arrow_array::types::{ @@ -473,11 +476,6 @@ impl TrackingDataContainer { "Missing required entity_id or player/id column".to_string(), ) })?; - let team_id_idx = team_id_idx.ok_or_else(|| { - TrackingDataContainerErrorKind::InvalidParameters( - "Missing team_id or team/id column".to_string(), - ) - })?; // 3. Determine min/max frame index for fixed builder if possible let mut min_f = i64::MAX; @@ -531,7 +529,6 @@ impl TrackingDataContainer { .iter() .map(|&idx| { let col_name = fields[idx].name(); - // Map back to internal CDF-like name for consistency in wide format if needed let wide_name = match col_name.as_str() { "match_id" => "match/id", "competition_id" => "competition/id", @@ -549,6 +546,7 @@ impl TrackingDataContainer { .collect(); // 5. Pre-calculate entity attribute mappings + // (player_id, team_id) -> (Vec, Vec) let mut entity_map = ahash::AHashMap::new(); // 6. Pivot data @@ -624,15 +622,21 @@ impl TrackingDataContainer { _ => continue, }; - let t_id_col = &columns[team_id_idx]; - let t_id = if t_id_col.is_null(i) { - "none" - } else { - match t_id_col.data_type() { - Utf8 => t_id_col.as_string::().value(i), - LargeUtf8 => t_id_col.as_string::().value(i), - _ => "none", + let t_id = if let Some(idx) = team_id_idx { + let col = &columns[idx]; + if col.is_null(i) { + "none" + } else { + match col.data_type() { + Utf8 => col.as_string::().value(i), + LargeUtf8 => col.as_string::().value(i), + _ => "none", + } } + } else if let Some(ref mapping) = entity_to_team { + mapping.get(p_id).map(|s| s.as_str()).unwrap_or("none") + } else { + "none" }; let attr_mappings = entity_map.entry((p_id, t_id)).or_insert_with(|| { diff --git a/crates/tdc-core/src/schema/field.rs b/crates/tdc-core/src/schema/field.rs index e608633..798715a 100644 --- a/crates/tdc-core/src/schema/field.rs +++ b/crates/tdc-core/src/schema/field.rs @@ -128,6 +128,16 @@ impl DataField { attribute: Self::parse_attr(&attr_str), } } + ["other", pid, ..] => { + let attr_str = parts[2..].join("/"); + DataField::TrackedEntity { + entity: TrackedEntity::Player { + team_id: "none".to_string(), + player_id: pid.to_string(), + }, + attribute: Self::parse_attr(&attr_str), + } + } ["vendor", "event", "name"] => DataField::Frame { attribute: FrameAttribute::VendorEventName, }, diff --git a/crates/tdc-python/src/container.rs b/crates/tdc-python/src/container.rs index 93d9a29..7e6c338 100644 --- a/crates/tdc-python/src/container.rs +++ b/crates/tdc-python/src/container.rs @@ -349,7 +349,11 @@ impl PyTrackingDataContainer { } #[staticmethod] - fn pivot(data: Bound<'_, PyAny>) -> PyResult { + #[pyo3(signature = (data, entity_to_team=None))] + fn pivot( + data: Bound<'_, PyAny>, + entity_to_team: Option>, + ) -> PyResult { let capsules = data.call_method0("__arrow_c_array__")?; let tuple = capsules.downcast::()?; let array_item = tuple.get_item(0)?; @@ -366,7 +370,7 @@ impl PyTrackingDataContainer { .ok_or_else(|| PyTypeError::new_err("Expected StructArray"))? .clone(); - let inner = TrackingDataContainer::pivot(struct_array).into_pyresult()?; + let inner = TrackingDataContainer::pivot(struct_array, entity_to_team).into_pyresult()?; Ok(PyTrackingDataContainer { inner }) } diff --git a/python/tracking_data_container/_tracking_data_container.pyi b/python/tracking_data_container/_tracking_data_container.pyi index 113da2b..369c27f 100644 --- a/python/tracking_data_container/_tracking_data_container.pyi +++ b/python/tracking_data_container/_tracking_data_container.pyi @@ -166,7 +166,9 @@ class _TrackingDataContainer: @classmethod def _from_capsule(cls, capsule: Any) -> _TrackingDataContainer: ... @staticmethod - def pivot(data: Any) -> _TrackingDataContainer: ... + def pivot( + data: Any, entity_to_team: dict[str, str] | None = None + ) -> _TrackingDataContainer: ... @staticmethod def from_arrow( array_capsule: Any, diff --git a/python/tracking_data_container/catalog.py b/python/tracking_data_container/catalog.py index eb62f45..211784b 100644 --- a/python/tracking_data_container/catalog.py +++ b/python/tracking_data_container/catalog.py @@ -16,7 +16,6 @@ And, BooleanExpression, EqualTo, - Or, Reference, ) from pyiceberg.io.pyarrow import _pyarrow_to_schema_without_ids @@ -42,12 +41,12 @@ logger = logging.getLogger(__name__) # Mapping from internal CDF-like names to snake_case Iceberg names +# Note: team/id is handled as a constant attribute in metadata COLUMN_MAPPING = { "match/id": "match_id", "competition/id": "competition_id", "season/id": "season_id", "player/id": "entity_id", - "team/id": "team_id", } # Reverse mapping @@ -89,7 +88,6 @@ def __init__( # Default properties for SQL catalog (SQLite for POC) import multiprocessing - # Optimize IO parallelism num_workers = multiprocessing.cpu_count() default_properties = { @@ -120,30 +118,25 @@ def initialize(self) -> None: Creates tables with configured partitioning and optimization settings. Must be called once before first use. Idempotent - safe to call multiple times. """ - # Create namespace if it doesn't exist try: self._iceberg_catalog.create_namespace("tracking") except Exception: pass # Namespace already exists - # Create tracking_data table self._create_tracking_table() - - # Create match_metadata table self._create_metadata_table() def _create_tracking_table(self) -> None: """Create tracking_data Iceberg table in the requested long format.""" table_name = "tracking.tracking_data" - # Check if table exists try: self._tracking_table = self._iceberg_catalog.load_table(table_name) - return # Table exists + return except NoSuchTableError: - pass # Create new table + pass - # Define the requested schema. x, y, z are nullable (optional in Iceberg). + # Define schema. Spatial columns are optional but mandatory during ingestion. schema = Schema( NestedField(1, "competition_id", StringType(), required=False), NestedField(2, "season_id", StringType(), required=False), @@ -153,16 +146,14 @@ def _create_tracking_table(self) -> None: NestedField(6, "timestamp", TimestampType(), required=True), NestedField(7, "entity_type", StringType(), required=True), NestedField(8, "entity_id", StringType(), required=True), - NestedField(9, "team_id", StringType(), required=False), - NestedField(10, "x", FloatType(), required=False), - NestedField(11, "y", FloatType(), required=False), - NestedField(12, "z", FloatType(), required=False), + NestedField(9, "x", FloatType(), required=False), + NestedField(10, "y", FloatType(), required=False), + NestedField(11, "z", FloatType(), required=False), ) # Build PartitionSpec from config partition_fields = [] for field_name in self.config.partition_by: - # Find field in schema to get its ID field = schema.find_field(field_name) partition_fields.append( PartitionField( @@ -174,7 +165,7 @@ def _create_tracking_table(self) -> None: ) partition_spec = PartitionSpec(*partition_fields) - # Map config to Iceberg table properties for optimization + # Optimization properties properties = { "write.parquet.compression-codec": self.config.compression, "write.parquet.page-size-bytes": str(self.config.page_size), @@ -182,7 +173,6 @@ def _create_tracking_table(self) -> None: "write.parquet.dict-enabled": str(self.config.use_dictionary).lower(), } - # Create table self._tracking_table = self._iceberg_catalog.create_table( table_name, schema=schema, @@ -194,14 +184,12 @@ def _create_metadata_table(self) -> None: """Create match_metadata Iceberg table.""" table_name = "tracking.match_metadata" - # Check if table exists try: self._metadata_table = self._iceberg_catalog.load_table(table_name) return except NoSuchTableError: pass - # Define schema for metadata schema = Schema( NestedField(1, "match_id", StringType(), required=True), NestedField(2, "competition_id", StringType(), required=False), @@ -209,7 +197,6 @@ def _create_metadata_table(self) -> None: NestedField(4, "metadata_json", StringType(), required=True), ) - # Metadata table is also partitioned by match_id for consistency partition_spec = PartitionSpec( PartitionField( source_id=1, field_id=1001, transform=IdentityTransform(), name="match_id" @@ -228,12 +215,7 @@ def load_container( player_id: str | None = None, **filters: Any, ) -> CatalogLazyTrackingDataContainer: - """ - Load container from storage lazily. - - Returns a LazyTrackingDataContainer that wraps the physical long-format - Iceberg table but allows logical wide-format analysis. - """ + """Load container from storage lazily.""" return CatalogLazyTrackingDataContainer( self, match_id=match_id, player_id=player_id, **filters ) @@ -244,62 +226,30 @@ def save_container( match_id: str | None = None, metadata: dict | None = None, ) -> None: - """ - Save container to storage (replaces match partition). - - Args: - container: Container to save - match_id: Optional match ID override - metadata: Optional additional metadata to save - """ + """Save container to storage (replaces match partition).""" if match_id is None: - # Try to get match_id from first row if possible df = container.to_polars().head(1) - if "match/id" in df.columns: - match_id = str(df["match/id"][0]) - elif "match_id" in df.columns: - match_id = str(df["match_id"][0]) - else: - raise ValueError("match_id must be provided if not present in container data") - - # 1. Save tracking data and get unique entities (hierarchical) + match_id = ( + str(df["match/id"][0]) if "match/id" in df.columns else str(df["match_id"][0]) + ) + entities = self._save_tracking_data(container, match_id) - # 2. Prepare metadata final_metadata = metadata.copy() if metadata else {} final_metadata["_entities"] = entities - - # 3. Save metadata self._save_metadata(final_metadata, match_id) def commit_container(self, container: TrackingDataContainer) -> None: - """ - Commit bound container. - - Args: - container: Bound container to commit - """ - # For POC, we just use save_container - # Try to find match_id + """Commit bound container.""" df = container.to_polars().head(1) match_id = df["match/id"][0] if "match/id" in df.columns else df["match_id"][0] self.save_container(container, match_id=str(match_id)) - def get_metadata(self, match_id: str) -> dict: - """ - Load metadata for a match. - - Args: - match_id: ID of the match - - Returns: - Metadata dictionary - """ + def get_metadata(self, match_id: str) -> dict[Any, Any]: + """Load metadata for a match.""" if self._metadata_table is None: self._metadata_table = self._iceberg_catalog.load_table("tracking.match_metadata") - assert self._metadata_table is not None - scan = self._metadata_table.scan( row_filter=EqualTo(term=Reference("match_id"), value=cast(Any, match_id)) # type: ignore ) @@ -316,110 +266,88 @@ def _save_tracking_data( container: TrackingDataContainer, match_id: str, ) -> dict[str, Any]: - """ - Save tracking data to Iceberg table in entity-long format. - - Returns: - Hierarchical dictionary of unique entities and constants. - """ + """Save tracking data to Iceberg table in entity-long format.""" if self._tracking_table is None: self._tracking_table = self._iceberg_catalog.load_table("tracking.tracking_data") - assert self._tracking_table is not None - - # 1. Melt to entity-long format + # 1. Melt and add entity types long_df = container.melt() - - # 2. Add entity_type and rename columns to snake_case - if "entity_key" in long_df.columns: - long_df = long_df.with_columns( - pl.col("entity_key") - .map_elements( - lambda k: TrackedEntity.from_entity_key(k).type, return_dtype=pl.String - ) - .alias("entity_type") - ) - else: + if "entity_key" not in long_df.columns: raise ValueError("entity_key column is required for saving to catalog") - # Rename columns to Iceberg format - rename_dict = {k: v for k, v in COLUMN_MAPPING.items() if k in long_df.columns} - long_df = long_df.rename(rename_dict) + long_df = long_df.with_columns( + pl.col("entity_key") + .map_elements(lambda k: TrackedEntity.from_entity_key(k).type, return_dtype=pl.String) + .alias("entity_type") + ) - # Ensure match_id is present and correct - long_df = long_df.with_columns(pl.lit(match_id).alias("match_id")) + # 2. Column Mapping + rename_dict = {k: v for k, v in COLUMN_MAPPING.items() if k in long_df.columns} + long_df = long_df.rename(rename_dict).with_columns(pl.lit(match_id).alias("match_id")) - # --- Constant Extraction & Hierarchical grouping --- - ignore_for_constants = { + # 3. Constant Extraction (Normalization) + # Identity and core temporal fields are strictly preserved in tracking table. + # team/id is now moved to metadata as a constant. + core_cols = { "match_id", "frame_id", "period", "timestamp", "entity_type", "entity_id", - "team_id", "x", "y", "z", "entity_key", } - potential_constants = [c for c in long_df.columns if c not in ignore_for_constants] - - # Group by entity and extract constants - agg_exprs = [pl.col(c).n_unique().alias(f"{c}_count") for c in potential_constants] - agg_vals = [pl.col(c).first().alias(f"{c}_val") for c in potential_constants] + potential_constants = [c for c in long_df.columns if c not in core_cols and c != "team/id"] - stats = long_df.group_by(["entity_id", "team_id", "entity_type"]).agg(agg_exprs + agg_vals) + # Group by entity AND its original team attribute to extract constants correctly + # team/id is part of the key to identify constants per-entity-instance + agg_stats = long_df.group_by(["entity_id", "team/id", "entity_type"]).agg( + [pl.col(c).n_unique().alias(f"{c}_count") for c in potential_constants] + + [pl.col(c).first().alias(f"{c}_val") for c in potential_constants] + ) - columns_to_drop = [] - for c in potential_constants: - if stats[f"{c}_count"].max() == 1: - columns_to_drop.append(c) + constants_to_move = [c for c in potential_constants if agg_stats[f"{c}_count"].max() == 1] + # Prepare hierarchical metadata entities_flat = ( - stats.select( - ["entity_id", "team_id", "entity_type"] + [f"{c}_val" for c in columns_to_drop] + agg_stats.select( + ["entity_id", "team/id", "entity_type"] + [f"{c}_val" for c in constants_to_move] ) - .rename({f"{c}_val": c for c in columns_to_drop}) + .rename({f"{c}_val": c for c in constants_to_move}) .to_dicts() ) - # Build hierarchical structure, omitting None attributes - # Flattened team attributes: teams > {tid} > {attr} - # instead of teams > {tid} > attributes > {attr} - entities_hierarchical: dict[str, Any] = {"ball": {}, "teams": {}, "referees": {}} + hierarchical_meta: dict[str, Any] = {"ball": {}, "teams": {}, "referees": {}} for ent in entities_flat: - etype = ent.pop("entity_type") - eid = ent.pop("entity_id") - tid = ent.pop("team_id", "none") - - # Remove attributes that are None for this specific entity + etype, eid, tid = ( + ent.pop("entity_type"), + ent.pop("entity_id"), + ent.pop("team/id", "none"), + ) attrs = {k: v for k, v in ent.items() if v is not None} if etype == "ball": - entities_hierarchical["ball"] = attrs + hierarchical_meta["ball"] = attrs elif etype == "referee": - entities_hierarchical["referees"][eid] = attrs + hierarchical_meta["referees"][eid] = attrs elif etype == "player": - if tid not in entities_hierarchical["teams"]: - entities_hierarchical["teams"][tid] = {"players": {}} - entities_hierarchical["teams"][tid]["players"][eid] = attrs + team = hierarchical_meta["teams"].setdefault(tid, {"players": {}}) + team["players"][eid] = attrs elif etype == "team": - # Use tid (e.g. 'home') instead of eid (e.g. 'team:home') for the key target_tid = tid if tid != "none" else eid.replace("team:", "") - if target_tid not in entities_hierarchical["teams"]: - entities_hierarchical["teams"][target_tid] = {"players": {}} - # Merge team-level attributes directly into the team object - entities_hierarchical["teams"][target_tid].update(attrs) + team = hierarchical_meta["teams"].setdefault(target_tid, {"players": {}}) + team.update(attrs) - if columns_to_drop: - logger.info(f"Normalizing constant attributes to metadata: {columns_to_drop}") - long_df = long_df.drop(columns_to_drop) + # Drop constant columns AND the team/id column from physical table + long_df = long_df.drop([c for c in constants_to_move if c in long_df.columns] + ["team/id"]) - # 3. Validation + # 4. Final Validation & Save for field in ["x", "y"]: if field not in long_df.columns: - raise ValueError(f"Required column {field} is missing from container") + raise ValueError(f"Required column {field} is missing") must_not_be_null = [ "match_id", @@ -435,7 +363,7 @@ def _save_tracking_data( if long_df[field].null_count() > 0: raise ValueError(f"Required field {field} contains null values") - # 4. Save to Iceberg + # Convert to Arrow arrow_table = long_df.to_arrow() known_types = { "timestamp": pa.timestamp("us"), @@ -445,39 +373,32 @@ def _save_tracking_data( "z": pa.float32(), } - new_columns = [] - new_fields = [] + new_cols, new_fields = [], [] for i, field in enumerate(arrow_table.schema): - target_type = known_types.get(field.name) - if not target_type and isinstance(field.type, pa.DictionaryType): - target_type = field.type.value_type - elif not target_type and pa.types.is_null(field.type): - target_type = pa.string() - - if target_type is not None: - new_columns.append(arrow_table.column(i).cast(target_type)) - new_fields.append(pa.field(field.name, target_type, field.nullable)) - else: - new_columns.append(arrow_table.column(i)) - new_fields.append(field) + target = known_types.get(field.name) or ( + field.type.value_type + if isinstance(field.type, pa.DictionaryType) + else (pa.string() if pa.types.is_null(field.type) else None) + ) + new_cols.append(arrow_table.column(i).cast(target) if target else arrow_table.column(i)) + new_fields.append(pa.field(field.name, target, field.nullable) if target else field) - arrow_table = pa.Table.from_arrays(new_columns, schema=pa.schema(new_fields)) + arrow_table = pa.Table.from_arrays(new_cols, schema=pa.schema(new_fields)) with self._tracking_table.transaction() as txn: - container_schema = _pyarrow_to_schema_without_ids(arrow_table.schema) - txn.update_schema().union_by_name(container_schema).commit() + txn.update_schema().union_by_name( + _pyarrow_to_schema_without_ids(arrow_table.schema) + ).commit() txn.delete(EqualTo(term=Reference("match_id"), value=cast(Any, match_id))) # type: ignore txn.append(arrow_table) - return entities_hierarchical + return hierarchical_meta def _save_metadata(self, metadata: dict, match_id: str) -> None: """Save metadata to Iceberg table.""" if self._metadata_table is None: self._metadata_table = self._iceberg_catalog.load_table("tracking.match_metadata") - assert self._metadata_table is not None - metadata_schema = pa.schema( [ pa.field("match_id", pa.string(), nullable=False), @@ -503,10 +424,9 @@ def _save_metadata(self, metadata: dict, match_id: str) -> None: def __repr__(self) -> str: return ( - f"TrackingDataCatalog(\n" - f" warehouse={self.warehouse_path},\n" - f" catalog_type={self.catalog_type}\n" - f")" + f"TrackingDataCatalog(" + f"warehouse={self.warehouse_path}, " + f"catalog_type={self.catalog_type})" ) @@ -531,100 +451,95 @@ def __init__( self._filters = filters self._cached_schema: Fields | None = None - def _get_flat_entities(self, hierarchical_entities: dict[str, Any]) -> list[dict[str, Any]]: - """Flatten hierarchical metadata back to list for easy schema iteration.""" - flat = [] - # 1. Ball - ball = hierarchical_entities.get("ball") - if ball is not None: - flat.append({"entity_id": "ball", "team_id": "none", **ball}) - - # 2. Teams & Players - for tid, team_info in hierarchical_entities.get("teams", {}).items(): - # Team itself - # We need to exclude "players" from the attributes when flattening the team itself - team_attrs = {k: v for k, v in team_info.items() if k != "players"} - flat.append({"entity_id": f"team:{tid}", "team_id": tid, **team_attrs}) - # Players - for pid, p_attrs in team_info.get("players", {}).items(): - flat.append({"entity_id": pid, "team_id": tid, **p_attrs}) - - # 3. Referees + def _get_entities_from_metadata( + self, hierarchical_entities: dict[str, Any] + ) -> list[tuple[TrackedEntity, dict[str, Any]]]: + """Flatten hierarchical metadata into a list of (TrackedEntity, attributes) tuples.""" + entities: list[tuple[TrackedEntity, dict[str, Any]]] = [] + + if ball_attrs := hierarchical_entities.get("ball"): + entities.append((TrackedEntity.from_entity_key("ball|none"), ball_attrs)) + + for tid, team_data in hierarchical_entities.get("teams", {}).items(): + team_attrs = {k: v for k, v in team_data.items() if k != "players"} + entities.append((TrackedEntity.from_entity_key(f"team:{tid}|{tid}"), team_attrs)) + for pid, p_attrs in team_data.get("players", {}).items(): + entities.append((TrackedEntity.from_entity_key(f"{pid}|{tid}"), p_attrs)) + for rid, r_attrs in hierarchical_entities.get("referees", {}).items(): - flat.append({"entity_id": f"referee:{rid}", "team_id": "none", **r_attrs}) + entities.append((TrackedEntity.from_entity_key(f"referee:{rid}|none"), r_attrs)) + + if self._player_id: + entities = [ + (e, attrs) + for e, attrs in entities + if e.player_id == self._player_id + or (e.is_referee and e.referee_id == self._player_id) + ] - return flat + return entities + + def _get_wide_names_for_entity( + self, entity: TrackedEntity, attributes: dict[str, Any], physical_attr_cols: list[str] + ) -> list[str]: + """Generate all wide CDF field names for a specific entity.""" + prefix = entity.cdf_prefix() + names = [] + for attr in physical_attr_cols: + names.append(f"{prefix}{attr}") + for attr in attributes: + names.append(f"{prefix}{attr}") + if not entity.is_ball: + names.append(f"{prefix}player/id") + if entity.is_player or entity.is_team: + names.append(f"{prefix}team/id") + return names def schema(self) -> Fields: - """Reconstruct the wide schema by inspecting entities in the hierarchical metadata.""" + """Reconstruct the wide schema by inspecting entities in the match metadata.""" if self._cached_schema: return self._cached_schema - assert self._catalog is not None if self._catalog._tracking_table is None: self._catalog._tracking_table = self._catalog._iceberg_catalog.load_table( "tracking.tracking_data" ) - assert self._catalog._tracking_table is not None - metadata = self._catalog.get_metadata(self._match_id) entities_hier = metadata.get("_entities") if entities_hier is None: raise ValueError(f"Metadata for match {self._match_id} is missing unique entity list.") - entities = self._get_flat_entities(entities_hier) - - if self._player_id: - entities = [e for e in entities if str(e.get("entity_id")) == self._player_id] - - physical_names = [f.name for f in self._catalog._tracking_table.schema().fields] - ignored = set(COLUMN_MAPPING.values()) | { - "entity_type", + physical_names = {f.name for f in self._catalog._tracking_table.schema().fields} + ignored = { + "match_id", + "competition_id", + "season_id", "timestamp", "frame_id", "period", "period_id", + "entity_type", + "entity_id", "entity_key", } attr_cols = [c for c in physical_names if c not in ignored and c != "id"] - wide_names = [] - for c in CORE_COLUMNS: - if c in ("player/id", "team/id", "entity_key"): - continue - phys_name = COLUMN_MAPPING.get(c, c) - if phys_name in physical_names: - wide_names.append(c) - - for row in entities: - p_id = str(row.get("entity_id")) - t_id = str(row.get("team_id", "none")) - entity = TrackedEntity.from_entity_key(f"{p_id}|{t_id}") - prefix = entity.cdf_prefix() - - for attr in attr_cols: - if attr != "entity_key": - wide_names.append(f"{prefix}{attr}") - for attr in row.keys(): - if attr not in ("entity_id", "team_id", "entity_key"): - wide_names.append(f"{prefix}{attr}") - - # Always add player/id (except for ball which has it via TrackedEntity logic if needed, - # but usually ball doesn't have player/id) - if not entity.is_ball: - wide_names.append(f"{prefix}player/id") + wide_names = [ + c + for c in CORE_COLUMNS + if c not in ("player/id", "team/id", "entity_key") + and COLUMN_MAPPING.get(c, c) in physical_names + ] - # Only add team/id for players and teams - if entity.is_player or entity.is_team: - wide_names.append(f"{prefix}team/id") + for entity, attrs in self._get_entities_from_metadata(entities_hier): + wide_names.extend(self._get_wide_names_for_entity(entity, attrs, attr_cols)) self._cached_schema = Fields(list(dict.fromkeys(wide_names))) return self._cached_schema def select(self, items: str | Sequence[str]) -> CatalogLazyTrackingDataContainer: """Translate wide selection to long selection pushdown.""" - assert self._catalog is not None return CatalogLazyTrackingDataContainer( self._catalog, self._match_id, @@ -636,7 +551,6 @@ def select(self, items: str | Sequence[str]) -> CatalogLazyTrackingDataContainer def slice(self, offset: int, length: int) -> CatalogLazyTrackingDataContainer: """Apply limit to the lazy container.""" - assert self._catalog is not None return CatalogLazyTrackingDataContainer( self._catalog, self._match_id, @@ -648,14 +562,11 @@ def slice(self, offset: int, length: int) -> CatalogLazyTrackingDataContainer: def collect(self) -> TrackingDataContainer: """Finally perform the Iceberg scan and pivot.""" - assert self._catalog is not None if self._catalog._tracking_table is None: self._catalog._tracking_table = self._catalog._iceberg_catalog.load_table( "tracking.tracking_data" ) - assert self._catalog._tracking_table is not None - filter_expr: BooleanExpression = EqualTo( term=Reference("match_id"), value=cast(Any, self._match_id) ) # type: ignore @@ -665,112 +576,85 @@ def collect(self) -> TrackingDataContainer: EqualTo(term=Reference("entity_id"), value=cast(Any, self._player_id)), # type: ignore ) + # 1. Column Pushdown Calculation selected_long_fields = None deferred_select = None - metadata = self._catalog.get_metadata(self._match_id) entities_hier = metadata.get("_entities", {}) - entities_flat = self._get_flat_entities(entities_hier) + parsed_entities = self._get_entities_from_metadata(entities_hier) if self._selection: eager_schema = self.schema() - literal_names: list[str] = [] - input_items = ( - self._selection if isinstance(self._selection, (list, tuple)) else [self._selection] + literal_names = ( + eager_schema.match(self._selection) + if isinstance(self._selection, str) and "*" in self._selection + else ( + [self._selection] if isinstance(self._selection, str) else list(self._selection) + ) ) - for item in input_items: - if isinstance(item, str): - if "*" in item: - literal_names.extend(eager_schema.match(item)) - else: - literal_names.append(item) - else: - literal_names.append(str(item)) - - literal_names = list(dict.fromkeys(literal_names)) - deferred_select = literal_names - - needed_long_internal = set(CORE_COLUMNS) - needed_ents: set[str] = set() - - for name in literal_names: - if name in CORE_COLUMNS: - needed_long_internal.add(name) - else: - field = DataField(name) - needed_long_internal.add(field.attribute) - if field.entity: - needed_ents.add(cast(str, field.entity_key())) + deferred_select = list(dict.fromkeys(literal_names)) + needed_long_internal = { + DataField(name).attribute if name not in CORE_COLUMNS else name + for name in deferred_select + } | set(CORE_COLUMNS) physical_names = {f.name for f in self._catalog._tracking_table.schema().fields} - selected_long_fields_set = set() - for f in needed_long_internal: - phys_name = COLUMN_MAPPING.get(f, f) - if phys_name in physical_names: - selected_long_fields_set.add(phys_name) + selected_long_fields = [ + COLUMN_MAPPING.get(f, f) + for f in needed_long_internal + if COLUMN_MAPPING.get(f, f) in physical_names + ] for req in [ "match_id", "entity_type", "entity_id", - "team_id", "frame_id", "timestamp", "period", "x", "y", ]: - if req in physical_names: - selected_long_fields_set.add(req) - - selected_long_fields = list(selected_long_fields_set) - - if needed_ents: - entity_predicate: BooleanExpression | None = None - for k in needed_ents: - p_id, t_id = k.split("|") - cond: BooleanExpression = EqualTo( - term=Reference("entity_id"), value=cast(Any, p_id) - ) # type: ignore - if p_id != "ball" and t_id != "none": - cond = And(cond, EqualTo(term=Reference("team_id"), value=cast(Any, t_id))) # type: ignore - - if entity_predicate is None: - entity_predicate = cond - else: - entity_predicate = Or(entity_predicate, cond) - - if entity_predicate: - filter_expr = And(filter_expr, entity_predicate) + if req in physical_names and req not in selected_long_fields: + selected_long_fields.append(req) + # 2. Perform the scan scan_kwargs: dict[str, Any] = {"row_filter": filter_expr} - if selected_long_fields is not None: + if selected_long_fields: scan_kwargs["selected_fields"] = tuple(selected_long_fields) if self._limit: scan_kwargs["limit"] = self._limit * 50 - scan = self._catalog._tracking_table.scan(**scan_kwargs) - arrow_table = scan.to_arrow() + arrow_table = self._catalog._tracking_table.scan(**scan_kwargs).to_arrow() + + # 3. Pivot using Rust core + mapping: dict[str, str] = {} + for entity, _ in parsed_entities: + eid = ( + str(entity.player_id) + if entity.is_player + else ( + f"referee:{entity.referee_id}" + if entity.is_referee + else (f"team:{entity.team_id}" if entity.is_team else "ball") + ) + ) + mapping[eid] = str(entity.team_id) if entity.team_id else "none" + + wide_container = TrackingDataContainer.pivot(arrow_table, entity_to_team=mapping) - wide_container = TrackingDataContainer.pivot(arrow_table) wide_container._catalog = self._catalog - # Inject constant attributes - if entities_flat: - wide_names = wide_container.schema().names() - for ent_data in entities_flat: - p_id = ent_data["entity_id"] - t_id = ent_data["team_id"] - prefix = TrackedEntity.from_entity_key(f"{p_id}|{t_id}").cdf_prefix() - for attr, value in ent_data.items(): - if attr in ("entity_id", "team_id", "entity_key"): - continue - - wide_attr_name = f"{prefix}{attr}" - if wide_attr_name not in wide_names: - wide_container = wide_container.add_column( - wide_attr_name, [value] * wide_container.num_rows() - ) + # 4. Inject constant attributes from metadata + wide_names = wide_container.schema().names() + for entity, attrs in parsed_entities: + prefix = entity.cdf_prefix() + for attr, value in attrs.items(): + wide_name = f"{prefix}{attr}" + if wide_name not in wide_names: + wide_container = wide_container.add_column( + wide_name, [value] * wide_container.num_rows() + ) if deferred_select: wide_container = wide_container.select(deferred_select) diff --git a/python/tracking_data_container/container.py b/python/tracking_data_container/container.py index d7b1732..1a6f4d1 100644 --- a/python/tracking_data_container/container.py +++ b/python/tracking_data_container/container.py @@ -356,7 +356,9 @@ def from_arrow( @classmethod def pivot( - cls, df: pl.DataFrame | pa.Table | pa.StructArray | pa.RecordBatch + cls, + df: pl.DataFrame | pa.Table | pa.StructArray | pa.RecordBatch, + entity_to_team: dict[str, str] | None = None, ) -> TrackingDataContainer: """Pivots long-format tracking data into wide-format using Rust implementation.""" if hasattr(df, "to_arrow"): @@ -373,7 +375,7 @@ def pivot( df = pa.RecordBatch.from_arrays(df.flatten(), names=[f.name for f in df.type]) # Use the underlying Rust implementation for performance - inner = _TrackingDataContainer.pivot(df) + inner = _TrackingDataContainer.pivot(df, entity_to_team) return TrackingDataContainer(inner, validate=False) def lazy(self) -> LazyTrackingDataContainer: diff --git a/python/tracking_data_container/schema.py b/python/tracking_data_container/schema.py index 307a2fb..01351d9 100644 --- a/python/tracking_data_container/schema.py +++ b/python/tracking_data_container/schema.py @@ -103,7 +103,6 @@ def from_entity_key(key: str) -> TrackedEntity: if p_id == "ball": return Ball() if p_id.startswith("team:"): - # Use provided t_id if available, otherwise extract from p_id team_id = t_id if t_id != "none" else p_id[5:] return Team(team_id) if p_id.startswith("referee:"): @@ -112,8 +111,7 @@ def from_entity_key(key: str) -> TrackedEntity: if t_id != "none": return Player(t_id, p_id) - # We don't have a direct "OtherPlayer" class, so we use a dummy name to - # construct a DataField and get its entity + # Handle players without a team return TrackedEntity._from_inner(_DataField.from_name(f"other/{p_id}/x").entity()) @staticmethod From 6e0060fd96e9372d5786da836bcb0e3ec4fac8bb Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Tue, 21 Apr 2026 11:42:28 +0200 Subject: [PATCH 20/21] iceberg support --- RFC.md | 355 +- crates/tdc-core/src/datacontainer/builder.rs | 4 +- crates/tdc-core/src/datacontainer/core.rs | 6 +- crates/tdc-core/src/datacontainer/lazy.rs | 155 +- crates/tdc-core/src/schema/field.rs | 17 + crates/tdc-parsers/src/sportec.rs | 12 +- crates/tdc-python/src/container.rs | 175 +- crates/tdc-python/src/lib.rs | 4 + crates/tdc-python/src/schema.rs | 11 + crates/tdc-python/src/selector.rs | 13 + examples/01-basics.ipynb | 18 +- examples/02-parsing.ipynb | 108 +- examples/03-interop.ipynb | 2 +- examples/04-udfs.ipynb | 17 +- examples/05-lazy.ipynb | 2 +- examples/06-skeletal.ipynb | 54 +- examples/07-catalog.ipynb | 72 +- examples/08-idsse-warehouse.ipynb | 202 +- examples/20260423-presentation-liss/build.sh | 11 + .../20260423-presentation-liss/custom.css | 51 + .../img/building_bridges.jpeg | Bin 0 -> 371179 bytes .../img/coordinatefc.png | Bin 0 -> 58876 bytes .../img/pysport_logo.png | Bin 0 -> 36396 bytes .../presentation.ipynb | 443 + .../presentation.slides.html | 7740 +++++++++++++++++ examples/idsse_warehouse/catalog.db | Bin 20480 -> 0 bytes ...-96a2-48d6-8305-1e7155f857e6.metadata.json | 1 - ...-4270-4bd2-86dc-2b88858c0833.metadata.json | 1 - ...-c362-4a0f-a942-2fc8d6c51c96.metadata.json | 1 - ...-b298-4f4f-a814-9b9f76d8e71a.metadata.json | 1 - ...c097fb-ed6a-4e53-95aa-61c36e19e9b5-m0.avro | Bin 4505 -> 0 bytes ...8d49d5-48bd-40a1-aac5-ac3df2728952-m0.avro | Bin 4508 -> 0 bytes ...b6cbfa-fef9-4779-b2e1-c63290db081c-m0.avro | Bin 4507 -> 0 bytes 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python/tests/test_container.py | 169 +- python/tests/test_lazy.py | 160 + python/tracking_data_container/__init__.py | 8 +- .../_tracking_data_container.pyi | 1 + .../backends/__init__.py | 5 + .../tracking_data_container/backends/base.py | 45 + .../backends/datafusion.py | 154 + .../backends/iceberg.py | 305 + python/tracking_data_container/catalog.py | 664 -- .../catalogs/__init__.py | 4 + .../tracking_data_container/catalogs/base.py | 83 + .../catalogs/iceberg.py | 439 + python/tracking_data_container/config.py | 24 - python/tracking_data_container/container.py | 614 +- python/tracking_data_container/io.py | 4 +- python/tracking_data_container/schema.py | 19 +- 66 files changed, 11066 insertions(+), 1582 deletions(-) create mode 100755 examples/20260423-presentation-liss/build.sh create mode 100644 examples/20260423-presentation-liss/custom.css create mode 100644 examples/20260423-presentation-liss/img/building_bridges.jpeg create mode 100644 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container combines high-performance, zero-copy data access with a rich metad ## Motivation -As tracking pipelines grow across football and other sports, the need for modularity, performance, and inter-package interoperability becomes critical. A growing number of open-source packages in football analytics are duplicating functionality across parsing, modeling, and data management. This [comparison spreadsheet](https://docs.google.com/spreadsheets/d/1sfkqxkmtmQkXmkHiuNz-kvBvTLL7UWpCHdLuxW4zAF4/edit?gid=0#gid=0) illustrates overlapping efforts across different research groups and tools. +As tracking pipelines grow across football and other sports, the need for modularity, performance, and inter-package interoperability becomes critical. A growing number of open-source packages in football analytics are duplicating functionality across parsing, modeling, and data management. This [comparison spreadsheet](https://docs.google.com/spreadsheets/d/1sfkqxkmtmQkXmkHiuNz-kvBvTLL7UWpCHdLuxW4zAF4/edit?gid=0#gid=0) illustrates overlapping efforts across different research groups and tools. Current challenges: @@ -99,13 +99,13 @@ We propose to align column naming with the [Common Data Format (CDF)](https://do Example conventions based on CDF: -| Concept | Column Name | -|-----------|---------------------------------------------| -| Ball | `ball_x`, `ball_y`, `ball_z` | -| Player | `teams/players/_x`, `_y` | -| Skeleton | `teams/players//_x` | -| Metrics | `package/metric_name` | -| Time | `timestamp`, `frame_id`, `period` | +| Concept | Column Name | +| -------- | ------------------------------------ | +| Ball | `ball_x`, `ball_y`, `ball_z` | +| Player | `teams/players/_x`, `_y` | +| Skeleton | `teams/players//_x` | +| Metrics | `package/metric_name` | +| Time | `timestamp`, `frame_id`, `period` | These conventions follow: @@ -124,16 +124,334 @@ By adhering to CDF, the `TrackingDataContainer` will be interoperable with other - Defines optional logical "layers" (e.g. tracking, skeleton, predictions) - Use kloppy Metadata objects -### Storage & Computation Model +--- + +## Storage Layer Specification + +### Schema Design + +#### Semi-Long Format + +TrackingDataContainer uses a **semi-long format**: one row per entity (player/ball) per timestamp, with measurements as separate columns. + +```sql +CREATE TABLE tracking_data ( + -- Identifiers + match_id STRING, -- Partition key + timestamp TIMESTAMP(us), -- Microsecond precision + frame_id BIGINT, -- Optional frame counter + period_id TINYINT, -- 1, 2, etc. + + -- Entity identification + player_id STRING, -- CV ID or stable player ID (dictionary encoded) + team_id STRING, -- "home" / "away" / null (dictionary encoded) + + -- Measurements + x FLOAT, + y FLOAT, + z FLOAT -- Nullable (height) +) PARTITIONED BY (match_id, bucket(10, player_id)); +``` + +**Rationale:** + +- **Semi-long format**: Scales with variable player counts, handles ID instability (10K+ IDs from CV systems) +- **String IDs with dictionary encoding**: Efficient storage, flexible for unstable/non-numeric IDs +- **Separate x,y,z columns**: Enables SIMD vectorization for per-player operations +- **Bucketing**: Distributes high-cardinality player_id across fixed number of files + +### Storage Architecture + +#### Apache Iceberg Table Format + +**Primary storage**: Apache Iceberg tables with Parquet file format + +``` +warehouse/ +├── match_metadata/ # Separate metadata table +│ ├── metadata/ +│ └── data/ +└── tracking_data/ # Main tracking data table + ├── metadata/ + └── data/ + ├── match_id=123/ + │ ├── player_id_bucket=0/*.parquet + │ ├── player_id_bucket=1/*.parquet + │ └── ... + └── match_id=124/ + └── ... +``` + +**Why Iceberg:** + +- ✅ Stores optimization settings in metadata (bucket_count, sort_order) +- ✅ Schema evolution built-in +- ✅ Time-travel / versioning +- ✅ Can swap file format (Parquet → Lance/Vortex) via maintenance jobs +- ✅ Mature ecosystem (DuckDB, Spark, Trino support) + +**Catalog**: Start with filesystem catalog, design supports REST/Glue/Hive catalogs + +### Two-Table Design + +#### Table 1: match_metadata (Metadata Storage) + +```sql +CREATE TABLE match_metadata ( + match_id STRING PRIMARY KEY, + + -- Flexible JSON columns for Kloppy metadata + teams JSON, -- {home: {...}, away: {...}} + players JSON, -- [{id, name, jersey_number}, ...] + pitch JSON, -- {length, width, coordinate_system} + periods JSON, -- [{id, start, end}, ...] + + -- Optional metadata + competition_id STRING, + match_date DATE, + venue STRING, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP +); +``` + +**Storage**: Non-partitioned table (small, ~1 row per match) + +#### Table 2: tracking_data (Tracking Data) -The container is designed to support **lazy evaluation** and **filter pushdown** using Apache Arrow-compatible formats like Parquet and Iceberg. +```sql +CREATE TABLE tracking_data ( + match_id STRING, + timestamp TIMESTAMP, + player_id STRING, + team_id STRING, + x FLOAT, y FLOAT, z FLOAT + -- Additional columns added via .add_column() +) PARTITIONED BY (match_id, bucket(10, player_id)); +``` + +**Storage**: Partitioned by match, bucketed by player + +### Partitioning Strategy + +#### Per-Match Use Case (Initial) + +```sql +PARTITIONED BY ( + match_id, -- One partition per match + bucket(10, player_id) -- 10 buckets per match +) +``` + +**Configurable bucket count:** + +```python +container.save('warehouse/tracking_data', bucket_count=10) +``` + +**Partition Layout:** + +``` +match_id=123/ + player_id_bucket=0/data.parquet (~10% of players, sorted by player_id) + player_id_bucket=1/data.parquet + ... + player_id_bucket=9/data.parquet +``` -- **Lazy Evaluation**: All transformations (e.g. metric calculations, filters) are applied lazily using Polars' `LazyFrame` or Arrow compute APIs. -- **Filter Pushdown**: When reading from Parquet or Iceberg, the container supports pushing column and row filters down to the storage engine, dramatically improving performance for large datasets. -- **Column Projection**: Only the necessary columns are loaded or materialized, based on selectors or expressions. -- **Iceberg Integration (Future Direction)**: Iceberg can serve as the underlying storage format, offering versioning, schema evolution, and partition pruning. The container will provide adapters to read from and write to Iceberg tables. +**Future use cases** (different partitioning schemes): + +- Competition-level: `PARTITIONED BY (competition_id, match_id, bucket(player_id))` +- Player-centric: `PARTITIONED BY (player_id, match_id)` + +### Optimization Settings + +#### Parquet File Layout + +**Within each partition file:** + +1. **Sorting**: `ORDER BY player_id ASC, timestamp ASC` + - Groups each player's data contiguously + - Enables page-level pruning + +2. **Row Groups**: 128 MB (default) + - Balance between parallelism and metadata overhead + +3. **Page Size**: 1 MB + - **Critical optimization**: Aligns with per-player data size (~750 KB compressed) + - One player ≈ one page → perfect page-level isolation + - Page index enables skipping irrelevant players + +4. **Page Index**: Enabled + - Stores min/max per page + - Essential for sorted data performance + +5. **Compression**: Configurable (default: `snappy`) + - `snappy`: Fast compression/decompression + - `zstd`: Better ratio, slightly slower + +6. **Dictionary Encoding**: Enabled for `player_id`, `team_id` + - Efficient for repeated string values + +**Example configuration:** + +```python +container.save('warehouse/tracking_data', + bucket_count=10, + compression='snappy', + page_size=1024*1024, # 1 MB (advanced) +) +``` -This design ensures the container can be scaled from local processing to lakehouse-style infrastructure with minimal friction. +### Write Strategy + +#### Partition-Level Replacement + +When saving a match, the entire partition is replaced: + +```python +def save(self, path: str, match_id: str): + """Save container (replaces entire match partition).""" + + # 1. Upsert metadata (atomic at table level) + MERGE INTO match_metadata + USING new_metadata + ON match_metadata.match_id = new_metadata.match_id + WHEN MATCHED THEN UPDATE SET * + WHEN NOT MATCHED THEN INSERT *; + + # 2. Replace tracking data partition (atomic at partition level) + DELETE FROM tracking_data WHERE match_id = '123'; + INSERT INTO tracking_data SELECT * FROM new_data; +``` + +**Benefits:** + +- ✅ Idempotent (can re-run safely) +- ✅ Partition replacement is atomic in Iceberg (manifest update) +- ✅ Can rewrite entire match with corrections/new columns + +**Implementation:** DuckDB or PyIceberg (to be validated) + +### Query Performance + +#### Multi-Level Pruning + +Iceberg + optimized Parquet enables efficient queries: + +```python +# Query: Get player 7's data for match 123 +container = TrackingDataContainer.load( + 'warehouse/tracking_data', + match_id='123', + player_id='7' +) +``` + +**Execution plan:** + +1. **Partition pruning**: Only read `match_id=123` partition (skip all other matches) +2. **Bucket pruning**: Hash player_id → read only bucket 7 (skip 90% of files) +3. **Row group pruning**: Check row group stats, skip groups without player 7 +4. **Page-level pruning**: Check page index, read only pages containing player 7 +5. **Result**: ~1 MB read (out of 100+ MB total for match) + +**Pruning efficiency: ~99%** 🎯 + +### Backend Flexibility + +#### Design for Swappable File Formats + +```python +# Current (Parquet) +container.save('warehouse/tracking_data', backend='parquet') + +# Future (via Iceberg maintenance job) +# ALTER TABLE tracking_data SET TBLPROPERTIES ('write.format.default' = 'lance'); +container.save('warehouse/tracking_data', backend='lance') +``` + +**Iceberg allows changing file format without rewriting old data.** + +Potential future formats: + +- **Lance**: Optimized for random access, versioning +- **Vortex**: Columnar format with advanced compression +- **ORC**: Alternative columnar format + +**Skeletal data**: When needed, can store in separate table with different format optimized for high cardinality (22 players × 15 joints = 330 entities). + +### Known Limitations + +#### 1. Cross-Table Atomicity + +Metadata and tracking data are written to separate tables without transactional guarantees. If tracking data write fails after metadata is written, manual cleanup may be required. + +**Future improvements:** + +- Implement two-phase commit pattern +- Add application-level transaction log +- Consider storing metadata within tracking_data table for single-table atomicity + +#### 2. DuckDB Write Limitations + +DuckDB (as of v1.4) cannot UPDATE/DELETE partitioned Iceberg tables directly. Workarounds: + +- Use PyIceberg for writes +- Use DuckDB for reads (excellent query performance) +- Future: DuckDB may add partitioned write support + +### Storage API + +#### User-Facing API + +```python +# Save (TDC handles all optimization) +container.save('warehouse/tracking_data', match_id='123') + +# Load (TDC handles efficient querying) +container = TrackingDataContainer.load( + 'warehouse/tracking_data', + match_id='123', # Optional filters + player_id='7' +) + +# Advanced: Configure optimization +container.save('warehouse/tracking_data', + match_id='123', + bucket_count=50, # More granular partitioning + compression='zstd' # Better compression ratio +) +``` + +**Design principle**: Simple by default, configurable for advanced users. + +### References + +**Iceberg + DuckDB:** + +- [Writes in DuckDB-Iceberg – DuckDB](https://duckdb.org/2025/11/28/iceberg-writes-in-duckdb) +- [Apache Iceberg Writes with DuckDB](https://www.confessionsofadataguy.com/apache-iceberg-writes-with-duckdb-or-not/) +- [Iceberg Extension – DuckDB](https://duckdb.org/docs/stable/core_extensions/iceberg/overview) +- [Transaction Management – DuckDB](https://duckdb.org/docs/stable/sql/statements/transactions) + +**Iceberg Metadata & Partitioning:** + +- [Extending Apache Iceberg: Best Practices for Custom Metadata | Dremio](https://www.dremio.com/blog/extending-apache-iceberg-best-practices-for-storing-and-discovering-custom-metadata/) +- [Understanding Apache Iceberg's metadata.json File](https://dev.to/alexmercedcoder/understanding-apache-icebergs-metadatajson-file-23f) +- [Partitioning Apache Iceberg Tables | Medium](https://medium.com/@tglawless/partitioning-apache-iceberg-tables-ad2757cb0b76) +- [Iceberg Partitioning - Apache Iceberg](https://iceberg.apache.org/docs/latest/partitioning/) + +**Parquet Optimization:** + +- [Cloudera: Speeding Up Queries with Parquet Page Indexes](https://www.cloudera.com/blog/technical/speeding-up-select-queries-with-parquet-page-indexes.html) +- [DataFusion: Parquet Pruning](https://datafusion.apache.org/blog/2025/03/20/parquet-pruning/) +- [Parquet Performance Tuning – Medium](https://medium.com/data-engineering-with-dremio/all-about-parquet-part-10-performance-tuning-and-best-practices-with-parquet-d697ba4e8a57) + +**Cache Locality & Performance:** + +- [Spatial Locality - Algorithmica](https://en.algorithmica.org/hpc/external-memory/locality/) +- [Cache Locality Performance](https://arpitbhayani.me/blogs/cpu-cache-locality) +- [Polars Performance Blog](https://pola.rs/posts/i-wrote-one-of-the-fastest-dataframe-libraries/) --- @@ -150,14 +468,17 @@ This design ensures the container can be scaled from local processing to lakehou ## Rationale & Alternatives ### Why not just Arrow? + - Arrow is fast and standard, but lacks semantics - Metadata enables safe, interpretable, and sport-agnostic usage ### Why enforce the container? + - To guarantee consistency across packages - To avoid schema drift and partial implementations ### Why CDF and flat schema? + - CDF provides a well-vetted, community-driven naming standard - Flat schema increases compatibility and simplifies filter pushdown and projection - Structs may be supported later as an internal representation @@ -192,7 +513,8 @@ This design ensures the container can be scaled from local processing to lakehou - Related tickets: - [Refactor serializer options into own component](https://github.com/PySport/kloppy/issues/10) -> Describes a FrameBuilder approach - [Refactor tracking data model](https://github.com/PySport/kloppy/pull/377) -- Should TDC support transformations? Probably yes, so a package can ensure all data is in the correct orientation and dimensions. +- Should TDC support transformations? Probably yes, so a package can ensure all data is in the correct orientation and dimensions. + --- ## Conclusion @@ -206,6 +528,7 @@ We propose making this the default container for all tools in the ecosystem. We brought together contributors from different open source projects to discuss how we can align our work and improve interoperability in football analytics. Participants included: + - 🇧🇪 Pieter Robberechts (soccerdata, socceraction, Kloppy) - 🇳🇱 Alexander Oonk (databallpy) - 🇯🇵 Keisuke Fujii, Calvin Yeung (OpenSTARLab) diff --git a/crates/tdc-core/src/datacontainer/builder.rs b/crates/tdc-core/src/datacontainer/builder.rs index e4a087c..2c1bc12 100644 --- a/crates/tdc-core/src/datacontainer/builder.rs +++ b/crates/tdc-core/src/datacontainer/builder.rs @@ -1343,8 +1343,8 @@ impl TrackingDataBuilder { for ((name, mut builder), domain_field) in self .column_names .into_iter() - .zip(self.columns.into_iter()) - .zip(self.domain_fields.into_iter()) + .zip(self.columns) + .zip(self.domain_fields) { builder.ensure_capacity(total_rows); let (array, dtype) = builder.finish(); diff --git a/crates/tdc-core/src/datacontainer/core.rs b/crates/tdc-core/src/datacontainer/core.rs index d42c570..539c09a 100644 --- a/crates/tdc-core/src/datacontainer/core.rs +++ b/crates/tdc-core/src/datacontainer/core.rs @@ -90,7 +90,7 @@ impl TrackingDataContainer { .map(|(i, f): (usize, &Arc)| (f.name().clone(), i)) .collect(); - for (field, array) in fields.into_iter().zip(arrays.into_iter()) { + for (field, array) in fields.into_iter().zip(arrays) { let name = field.name(); if let Some(&i) = field_map.get(&name) { new_columns[i] = array.clone(); @@ -123,7 +123,7 @@ impl TrackingDataContainer { ) -> TrackingDataContainerResult { let mut df = ctx.read_batch(RecordBatch::from(self.data.clone()))?; let new_names = names.clone(); - for (name, expr) in names.into_iter().zip(exprs.into_iter()) { + for (name, expr) in names.into_iter().zip(exprs) { df = df.with_column(&name, expr)?; } df = df.select_columns(&new_names.iter().map(|s| s.as_str()).collect::>())?; @@ -747,7 +747,7 @@ impl crate::selector::Select for TrackingDataContainer { let mut new_arrow_fields = Vec::with_capacity(fields.len()); let mut new_schema_fields = Vec::with_capacity(fields.len()); - for (field, maybe_idx) in fields.into_iter().zip(indices.into_iter()) { + for (field, maybe_idx) in fields.into_iter().zip(indices) { if let Some(idx) = maybe_idx { new_arrays.push(self.data.column(idx).clone()); new_arrow_fields.push(self.data.fields()[idx].clone()); diff --git a/crates/tdc-core/src/datacontainer/lazy.rs b/crates/tdc-core/src/datacontainer/lazy.rs index 62889d5..f98f2bc 100644 --- a/crates/tdc-core/src/datacontainer/lazy.rs +++ b/crates/tdc-core/src/datacontainer/lazy.rs @@ -237,7 +237,7 @@ impl LazyTrackingDataContainer { let mut df = self.df.clone(); let mut new_schema_fields = self.schema.fields.clone(); - for (name, expr) in names.into_iter().zip(exprs.into_iter()) { + for (name, expr) in names.into_iter().zip(exprs) { df = df.with_column(&name, expr)?; let field = DataField::from_name(&name); let mut found = false; @@ -263,6 +263,159 @@ impl LazyTrackingDataContainer { }) } + pub fn melt(&self) -> TrackingDataContainerResult { + use crate::schema::TrackedEntity; + use crate::schema::attribute::Attribute; + use datafusion::logical_expr::ExprSchemable; + use std::collections::{HashMap, HashSet}; + + let core_names: HashSet<&str> = [ + "match/id", + "competition/id", + "season/id", + "timestamp", + "frame_id", + "period", + "period_id", + "ball_status", + "ball_poss_team_id", + "ball_poss_status", + ] + .iter() + .cloned() + .collect(); + + let mut core_cols = Vec::new(); + for f in &self.schema.fields { + let name = f.name(); + if core_names.contains(name.as_str()) { + core_cols.push(name); + } + } + + let mut entity_groups: HashMap> = HashMap::new(); + let mut all_attributes: HashSet = HashSet::new(); + let mut attr_datatypes: HashMap = HashMap::new(); + + let df_schema = self.df.schema(); + + for f in &self.schema.fields { + if let DataField::TrackedEntity { entity, attribute } = f { + let attr_name = attribute.name().to_string(); + if attr_name == "player/id" || attr_name == "team/id" || attr_name == "entity_key" { + continue; + } + all_attributes.insert(attr_name.clone()); + entity_groups + .entry(entity.clone()) + .or_default() + .insert(attr_name.clone(), f.name()); + + if !attr_datatypes.contains_key(&attr_name) + && let Ok(arrow_field) = df_schema.field_with_unqualified_name(&f.name()) + { + let mut dt = arrow_field.data_type().clone(); + if attr_name == "x" || attr_name == "y" || attr_name == "z" { + dt = arrow_schema::DataType::Float32; + } + attr_datatypes.insert(attr_name, dt); + } + } + } + + let mut all_attributes_vec: Vec = all_attributes.into_iter().collect(); + all_attributes_vec.sort(); + + let mut melted_dfs = Vec::new(); + + for (entity, attrs) in entity_groups { + let mut exprs = Vec::new(); + for c in &core_cols { + exprs.push(col(c)); + } + + let etype = match &entity { + TrackedEntity::Ball => "ball", + TrackedEntity::Player { .. } => "player", + TrackedEntity::Referee { .. } => "referee", + TrackedEntity::Team { .. } => "team", + }; + + let ekey = match &entity { + TrackedEntity::Ball => "ball".to_string(), + TrackedEntity::Player { team_id, player_id } => { + format!("{}|{}", player_id, team_id) + } + TrackedEntity::Referee { referee_id } => format!("referee:{}", referee_id), + TrackedEntity::Team { team_id } => format!("team:{}", team_id), + }; + + let parts: Vec<&str> = ekey.split('|').collect(); + let p_id = parts[0].to_string(); + let t_id = if parts.len() > 1 { + parts[1].to_string() + } else { + "none".to_string() + }; + + exprs.push(lit(p_id).alias("player/id")); + exprs.push( + lit(if t_id == "none" { + datafusion::scalar::ScalarValue::Utf8(None) + } else { + datafusion::scalar::ScalarValue::Utf8(Some(t_id)) + }) + .alias("team/id"), + ); + exprs.push(lit(ekey).alias("entity_key")); + exprs.push(lit(etype).alias("entity_type")); + + let mut filter_exprs = Vec::new(); + + for attr in &all_attributes_vec { + if let Some(col_name) = attrs.get(attr) { + let mut e = col(col_name); + if attr == "x" || attr == "y" || attr == "z" { + e = e.cast_to(&arrow_schema::DataType::Float32, df_schema)?; + } + exprs.push(e.alias(attr)); + filter_exprs.push(col(col_name).is_not_null()); + } else { + let dt = attr_datatypes + .get(attr) + .cloned() + .unwrap_or(arrow_schema::DataType::Float32); + exprs.push( + datafusion::logical_expr::Expr::Cast(datafusion::logical_expr::Cast { + expr: Box::new(lit(datafusion::scalar::ScalarValue::Null)), + data_type: dt, + }) + .alias(attr), + ); + } + } + + let filter_expr = filter_exprs.into_iter().reduce(|a, b| a.or(b)); + let mut entity_df = self.df.clone(); + if let Some(f) = filter_expr { + entity_df = entity_df.filter(f)?; + } + entity_df = entity_df.select(exprs)?; + melted_dfs.push(entity_df); + } + + if melted_dfs.is_empty() { + return Ok(self.df.clone().limit(0, Some(0))?); + } + + let mut final_df = melted_dfs[0].clone(); + for df in melted_dfs.into_iter().skip(1) { + final_df = final_df.union(df)?; + } + + Ok(final_df) + } + pub fn add_array_column( &self, _name: &str, diff --git a/crates/tdc-core/src/schema/field.rs b/crates/tdc-core/src/schema/field.rs index 798715a..ab89095 100644 --- a/crates/tdc-core/src/schema/field.rs +++ b/crates/tdc-core/src/schema/field.rs @@ -5,6 +5,23 @@ use arrow_schema::DataType; use std::fmt; use std::str::FromStr; +/// Standard core columns that identify the dataset, match, or frame context. +pub const CORE_COLUMNS: &[&str] = &[ + "match/id", + "competition/id", + "season/id", + "timestamp", + "frame_id", + "period", + "period_id", + "player/id", + "team/id", + "entity_key", + "ball_status", + "ball_poss_team_id", + "ball_poss_status", +]; + /// Represents a single column (field) in a tracking dataset. /// /// A `DataField` maps an Arrow column name to its domain-specific meaning, diff --git a/crates/tdc-parsers/src/sportec.rs b/crates/tdc-parsers/src/sportec.rs index 85131b0..dd63aec 100644 --- a/crates/tdc-parsers/src/sportec.rs +++ b/crates/tdc-parsers/src/sportec.rs @@ -144,15 +144,11 @@ pub fn load_sportec(path: &str, meta_path: Option<&str>) -> Result n = val.parse().unwrap_or(-1), - b"T" => { - if !val.is_empty() { - ts = Some(val.to_string()); - } + b"T" if !val.is_empty() => { + ts = Some(val.to_string()); } - b"M" => { - if !val.is_empty() { - p = Some(val.parse::().unwrap_or(0)); - } + b"M" if !val.is_empty() => { + p = Some(val.parse::().unwrap_or(0)); } _ => (), } diff --git a/crates/tdc-python/src/container.rs b/crates/tdc-python/src/container.rs index 7e6c338..a3085f0 100644 --- a/crates/tdc-python/src/container.rs +++ b/crates/tdc-python/src/container.rs @@ -223,7 +223,7 @@ impl PyTrackingDataContainer { let mut expr_values = Vec::new(); let mut array_names = Vec::new(); let mut array_values = Vec::new(); - for (i, (name, value)) in names.into_iter().zip(values.into_iter()).enumerate() { + for (i, (name, value)) in names.into_iter().zip(values).enumerate() { match value { AddColumnValue::Expr(e) => { expr_names.push(name); @@ -536,7 +536,7 @@ impl PyLazyTrackingDataContainer { let mut expr_names = Vec::new(); let mut expr_values = Vec::new(); - for (name, value) in names.into_iter().zip(values.into_iter()) { + for (name, value) in names.into_iter().zip(values) { match value { AddColumnValue::Expr(e) => { expr_names.push(name); @@ -631,6 +631,164 @@ impl PyLazyTrackingDataContainer { .call_method0(py, "__arrow_c_stream__")? .into_bound(py)) } + + fn melt(&self) -> PyResult { + Ok(PyLazyDataFrame { + df: self.inner.melt().into_pyresult()?, + }) + } +} + +#[pyclass(name = "LazyDataFrame", module = "tracking_data_container")] +pub struct PyLazyDataFrame { + df: datafusion::dataframe::DataFrame, +} + +#[pymethods] +impl PyLazyDataFrame { + fn __arrow_c_stream__<'py>( + &self, + py: Python<'py>, + _requested_schema: Option>, + ) -> PyResult> { + let stream = py + .allow_threads(|| block_on(async { self.df.clone().execute_stream().await })) + .map_err(|e| PyValueError::new_err(e.to_string()))?; + let schema = stream.schema(); + struct StreamReader { + stream: std::pin::Pin>, + schema: arrow_schema::SchemaRef, + } + impl Iterator for StreamReader { + type Item = Result; + fn next(&mut self) -> Option { + let next_batch = block_on(async { self.stream.next().await }); + next_batch.map(|res| { + res.map_err(|e| arrow_schema::ArrowError::ExternalError(Box::new(e))) + }) + } + } + impl datafusion::arrow::record_batch::RecordBatchReader for StreamReader { + fn schema(&self) -> arrow_schema::SchemaRef { + self.schema.clone() + } + } + let pyarrow_reader = + pyo3_arrow::PyRecordBatchReader::new(Box::new(StreamReader { stream, schema })) + .to_pyarrow(py)?; + Ok(pyarrow_reader + .call_method0(py, "__arrow_c_stream__")? + .into_bound(py)) + } + + fn extract_iceberg_metadata<'py>( + &self, + py: Python<'py>, + match_id: &str, + potential_constants: Vec, + ) -> PyResult<(Bound<'py, PyAny>, Bound<'py, PyAny>)> { + use datafusion::prelude::*; + use pyo3::IntoPyObjectExt; + + // 1. Rename columns according to mapping + // COLUMN_MAPPING = {"match/id": "match_id", "competition/id": "competition_id", ...} + let df = self.df.clone(); + let mut select_exprs = Vec::new(); + for field in df.schema().fields() { + let name = field.name(); + if name == "match/id" { + select_exprs.push(col(name).alias("match_id")); + } else if name == "competition/id" { + select_exprs.push(col(name).alias("competition_id")); + } else if name == "season/id" { + select_exprs.push(col(name).alias("season_id")); + } else if name == "period_id" { + select_exprs.push(col(name).alias("period")); + } else if name == "player/id" { + select_exprs.push(col(name).alias("entity_id")); + } else { + select_exprs.push(col(name)); + } + } + + let df = py + .allow_threads(|| block_on(async { df.select(select_exprs) })) + .map_err(|e| PyValueError::new_err(e.to_string()))?; + + // 2. Add match_id column if not exists (we overwrite it for iceberg) + let df = py + .allow_threads(|| block_on(async { df.with_column("match_id", lit(match_id)) })) + .map_err(|e| PyValueError::new_err(e.to_string()))?; + + // 3. Aggregate constants + let group_exprs = vec![col("entity_id"), col("team/id"), col("entity_type")]; + let mut agg_exprs = Vec::new(); + for c in &potential_constants { + agg_exprs.push( + datafusion::functions_aggregate::expr_fn::count_distinct(col(c)) + .alias(format!("{}_count", c)), + ); + agg_exprs.push( + datafusion::functions_aggregate::expr_fn::first_value(col(c), None) + .alias(format!("{}_val", c)), + ); + } + + let agg_df = py + .allow_threads(|| block_on(async { df.clone().aggregate(group_exprs, agg_exprs) })) + .map_err(|e| PyValueError::new_err(e.to_string()))?; + + // 4. Collect agg_df to get the constants + let batches = py + .allow_threads(|| block_on(async { agg_df.collect().await })) + .map_err(|e| PyValueError::new_err(e.to_string()))?; + + // We will return the original (renamed, match_id added) PyLazyDataFrame, + // and the Arrow table of the aggregated stats. + // Then Python can figure out which columns are constant and drop them. + + // Convert batches to PyArrow Table + let schema = batches + .first() + .map(|b| b.schema()) + .unwrap_or_else(|| Arc::new(arrow_schema::Schema::empty())); + let mut arrow_batches = Vec::new(); + for b in batches { + arrow_batches.push(b); + } + + // Export aggregated table + let pyarrow = py.import_bound("pyarrow")?; + let table_class = pyarrow.getattr("Table")?; + let pa_table = pyo3_arrow::PyRecordBatchReader::new(Box::new( + datafusion::arrow::record_batch::RecordBatchIterator::new( + arrow_batches.into_iter().map(Ok), + schema, + ), + )) + .to_pyarrow(py)?; + let agg_table = table_class.call_method1("from_batches", (pa_table,))?; + + // Return a new PyLazyDataFrame for the main df + let new_df = PyLazyDataFrame { df }; + Ok((new_df.into_bound_py_any(py)?, agg_table)) + } + + fn drop_columns<'py>(&self, py: Python<'py>, cols: Vec) -> PyResult> { + let mut select_exprs = Vec::new(); + for field in self.df.schema().fields() { + let name = field.name(); + if !cols.contains(name) { + select_exprs.push(datafusion::prelude::col(name)); + } + } + let df = py + .allow_threads(|| block_on(async { self.df.clone().select(select_exprs) })) + .map_err(|e| PyValueError::new_err(e.to_string()))?; + let new_df = PyLazyDataFrame { df }; + use pyo3::IntoPyObjectExt; + new_df.into_bound_py_any(py) + } } impl PyLazyTrackingDataContainer { @@ -689,7 +847,7 @@ fn build_arrow_array(list: &Bound<'_, PyList>, dtype: &DataType) -> PyResult()?); + b.append_value(i.extract::()? as f32); } } Ok(Arc::new(b.finish())) @@ -743,8 +901,15 @@ fn build_arrow_array(list: &Bound<'_, PyList>, dtype: &DataType) -> PyResult() { + b.append_value(ts); + } else if let Ok(ts_val) = i.call_method0("timestamp") { + let ts = (ts_val.extract::()? * 1_000_000.0) as i64; + b.append_value(ts); } else { - b.append_value(i.extract::()?); + return Err(PyTypeError::new_err( + "Expected integer or datetime with timestamp method", + )); } } Ok(Arc::new(b.finish())) @@ -755,7 +920,7 @@ fn build_arrow_array(list: &Bound<'_, PyList>, dtype: &DataType) -> PyResult()?); + b.append_value(i.to_string()); } } Ok(Arc::new(b.finish())) diff --git a/crates/tdc-python/src/lib.rs b/crates/tdc-python/src/lib.rs index 2ab7ca8..ecc4158 100644 --- a/crates/tdc-python/src/lib.rs +++ b/crates/tdc-python/src/lib.rs @@ -26,6 +26,10 @@ fn _tracking_data_container(m: &Bound<'_, PyModule>) -> PyResult<()> { ops::register(m)?; m.add("__version__", env!("CARGO_PKG_VERSION"))?; + m.add( + "CORE_COLUMNS", + tdc_core::schema::field::CORE_COLUMNS.to_vec(), + )?; Ok(()) } diff --git a/crates/tdc-python/src/schema.rs b/crates/tdc-python/src/schema.rs index 84fd618..0d2d07f 100644 --- a/crates/tdc-python/src/schema.rs +++ b/crates/tdc-python/src/schema.rs @@ -41,6 +41,17 @@ impl PyDataField { DataField::TrackedEntity { attribute, .. } => attribute.name().to_string(), } } + + fn to_iceberg_name(&self) -> String { + match self.inner.name().as_str() { + "match/id" => "match_id".to_string(), + "competition/id" => "competition_id".to_string(), + "season/id" => "season_id".to_string(), + "player/id" => "entity_id".to_string(), + "team/id" => "team_id".to_string(), + other => other.to_string(), + } + } fn entity(&self) -> Option { match &self.inner { DataField::Frame { .. } => None, diff --git a/crates/tdc-python/src/selector.rs b/crates/tdc-python/src/selector.rs index 42cc6bb..8912362 100644 --- a/crates/tdc-python/src/selector.rs +++ b/crates/tdc-python/src/selector.rs @@ -37,6 +37,11 @@ pub struct PyColumnSelector; #[pymethods] impl PyColumnSelector { + #[new] + fn new() -> Self { + PyColumnSelector + } + fn ball(&self) -> PySelection { PySelection { expr: CoreExpr::Ball, @@ -69,6 +74,14 @@ pub struct PyExpr { #[pymethods] impl PyExpr { + fn __repr__(&self) -> String { + format!("{:?}", self.inner) + } + + fn __str__(&self) -> String { + format!("{}", self.inner) + } + fn __add__(&self, other: PyExpr) -> PyExpr { PyExpr { inner: self.inner.clone() + other.inner, diff --git a/examples/01-basics.ipynb b/examples/01-basics.ipynb index 93f9b5a..7ecc21c 100644 --- a/examples/01-basics.ipynb +++ b/examples/01-basics.ipynb @@ -2,7 +2,13 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "# Basics: TrackingDataContainer and CDF Schema\n", "\n", @@ -53,7 +59,13 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -89,7 +101,7 @@ " \"teams/away/players/p2/y\": [34.9, 35.0, 35.1],\n", "}\n", "\n", - "container = tdc.TrackingDataContainer(data)\n", + "container = tdc.TrackingDataContainer(data, validate=False)\n", "print(f\"Container created with {container.num_rows()} frames.\")" ] }, diff --git a/examples/02-parsing.ipynb b/examples/02-parsing.ipynb index f1e0962..c6e0e7e 100644 --- a/examples/02-parsing.ipynb +++ b/examples/02-parsing.ipynb @@ -2,7 +2,13 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "# Efficient Parsing with TrackingDataBuilder\n", "\n", @@ -29,7 +35,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Ingested 10000 frames in 0.0715 seconds.\n" + "Ingested 10000 frames in 0.0768 seconds.\n" ] } ], @@ -152,9 +158,9 @@ "output_type": "stream", "text": [ "--- Loading benchmark ---\n", - "TDC (Rust): 1.18s\n", - "Fast-Forward: 1.60s\n", - "Kloppy: 19.38s\n" + "TDC (Rust): 1.41s\n", + "Fast-Forward: 1.69s\n", + "Kloppy: 20.04s\n" ] } ], @@ -185,6 +191,48 @@ "print(f\"Kloppy: {kloppy_load_time:.2f}s\")" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Data for plotting\n", + "labels = ['Kloppy', 'Fast-Forward', 'TDC (Rust)']\n", + "times = [kloppy_load_time, fff_load_time, tdc_load_time]\n", + "colors = ['#f87171', '#fbbf24', '#34d399'] # Red, Yellow, Green\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "bars = ax.barh(labels, times, color=colors)\n", + "\n", + "# Add titles and labels\n", + "ax.set_xlabel('Load Time (seconds)')\n", + "ax.set_title('Benchmark: Sportec XML Loading Performance')\n", + "ax.grid(axis='x', linestyle='--', alpha=0.7)\n", + "\n", + "# Add value labels to the end of each bar\n", + "for bar in bars:\n", + " width = bar.get_width()\n", + " ax.text(width + 0.2, bar.get_y() + bar.get_height()/2, \n", + " f'{width:.2f}s', ha='left', va='center', fontweight='bold')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -196,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -204,11 +252,11 @@ "output_type": "stream", "text": [ "--- to_polars benchmark ---\n", - "TDC to_polars: 13.31ms\n", - "Fast-Forward tracking access: 0.02ms\n", - "Kloppy to_df(engine=\"polars\"): 1133.28ms\n", + "TDC to_polars: 6.66ms\n", + "Fast-Forward tracking access: 0.01ms\n", + "Kloppy to_df(engine=\"polars\"): 1076.91ms\n", "\n", - "Speedup TDC vs Kloppy: 85.2x\n" + "Speedup TDC vs Kloppy: 161.7x\n" ] } ], @@ -235,6 +283,46 @@ "print(f\"\\nSpeedup TDC vs Kloppy: {kloppy_conv_time / tdc_conv_time:.1f}x\")" ] }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data for plotting\n", + "labels = ['Kloppy', 'TDC (Zero-Copy)']\n", + "times = [kloppy_conv_time * 1000, tdc_conv_time * 1000]\n", + "colors = ['#f87171', '#34d399'] # Red, Green\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 3))\n", + "bars = ax.barh(labels, times, color=colors)\n", + "\n", + "# Add titles and labels\n", + "ax.set_xlabel('Conversion Time (milliseconds)')\n", + "ax.set_title('Benchmark: Polars Conversion Performance')\n", + "ax.grid(axis='x', linestyle='--', alpha=0.7)\n", + "\n", + "# Add value labels\n", + "for bar in bars:\n", + " width = bar.get_width()\n", + " ax.text(width + 5, bar.get_y() + bar.get_height()/2, \n", + " f'{width:.2f}ms', ha='left', va='center', fontweight='bold')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/examples/03-interop.ipynb b/examples/03-interop.ipynb index c363521..03e1c06 100644 --- a/examples/03-interop.ipynb +++ b/examples/03-interop.ipynb @@ -49,7 +49,7 @@ " origin=tdc.Origin.TopLeft,\n", " length_unit=tdc.LengthUnit.Percent,\n", ")\n", - "container = tdc.TrackingDataContainer(data).with_coordinate_system(cs)\n", + "container = tdc.TrackingDataContainer(data, validate=False).with_coordinate_system(cs)\n", "print(f\"Container created with {container.num_rows()} rows.\")" ] }, diff --git a/examples/04-udfs.ipynb b/examples/04-udfs.ipynb index 9de72b3..fb992ac 100644 --- a/examples/04-udfs.ipynb +++ b/examples/04-udfs.ipynb @@ -68,7 +68,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Expression Engine time: 0.5881s\n" + "Expression Engine time: 0.4595s\n" ] } ], @@ -97,7 +97,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Polars Vectorized time: 0.3581s\n" + "Polars Vectorized time: 0.4631s\n" ] } ], @@ -123,7 +123,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Numpy Vectorized time: 0.0208s\n" + "Numpy Vectorized time: 0.0394s\n" ] } ], @@ -161,7 +161,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimized Rust Kernel time: 0.0463s\n" + "Optimized Rust Kernel time: 0.0359s\n" ] } ], @@ -222,7 +222,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Native Polars Batch time: 0.9378s\n" + "Native Polars Batch time: 0.8644s\n" ] } ], @@ -289,6 +289,13 @@ "if mismatch_polars < 1e-6 and mismatch_rust < 1e-6:\n", " print(\"\\nVerification Successful: All methods produced identical results.\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/05-lazy.ipynb b/examples/05-lazy.ipynb index a489553..b25ecbd 100644 --- a/examples/05-lazy.ipynb +++ b/examples/05-lazy.ipynb @@ -126,7 +126,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collected 1500 frames in 0.0050s.\n", + "Collected 1500 frames in 0.0165s.\n", "shape: (5, 6)\n", "┌──────────┬────────┬────────┬──────────┬────────┬──────────┐\n", "│ frame_id ┆ ball/z ┆ ball/y ┆ ball/vel ┆ ball/x ┆ ball/acc │\n", diff --git a/examples/06-skeletal.ipynb b/examples/06-skeletal.ipynb index 540fb80..36dbb21 100644 --- a/examples/06-skeletal.ipynb +++ b/examples/06-skeletal.ipynb @@ -58,8 +58,8 @@ "output_type": "stream", "text": [ "Found 95 minutes of data.\n", - "Loaded 132917 frames in 25.05 seconds.\n", - "Memory used: 3578.27 MB\n", + "Loaded 132917 frames in 29.43 seconds.\n", + "Memory used: 2692.30 MB\n", "Total columns: 3552\n" ] } @@ -139,8 +139,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loaded 118 columns for player #10 in 0.1026 seconds.\n", - "Memory used for selection: 71.14 MB\n" + "Loaded 118 columns for player #10 in 0.4122 seconds.\n", + "Memory used for selection: -54.38 MB\n" ] } ], @@ -366,42 +366,42 @@ "\n", "\n", "
\n", - " \n", + " \n", "
\n", - " \n", + " oninput=\"anima73aa44715804f77a7b32c6bca3f75d9.set_frame(parseInt(this.value));\">\n", "
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", "
\n", - "
\n", - " \n", - " \n", - " Once\n", + " \n", - " \n", - " Loop\n", + " \n", - " \n", + " \n", "
\n", "
\n", "
\n", @@ -411,9 +411,9 @@ " /* Instantiate the Animation class. */\n", " /* The IDs given should match those used in the template above. */\n", " (function() {\n", - " var img_id = \"_anim_imge9b75df42be44b978907a50a64638a19\";\n", - " var slider_id = \"_anim_slidere9b75df42be44b978907a50a64638a19\";\n", - " var loop_select_id = \"_anim_loop_selecte9b75df42be44b978907a50a64638a19\";\n", + " var img_id = \"_anim_imga73aa44715804f77a7b32c6bca3f75d9\";\n", + " var slider_id = \"_anim_slidera73aa44715804f77a7b32c6bca3f75d9\";\n", + " var loop_select_id = \"_anim_loop_selecta73aa44715804f77a7b32c6bca3f75d9\";\n", " var frames = new Array(150);\n", " \n", " frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAMgCAYAAACwGEg9AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n", @@ -226848,7 +226848,7 @@ " /* set a timeout to make sure all the above elements are created before\n", " the object is initialized. */\n", " setTimeout(function() {\n", - " anime9b75df42be44b978907a50a64638a19 = new Animation(frames, img_id, slider_id, 40.0,\n", + " anima73aa44715804f77a7b32c6bca3f75d9 = new Animation(frames, img_id, slider_id, 40.0,\n", " loop_select_id);\n", " }, 0);\n", " })()\n", diff --git a/examples/07-catalog.ipynb b/examples/07-catalog.ipynb index 901392e..4312109 100644 --- a/examples/07-catalog.ipynb +++ b/examples/07-catalog.ipynb @@ -56,7 +56,6 @@ " shutil.rmtree(warehouse_path)\n", "\n", "config = tdc.IcebergConfig(\n", - " bucket_count=10,\n", " compression=\"zstd\"\n", ")\n", "\n", @@ -96,6 +95,8 @@ "source": [ "data = {\n", " \"match/id\": [\"match_A\"] * 5,\n", + " \"frame_id\": list(range(5)),\n", + " \"period\": [\"first_half\"] * 5,\n", " \"timestamp\": [1000000 + i * 40000 for i in range(5)],\n", " \"ball/x\": [0.0, 1.0, 2.0, 3.0, 4.0],\n", " \"ball/y\": [0.0, 0.5, 1.0, 1.5, 2.0],\n", @@ -145,21 +146,26 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (5, 6)
match/idtimestampframe_idperiodball/xball/y
strdatetime[μs]u64strf32f32
"match_A"1969-12-31 22:00:01nullnull0.00.0
"match_A"1969-12-31 22:00:01.040nullnull1.00.5
"match_A"1969-12-31 22:00:01.080nullnull2.01.0
"match_A"1969-12-31 22:00:01.120nullnull3.01.5
"match_A"1969-12-31 22:00:01.160nullnull4.02.0
" + "shape: (5, 9)
competition/idseason/idmatch/idframe_idperiodtimestampball/xball/yball/z
strstrstri64catdatetime[μs]f32f32f32
nullnull"match_A"0"first_half"1970-01-01 00:00:010.00.0null
nullnull"match_A"1"first_half"1970-01-01 00:00:01.0401.00.5null
nullnull"match_A"2"first_half"1970-01-01 00:00:01.0802.01.0null
nullnull"match_A"3"first_half"1970-01-01 00:00:01.1203.01.5null
nullnull"match_A"4"first_half"1970-01-01 00:00:01.1604.02.0null
" ], "text/plain": [ - "shape: (5, 6)\n", - "┌──────────┬─────────────────────────┬──────────┬────────┬────────┬────────┐\n", - "│ match/id ┆ timestamp ┆ frame_id ┆ period ┆ ball/x ┆ ball/y │\n", - "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ str ┆ datetime[μs] ┆ u64 ┆ str ┆ f32 ┆ f32 │\n", - "╞══════════╪═════════════════════════╪══════════╪════════╪════════╪════════╡\n", - "│ match_A ┆ 1969-12-31 22:00:01 ┆ null ┆ null ┆ 0.0 ┆ 0.0 │\n", - "│ match_A ┆ 1969-12-31 22:00:01.040 ┆ null ┆ null ┆ 1.0 ┆ 0.5 │\n", - "│ match_A ┆ 1969-12-31 22:00:01.080 ┆ null ┆ null ┆ 2.0 ┆ 1.0 │\n", - "│ match_A ┆ 1969-12-31 22:00:01.120 ┆ null ┆ null ┆ 3.0 ┆ 1.5 │\n", - "│ match_A ┆ 1969-12-31 22:00:01.160 ┆ null ┆ null ┆ 4.0 ┆ 2.0 │\n", - "└──────────┴─────────────────────────┴──────────┴────────┴────────┴────────┘" + "shape: (5, 9)\n", + "┌────────────────┬───────────┬──────────┬──────────┬───┬────────────────┬────────┬────────┬────────┐\n", + "│ competition/id ┆ season/id ┆ match/id ┆ frame_id ┆ … ┆ timestamp ┆ ball/x ┆ ball/y ┆ ball/z │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ str ┆ str ┆ i64 ┆ ┆ datetime[μs] ┆ f32 ┆ f32 ┆ f32 │\n", + "╞════════════════╪═══════════╪══════════╪══════════╪═══╪════════════════╪════════╪════════╪════════╡\n", + "│ null ┆ null ┆ match_A ┆ 0 ┆ … ┆ 1970-01-01 ┆ 0.0 ┆ 0.0 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ 00:00:01 ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ match_A ┆ 1 ┆ … ┆ 1970-01-01 ┆ 1.0 ┆ 0.5 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ 00:00:01.040 ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ match_A ┆ 2 ┆ … ┆ 1970-01-01 ┆ 2.0 ┆ 1.0 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ 00:00:01.080 ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ match_A ┆ 3 ┆ … ┆ 1970-01-01 ┆ 3.0 ┆ 1.5 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ 00:00:01.120 ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ match_A ┆ 4 ┆ … ┆ 1970-01-01 ┆ 4.0 ┆ 2.0 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ 00:00:01.160 ┆ ┆ ┆ │\n", + "└────────────────┴───────────┴──────────┴──────────┴───┴────────────────┴────────┴────────┴────────┘" ] }, "execution_count": 4, @@ -240,21 +246,26 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (5, 7)
match/idtimestampframe_idperiodball/xball/yball/z
strdatetime[μs]u64strf32f32f32
"match_A"1969-12-31 20:00:01nullnull0.00.00.5
"match_A"1969-12-31 20:00:01.040nullnull1.00.50.5
"match_A"1969-12-31 20:00:01.080nullnull2.01.00.5
"match_A"1969-12-31 20:00:01.120nullnull3.01.50.5
"match_A"1969-12-31 20:00:01.160nullnull4.02.00.5
" + "shape: (5, 9)
competition/idseason/idmatch/idframe_idperiodtimestampball/xball/yball/z
strstrstri64catdatetime[μs]f32f32f32
nullnull"match_A"0"first_half"1970-01-01 00:00:010.00.00.5
nullnull"match_A"1"first_half"1970-01-01 00:00:01.0401.00.50.5
nullnull"match_A"2"first_half"1970-01-01 00:00:01.0802.01.00.5
nullnull"match_A"3"first_half"1970-01-01 00:00:01.1203.01.50.5
nullnull"match_A"4"first_half"1970-01-01 00:00:01.1604.02.00.5
" ], "text/plain": [ - "shape: (5, 7)\n", - "┌──────────┬─────────────────────────┬──────────┬────────┬────────┬────────┬────────┐\n", - "│ match/id ┆ timestamp ┆ frame_id ┆ period ┆ ball/x ┆ ball/y ┆ ball/z │\n", - "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ str ┆ datetime[μs] ┆ u64 ┆ str ┆ f32 ┆ f32 ┆ f32 │\n", - "╞══════════╪═════════════════════════╪══════════╪════════╪════════╪════════╪════════╡\n", - "│ match_A ┆ 1969-12-31 20:00:01 ┆ null ┆ null ┆ 0.0 ┆ 0.0 ┆ 0.5 │\n", - "│ match_A ┆ 1969-12-31 20:00:01.040 ┆ null ┆ null ┆ 1.0 ┆ 0.5 ┆ 0.5 │\n", - "│ match_A ┆ 1969-12-31 20:00:01.080 ┆ null ┆ null ┆ 2.0 ┆ 1.0 ┆ 0.5 │\n", - "│ match_A ┆ 1969-12-31 20:00:01.120 ┆ null ┆ null ┆ 3.0 ┆ 1.5 ┆ 0.5 │\n", - "│ match_A ┆ 1969-12-31 20:00:01.160 ┆ null ┆ null ┆ 4.0 ┆ 2.0 ┆ 0.5 │\n", - "└──────────┴─────────────────────────┴──────────┴────────┴────────┴────────┴────────┘" + "shape: (5, 9)\n", + "┌────────────────┬───────────┬──────────┬──────────┬───┬────────────────┬────────┬────────┬────────┐\n", + "│ competition/id ┆ season/id ┆ match/id ┆ frame_id ┆ … ┆ timestamp ┆ ball/x ┆ ball/y ┆ ball/z │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ str ┆ str ┆ i64 ┆ ┆ datetime[μs] ┆ f32 ┆ f32 ┆ f32 │\n", + "╞════════════════╪═══════════╪══════════╪══════════╪═══╪════════════════╪════════╪════════╪════════╡\n", + "│ null ┆ null ┆ match_A ┆ 0 ┆ … ┆ 1970-01-01 ┆ 0.0 ┆ 0.0 ┆ 0.5 │\n", + "│ ┆ ┆ ┆ ┆ ┆ 00:00:01 ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ match_A ┆ 1 ┆ … ┆ 1970-01-01 ┆ 1.0 ┆ 0.5 ┆ 0.5 │\n", + "│ ┆ ┆ ┆ ┆ ┆ 00:00:01.040 ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ match_A ┆ 2 ┆ … ┆ 1970-01-01 ┆ 2.0 ┆ 1.0 ┆ 0.5 │\n", + "│ ┆ ┆ ┆ ┆ ┆ 00:00:01.080 ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ match_A ┆ 3 ┆ … ┆ 1970-01-01 ┆ 3.0 ┆ 1.5 ┆ 0.5 │\n", + "│ ┆ ┆ ┆ ┆ ┆ 00:00:01.120 ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ match_A ┆ 4 ┆ … ┆ 1970-01-01 ┆ 4.0 ┆ 2.0 ┆ 0.5 │\n", + "│ ┆ ┆ ┆ ┆ ┆ 00:00:01.160 ┆ ┆ ┆ │\n", + "└────────────────┴───────────┴──────────┴──────────┴───┴────────────────┴────────┴────────┴────────┘" ] }, "execution_count": 6, @@ -294,6 +305,13 @@ " shutil.rmtree(warehouse_path)\n", "print(\"Warehouse cleaned up.\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/08-idsse-warehouse.ipynb b/examples/08-idsse-warehouse.ipynb index 23f1014..c428c04 100644 --- a/examples/08-idsse-warehouse.ipynb +++ b/examples/08-idsse-warehouse.ipynb @@ -138,15 +138,15 @@ "text": [ "\n", "Processing match DFL-MAT-J03WMX...\n", - " ✅ Parsed in 14.33s, Saved in 12.99s\n", + " ✅ Parsed in 1.48s, Saved in 1.36s\n", " ✅ Rows: 145,967\n", "\n", "Processing match DFL-MAT-J03WN1...\n", - " ✅ Parsed in 14.03s, Saved in 12.06s\n", + " ✅ Parsed in 1.22s, Saved in 1.26s\n", " ✅ Rows: 141,561\n", "\n", "Processing match DFL-MAT-J03WOH...\n", - " ✅ Parsed in 12.71s, Saved in 10.63s\n", + " ✅ Parsed in 1.19s, Saved in 1.08s\n", " ✅ Rows: 137,214\n" ] } @@ -242,7 +242,7 @@ "output_type": "stream", "text": [ "Loading data for DFL-MAT-J03WMX...\n", - " ✅ Loaded and pivoted 165259 rows in 7.1870s\n" + " ✅ Loaded and pivoted 165259 rows in 1.0861s\n" ] }, { @@ -255,27 +255,27 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (5, 273)
competition/idseason/idmatch/idframe_idperiodtimestampteams/home/players/DFL-OBJ-0026ZI/player/idteams/home/players/DFL-OBJ-0026ZI/team/idteams/home/players/DFL-OBJ-0026ZI/xteams/home/players/DFL-OBJ-0026ZI/yteams/home/players/DFL-OBJ-0026ZI/zteams/home/players/DFL-OBJ-0026ZI/velteams/home/players/DFL-OBJ-0026ZI/accteams/home/players/DFL-OBJ-0002BO/player/idteams/home/players/DFL-OBJ-0002BO/team/idteams/home/players/DFL-OBJ-0002BO/xteams/home/players/DFL-OBJ-0002BO/yteams/home/players/DFL-OBJ-0002BO/zteams/home/players/DFL-OBJ-0002BO/velteams/home/players/DFL-OBJ-0002BO/accteams/home/players/DFL-OBJ-0027B6/player/idteams/home/players/DFL-OBJ-0027B6/team/idteams/home/players/DFL-OBJ-0027B6/xteams/home/players/DFL-OBJ-0027B6/yteams/home/players/DFL-OBJ-0027B6/zteams/home/players/DFL-OBJ-0027B6/velteams/home/players/DFL-OBJ-0027B6/accteams/away/players/DFL-OBJ-002GCR/player/idteams/away/players/DFL-OBJ-002GCR/team/idteams/away/players/DFL-OBJ-002GCR/xteams/away/players/DFL-OBJ-002GCR/yteams/away/players/DFL-OBJ-002GCR/zteams/away/players/DFL-OBJ-002GCR/velteams/away/players/DFL-OBJ-002GCR/accteams/away/players/DFL-OBJ-0027G0/player/idteams/away/players/DFL-OBJ-0027G0/team/idteams/away/players/DFL-OBJ-0027G0/x…teams/home/players/DFL-OBJ-002GBW/zteams/home/players/DFL-OBJ-002GBW/velteams/home/players/DFL-OBJ-002GBW/accteams/away/player/idteams/away/team/idteams/away/xteams/away/yteams/away/zteams/away/velteams/away/accteams/home/players/DFL-OBJ-0028BD/player/idteams/home/players/DFL-OBJ-0028BD/team/idteams/home/players/DFL-OBJ-0028BD/xteams/home/players/DFL-OBJ-0028BD/yteams/home/players/DFL-OBJ-0028BD/zteams/home/players/DFL-OBJ-0028BD/velteams/home/players/DFL-OBJ-0028BD/accteams/away/players/DFL-OBJ-0002F5/player/idteams/away/players/DFL-OBJ-0002F5/team/idteams/away/players/DFL-OBJ-0002F5/xteams/away/players/DFL-OBJ-0002F5/yteams/away/players/DFL-OBJ-0002F5/zteams/away/players/DFL-OBJ-0002F5/velteams/away/players/DFL-OBJ-0002F5/accteams/away/players/DFL-OBJ-J015S4/player/idteams/away/players/DFL-OBJ-J015S4/team/idteams/away/players/DFL-OBJ-J015S4/xteams/away/players/DFL-OBJ-J015S4/yteams/away/players/DFL-OBJ-J015S4/zteams/away/players/DFL-OBJ-J015S4/velteams/away/players/DFL-OBJ-J015S4/accteams/away/jersey_colourteams/away/nameteams/away/idteams/home/jersey_colourteams/home/nameteams/home/id
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" ], "text/plain": [ - "shape: (5, 273)\n", + "shape: (5, 241)\n", "┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", - "│ competiti ┆ season/id ┆ match/id ┆ frame_id ┆ … ┆ teams/awa ┆ teams/hom ┆ teams/hom ┆ teams/hom │\n", - "│ on/id ┆ --- ┆ --- ┆ --- ┆ ┆ y/id ┆ e/jersey_ ┆ e/name ┆ e/id │\n", + "│ competiti ┆ season/id ┆ match/id ┆ frame_id ┆ … ┆ teams/hom ┆ teams/awa ┆ teams/awa ┆ teams/awa │\n", + "│ on/id ┆ --- ┆ --- ┆ --- ┆ ┆ e/id ┆ y/jersey_ ┆ y/name ┆ y/id │\n", "│ --- ┆ str ┆ str ┆ i64 ┆ ┆ --- ┆ colour ┆ --- ┆ --- │\n", "│ str ┆ ┆ ┆ ┆ ┆ str ┆ --- ┆ str ┆ str │\n", "│ ┆ ┆ ┆ ┆ ┆ ┆ str ┆ ┆ │\n", "╞═══════════╪═══════════╪═══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10000 ┆ … ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC ┆ DFL-CLU-0 │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ 0000G ┆ ┆ Köln ┆ 00008 │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10001 ┆ … ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC ┆ DFL-CLU-0 │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ 0000G ┆ ┆ Köln ┆ 00008 │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10002 ┆ … ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC ┆ DFL-CLU-0 │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ 0000G ┆ ┆ Köln ┆ 00008 │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10003 ┆ … ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC ┆ DFL-CLU-0 │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ 0000G ┆ ┆ Köln ┆ 00008 │\n", - "│ null ┆ null ┆ DFL-MAT-J ┆ 10004 ┆ … ┆ DFL-CLU-0 ┆ #F4F5F0 ┆ 1. FC ┆ DFL-CLU-0 │\n", - "│ ┆ ┆ 03WMX ┆ ┆ ┆ 0000G ┆ ┆ Köln ┆ 00008 │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10000 ┆ … ┆ DFL-CLU-0 ┆ #25282A ┆ FC Bayern ┆ DFL-CLU-0 │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00008 ┆ ┆ München ┆ 0000G │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10001 ┆ … ┆ DFL-CLU-0 ┆ #25282A ┆ FC Bayern ┆ DFL-CLU-0 │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00008 ┆ ┆ München ┆ 0000G │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10002 ┆ … ┆ DFL-CLU-0 ┆ #25282A ┆ FC Bayern ┆ DFL-CLU-0 │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00008 ┆ ┆ München ┆ 0000G │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10003 ┆ … ┆ DFL-CLU-0 ┆ #25282A ┆ FC Bayern ┆ DFL-CLU-0 │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00008 ┆ ┆ München ┆ 0000G │\n", + "│ null ┆ null ┆ DFL-MAT-J ┆ 10004 ┆ … ┆ DFL-CLU-0 ┆ #25282A ┆ FC Bayern ┆ DFL-CLU-0 │\n", + "│ ┆ ┆ 03WMX ┆ ┆ ┆ 00008 ┆ ┆ München ┆ 0000G │\n", "└───────────┴───────────┴───────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘" ] }, @@ -307,47 +307,15 @@ "data": { "text/plain": [ "{'_entities': {'ball': {},\n", - " 'teams': {'away': {'players': {'DFL-OBJ-J015S4': {},\n", - " 'DFL-OBJ-J017RE': {},\n", - " 'DFL-OBJ-0000IA': {},\n", - " 'DFL-OBJ-0002DR': {},\n", - " 'DFL-OBJ-000191': {},\n", - " 'DFL-OBJ-0027G6': {},\n", - " 'DFL-OBJ-0000RP': {},\n", - " 'DFL-OBJ-J01B8N': {},\n", - " 'DFL-OBJ-0027G0': {},\n", - " 'DFL-OBJ-0002AU': {},\n", - " 'DFL-OBJ-002GCR': {},\n", - " 'DFL-OBJ-0026PM': {},\n", - " 'DFL-OBJ-0002F5': {},\n", - " 'DFL-OBJ-J01APO': {},\n", - " 'DFL-OBJ-J01N65': {},\n", - " 'DFL-OBJ-0027KL': {}},\n", - " 'jersey_colour': '#25282A',\n", - " 'name': 'FC Bayern München',\n", - " 'id': 'DFL-CLU-00000G'},\n", - " 'home': {'players': {'DFL-OBJ-0026ZI': {},\n", - " 'DFL-OBJ-002GBW': {},\n", - " 'DFL-OBJ-002GKO': {},\n", - " 'DFL-OBJ-0002BO': {},\n", - " 'DFL-OBJ-0027V2': {},\n", - " 'DFL-OBJ-0002HE': {},\n", - " 'DFL-OBJ-00012X': {},\n", - " 'DFL-OBJ-0027B6': {},\n", - " 'DFL-OBJ-002G4A': {},\n", - " 'DFL-OBJ-0027AX': {},\n", - " 'DFL-OBJ-J01DVC': {},\n", - " 'DFL-OBJ-0028BD': {},\n", - " 'DFL-OBJ-J01D1W': {},\n", - " 'DFL-OBJ-000270': {},\n", - " 'DFL-OBJ-J01BGM': {},\n", - " 'DFL-OBJ-002G89': {}},\n", + " 'teams': {'home': {'players': {},\n", " 'jersey_colour': '#F4F5F0',\n", " 'name': '1. FC Köln',\n", - " 'id': 'DFL-CLU-000008'}},\n", - " 'referees': {'referee:DFL-OBJ-00011J': {},\n", - " 'referee:DFL-OBJ-00011W': {},\n", - " 'referee:DFL-OBJ-000141': {}}}}" + " 'id': 'DFL-CLU-000008'},\n", + " 'away': {'players': {},\n", + " 'jersey_colour': '#25282A',\n", + " 'name': 'FC Bayern München',\n", + " 'id': 'DFL-CLU-00000G'}},\n", + " 'referees': {}}}" ] }, "execution_count": 7, @@ -366,27 +334,100 @@ "metadata": {}, "outputs": [ { - "ename": "FileNotFoundError", - "evalue": "No such file or directory (os error 2): ...rehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet (set POLARS_VERBOSE=1 to see full path)\n\nThis error occurred with the following context stack:\n\t[1] 'parquet scan'\n\t[2] 'sink'\n", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m start = time.perf_counter()\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m df = \u001b[43mpl\u001b[49m\u001b[43m.\u001b[49m\u001b[43mscan_parquet\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43midsse_warehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m query_time = time.perf_counter() - start\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m ✅ Loaded and pivoted \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(df)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m rows in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquery_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33ms\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/_utils/deprecation.py:97\u001b[39m, in \u001b[36mdeprecate_streaming_parameter..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33mengine\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33min-memory\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m kwargs[\u001b[33m\"\u001b[39m\u001b[33mstreaming\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m---> \u001b[39m\u001b[32m97\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/lazyframe/opt_flags.py:328\u001b[39m, in \u001b[36mforward_old_opt_flags..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 325\u001b[39m optflags = cb(optflags, kwargs.pop(key)) \u001b[38;5;66;03m# type: ignore[no-untyped-call,unused-ignore]\u001b[39;00m\n\u001b[32m 327\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33moptimizations\u001b[39m\u001b[33m\"\u001b[39m] = optflags\n\u001b[32m--> \u001b[39m\u001b[32m328\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/polars/lazyframe/frame.py:2429\u001b[39m, in \u001b[36mLazyFrame.collect\u001b[39m\u001b[34m(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, cluster_with_columns, collapse_joins, no_optimization, engine, background, optimizations, **_kwargs)\u001b[39m\n\u001b[32m 2427\u001b[39m \u001b[38;5;66;03m# Only for testing purposes\u001b[39;00m\n\u001b[32m 2428\u001b[39m callback = _kwargs.get(\u001b[33m\"\u001b[39m\u001b[33mpost_opt_callback\u001b[39m\u001b[33m\"\u001b[39m, callback)\n\u001b[32m-> \u001b[39m\u001b[32m2429\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m wrap_df(\u001b[43mldf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[43m)\u001b[49m)\n", - "\u001b[31mFileNotFoundError\u001b[39m: No such file or directory (os error 2): ...rehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet (set POLARS_VERBOSE=1 to see full path)\n\nThis error occurred with the following context stack:\n\t[1] 'parquet scan'\n\t[2] 'sink'\n" + "data": { + "text/plain": [ + "'./idsse_warehouse'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "warehouse_path" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "catalog.db \u001b[34mtracking\u001b[m\u001b[m\n" + ] + } + ], + "source": [ + "!ls idsse_warehouse/" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ✅ Loaded 11350189 rows in 0.3305s\n" ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (5, 16)
competition_idseason_idmatch_idframe_idperiodtimestampentity_typeentity_idxyzball_statusball_poss_team_identity_keyaccvel
strstrstri64strdatetime[μs]strstrf32f32f32boolstrstrf64f64
nullnull"DFL-MAT-J03WN1"152866"other"2023-05-27 15:09:10.480"player""DFL-OBJ-00002Z"-1.83-34.169998nullfalse"DFL-CLU-00000B""DFL-OBJ-00002Z|home"0.00.0
nullnull"DFL-MAT-J03WN1"152867"other"2023-05-27 15:09:10.520"player""DFL-OBJ-00002Z"-1.89-34.07nullfalse"DFL-CLU-00000B""DFL-OBJ-00002Z|home"0.00.0
nullnull"DFL-MAT-J03WN1"152868"other"2023-05-27 15:09:10.560"player""DFL-OBJ-00002Z"-1.93-33.990002nullfalse"DFL-CLU-00000B""DFL-OBJ-00002Z|home"0.09.58
nullnull"DFL-MAT-J03WN1"152869"other"2023-05-27 15:09:10.600"player""DFL-OBJ-00002Z"-1.98-33.889999nullfalse"DFL-CLU-00000B""DFL-OBJ-00002Z|home"0.09.58
nullnull"DFL-MAT-J03WN1"152870"other"2023-05-27 15:09:10.640"player""DFL-OBJ-00002Z"-2.03-33.790001nullfalse"DFL-CLU-00000B""DFL-OBJ-00002Z|home"0.09.58
" + ], + "text/plain": [ + "shape: (5, 16)\n", + "┌──────────────┬───────────┬──────────────┬──────────┬───┬──────────────┬─────────────┬─────┬──────┐\n", + "│ competition_ ┆ season_id ┆ match_id ┆ frame_id ┆ … ┆ ball_poss_te ┆ entity_key ┆ acc ┆ vel │\n", + "│ id ┆ --- ┆ --- ┆ --- ┆ ┆ am_id ┆ --- ┆ --- ┆ --- │\n", + "│ --- ┆ str ┆ str ┆ i64 ┆ ┆ --- ┆ str ┆ f64 ┆ f64 │\n", + "│ str ┆ ┆ ┆ ┆ ┆ str ┆ ┆ ┆ │\n", + "╞══════════════╪═══════════╪══════════════╪══════════╪═══╪══════════════╪═════════════╪═════╪══════╡\n", + "│ null ┆ null ┆ DFL-MAT-J03W ┆ 152866 ┆ … ┆ DFL-CLU-0000 ┆ DFL-OBJ-000 ┆ 0.0 ┆ 0.0 │\n", + "│ ┆ ┆ N1 ┆ ┆ ┆ 0B ┆ 02Z|home ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J03W ┆ 152867 ┆ … ┆ DFL-CLU-0000 ┆ DFL-OBJ-000 ┆ 0.0 ┆ 0.0 │\n", + "│ ┆ ┆ N1 ┆ ┆ ┆ 0B ┆ 02Z|home ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J03W ┆ 152868 ┆ … ┆ DFL-CLU-0000 ┆ DFL-OBJ-000 ┆ 0.0 ┆ 9.58 │\n", + "│ ┆ ┆ N1 ┆ ┆ ┆ 0B ┆ 02Z|home ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J03W ┆ 152869 ┆ … ┆ DFL-CLU-0000 ┆ DFL-OBJ-000 ┆ 0.0 ┆ 9.58 │\n", + "│ ┆ ┆ N1 ┆ ┆ ┆ 0B ┆ 02Z|home ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J03W ┆ 152870 ┆ … ┆ DFL-CLU-0000 ┆ DFL-OBJ-000 ┆ 0.0 ┆ 9.58 │\n", + "│ ┆ ┆ N1 ┆ ┆ ┆ 0B ┆ 02Z|home ┆ ┆ │\n", + "└──────────────┴───────────┴──────────────┴──────────┴───┴──────────────┴─────────────┴─────┴──────┘" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "start = time.perf_counter()\n", "\n", - "df = pl.scan_parquet(\"idsse_warehouse/tracking/tracking_data/data/00000-0-2ce0e3ca-73c0-404e-9596-10570ff4a509.parquet\").collect()\n", + "import glob\n", + "parquet_files = glob.glob(\n", + " os.path.join(warehouse_path, \"tracking\", \"tracking_data\", \"**\", \"*.parquet\"),\n", + " recursive=True,\n", + ")\n", + "\n", + "df = pl.scan_parquet(parquet_files).collect()\n", "query_time = time.perf_counter() - start\n", - "print(f\" ✅ Loaded and pivoted {len(df)} rows in {query_time:.4f}s\")\n", - "container.to_polars().head()" + "print(f\" ✅ Loaded {len(df)} rows in {query_time:.4f}s\")\n", + "df.head()" ] }, { @@ -398,14 +439,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warehouse cleaned up.\n" + ] + } + ], "source": [ "if Path(warehouse_path).exists():\n", " shutil.rmtree(warehouse_path)\n", "print(\"Warehouse cleaned up.\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/20260423-presentation-liss/build.sh b/examples/20260423-presentation-liss/build.sh new file mode 100755 index 0000000..85e7957 --- /dev/null +++ b/examples/20260423-presentation-liss/build.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +# Build and serve the presentation +# This will generate presentation.slides.html and open it in your browser. + +jupyter nbconvert presentation.ipynb \ + --to slides \ + 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Metadata-Aware High-Performance Container for Sports Tracking Data\n", + "\n", + "**Pieter Robberechts, Koen Vossen, et al.**\n", + "\n", + "April 2026\n", + "\n", + "\"logo\"\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## The Fragmentation of Open-Source\n", + "\n", + "People are building great open source packages but there is little to no coordination and therefore no interoperability. \n", + "\n", + "\n", + "\n", + "See " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "jp-MarkdownHeadingCollapsed": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "Lots of time is wasted on transforming output from one package into input for another one. This can be solved by a common standard. This increases interoperability and saves you time.\n", + "\n", + "Sports analytics is currently suffering from **duplicated effort** across various open-source packages.\n", + "\n", + "- **Parsing & Modeling**: Kloppy, Floodlight, and others often build similar logic.\n", + "- **The Scalability Wall**: Object-based models (like Kloppy) are excellent for small datasets but fail when processing millions of rows of tracking data.\n", + "- **Inconsistent Schemas**: Every tool uses different column names, making interoperability a nightmare.\n", + "- Lots of time is wasted on transforming output from one package into input for another one. This can be solved by a common standard. \n", + "\n", + "- Lots of implemetation effort is wasted by re-implementing data parsers across various open-source packages. \n", + "\n", + "\n", + "| | databallpy | PreProcessing/Event | floodlight | gandula | soccercpd | defcon | ballradar | socceraction | soccer-xg | unravelsports | kloppy | ingestify |\n", + "|------------------|------------|---------------------|---------------|----------|-----------|----------|-----------|--------------|-----------|---------------|----------|-----------|\n", + "| Ingestion | | | | | | | | | | | | ✔️ |\n", + "| Data management | | | | | | | | | | | | ✔️ |\n", + "| Parsing Events | ✔️ | ✔️ | ✔️ | ✔️ | | ✔️ | ✔️ | ✔️ | ✔️ | | ✔️ | |\n", + "| Parsing Tracking | ✔️ | ✔️ | ✔️ | ✔️ | | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | |\n", + "| Event Sync | ✔️ | | | | | ✔️ | | | | | | |\n", + "| Metrics | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | | |\n", + "| Visuals | ✔️ | | | ✔️ | | | ✔️ | ✔️ | | | | |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## 🌉🌁 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐛𝐫𝐢𝐝𝐠𝐞𝐬! \n", + "\n", + "We brought together contributors from different open source projects to discuss how we can align our work and improve interoperability in football analytics. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "Socceraction v1.5.0 added support for loading data using kloppy." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/probberechts/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/kloppy/_providers/statsbomb.py:86: UserWarning: \n", + "\n", + "You are about to use StatsBomb public data.\n", + "By using this data, you are agreeing to the user agreement. \n", + "The user agreement can be found here: https://github.com/statsbomb/open-data/blob/master/LICENSE.pdf\n", + "\n", + " warnings.warn(\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "FIXED_HOME_AWAY", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 2\u001b[39m dataset = statsbomb.load_open_data(match_id=\u001b[32m8657\u001b[39m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msocceraction\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mspadl\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mkloppy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m convert_to_actions\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m spadl_actions = \u001b[43mconvert_to_actions\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgame_id\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m8657\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msocceraction\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01matomic\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mspadl\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01matomicspadl\u001b[39;00m\n\u001b[32m 8\u001b[39m atomic_spadl_actions = atomicspadl.convert_to_atomic(spadl_actions)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Developer/tracking_data_container/.venv/lib/python3.11/site-packages/socceraction/spadl/kloppy.py:90\u001b[39m, in \u001b[36mconvert_to_actions\u001b[39m\u001b[34m(dataset, game_id)\u001b[39m\n\u001b[32m 82\u001b[39m warnings.warn(\n\u001b[32m 83\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mConverting \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdataset.metadata.provider\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m data is only supported from \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 84\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mKloppy version \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_SUPPORTED_PROVIDERS[dataset.metadata.provider]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m (you have \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_KLOPPY_VERSION\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m). \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 85\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mThe result may be incorrect or incomplete.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 86\u001b[39m )\n\u001b[32m 88\u001b[39m \u001b[38;5;66;03m# Convert the dataset to the SPADL coordinate system\u001b[39;00m\n\u001b[32m 89\u001b[39m new_dataset = dataset.transform(\n\u001b[32m---> \u001b[39m\u001b[32m90\u001b[39m to_orientation=\u001b[43mOrientation\u001b[49m\u001b[43m.\u001b[49m\u001b[43mFIXED_HOME_AWAY\u001b[49m, \u001b[38;5;66;03m# FIXME\u001b[39;00m\n\u001b[32m 91\u001b[39m to_coordinate_system=_SoccerActionCoordinateSystem(normalized=\u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[32m 92\u001b[39m )\n\u001b[32m 94\u001b[39m \u001b[38;5;66;03m# Convert the events to SPADL actions\u001b[39;00m\n\u001b[32m 95\u001b[39m actions = []\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/share/uv/python/cpython-3.11.14-macos-aarch64-none/lib/python3.11/enum.py:786\u001b[39m, in \u001b[36mEnumType.__getattr__\u001b[39m\u001b[34m(cls, name)\u001b[39m\n\u001b[32m 784\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m._member_map_[name]\n\u001b[32m 785\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m786\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(name) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[31mAttributeError\u001b[39m: FIXED_HOME_AWAY" + ] + } + ], + "source": [ + "from kloppy import statsbomb\n", + "dataset = statsbomb.load_open_data(match_id=8657)\n", + "\n", + "from socceraction.spadl.kloppy import convert_to_actions\n", + "spadl_actions = convert_to_actions(dataset, game_id=8657)\n", + "\n", + "import socceraction.atomic.spadl as atomicspadl\n", + "atomic_spadl_actions = atomicspadl.convert_to_atomic(spadl_actions)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "Databallpy added support for loading data using kloppy." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Succesfully loaded databallpy.game.Game object: Fortuna Düsseldorf 0 - 1 1. FC Nürnberg\n" + ] + } + ], + "source": [ + "# create a Kloppy TrackingDataset and/or EventDataset\n", + "from kloppy import sportec\n", + "\n", + "event_dataset = sportec.load_open_event_data(match_id=\"J03WPY\")\n", + "tracking_dataset = sportec.load_open_tracking_data(match_id=\"J03WPY\", only_alive=False)\n", + "\n", + "# transform the kloppy dataset to a databallpy game object:\n", + "from databallpy import get_game_from_kloppy\n", + "\n", + "game = get_game_from_kloppy(tracking_dataset=tracking_dataset,event_dataset=event_dataset)\n", + "print(f\"Succesfully loaded {game}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Is a Dataframe Agreement Enough?\n", + "\n", + "A common suggestion is: *\"Why don't we just all use Polars and agree on column names?\"*\n", + "\n", + "We believe this is **insufficient** because:\n", + "- **Metadata Loss**: Dataframes lose the context of coordinate systems, field sizes, and team orientations.\n", + "- **Semantic Fragility**: Column names like `x_1` or `home_7_y` are fragile and lack domain-specific logic.\n", + "- **Logic Duplication**: Operations like coordinate normalization shouldn't be re-implemented for every project." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## The Proposed Solution: TDC\n", + "\n", + "The **Tracking Data Container** is a shared, standardized, metadata-aware interface.\n", + "\n", + "- **Implemented in Rust**: Fast.\n", + "- **Backed by Apache Arrow**: Zero-copy, memory-efficient.\n", + "- **(Soft) Enforced Metadata**: The container *knows* what it's holding.\n", + "- **Shared CDF Schema**: Guaranteed interoperability across the ecosystem." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Why Rust, Arrow & CDF?\n", + "\n", + "### Apache Arrow\n", + "- A community-driven standard for columnar in-memory data.\n", + "- Enables **zero-copy** transfer between Rust and Python." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "### Common Data Format (CDF)\n", + "- We align with the **CDF v0.2.2** standard for hierarchical naming.\n", + "- `ball/x`, `teams/{tid}/players/{pid}/y`\n", + "- This ensures compatibility with research and federation-level tooling." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## 5. Design & Functionalities\n", + "\n", + "- **TrackingDataContainer**: The eager interface for interactive work.\n", + "- **DataField**: Domain-specific representation of a column.\n", + "- **ColumnSelector**: Metadata-aware querying (e.g., `selector.players('home').x`).\n", + "- **CoordinateSystem**: Built-in support for transformations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "import tracking_data_container as tdc\n", + "from pathlib import Path\n", + "\n", + "# Load and transform in 3 lines\n", + "container = tdc.load_sportec(\"../../data/sportec/tracking.xml\")\n", + "target_cs = tdc.CoordinateSystem(x_range=(-52.5, 52.5), y_range=(-34, 34), origin=\"center\")\n", + "container = container.transform_coordinates(target_cs)\n", + "\n", + "# Metadata-aware selection\n", + "home_x = container.select(container.selector.players(team_id=\"home\").attributes([\"x\"]))\n", + "home_x.to_polars().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 6. Advanced: Lazy Eval & Filter Pushdown\n", + "\n", + "For high-cardinality data like **Pose Tracking (Skeletal)**, eager loading is impossible.\n", + "\n", + "- **LazyTrackingDataContainer**: Uses DataFusion logical plans.\n", + "- **Filter Pushdown**: Queries like `filter(period == 1)` skip reading irrelevant data from disk.\n", + "- **Performance**: Scales to hundreds of joints across multiple players." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "# Lazy load Hawkeye skeletal data\n", + "H_DIR = Path(\"../../data/hawkeye\")\n", + "hawkeye = tdc.load_hawkeye(\n", + " list(H_DIR.glob(\"*.ball.json\"))[:5], \n", + " list(H_DIR.glob(\"*.centroids.json\"))[:5], \n", + " joint_paths=list(H_DIR.glob(\"*.joints.json\"))[:5], \n", + " meta_path=H_DIR / \"meta.json\"\n", + ").lazy()\n", + "\n", + "# Pushdown: Only process what we need\n", + "subset = hawkeye.filter(tdc.col(\"period\") == 1).select([\"timestamp\", \"ball/x\", \"ball/y\"])\n", + "subset.collect().to_polars().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 7. Warehouse Scale: Apache Iceberg\n", + "\n", + "Storage is optimized via **Apache Iceberg** and a **Semi-Long Format**.\n", + "\n", + "### Why Iceberg?\n", + "- **Partition Pruning**: Skip entire matches during scan.\n", + "- **Bucket Pruning**: Read only specific players from a match file.\n", + "- **Schema Evolution**: Add new metrics/columns without rewriting the entire warehouse.\n", + "- **99% Pruning Efficiency**: Read 1MB of data for a specific player query vs. 100MB+ for the full match." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 8. Get Involved & Discussion\n", + "\n", + "TDC is **open-source** and built on community collaboration.\n", + "\n", + "### Open Questions for Discussion:\n", + "- Should we support nested structs for joint data, or stick to flat hierarchical names?\n", + "- How do we best integrate event data (which doesn't fit the Arrow table model perfectly)?\n", + "- What are the priority formats for the next batch of loaders?\n", + "\n", + "**GitHub**: `PySport/tracking-data-container` \n", + "**RFC**: Join the conversation on the RFC document!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + }, + "livereveal": { + "scroll": true, + "theme": "simple", + "transition": "slide" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/20260423-presentation-liss/presentation.slides.html b/examples/20260423-presentation-liss/presentation.slides.html new file mode 100644 index 0000000..35a3cc1 --- /dev/null +++ b/examples/20260423-presentation-liss/presentation.slides.html @@ -0,0 +1,7740 @@ + + + + + + + +presentation slides + + + + + + + + + + + + + + + + + +

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test_add_column_per_player_udf(self, container): - def compute_pos_sum(x, y): - return x.to_numpy() + y.to_numpy() - - container = container.add_column_per_player("pos_sum", compute_pos_sum, on=["x", "y"]) - assert "teams/home/players/player_1/pos_sum" in container.schema().names() - - # home player_1 x: [23.5, 23.6, 23.7], y: [24.2, 24.3, 24.4] - expected = np.array([23.5 + 24.2, 23.6 + 24.3, 23.7 + 24.4]) - actual = container.to_arrow().column("teams/home/players/player_1/pos_sum").to_numpy() - np.testing.assert_allclose(actual, expected) - - def test_add_column_per_player_ops(self, container): - container = container.add_column_per_player( - "speed_rust", tdc.ops.compute_speed, on=["x", "y"] - ) - assert "teams/home/players/player_1/speed_rust" in container.schema().names() - - # home player_1 x: [23.5, 23.6, 23.7], y: [24.2, 24.3, 24.4] - # dx=0.1, dy=0.1 -> speed=sqrt(0.01 + 0.01) = sqrt(0.02) approx 0.1414 - expected = np.array([0.0, np.sqrt(0.02), np.sqrt(0.02)]) - actual = container.to_arrow().column("teams/home/players/player_1/speed_rust").to_numpy() - np.testing.assert_allclose(actual, expected, atol=1e-6) diff --git a/python/tests/test_advanced_features.py b/python/tests/test_advanced_features.py deleted file mode 100644 index 9982dca..0000000 --- a/python/tests/test_advanced_features.py +++ /dev/null @@ -1,102 +0,0 @@ -from datetime import datetime, timedelta, timezone - -import polars as pl -import tracking_data_container as tdc - - -def test_materialize_frame_temporal(container): - """Test materializing frames by datetime and timedelta.""" - base_time = datetime(2023, 10, 1, 12, 0, 0, tzinfo=timezone.utc) - - # 1. By datetime - # Frame 1 timestamp is base_time + 40ms = 1696161600040000 microseconds - dt = base_time + timedelta(milliseconds=40) - frame_dt = container.materialize_frame(dt) - assert frame_dt["frame_id"] == 123457 - - # 2. By timedelta - # 40ms offset should be Frame 1. - td = timedelta(milliseconds=40) - frame_td = container.materialize_frame(td) - assert frame_td["frame_id"] == 123457 - - -def test_melt_pivot_roundtrip(): - """Test that melting and pivoting correctly reconstructs the wide format.""" - data = { - "match/id": ["m1", "m1"], - "timestamp": [1000000, 1040000], - "frame_id": [1, 2], - "period": ["1", "1"], - "ball/x": [10.0, 11.0], - "ball/y": [20.0, 21.0], - "teams/home/players/p1/x": [30.0, 31.0], - } - container = tdc.TrackingDataContainer(data, validate=False) - - # Melt to long format - long_df = container.melt() - assert "player/id" in long_df.columns - assert "entity_key" in long_df.columns - - # Pivot back to wide - pivoted = tdc.TrackingDataContainer.pivot(long_df) - - assert pivoted.num_rows() == 2 - assert "ball/x" in pivoted.schema().names() - assert "teams/home/players/p1/x" in pivoted.schema().names() - - # Check values - df_piv = pivoted.to_polars().sort("frame_id") - assert df_piv["ball/x"].to_list() == [10.0, 11.0] - assert df_piv["teams/home/players/p1/x"].to_list() == [30.0, 31.0] - - -def test_lazy_schema_caching(container): - """Test that lazy schema caching works and propagates.""" - lazy = container.lazy() - - # First call reconstructions schema - lazy.schema() - assert lazy._cached_schema is not None - - # Transformation should propagate/subset cache - selected = lazy.select(["ball/x", "ball/y"]) - assert selected._cached_schema is not None - assert len(selected._cached_schema.names()) == 2 - - # Slice should preserve cache - sliced = selected.slice(0, 1) - assert sliced._cached_schema == selected._cached_schema - - -def test_pivot_global_constants(): - """Test that global constants are correctly handled in pivot.""" - data = { - "match_id": ["constant_match"] * 4, - "competition_id": ["constant_comp"] * 4, - "timestamp": [1000, 1000, 2000, 2000], - "period": ["1", "1", "1", "1"], - "entity_id": ["p1", "p2", "p1", "p2"], - "team_id": ["home", "home", "home", "home"], - "entity_key": ["p1|home", "p2|home", "p1|home", "p2|home"], - "x": [1.0, 2.0, 3.0, 4.0], - "y": [1.0, 2.0, 3.0, 4.0], - } - df = pl.DataFrame(data) - - pivoted = tdc.TrackingDataContainer.pivot(df) - - # Check that constants are back (mapped to internal CDF names by Rust) - res = pivoted.to_polars() - assert "match/id" in res.columns - assert "competition/id" in res.columns - assert res["match/id"][0] == "constant_match" - - assert "competition/id" in res.columns - assert res["competition/id"][0] == "constant_comp" - - # Check pivoted columns - assert "teams/home/players/p1/x" in res.columns - assert "teams/home/players/p2/x" in res.columns - assert res.sort("timestamp")["teams/home/players/p1/x"].to_list() == [1.0, 3.0] diff --git a/python/tests/test_catalog.py b/python/tests/test_catalog.py index 2ba3b6f..ac597c6 100644 --- a/python/tests/test_catalog.py +++ b/python/tests/test_catalog.py @@ -1,93 +1,191 @@ +import polars as pl +import pytest import tracking_data_container as tdc -def test_catalog_lifecycle(tmp_path): - warehouse_path = tmp_path / "warehouse" - catalog = tdc.TrackingDataCatalog(str(warehouse_path)) - catalog.initialize() - - # Create a container with all required fields for Iceberg - # and some constant attributes (jersey_color, player_name) - # Including a NULL value in x to verify it's allowed - data = { - "match/id": ["match_1", "match_1"], - "timestamp": [1000000, 1040000], # microseconds - "frame_id": [100, 101], - "period": ["1", "1"], - "ball/x": [0.0, None], - "ball/y": [0.0, 1.0], - "teams/home/players/p1/x": [10.0, 11.0], - "teams/home/players/p1/y": [20.0, 21.0], - "teams/home/players/p1/jersey_color": ["red", "red"], - "teams/home/players/p1/name": ["Player 1", "Player 1"], - } - container = tdc.TrackingDataContainer(data, validate=False) - - # Save container - normalization should happen here - catalog.save_container(container) - - # Verify normalization: physical table should not have jersey_color - table = catalog._iceberg_catalog.load_table("tracking.tracking_data") - physical_cols = [f.name for f in table.schema().fields] - assert "jersey_color" not in physical_cols - assert "name" not in physical_cols - - # Load container - lazy_loaded = catalog.load_container(match_id="match_1") - assert isinstance(lazy_loaded, tdc.LazyTrackingDataContainer) - - # Schema should include normalized columns - schema_names = lazy_loaded.schema().names() - assert "teams/home/players/p1/jersey_color" in schema_names - assert "teams/home/players/p1/name" in schema_names - - loaded = lazy_loaded.collect() - assert loaded.num_rows() == 2 - assert loaded.is_bound() - - # Check data - df = loaded.to_polars() - assert df["match/id"][0] == "match_1" - # ball should NOT have player/id - assert "ball/player/id" not in df.columns - assert df["ball/x"][0] == 0.0 - assert df["ball/x"][1] is None - - # Check normalized constants - assert df["teams/home/players/p1/jersey_color"][0] == "red" - assert df["teams/home/players/p1/name"][0] == "Player 1" - - -def test_bound_container_save(tmp_path): - warehouse_path = tmp_path / "warehouse" - catalog = tdc.TrackingDataCatalog(str(warehouse_path)) - catalog.initialize() - - # Create and save with required fields - data = { - "match/id": ["match_1"], - "timestamp": [1000000], - "frame_id": [100], - "period": ["1"], - "ball/x": [0.0], - "ball/y": [0.0], - } - container = tdc.TrackingDataContainer(data, validate=False) - catalog.save_container(container) - - # Load (becomes bound) - lazy_bound = catalog.load_container(match_id="match_1") - bound = lazy_bound.collect() - assert bound.is_bound() - - # Modify and save via bound method - # Preserves catalog binding - new_bound = bound.add_column("ball/z", [1.0]) - assert new_bound.is_bound() - - new_bound.save() - - # Load again to verify - final = catalog.load_container(match_id="match_1").collect() - assert "ball/z" in final.to_polars().columns - assert final.to_polars()["ball/z"][0] == 1.0 +class TestIcebergCatalog: + def test_catalog_lifecycle(self, empty_catalog): + # Create a container with all required fields for Iceberg + # and some constant attributes (jersey_color, player_name) + # Including a NULL value in x to verify it's allowed + data = { + "match/id": ["match_1", "match_1"], + "timestamp": [1000000, 1040000], # microseconds + "frame_id": [100, 101], + "period": ["1", "1"], + "ball/x": [0.0, None], + "ball/y": [0.0, 1.0], + "ball/radius": [0.11, 0.11], # Ball constant + "teams/home/players/p1/x": [10.0, 11.0], + "teams/home/players/p1/y": [20.0, 21.0], + "teams/home/players/p1/jersey_color": ["red", "red"], + "teams/home/players/p1/name": ["Player 1", "Player 1"], + } + container = tdc.TrackingDataContainer(data, validate=False) + + # Save container - normalization should happen here + empty_catalog.save_container(container) + + # Verify normalization: physical table should not have jersey_color + table = empty_catalog._iceberg_catalog.load_table("tracking.tracking_data") + physical_cols = [f.name for f in table.schema().fields] + assert "jersey_color" not in physical_cols + assert "name" not in physical_cols + assert "radius" not in physical_cols + + # Load container + lazy_loaded = empty_catalog.load_container(match_id="match_1") + assert isinstance(lazy_loaded, tdc.LazyTrackingDataContainer) + + # Schema should include normalized columns + schema_names = lazy_loaded.schema().names() + assert "teams/home/players/p1/jersey_color" in schema_names + assert "teams/home/players/p1/name" in schema_names + assert "ball/radius" in schema_names + + loaded = lazy_loaded.collect() + assert loaded.num_rows() == 2 + assert loaded.is_bound() + + # Check data + df = loaded.to_polars() + assert df["match/id"][0] == "match_1" + # ball should NOT have player/id + assert "ball/player/id" not in df.columns + assert df["ball/x"][0] == 0.0 + assert df["ball/x"][1] is None + + # Check normalized constants + assert df["teams/home/players/p1/jersey_color"][0] == "red" + assert df["teams/home/players/p1/name"][0] == "Player 1" + assert df["ball/radius"][0] == "0.11" + + def test_bound_container_save(self, iceberg_catalog): + # Load (becomes bound) + lazy_bound = iceberg_catalog.load_container(match_id="match_12345") + bound = lazy_bound.collect() + assert bound.is_bound() + + # Modify and save via bound method + # Preserves catalog binding + new_bound = bound.add_column("ball/z2", [1.0, 1.0, 1.0]) + assert new_bound.is_bound() + + new_bound.save() + + # Load again to verify + final = iceberg_catalog.load_container(match_id="match_12345").collect() + assert "ball/z2" in final.to_polars().columns + assert final.to_polars()["ball/z2"][0] == 1.0 + + def test_iceberg_backend_errors(self, iceberg_catalog): + lazy = iceberg_catalog.load_container(match_id="match_12345") + + # Filter is now supported via deferred evaluation + filtered = lazy.filter(tdc.col("ball/x") > 0) + assert isinstance(filtered, tdc.LazyTrackingDataContainer) + + with pytest.raises(NotImplementedError, match="Iceberg backend does not support"): + lazy.transform_coordinates(tdc.CoordinateSystem(origin="bottom_left")) + + def test_load_non_existent(self, empty_catalog): + # Loading a non-existent match id should return a lazy container that collects to 0 rows + lazy = empty_catalog.load_container(match_id="non_existent") + loaded = lazy.collect() + assert loaded.num_rows() == 0 + + def test_physical_parquet_format(self, iceberg_catalog): + import glob + import os + + # Find the physical parquet files written by Iceberg + warehouse_dir = iceberg_catalog.warehouse_path + parquet_files = glob.glob( + os.path.join(warehouse_dir, "tracking", "tracking_data", "**", "*.parquet"), + recursive=True, + ) + assert len(parquet_files) > 0 + + # Read the parquet files directly with Polars + df = pl.read_parquet(parquet_files[0]) + + # Verify it is in long format with the expected columns + expected_cols = { + "competition_id", + "season_id", + "match_id", + "frame_id", + "period", + "timestamp", + "ball_status", + "entity_type", + "entity_id", + "entity_key", + "x", + "y", + "z", + } + assert set(df.columns) == expected_cols + + +class TestMeltPivot: + def test_melt_pivot_roundtrip(self): + """Test that melting and pivoting correctly reconstructs the wide format.""" + data = { + "match/id": ["m1", "m1"], + "timestamp": [1000000, 1040000], + "frame_id": [1, 2], + "period": ["1", "1"], + "ball/x": [10.0, 11.0], + "ball/y": [20.0, 21.0], + "teams/home/players/p1/x": [30.0, 31.0], + } + container = tdc.TrackingDataContainer(data, validate=False) + + # Melt to long format + long_df = container.melt() + assert "player/id" in long_df.columns + assert "entity_key" in long_df.columns + + # Pivot back to wide + pivoted = tdc.TrackingDataContainer.pivot(long_df) + + assert pivoted.num_rows() == 2 + assert "ball/x" in pivoted.schema().names() + assert "teams/home/players/p1/x" in pivoted.schema().names() + + # Check values + df_piv = pivoted.to_polars().sort("frame_id") + assert df_piv["ball/x"].to_list() == [10.0, 11.0] + assert df_piv["teams/home/players/p1/x"].to_list() == [30.0, 31.0] + + def test_pivot_global_constants(self): + """Test that global constants are correctly handled in pivot.""" + data = { + "match_id": ["constant_match"] * 4, + "competition_id": ["constant_comp"] * 4, + "timestamp": [1000, 1000, 2000, 2000], + "period": ["1", "1", "1", "1"], + "entity_id": ["p1", "p2", "p1", "p2"], + "team_id": ["home", "home", "home", "home"], + "entity_key": ["p1|home", "p2|home", "p1|home", "p2|home"], + "x": [1.0, 2.0, 3.0, 4.0], + "y": [1.0, 2.0, 3.0, 4.0], + } + df = pl.DataFrame(data) + + pivoted = tdc.TrackingDataContainer.pivot(df) + + # Check that constants are back (mapped to internal CDF names by Rust) + res = pivoted.to_polars() + assert "match/id" in res.columns + assert "competition/id" in res.columns + assert res["match/id"][0] == "constant_match" + + assert "competition/id" in res.columns + assert res["competition/id"][0] == "constant_comp" + + # Check pivoted columns + assert "teams/home/players/p1/x" in res.columns + assert "teams/home/players/p2/x" in res.columns + assert res.sort("timestamp")["teams/home/players/p1/x"].to_list() == [1.0, 3.0] diff --git a/python/tests/test_container.py b/python/tests/test_container.py index 0615ab0..7dfc481 100644 --- a/python/tests/test_container.py +++ b/python/tests/test_container.py @@ -1,3 +1,4 @@ +import numpy as np import pytest import tracking_data_container as tdc @@ -6,10 +7,7 @@ class TestBasics: def test_container_init(self, container) -> None: assert isinstance(container, tdc.TrackingDataContainer) assert container.num_rows() == 3 - # In conftest.py: 5 ball cols, 8 player cols, frame_id, timestamp, period, - # match/id, ball_status, teams/home/id, teams/away/id - # Total = 5+8+7 = 20. But let's just check it's >= 18 - assert len(container.schema()) >= 18 + assert len(container.schema()) == 18 def test_data_field_init(self) -> None: field = tdc.DataField("ball/x") @@ -26,7 +24,72 @@ def test_orientation_mode(self, container) -> None: assert container.orientation_mode == tdc.OrientationMode.HomeAway -class TestColumnAddition: +class TestAddColumn: + def test_add_column_array(self, container): + data = [1.0, 2.0, 3.0] + container = container.add_column("ball/new", data) + assert "ball/new" in container.schema().names() + assert container.to_arrow().column("ball/new").to_pylist() == data + + def test_add_column_expr(self, container): + expr = (tdc.col("ball/x") + tdc.col("ball/y")).sqrt() + container = container.add_column("ball/dist", expr) + assert "ball/dist" in container.schema().names() + + # ball/x: [50.0, 51.0, 52.0], ball/y: [12.0, 13.0, 14.0] + expected = np.sqrt(np.array([50.0, 51.0, 52.0]) + np.array([12.0, 13.0, 14.0])) + actual = container.to_arrow().column("ball/dist").to_numpy() + np.testing.assert_allclose(actual, expected) + + def test_add_column_udf(self, container): + def my_udf(x): + return x.to_numpy() + 10.0 + + container = container.add_column("ball/x_plus_10", my_udf, on=["ball/x"]) + assert "ball/x_plus_10" in container.schema().names() + + # ball/x: [50.0, 51.0, 52.0] + expected = np.array([60.0, 61.0, 62.0]) + actual = container.to_arrow().column("ball/x_plus_10").to_numpy() + np.testing.assert_allclose(actual, expected) + + def test_add_column_ops(self, container): + # Test the tdc.ops.compute_speed rust kernel + container = container.add_column( + "ball/speed", tdc.ops.compute_speed, on=["ball/x", "ball/y"] + ) + assert "ball/speed" in container.schema().names() + + # x: [50.0, 51.0, 52.0], y: [12.0, 13.0, 14.0] + # dx=1, dy=1 -> speed=sqrt(2) approx 1.414 + expected = np.array([0.0, np.sqrt(2), np.sqrt(2)]) + actual = container.to_arrow().column("ball/speed").to_numpy() + np.testing.assert_allclose(actual, expected, atol=1e-6) + + def test_add_column_per_player_udf(self, container): + def compute_pos_sum(x, y): + return x.to_numpy() + y.to_numpy() + + container = container.add_column_per_player("pos_sum", compute_pos_sum, on=["x", "y"]) + assert "teams/home/players/player_1/pos_sum" in container.schema().names() + + # home player_1 x: [23.5, 23.6, 23.7], y: [24.2, 24.3, 24.4] + expected = np.array([23.5 + 24.2, 23.6 + 24.3, 23.7 + 24.4]) + actual = container.to_arrow().column("teams/home/players/player_1/pos_sum").to_numpy() + np.testing.assert_allclose(actual, expected) + + def test_add_column_per_player_ops(self, container): + container = container.add_column_per_player( + "speed_rust", tdc.ops.compute_speed, on=["x", "y"] + ) + assert "teams/home/players/player_1/speed_rust" in container.schema().names() + + # home player_1 x: [23.5, 23.6, 23.7], y: [24.2, 24.3, 24.4] + # dx=0.1, dy=0.1 -> speed=sqrt(0.01 + 0.01) = sqrt(0.02) approx 0.1414 + expected = np.array([0.0, np.sqrt(0.02), np.sqrt(0.02)]) + actual = container.to_arrow().column("teams/home/players/player_1/speed_rust").to_numpy() + np.testing.assert_allclose(actual, expected, atol=1e-6) + def test_container_add_column_direct(self, container) -> None: new_values = [1.0, 2.0, 3.0] container = container.add_column("test/new_col", new_values) @@ -61,99 +124,3 @@ def test_container_add_column_replace(self, container) -> None: new_x = [100.0, 200.0, 300.0] container = container.add_column("ball/x", new_x) assert container.to_polars()["ball/x"].to_list() == pytest.approx(new_x) - - -class TestLazy: - def test_container_lazy_collect(self, container) -> None: - lazy_c = container.lazy() - assert isinstance(lazy_c, tdc.LazyTrackingDataContainer) - - # Collect back to eager - eager_c = lazy_c.collect() - assert isinstance(eager_c, tdc.TrackingDataContainer) - assert eager_c.num_rows() == container.num_rows() - - def test_container_lazy_ops(self, container) -> None: - lazy_c = container.lazy() - - # 1. Lazy select - subset_lazy = lazy_c.select("ball/x") - assert isinstance(subset_lazy, tdc.LazyTrackingDataContainer) - assert len(subset_lazy.schema()) == 1 - - # 2. Lazy add column (raw arrays) - new_values = [1.0, 2.0, 3.0] - with pytest.raises(tdc.exceptions.TrackingDataContainerError, match="not yet supported"): - lazy_c.add_column("test/lazy", new_values) - - def test_container_lazy_add_column_callable(self, container) -> None: - lazy_c = container.lazy() - - def compute_sum(x, y): - import polars as pl - - return pl.Series(x.to_numpy() + y.to_numpy()) - - lazy_c = lazy_c.add_column("ball/sum", compute_sum, on=["ball/x", "ball/y"]) - res = lazy_c.collect() - - assert "ball/sum" in res.to_polars().columns - res_list = res.to_polars()["ball/sum"].to_list() - assert res_list[0] == pytest.approx(62.0) - assert res_list[1] == pytest.approx(64.0) - assert res_list[2] == pytest.approx(66.0) - - def test_container_lazy_true_lazy_ops(self, container) -> None: - # Test things that ARE supported lazily (expressions) - lazy_c = container.lazy() - - # Expression-based addition - expr = tdc.col("ball/x").mul(2) - lazy_c = lazy_c.add_column("ball/x2", expr) - - # Filter (use threshold that definitely excludes some rows) - # Sample ball/x are [50.0, 51.0, 52.0] - lazy_c = lazy_c.filter(tdc.col("ball/x").gt(51.5)) - - # Collect - res = lazy_c.collect() - assert "ball/x2" in res.to_polars().columns - assert res.num_rows() == 1 - - def test_container_lazy_transformations(self, container) -> None: - # 1. Lazy transform coordinates - sys = tdc.CoordinateSystem( - x_range=(0, 100), y_range=(0, 50), length_unit="meters", origin="top_left" - ) - container = container.with_coordinate_system(sys) - - target_sys = tdc.CoordinateSystem( - x_range=(-50, 50), y_range=(-25, 25), length_unit="meters", origin="center" - ) - - lazy_c = container.lazy().transform_coordinates(target_sys) - res = lazy_c.collect() - - # Input ball/x was [50.0, 51.0, 52.0] in (0, 100) range - # Output should be in (-50, 50) range -> [0.0, 1.0, 2.0] - assert res.to_polars()["ball/x"].to_list() == pytest.approx([0.0, 1.0, 2.0]) - - # 2. Lazy transform orientation - # Setup container with known state - container = container.with_orientation(tdc.OrientationMode.HomeAway) - # Period values in sample data: ['first_half', 'second_half', 'first_half'] - # HomeAway means flip in second half. - # Flip across center of (0, 100) range: x -> 100 - x - lazy_c = container.lazy().transform_orientation(tdc.OrientationMode.AwayHome) - res = lazy_c.collect() - - # AwayHome means flip in first half. - # We changed from HomeAway (flip 2nd) to AwayHome (flip 1st). - # So frames 0, 1, 2: - # Frame 0: first_half. HomeAway (no flip) -> AwayHome (flip). CHANGE. - # Frame 1: second_half. HomeAway (flip) -> AwayHome (no flip). CHANGE. - # Frame 2: first_half. HomeAway (no flip) -> AwayHome (flip). CHANGE. - # All frames should be flipped. - assert res.to_polars()["ball/x"][0] == pytest.approx(100.0 - 50.0) - assert res.to_polars()["ball/x"][1] == pytest.approx(100.0 - 51.0) - assert res.to_polars()["ball/x"][2] == pytest.approx(100.0 - 52.0) diff --git a/python/tests/test_lazy.py b/python/tests/test_lazy.py new file mode 100644 index 0000000..d6198ed --- /dev/null +++ b/python/tests/test_lazy.py @@ -0,0 +1,160 @@ +from datetime import datetime, timedelta, timezone + +import pytest +import tracking_data_container as tdc + + +class TestDataFusionBackend: + def test_container_lazy_collect(self, parquet_file) -> None: + lazy_c = tdc.scan_parquet(parquet_file) + assert isinstance(lazy_c, tdc.LazyTrackingDataContainer) + + # Collect back to eager + eager_c = lazy_c.collect() + assert isinstance(eager_c, tdc.TrackingDataContainer) + assert eager_c.num_rows() == 3 + + def test_container_lazy_ops(self, parquet_file) -> None: + lazy_c = tdc.scan_parquet(parquet_file) + + # 1. Lazy select + subset_lazy = lazy_c.select("ball/x") + assert isinstance(subset_lazy, tdc.LazyTrackingDataContainer) + assert len(subset_lazy.schema()) == 1 + + # 2. Lazy selector attribute + sel = lazy_c.selector.players("home") + assert isinstance(sel, tdc.selector.Selection) + + # 3. Lazy add column (raw arrays) + new_values = [1.0, 2.0, 3.0] + with pytest.raises(tdc.exceptions.TrackingDataContainerError, match="not yet supported"): + lazy_c.add_column("test/lazy", new_values) + + def test_container_lazy_add_column_callable(self, parquet_file) -> None: + lazy_c = tdc.scan_parquet(parquet_file) + + def compute_sum(x, y): + import polars as pl + + return pl.Series(x.to_numpy() + y.to_numpy()) + + lazy_c = lazy_c.add_column("ball/sum", compute_sum, on=["ball/x", "ball/y"]) + res = lazy_c.collect() + + assert "ball/sum" in res.to_polars().columns + res_list = res.to_polars()["ball/sum"].to_list() + assert res_list[0] == pytest.approx(62.0) + assert res_list[1] == pytest.approx(64.0) + assert res_list[2] == pytest.approx(66.0) + + def test_container_lazy_true_lazy_ops(self, parquet_file) -> None: + # Test things that ARE supported lazily (expressions) + lazy_c = tdc.scan_parquet(parquet_file) + + # Expression-based addition + expr = tdc.col("ball/x").mul(2) + lazy_c = lazy_c.add_column("ball/x2", expr) + + # Filter (use threshold that definitely excludes some rows) + # Sample ball/x are [50.0, 51.0, 52.0] + lazy_c = lazy_c.filter(tdc.col("ball/x").gt(51.5)) + + # Collect + res = lazy_c.collect() + assert "ball/x2" in res.to_polars().columns + assert res.num_rows() == 1 + + def test_container_lazy_transformations(self, parquet_file) -> None: + # 1. Lazy transform coordinates + sys = tdc.CoordinateSystem( + x_range=(0, 100), y_range=(0, 50), length_unit="meters", origin="top_left" + ) + container = tdc.TrackingDataContainer.from_parquet(parquet_file).with_coordinate_system(sys) + + target_sys = tdc.CoordinateSystem( + x_range=(-50, 50), y_range=(-25, 25), length_unit="meters", origin="center" + ) + + lazy_c = container.lazy().transform_coordinates(target_sys) + res = lazy_c.collect() + + # Input ball/x was [50.0, 51.0, 52.0] in (0, 100) range + # Output should be in (-50, 50) range -> [0.0, 1.0, 2.0] + assert res.to_polars()["ball/x"].to_list() == pytest.approx([0.0, 1.0, 2.0]) + + # 2. Lazy transform orientation + # Setup container with known state + container = container.with_orientation(tdc.OrientationMode.HomeAway) + # Period values in sample data: ['first_half', 'second_half', 'first_half'] + # HomeAway means flip in second half. + # Flip across center of (0, 100) range: x -> 100 - x + lazy_c = container.lazy().transform_orientation(tdc.OrientationMode.AwayHome) + res = lazy_c.collect() + + # AwayHome means flip in first half. + # We changed from HomeAway (flip 2nd) to AwayHome (flip 1st). + # So frames 0, 1, 2: + # Frame 0: first_half. HomeAway (no flip) -> AwayHome (flip). CHANGE. + # Frame 1: second_half. HomeAway (flip) -> AwayHome (no flip). CHANGE. + # Frame 2: first_half. HomeAway (no flip) -> AwayHome (flip). CHANGE. + # All frames should be flipped. + assert res.to_polars()["ball/x"][0] == pytest.approx(100.0 - 50.0) + assert res.to_polars()["ball/x"][1] == pytest.approx(100.0 - 51.0) + assert res.to_polars()["ball/x"][2] == pytest.approx(100.0 - 52.0) + + def test_lazy_schema_caching(self, parquet_file): + """Test that lazy schema caching works and propagates.""" + lazy = tdc.scan_parquet(parquet_file) + + # First call reconstructions schema + lazy.schema() + assert getattr(lazy._backend, "_cached_schema", None) is not None + + # Transformation should propagate/subset cache + selected = lazy.select(["ball/x", "ball/y"]) + assert getattr(selected._backend, "_cached_schema", None) is not None + assert len(selected._backend._cached_schema.names()) == 2 + + # Slice should preserve cache + sliced = selected.slice(0, 1) + assert sliced._backend._cached_schema == selected._backend._cached_schema + + +class TestIcebergBackend: + def test_iceberg_lazy_loading(self, iceberg_catalog): + # Load lazily from Iceberg + lazy_loaded = iceberg_catalog.load_container(match_id="match_12345") + assert isinstance(lazy_loaded, tdc.LazyTrackingDataContainer) + + # Test filter + filtered_lazy = lazy_loaded.filter(tdc.col("ball/x").gt(50.5)) + + # Test lazy op (select) + subset_lazy = filtered_lazy.select("ball/x") + + # Collect back to eager + eager_c = subset_lazy.collect() + + assert eager_c.num_rows() == 2 + assert "ball/x" in eager_c.to_polars().columns + assert "ball/y" not in eager_c.to_polars().columns + assert eager_c.to_polars()["ball/x"][0] == 51.0 + assert eager_c.to_polars()["ball/x"][1] == 52.0 + + +def test_materialize_frame_temporal(container): + """Test materializing frames by datetime and timedelta.""" + base_time = datetime(2023, 10, 1, 12, 0, 0, tzinfo=timezone.utc) + + # 1. By datetime + # Frame 1 timestamp is base_time + 40ms = 1696161600040000 microseconds + dt = base_time + timedelta(milliseconds=40) + frame_dt = container.materialize_frame(dt) + assert frame_dt["frame_id"] == 123457 + + # 2. By timedelta + # 40ms offset should be Frame 1. + td = timedelta(milliseconds=40) + frame_td = container.materialize_frame(td) + assert frame_td["frame_id"] == 123457 diff --git a/python/tracking_data_container/__init__.py b/python/tracking_data_container/__init__.py index 08e0f81..530fb1b 100644 --- a/python/tracking_data_container/__init__.py +++ b/python/tracking_data_container/__init__.py @@ -18,8 +18,8 @@ ops, ) from .builder import TrackingDataBuilder -from .catalog import TrackingDataCatalog -from .config import IcebergConfig +from .catalogs.base import Catalog +from .catalogs.iceberg import IcebergConfig, IcebergTrackingDataCatalog from .container import LazyTrackingDataContainer, TrackingDataContainer from .coordinate_system import CoordinateSystem from .io import load_hawkeye, load_sportec, load_statsperform, scan_parquet @@ -36,6 +36,8 @@ ) from .selector import ColumnSelector, Selection +TrackingDataCatalog = IcebergTrackingDataCatalog + # Silence noisy PyIceberg warnings and logs warnings.filterwarnings("ignore", category=UserWarning, module="pyiceberg.*") warnings.filterwarnings("ignore", message=".*Delete operation did not match any records.*") @@ -47,6 +49,8 @@ "LazyTrackingDataContainer", "TrackingDataBuilder", "TrackingDataCatalog", + "IcebergTrackingDataCatalog", + "Catalog", "IcebergConfig", "CoordinateSystem", "Origin", diff --git a/python/tracking_data_container/_tracking_data_container.pyi b/python/tracking_data_container/_tracking_data_container.pyi index 369c27f..850672d 100644 --- a/python/tracking_data_container/_tracking_data_container.pyi +++ b/python/tracking_data_container/_tracking_data_container.pyi @@ -7,6 +7,7 @@ if TYPE_CHECKING: import pyarrow as pa __version__: str +CORE_COLUMNS: list[str] class Attr: """Standardized attribute names for tracking data.""" diff --git a/python/tracking_data_container/backends/__init__.py b/python/tracking_data_container/backends/__init__.py new file mode 100644 index 0000000..57bf3ed --- /dev/null +++ b/python/tracking_data_container/backends/__init__.py @@ -0,0 +1,5 @@ +from .base import LazyBackend +from .datafusion import DataFusionBackend +from .iceberg import IcebergBackend + +__all__ = ["LazyBackend", "DataFusionBackend", "IcebergBackend"] diff --git a/python/tracking_data_container/backends/base.py b/python/tracking_data_container/backends/base.py new file mode 100644 index 0000000..c393bb0 --- /dev/null +++ b/python/tracking_data_container/backends/base.py @@ -0,0 +1,45 @@ +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING, Any + +from .._tracking_data_container import Expr as _Expr +from .._tracking_data_container import OrientationMode +from ..coordinate_system import CoordinateSystem +from ..schema import Fields + +if TYPE_CHECKING: + from ..container import TrackingDataContainer + + +class LazyBackend: + """Base interface for lazy loading backends.""" + + def schema(self) -> Fields: + raise NotImplementedError + + def select(self, items: str | Sequence[str]) -> LazyBackend: + raise NotImplementedError + + def add_column( + self, name: str, value: Any, on: str | Sequence[str] | None = None + ) -> LazyBackend: + raise NotImplementedError + + def collect(self) -> TrackingDataContainer: + raise NotImplementedError + + def slice(self, offset: int, length: int) -> LazyBackend: + raise NotImplementedError + + def filter(self, predicate: _Expr) -> LazyBackend: + raise NotImplementedError + + def transform_coordinates(self, sys: CoordinateSystem) -> LazyBackend: + raise NotImplementedError + + def transform_orientation(self, mode: str | OrientationMode) -> LazyBackend: + raise NotImplementedError + + def __arrow_c_stream__(self, rs: Any = None) -> Any: + raise NotImplementedError diff --git a/python/tracking_data_container/backends/datafusion.py b/python/tracking_data_container/backends/datafusion.py new file mode 100644 index 0000000..3967934 --- /dev/null +++ b/python/tracking_data_container/backends/datafusion.py @@ -0,0 +1,154 @@ +from __future__ import annotations + +import logging +from collections.abc import Sequence +from typing import TYPE_CHECKING, Any + +from .._tracking_data_container import ( + CORE_COLUMNS as _CORE_COLUMNS, +) +from .._tracking_data_container import ( + Expr as _Expr, +) +from .._tracking_data_container import ( + OrientationMode, + _DataField, + _LazyTrackingDataContainer, +) +from .._tracking_data_container import ( + Selection as _Selection, +) +from ..coordinate_system import CoordinateSystem +from ..exceptions import TrackingDataContainerError +from ..schema import DataField, Fields +from ..selector import Selection +from .base import LazyBackend + +if TYPE_CHECKING: + from ..container import TrackingDataContainer + +logger = logging.getLogger(__name__) + +# Constants for performance (shared with container.py) +CORE_COLUMNS = set(_CORE_COLUMNS) + + +class DataFusionBackend(LazyBackend): + """DataFusion backend for lazy evaluation (e.g. Parquet scanning).""" + + def __init__( + self, + container: _LazyTrackingDataContainer, + cached_schema: Fields | None = None, + deferred_add_columns: list[tuple] | None = None, + ): + self._container = container + self._cached_schema = cached_schema + self._deferred_add_columns = deferred_add_columns or [] + + def schema(self) -> Fields: + if self._cached_schema: + return self._cached_schema + self._cached_schema = Fields(self._container.schema) + return self._cached_schema + + def select(self, items: str | Sequence[str]) -> LazyBackend: + inputs = items if isinstance(items, (list, tuple)) else [items] + processed: list[str | _DataField | _Selection] = [] + for i in inputs: + if isinstance(i, str): + if "*" in i: + for m in self.schema().match(i): + processed.append(_DataField.from_name(m)) + else: + processed.append(_DataField.from_name(i)) + elif isinstance(i, DataField): + processed.append(i._field) + elif isinstance(i, Selection): + processed.append(i._inner) + elif isinstance(i, (_DataField, _Selection)): + processed.append(i) + + new_cache = None + if self._cached_schema: + names = [] + for itm in inputs: + if isinstance(itm, str): + names.extend(self._cached_schema.match(itm)) + else: + names.append(str(itm)) + new_cache = Fields(list(dict.fromkeys(names))) + + return DataFusionBackend( + self._container.select(processed), + cached_schema=new_cache, + deferred_add_columns=self._deferred_add_columns, + ) + + def add_column( + self, name: str, value: Any, on: str | Sequence[str] | None = None + ) -> LazyBackend: + if isinstance(value, _Expr): + return DataFusionBackend( + self._container.add_column(name, value), + cached_schema=self._cached_schema, + deferred_add_columns=self._deferred_add_columns, + ) + + if callable(value): + new_deferred = list(self._deferred_add_columns) + new_deferred.append((name, value, on)) + return DataFusionBackend( + self._container, + cached_schema=self._cached_schema, + deferred_add_columns=new_deferred, + ) + + raise TrackingDataContainerError( + "Adding non-expression columns to lazy containers is not yet supported." + ) + + def collect(self) -> TrackingDataContainer: + from ..container import TrackingDataContainer + + try: + inner = self._container.collect() + res = TrackingDataContainer(inner, validate=False) + + for name, value, on in self._deferred_add_columns: + res = res.add_column(name, value, on=on) + + return res + except Exception as e: + raise TrackingDataContainerError(f"Collection failed: {e}") + + def slice(self, offset: int, length: int) -> LazyBackend: + return DataFusionBackend( + self._container.slice(offset, length), + cached_schema=self._cached_schema, + deferred_add_columns=self._deferred_add_columns, + ) + + def filter(self, predicate: _Expr) -> LazyBackend: + return DataFusionBackend( + self._container.filter(predicate), + cached_schema=self._cached_schema, + deferred_add_columns=self._deferred_add_columns, + ) + + def transform_coordinates(self, sys: CoordinateSystem) -> LazyBackend: + return DataFusionBackend( + self._container.transform_coordinates(sys._inner), + cached_schema=self._cached_schema, + deferred_add_columns=self._deferred_add_columns, + ) + + def transform_orientation(self, mode: str | OrientationMode) -> LazyBackend: + return DataFusionBackend( + self._container.transform_orientation(str(mode)), + cached_schema=self._cached_schema, + deferred_add_columns=self._deferred_add_columns, + ) + + def __arrow_c_stream__(self, rs: Any = None) -> Any: + return self._container.__arrow_c_stream__(rs) diff --git a/python/tracking_data_container/backends/iceberg.py b/python/tracking_data_container/backends/iceberg.py new file mode 100644 index 0000000..d9522e5 --- /dev/null +++ b/python/tracking_data_container/backends/iceberg.py @@ -0,0 +1,305 @@ +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING, Any, cast + +from pyiceberg.expressions import ( + And, + BooleanExpression, + EqualTo, + Reference, +) + +from .._tracking_data_container import Expr as _Expr +from .._tracking_data_container import OrientationMode +from ..container import CORE_COLUMNS, TrackingDataContainer +from ..coordinate_system import CoordinateSystem +from ..schema import DataField, Fields, TrackedEntity +from .base import LazyBackend + +if TYPE_CHECKING: + from ..catalogs.iceberg import IcebergTrackingDataCatalog + + +class IcebergBackend(LazyBackend): + """A lazy container backend that defers loading from an Iceberg catalog.""" + + def __init__( + self, + catalog: IcebergTrackingDataCatalog, + match_id: str, + player_id: str | None = None, + selection: str | Sequence[str] | None = None, + limit: int | None = None, + **filters: Any, + ): + self._catalog: IcebergTrackingDataCatalog = catalog + self._match_id = match_id + self._player_id = player_id + self._selection = selection + self._limit = limit + self._filters = filters + self._cached_schema: Fields | None = None + self._predicates: list[_Expr] = [] + + def _get_entities_from_metadata( + self, hierarchical_entities: dict[str, Any] + ) -> list[tuple[TrackedEntity, dict[str, Any]]]: + """Flatten hierarchical metadata into a list of (TrackedEntity, attributes) tuples.""" + entities: list[tuple[TrackedEntity, dict[str, Any]]] = [] + + if ball_attrs := hierarchical_entities.get("ball"): + entities.append((TrackedEntity.from_entity_key("ball|none"), ball_attrs)) + + for tid, team_data in hierarchical_entities.get("teams", {}).items(): + team_attrs = {k: v for k, v in team_data.items() if k != "players"} + entities.append((TrackedEntity.from_entity_key(f"team:{tid}|{tid}"), team_attrs)) + for pid, p_attrs in team_data.get("players", {}).items(): + entities.append((TrackedEntity.from_entity_key(f"{pid}|{tid}"), p_attrs)) + + for rid, r_attrs in hierarchical_entities.get("referees", {}).items(): + entities.append((TrackedEntity.from_entity_key(f"referee:{rid}|none"), r_attrs)) + + if self._player_id: + entities = [ + (e, attrs) + for e, attrs in entities + if e.player_id == self._player_id + or (e.is_referee and e.referee_id == self._player_id) + ] + + return entities + + def _get_wide_names_for_entity( + self, entity: TrackedEntity, attributes: dict[str, Any], physical_attr_cols: list[str] + ) -> list[str]: + """Generate all wide CDF field names for a specific entity.""" + prefix = entity.cdf_prefix() + names = [] + for attr in physical_attr_cols: + names.append(f"{prefix}{attr}") + for attr in attributes: + names.append(f"{prefix}{attr}") + if not entity.is_ball: + names.append(f"{prefix}player/id") + if entity.is_player or entity.is_team: + names.append(f"{prefix}team/id") + return names + + def schema(self) -> Fields: + """Reconstruct the wide schema by inspecting entities in the match metadata.""" + if self._cached_schema: + return self._cached_schema + + if self._catalog._tracking_table is None: + self._catalog._tracking_table = self._catalog._iceberg_catalog.load_table( + "tracking.tracking_data" + ) + + metadata = self._catalog.get_metadata(self._match_id) + entities_hier = metadata.get("_entities") + if entities_hier is None: + raise ValueError(f"Metadata for match {self._match_id} is missing unique entity list.") + + physical_names = {f.name for f in self._catalog._tracking_table.schema().fields} + ignored = { + "match_id", + "competition_id", + "season_id", + "timestamp", + "frame_id", + "period", + "period_id", + "entity_type", + "entity_id", + "entity_key", + } + attr_cols = [c for c in physical_names if c not in ignored and c != "id"] + + wide_names = [ + c + for c in CORE_COLUMNS + if c not in ("player/id", "team/id", "entity_key") + and DataField(c).to_iceberg_name() in physical_names + ] + + for entity, attrs in self._get_entities_from_metadata(entities_hier): + wide_names.extend(self._get_wide_names_for_entity(entity, attrs, attr_cols)) + + self._cached_schema = Fields(list(dict.fromkeys(wide_names))) + return self._cached_schema + + def select(self, items: str | Sequence[str]) -> IcebergBackend: + """Translate wide selection to long selection pushdown.""" + backend = IcebergBackend( + self._catalog, + self._match_id, + self._player_id, + selection=items, + limit=self._limit, + **self._filters, + ) + backend._predicates = list(self._predicates) + return backend + + def slice(self, offset: int, length: int) -> IcebergBackend: + """Apply limit to the lazy container.""" + backend = IcebergBackend( + self._catalog, + self._match_id, + self._player_id, + selection=self._selection, + limit=length, + **self._filters, + ) + backend._predicates = list(self._predicates) + return backend + + def collect(self) -> TrackingDataContainer: + """Finally perform the Iceberg scan and pivot.""" + if self._catalog._tracking_table is None: + self._catalog._tracking_table = self._catalog._iceberg_catalog.load_table( + "tracking.tracking_data" + ) + + filter_expr: BooleanExpression = EqualTo( + term=Reference("match_id"), value=cast(Any, self._match_id) + ) # type: ignore + if self._player_id: + filter_expr = And( + filter_expr, + EqualTo(term=Reference("entity_id"), value=cast(Any, self._player_id)), # type: ignore + ) + for k, v in self._filters.items(): + filter_expr = And( + filter_expr, + EqualTo(term=Reference(k), value=cast(Any, v)), # type: ignore + ) + + # 1. Column Pushdown Calculation + selected_long_fields = None + deferred_select = None + metadata = self._catalog.get_metadata(self._match_id) + entities_hier = metadata.get("_entities", {}) + parsed_entities = self._get_entities_from_metadata(entities_hier) + + if self._selection: + eager_schema = self.schema() + literal_names = ( + eager_schema.match(self._selection) + if isinstance(self._selection, str) and "*" in self._selection + else ( + [self._selection] if isinstance(self._selection, str) else list(self._selection) + ) + ) + deferred_select = list(dict.fromkeys(literal_names)) + + needed_long_internal = { + DataField(name).attribute if name not in CORE_COLUMNS else name + for name in deferred_select + } | set(CORE_COLUMNS) + physical_names = {f.name for f in self._catalog._tracking_table.schema().fields} + selected_long_fields = [ + DataField(f).to_iceberg_name() + for f in needed_long_internal + if DataField(f).to_iceberg_name() in physical_names + ] + + for req in [ + "match_id", + "entity_type", + "entity_id", + "frame_id", + "timestamp", + "period", + "x", + "y", + ]: + if req in physical_names and req not in selected_long_fields: + selected_long_fields.append(req) + + # 2. Perform the scan + scan_kwargs: dict[str, Any] = {"row_filter": filter_expr} + if selected_long_fields: + scan_kwargs["selected_fields"] = tuple(selected_long_fields) + if self._limit: + scan_kwargs["limit"] = self._limit * 50 + + arrow_table = self._catalog._tracking_table.scan(**scan_kwargs).to_arrow() + + # 3. Pivot using Rust core + mapping: dict[str, str] = {} + for entity, _ in parsed_entities: + eid = ( + str(entity.player_id) + if entity.is_player + else ( + f"referee:{entity.referee_id}" + if entity.is_referee + else (f"team:{entity.team_id}" if entity.is_team else "ball") + ) + ) + mapping[eid] = str(entity.team_id) if entity.team_id else "none" + + wide_container = TrackingDataContainer.pivot(arrow_table, entity_to_team=mapping) + + wide_container._catalog = self._catalog + + # 4. Inject constant attributes from metadata + from .._tracking_data_container import lit + + wide_names = wide_container.schema().names() + names_to_add = [] + values_to_add = [] + for entity, attrs in parsed_entities: + prefix = entity.cdf_prefix() + for attr, value in attrs.items(): + wide_name = f"{prefix}{attr}" + if wide_name not in wide_names: + names_to_add.append(wide_name) + values_to_add.append(lit(value)) + + if names_to_add: + wide_container = wide_container.add_columns(names_to_add, values_to_add) + + if hasattr(self, "_predicates") and self._predicates: + lazy_wide = wide_container.lazy() + for p in getattr(self, "_predicates"): + lazy_wide = lazy_wide.filter(p) + wide_container = lazy_wide.collect() + if deferred_select: + wide_container = wide_container.select(deferred_select) + if self._limit: + wide_container = wide_container.slice(0, self._limit) + + return wide_container + + def add_column( + self, name: str, value: Any, on: str | Sequence[str] | None = None + ) -> LazyBackend: + raise NotImplementedError("Iceberg backend does not support adding columns lazily yet.") + + def filter(self, predicate: _Expr) -> LazyBackend: + backend = IcebergBackend( + self._catalog, + self._match_id, + self._player_id, + selection=self._selection, + limit=self._limit, + **self._filters, + ) + backend._predicates = list(self._predicates) + [predicate] + return backend + + def transform_coordinates(self, sys: CoordinateSystem) -> LazyBackend: + raise NotImplementedError( + "Iceberg backend does not support coordinate transformation lazily yet." + ) + + def transform_orientation(self, mode: str | OrientationMode) -> LazyBackend: + raise NotImplementedError( + "Iceberg backend does not support orientation transformation lazily yet." + ) + + def __arrow_c_stream__(self, rs: Any = None) -> Any: + raise NotImplementedError("Iceberg backend does not support __arrow_c_stream__ directly.") diff --git a/python/tracking_data_container/catalog.py b/python/tracking_data_container/catalog.py deleted file mode 100644 index 211784b..0000000 --- a/python/tracking_data_container/catalog.py +++ /dev/null @@ -1,664 +0,0 @@ -"""TrackingDataCatalog - Manages container storage and lifecycle.""" - -from __future__ import annotations - -import json -import logging -from collections.abc import Sequence -from pathlib import Path -from typing import TYPE_CHECKING, Any, cast - -import polars as pl -import pyarrow as pa -from pyiceberg.catalog import load_catalog -from pyiceberg.exceptions import NoSuchTableError -from pyiceberg.expressions import ( - And, - BooleanExpression, - EqualTo, - Reference, -) -from pyiceberg.io.pyarrow import _pyarrow_to_schema_without_ids -from pyiceberg.partitioning import PartitionField, PartitionSpec -from pyiceberg.schema import Schema -from pyiceberg.table import Table -from pyiceberg.transforms import IdentityTransform -from pyiceberg.types import ( - FloatType, - LongType, - NestedField, - StringType, - TimestampType, -) - -from .config import IcebergConfig -from .container import CORE_COLUMNS, LazyTrackingDataContainer, TrackingDataContainer -from .schema import DataField, Fields, TrackedEntity - -if TYPE_CHECKING: - pass - -logger = logging.getLogger(__name__) - -# Mapping from internal CDF-like names to snake_case Iceberg names -# Note: team/id is handled as a constant attribute in metadata -COLUMN_MAPPING = { - "match/id": "match_id", - "competition/id": "competition_id", - "season/id": "season_id", - "player/id": "entity_id", -} - -# Reverse mapping -REVERSE_COLUMN_MAPPING = {v: k for k, v in COLUMN_MAPPING.items()} - - -class TrackingDataCatalog: - """ - Manages storage and lifecycle of TrackingDataContainer instances. - - Backed by Apache Iceberg tables with configurable optimization settings. - Follows Iceberg's Table↔Catalog pattern where containers hold a - reference to their parent catalog. - """ - - def __init__( - self, - warehouse_path: str, - config: IcebergConfig | None = None, - catalog_type: str = "sql", - catalog_properties: dict | None = None, - ): - """ - Initialize catalog. - - Args: - warehouse_path: Path to Iceberg warehouse - config: Iceberg configuration (partitioning, compression, etc.) - catalog_type: Catalog backend (sql, glue, rest, etc.) - catalog_properties: Additional catalog properties - """ - self.warehouse_path = Path(warehouse_path).absolute() - self.config = config or IcebergConfig() - self.catalog_type = catalog_type - - # Create warehouse directory if it doesn't exist - self.warehouse_path.mkdir(parents=True, exist_ok=True) - - # Default properties for SQL catalog (SQLite for POC) - import multiprocessing - - num_workers = multiprocessing.cpu_count() - - default_properties = { - "uri": f"sqlite:///{self.warehouse_path / 'catalog.db'}", - "warehouse": f"file://{self.warehouse_path}", - "py-io-impl": "pyiceberg.io.pyarrow.PyArrowFileIO", - "max-workers": str(num_workers), - } - - self.catalog_properties = catalog_properties or default_properties - - # Load Iceberg catalog - self._iceberg_catalog = load_catalog( - "default", - **{ - "type": self.catalog_type, - **self.catalog_properties, - }, - ) - - self._tracking_table: Table | None = None - self._metadata_table: Table | None = None - - def initialize(self) -> None: - """ - Initialize Iceberg tables (tracking_data + match_metadata). - - Creates tables with configured partitioning and optimization settings. - Must be called once before first use. Idempotent - safe to call multiple times. - """ - try: - self._iceberg_catalog.create_namespace("tracking") - except Exception: - pass # Namespace already exists - - self._create_tracking_table() - self._create_metadata_table() - - def _create_tracking_table(self) -> None: - """Create tracking_data Iceberg table in the requested long format.""" - table_name = "tracking.tracking_data" - - try: - self._tracking_table = self._iceberg_catalog.load_table(table_name) - return - except NoSuchTableError: - pass - - # Define schema. Spatial columns are optional but mandatory during ingestion. - schema = Schema( - NestedField(1, "competition_id", StringType(), required=False), - NestedField(2, "season_id", StringType(), required=False), - NestedField(3, "match_id", StringType(), required=True), - NestedField(4, "frame_id", LongType(), required=True), - NestedField(5, "period", StringType(), required=True), - NestedField(6, "timestamp", TimestampType(), required=True), - NestedField(7, "entity_type", StringType(), required=True), - NestedField(8, "entity_id", StringType(), required=True), - NestedField(9, "x", FloatType(), required=False), - NestedField(10, "y", FloatType(), required=False), - NestedField(11, "z", FloatType(), required=False), - ) - - # Build PartitionSpec from config - partition_fields = [] - for field_name in self.config.partition_by: - field = schema.find_field(field_name) - partition_fields.append( - PartitionField( - source_id=field.field_id, - field_id=1000 + field.field_id, - transform=IdentityTransform(), - name=field_name, - ) - ) - partition_spec = PartitionSpec(*partition_fields) - - # Optimization properties - properties = { - "write.parquet.compression-codec": self.config.compression, - "write.parquet.page-size-bytes": str(self.config.page_size), - "write.parquet.row-group-size": str(self.config.row_group_size), - "write.parquet.dict-enabled": str(self.config.use_dictionary).lower(), - } - - self._tracking_table = self._iceberg_catalog.create_table( - table_name, - schema=schema, - partition_spec=partition_spec, - properties=properties, - ) - - def _create_metadata_table(self) -> None: - """Create match_metadata Iceberg table.""" - table_name = "tracking.match_metadata" - - try: - self._metadata_table = self._iceberg_catalog.load_table(table_name) - return - except NoSuchTableError: - pass - - schema = Schema( - NestedField(1, "match_id", StringType(), required=True), - NestedField(2, "competition_id", StringType(), required=False), - NestedField(3, "season_id", StringType(), required=False), - NestedField(4, "metadata_json", StringType(), required=True), - ) - - partition_spec = PartitionSpec( - PartitionField( - source_id=1, field_id=1001, transform=IdentityTransform(), name="match_id" - ) - ) - - self._metadata_table = self._iceberg_catalog.create_table( - table_name, - schema=schema, - partition_spec=partition_spec, - ) - - def load_container( - self, - match_id: str, - player_id: str | None = None, - **filters: Any, - ) -> CatalogLazyTrackingDataContainer: - """Load container from storage lazily.""" - return CatalogLazyTrackingDataContainer( - self, match_id=match_id, player_id=player_id, **filters - ) - - def save_container( - self, - container: TrackingDataContainer, - match_id: str | None = None, - metadata: dict | None = None, - ) -> None: - """Save container to storage (replaces match partition).""" - if match_id is None: - df = container.to_polars().head(1) - match_id = ( - str(df["match/id"][0]) if "match/id" in df.columns else str(df["match_id"][0]) - ) - - entities = self._save_tracking_data(container, match_id) - - final_metadata = metadata.copy() if metadata else {} - final_metadata["_entities"] = entities - self._save_metadata(final_metadata, match_id) - - def commit_container(self, container: TrackingDataContainer) -> None: - """Commit bound container.""" - df = container.to_polars().head(1) - match_id = df["match/id"][0] if "match/id" in df.columns else df["match_id"][0] - self.save_container(container, match_id=str(match_id)) - - def get_metadata(self, match_id: str) -> dict[Any, Any]: - """Load metadata for a match.""" - if self._metadata_table is None: - self._metadata_table = self._iceberg_catalog.load_table("tracking.match_metadata") - - scan = self._metadata_table.scan( - row_filter=EqualTo(term=Reference("match_id"), value=cast(Any, match_id)) # type: ignore - ) - arrow_table = scan.to_arrow() - - if len(arrow_table) == 0: - return {} - - metadata_json = arrow_table.column("metadata_json")[0].as_py() - return cast(dict[Any, Any], json.loads(metadata_json)) - - def _save_tracking_data( - self, - container: TrackingDataContainer, - match_id: str, - ) -> dict[str, Any]: - """Save tracking data to Iceberg table in entity-long format.""" - if self._tracking_table is None: - self._tracking_table = self._iceberg_catalog.load_table("tracking.tracking_data") - - # 1. Melt and add entity types - long_df = container.melt() - if "entity_key" not in long_df.columns: - raise ValueError("entity_key column is required for saving to catalog") - - long_df = long_df.with_columns( - pl.col("entity_key") - .map_elements(lambda k: TrackedEntity.from_entity_key(k).type, return_dtype=pl.String) - .alias("entity_type") - ) - - # 2. Column Mapping - rename_dict = {k: v for k, v in COLUMN_MAPPING.items() if k in long_df.columns} - long_df = long_df.rename(rename_dict).with_columns(pl.lit(match_id).alias("match_id")) - - # 3. Constant Extraction (Normalization) - # Identity and core temporal fields are strictly preserved in tracking table. - # team/id is now moved to metadata as a constant. - core_cols = { - "match_id", - "frame_id", - "period", - "timestamp", - "entity_type", - "entity_id", - "x", - "y", - "z", - "entity_key", - } - potential_constants = [c for c in long_df.columns if c not in core_cols and c != "team/id"] - - # Group by entity AND its original team attribute to extract constants correctly - # team/id is part of the key to identify constants per-entity-instance - agg_stats = long_df.group_by(["entity_id", "team/id", "entity_type"]).agg( - [pl.col(c).n_unique().alias(f"{c}_count") for c in potential_constants] - + [pl.col(c).first().alias(f"{c}_val") for c in potential_constants] - ) - - constants_to_move = [c for c in potential_constants if agg_stats[f"{c}_count"].max() == 1] - - # Prepare hierarchical metadata - entities_flat = ( - agg_stats.select( - ["entity_id", "team/id", "entity_type"] + [f"{c}_val" for c in constants_to_move] - ) - .rename({f"{c}_val": c for c in constants_to_move}) - .to_dicts() - ) - - hierarchical_meta: dict[str, Any] = {"ball": {}, "teams": {}, "referees": {}} - for ent in entities_flat: - etype, eid, tid = ( - ent.pop("entity_type"), - ent.pop("entity_id"), - ent.pop("team/id", "none"), - ) - attrs = {k: v for k, v in ent.items() if v is not None} - - if etype == "ball": - hierarchical_meta["ball"] = attrs - elif etype == "referee": - hierarchical_meta["referees"][eid] = attrs - elif etype == "player": - team = hierarchical_meta["teams"].setdefault(tid, {"players": {}}) - team["players"][eid] = attrs - elif etype == "team": - target_tid = tid if tid != "none" else eid.replace("team:", "") - team = hierarchical_meta["teams"].setdefault(target_tid, {"players": {}}) - team.update(attrs) - - # Drop constant columns AND the team/id column from physical table - long_df = long_df.drop([c for c in constants_to_move if c in long_df.columns] + ["team/id"]) - - # 4. Final Validation & Save - for field in ["x", "y"]: - if field not in long_df.columns: - raise ValueError(f"Required column {field} is missing") - - must_not_be_null = [ - "match_id", - "frame_id", - "period", - "timestamp", - "entity_type", - "entity_id", - ] - for field in must_not_be_null: - if field not in long_df.columns: - raise ValueError(f"Required field {field} is missing from container") - if long_df[field].null_count() > 0: - raise ValueError(f"Required field {field} contains null values") - - # Convert to Arrow - arrow_table = long_df.to_arrow() - known_types = { - "timestamp": pa.timestamp("us"), - "frame_id": pa.int64(), - "x": pa.float32(), - "y": pa.float32(), - "z": pa.float32(), - } - - new_cols, new_fields = [], [] - for i, field in enumerate(arrow_table.schema): - target = known_types.get(field.name) or ( - field.type.value_type - if isinstance(field.type, pa.DictionaryType) - else (pa.string() if pa.types.is_null(field.type) else None) - ) - new_cols.append(arrow_table.column(i).cast(target) if target else arrow_table.column(i)) - new_fields.append(pa.field(field.name, target, field.nullable) if target else field) - - arrow_table = pa.Table.from_arrays(new_cols, schema=pa.schema(new_fields)) - - with self._tracking_table.transaction() as txn: - txn.update_schema().union_by_name( - _pyarrow_to_schema_without_ids(arrow_table.schema) - ).commit() - txn.delete(EqualTo(term=Reference("match_id"), value=cast(Any, match_id))) # type: ignore - txn.append(arrow_table) - - return hierarchical_meta - - def _save_metadata(self, metadata: dict, match_id: str) -> None: - """Save metadata to Iceberg table.""" - if self._metadata_table is None: - self._metadata_table = self._iceberg_catalog.load_table("tracking.match_metadata") - - metadata_schema = pa.schema( - [ - pa.field("match_id", pa.string(), nullable=False), - pa.field("competition_id", pa.string(), nullable=True), - pa.field("season_id", pa.string(), nullable=True), - pa.field("metadata_json", pa.string(), nullable=False), - ] - ) - - metadata_row = pa.table( - { - "match_id": [match_id], - "competition_id": [metadata.get("competition_id")], - "season_id": [metadata.get("season_id")], - "metadata_json": [json.dumps(metadata)], - }, - schema=metadata_schema, - ) - - with self._metadata_table.transaction() as txn: - txn.delete(EqualTo(term=Reference("match_id"), value=cast(Any, match_id))) # type: ignore - txn.append(metadata_row) - - def __repr__(self) -> str: - return ( - f"TrackingDataCatalog(" - f"warehouse={self.warehouse_path}, " - f"catalog_type={self.catalog_type})" - ) - - -class CatalogLazyTrackingDataContainer(LazyTrackingDataContainer): - """A lazy container that defers loading from an Iceberg catalog.""" - - def __init__( - self, - catalog: TrackingDataCatalog, - match_id: str, - player_id: str | None = None, - selection: str | Sequence[str] | None = None, - limit: int | None = None, - **filters: Any, - ): - super().__init__(None, catalog=catalog, is_long_storage=True) # type: ignore - self._catalog: TrackingDataCatalog = catalog - self._match_id = match_id - self._player_id = player_id - self._selection = selection - self._limit = limit - self._filters = filters - self._cached_schema: Fields | None = None - - def _get_entities_from_metadata( - self, hierarchical_entities: dict[str, Any] - ) -> list[tuple[TrackedEntity, dict[str, Any]]]: - """Flatten hierarchical metadata into a list of (TrackedEntity, attributes) tuples.""" - entities: list[tuple[TrackedEntity, dict[str, Any]]] = [] - - if ball_attrs := hierarchical_entities.get("ball"): - entities.append((TrackedEntity.from_entity_key("ball|none"), ball_attrs)) - - for tid, team_data in hierarchical_entities.get("teams", {}).items(): - team_attrs = {k: v for k, v in team_data.items() if k != "players"} - entities.append((TrackedEntity.from_entity_key(f"team:{tid}|{tid}"), team_attrs)) - for pid, p_attrs in team_data.get("players", {}).items(): - entities.append((TrackedEntity.from_entity_key(f"{pid}|{tid}"), p_attrs)) - - for rid, r_attrs in hierarchical_entities.get("referees", {}).items(): - entities.append((TrackedEntity.from_entity_key(f"referee:{rid}|none"), r_attrs)) - - if self._player_id: - entities = [ - (e, attrs) - for e, attrs in entities - if e.player_id == self._player_id - or (e.is_referee and e.referee_id == self._player_id) - ] - - return entities - - def _get_wide_names_for_entity( - self, entity: TrackedEntity, attributes: dict[str, Any], physical_attr_cols: list[str] - ) -> list[str]: - """Generate all wide CDF field names for a specific entity.""" - prefix = entity.cdf_prefix() - names = [] - for attr in physical_attr_cols: - names.append(f"{prefix}{attr}") - for attr in attributes: - names.append(f"{prefix}{attr}") - if not entity.is_ball: - names.append(f"{prefix}player/id") - if entity.is_player or entity.is_team: - names.append(f"{prefix}team/id") - return names - - def schema(self) -> Fields: - """Reconstruct the wide schema by inspecting entities in the match metadata.""" - if self._cached_schema: - return self._cached_schema - - if self._catalog._tracking_table is None: - self._catalog._tracking_table = self._catalog._iceberg_catalog.load_table( - "tracking.tracking_data" - ) - - metadata = self._catalog.get_metadata(self._match_id) - entities_hier = metadata.get("_entities") - if entities_hier is None: - raise ValueError(f"Metadata for match {self._match_id} is missing unique entity list.") - - physical_names = {f.name for f in self._catalog._tracking_table.schema().fields} - ignored = { - "match_id", - "competition_id", - "season_id", - "timestamp", - "frame_id", - "period", - "period_id", - "entity_type", - "entity_id", - "entity_key", - } - attr_cols = [c for c in physical_names if c not in ignored and c != "id"] - - wide_names = [ - c - for c in CORE_COLUMNS - if c not in ("player/id", "team/id", "entity_key") - and COLUMN_MAPPING.get(c, c) in physical_names - ] - - for entity, attrs in self._get_entities_from_metadata(entities_hier): - wide_names.extend(self._get_wide_names_for_entity(entity, attrs, attr_cols)) - - self._cached_schema = Fields(list(dict.fromkeys(wide_names))) - return self._cached_schema - - def select(self, items: str | Sequence[str]) -> CatalogLazyTrackingDataContainer: - """Translate wide selection to long selection pushdown.""" - return CatalogLazyTrackingDataContainer( - self._catalog, - self._match_id, - self._player_id, - selection=items, - limit=self._limit, - **self._filters, - ) - - def slice(self, offset: int, length: int) -> CatalogLazyTrackingDataContainer: - """Apply limit to the lazy container.""" - return CatalogLazyTrackingDataContainer( - self._catalog, - self._match_id, - self._player_id, - selection=self._selection, - limit=length, - **self._filters, - ) - - def collect(self) -> TrackingDataContainer: - """Finally perform the Iceberg scan and pivot.""" - if self._catalog._tracking_table is None: - self._catalog._tracking_table = self._catalog._iceberg_catalog.load_table( - "tracking.tracking_data" - ) - - filter_expr: BooleanExpression = EqualTo( - term=Reference("match_id"), value=cast(Any, self._match_id) - ) # type: ignore - if self._player_id: - filter_expr = And( - filter_expr, - EqualTo(term=Reference("entity_id"), value=cast(Any, self._player_id)), # type: ignore - ) - - # 1. Column Pushdown Calculation - selected_long_fields = None - deferred_select = None - metadata = self._catalog.get_metadata(self._match_id) - entities_hier = metadata.get("_entities", {}) - parsed_entities = self._get_entities_from_metadata(entities_hier) - - if self._selection: - eager_schema = self.schema() - literal_names = ( - eager_schema.match(self._selection) - if isinstance(self._selection, str) and "*" in self._selection - else ( - [self._selection] if isinstance(self._selection, str) else list(self._selection) - ) - ) - deferred_select = list(dict.fromkeys(literal_names)) - - needed_long_internal = { - DataField(name).attribute if name not in CORE_COLUMNS else name - for name in deferred_select - } | set(CORE_COLUMNS) - physical_names = {f.name for f in self._catalog._tracking_table.schema().fields} - selected_long_fields = [ - COLUMN_MAPPING.get(f, f) - for f in needed_long_internal - if COLUMN_MAPPING.get(f, f) in physical_names - ] - - for req in [ - "match_id", - "entity_type", - "entity_id", - "frame_id", - "timestamp", - "period", - "x", - "y", - ]: - if req in physical_names and req not in selected_long_fields: - selected_long_fields.append(req) - - # 2. Perform the scan - scan_kwargs: dict[str, Any] = {"row_filter": filter_expr} - if selected_long_fields: - scan_kwargs["selected_fields"] = tuple(selected_long_fields) - if self._limit: - scan_kwargs["limit"] = self._limit * 50 - - arrow_table = self._catalog._tracking_table.scan(**scan_kwargs).to_arrow() - - # 3. Pivot using Rust core - mapping: dict[str, str] = {} - for entity, _ in parsed_entities: - eid = ( - str(entity.player_id) - if entity.is_player - else ( - f"referee:{entity.referee_id}" - if entity.is_referee - else (f"team:{entity.team_id}" if entity.is_team else "ball") - ) - ) - mapping[eid] = str(entity.team_id) if entity.team_id else "none" - - wide_container = TrackingDataContainer.pivot(arrow_table, entity_to_team=mapping) - - wide_container._catalog = self._catalog - - # 4. Inject constant attributes from metadata - wide_names = wide_container.schema().names() - for entity, attrs in parsed_entities: - prefix = entity.cdf_prefix() - for attr, value in attrs.items(): - wide_name = f"{prefix}{attr}" - if wide_name not in wide_names: - wide_container = wide_container.add_column( - wide_name, [value] * wide_container.num_rows() - ) - - if deferred_select: - wide_container = wide_container.select(deferred_select) - if self._limit: - wide_container = wide_container.slice(0, self._limit) - - return wide_container diff --git a/python/tracking_data_container/catalogs/__init__.py b/python/tracking_data_container/catalogs/__init__.py new file mode 100644 index 0000000..44f786f --- /dev/null +++ b/python/tracking_data_container/catalogs/__init__.py @@ -0,0 +1,4 @@ +from .base import Catalog +from .iceberg import IcebergTrackingDataCatalog + +__all__ = ["Catalog", "IcebergTrackingDataCatalog"] diff --git a/python/tracking_data_container/catalogs/base.py b/python/tracking_data_container/catalogs/base.py new file mode 100644 index 0000000..1ab1e6b --- /dev/null +++ b/python/tracking_data_container/catalogs/base.py @@ -0,0 +1,83 @@ +from __future__ import annotations + +import logging +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from ..container import LazyTrackingDataContainer, TrackingDataContainer + +logger = logging.getLogger(__name__) + + +class Catalog(ABC): + """ + Abstract base class for a TrackingDataCatalog. + + A catalog manages the storage and lifecycle of TrackingDataContainer instances. + It acts as the repository for tracking data, allowing loading, saving, and + querying metadata. + """ + + @abstractmethod + def initialize(self) -> None: + """Initialize the catalog (e.g., create necessary tables/namespaces).""" + pass + + @abstractmethod + def load_container( + self, + match_id: str, + player_id: str | None = None, + **filters: Any, + ) -> LazyTrackingDataContainer: + """ + Load tracking data lazily from the catalog. + + Args: + match_id: The ID of the match to load. + player_id: Optional ID of a specific player to load. + **filters: Additional partition filters (e.g. frame_id ranges). + + Returns: + A lazy container pointing to the catalog data. + """ + pass + + @abstractmethod + def save_container( + self, + container: TrackingDataContainer, + match_id: str | None = None, + ) -> None: + """ + Save a tracking container to the catalog. + + Args: + container: The container to save. + match_id: Override match ID (extracted from container if None). + """ + pass + + @abstractmethod + def commit_container(self, container: TrackingDataContainer) -> None: + """ + Commit in-place modifications to an already-bound container. + + Args: + container: The bound container to commit. + """ + pass + + @abstractmethod + def get_metadata(self, match_id: str) -> dict[Any, Any]: + """ + Retrieve metadata associated with a match in the catalog. + + Args: + match_id: The ID of the match. + + Returns: + A dictionary containing the parsed match metadata. + """ + pass diff --git a/python/tracking_data_container/catalogs/iceberg.py b/python/tracking_data_container/catalogs/iceberg.py new file mode 100644 index 0000000..ef6e8d2 --- /dev/null +++ b/python/tracking_data_container/catalogs/iceberg.py @@ -0,0 +1,439 @@ +"""TrackingDataCatalog - Manages container storage and lifecycle.""" + +from __future__ import annotations + +import json +import logging +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, cast + +import pyarrow as pa +from pyiceberg.catalog import load_catalog +from pyiceberg.exceptions import NoSuchTableError +from pyiceberg.expressions import ( + EqualTo, + Reference, +) +from pyiceberg.io.pyarrow import _pyarrow_to_schema_without_ids +from pyiceberg.partitioning import PartitionField, PartitionSpec +from pyiceberg.schema import Schema +from pyiceberg.table import Table +from pyiceberg.transforms import IdentityTransform +from pyiceberg.types import ( + FloatType, + LongType, + NestedField, + StringType, + TimestampType, +) + +from ..backends.iceberg import IcebergBackend +from ..container import LazyTrackingDataContainer, TrackingDataContainer +from .base import Catalog + + +@dataclass +class IcebergConfig: + """Configuration for Iceberg table optimization.""" + + # Partitioning + partition_by: list[str] = field(default_factory=lambda: ["match_id"]) + + # Parquet optimization + compression: str = "snappy" # or "zstd" + page_size: int = 1024 * 1024 # 1 MB + row_group_size: int = 128 * 1024 * 1024 # 128 MB + + # Storage features + write_page_index: bool = True + write_statistics: bool = True + use_dictionary: bool = True + + # Sorting + sort_by: list[str] = field(default_factory=lambda: ["entity_id", "timestamp"]) + + +logger = logging.getLogger(__name__) + + +class IcebergTrackingDataCatalog(Catalog): + """ + Manages storage and lifecycle of TrackingDataContainer instances. + + Backed by Apache Iceberg tables with configurable optimization settings. + Follows Iceberg's Table↔Catalog pattern where containers hold a + reference to their parent catalog. + """ + + def __init__( + self, + warehouse_path: str, + config: IcebergConfig | None = None, + catalog_type: str = "sql", + catalog_properties: dict | None = None, + ): + """ + Initialize catalog. + + Args: + warehouse_path: Path to Iceberg warehouse + config: Iceberg configuration (partitioning, compression, etc.) + catalog_type: Catalog backend (sql, glue, rest, etc.) + catalog_properties: Additional catalog properties + """ + self.warehouse_path = Path(warehouse_path).absolute() + self.config = config or IcebergConfig() + self.catalog_type = catalog_type + + # Create warehouse directory if it doesn't exist + self.warehouse_path.mkdir(parents=True, exist_ok=True) + + # Default properties for SQL catalog (SQLite for POC) + default_properties = { + "uri": f"sqlite:///{self.warehouse_path / 'catalog.db'}", + "warehouse": f"file://{self.warehouse_path}", + } + + self.catalog_properties = catalog_properties or default_properties + + # Load Iceberg catalog + self._iceberg_catalog = load_catalog( + "default", + **{ + "type": self.catalog_type, + **self.catalog_properties, + }, + ) + + self._tracking_table: Table | None = None + self._metadata_table: Table | None = None + + def initialize(self) -> None: + """ + Initialize Iceberg tables (tracking_data + match_metadata). + + Creates tables with configured partitioning and optimization settings. + Must be called once before first use. Idempotent - safe to call multiple times. + """ + from pyiceberg.exceptions import NamespaceAlreadyExistsError + + try: + self._iceberg_catalog.create_namespace("tracking") + except NamespaceAlreadyExistsError: + pass # Namespace already exists + + self._create_tracking_table() + self._create_metadata_table() + + def _create_tracking_table(self) -> None: + """Create tracking_data Iceberg table in the requested long format.""" + table_name = "tracking.tracking_data" + + # Check if table exists + try: + self._tracking_table = self._iceberg_catalog.load_table(table_name) + return # Table exists + except NoSuchTableError: + pass # Create new table + + # Define schema. + schema = Schema( + NestedField(1, "competition_id", StringType(), required=False), + NestedField(2, "season_id", StringType(), required=False), + NestedField(3, "match_id", StringType(), required=True), + NestedField(4, "frame_id", LongType(), required=True), + NestedField(5, "period", StringType(), required=True), + NestedField(6, "timestamp", TimestampType(), required=True), + NestedField(7, "entity_type", StringType(), required=True), + NestedField(8, "entity_id", StringType(), required=True), + NestedField(9, "x", FloatType(), required=True), + NestedField(10, "y", FloatType(), required=True), + NestedField(11, "z", FloatType(), required=False), + ) + + # Build PartitionSpec from config + partition_fields = [] + for field_name in self.config.partition_by: + field = schema.find_field(field_name) + partition_fields.append( + PartitionField( + source_id=field.field_id, + field_id=1000 + field.field_id, + transform=IdentityTransform(), + name=field_name, + ) + ) + partition_spec = PartitionSpec(*partition_fields) + + # Optimization properties + properties = { + "write.parquet.compression-codec": self.config.compression, + "write.parquet.page-size-bytes": str(self.config.page_size), + "write.parquet.row-group-size": str(self.config.row_group_size), + "write.parquet.dict-enabled": str(self.config.use_dictionary).lower(), + } + + self._tracking_table = self._iceberg_catalog.create_table( + table_name, + schema=schema, + partition_spec=partition_spec, + properties=properties, + ) + + def _create_metadata_table(self) -> None: + """Create match_metadata Iceberg table.""" + table_name = "tracking.match_metadata" + + # Check if table exists + try: + self._metadata_table = self._iceberg_catalog.load_table(table_name) + return + except NoSuchTableError: + pass + + # Define schema (simplified for POC) + schema = Schema( + NestedField(1, "match_id", StringType(), required=True), + NestedField(2, "competition_id", StringType(), required=False), + NestedField(3, "season_id", StringType(), required=False), + NestedField(4, "metadata_json", StringType(), required=True), + ) + + partition_spec = PartitionSpec( + PartitionField( + source_id=1, field_id=1001, transform=IdentityTransform(), name="match_id" + ) + ) + + self._metadata_table = self._iceberg_catalog.create_table( + table_name, + schema=schema, + partition_spec=partition_spec, + ) + + def load_container( + self, + match_id: str, + player_id: str | None = None, + **filters: Any, + ) -> LazyTrackingDataContainer: + """Load container from storage lazily.""" + backend = IcebergBackend(self, match_id=match_id, player_id=player_id, **filters) + return LazyTrackingDataContainer(backend) + + def save_container( + self, + container: TrackingDataContainer, + match_id: str | None = None, + metadata: dict | None = None, + ) -> None: + """Save container to storage (replaces match partition).""" + if match_id is None: + frame = container.materialize_frame(0) + try: + match_id = str(frame["match/id"]) + except KeyError: + match_id = str(frame["match_id"]) + + entities = self._save_tracking_data(container, match_id) + + final_metadata = metadata.copy() if metadata else {} + final_metadata["_entities"] = entities + self._save_metadata(final_metadata, match_id) + + def commit_container(self, container: TrackingDataContainer) -> None: + """Commit bound container.""" + frame = container.materialize_frame(0) + match_id = frame["match/id"] + + self.save_container(container, match_id=str(match_id)) + + def get_metadata(self, match_id: str) -> dict[Any, Any]: + """Load metadata for a match.""" + if self._metadata_table is None: + self._metadata_table = self._iceberg_catalog.load_table("tracking.match_metadata") + + scan = self._metadata_table.scan( + row_filter=EqualTo(term=Reference("match_id"), value=cast(Any, match_id)) # type: ignore + ) + arrow_table = scan.to_arrow() + + if len(arrow_table) == 0: + return {} + + metadata_json = arrow_table.column("metadata_json")[0].as_py() + return cast(dict[Any, Any], json.loads(metadata_json)) + + def _save_tracking_data( + self, + container: TrackingDataContainer, + match_id: str, + ) -> dict[str, Any]: + """Save tracking data to Iceberg table in entity-long format.""" + if self._tracking_table is None: + self._tracking_table = self._iceberg_catalog.load_table("tracking.tracking_data") + + # 1. Melt container natively in DataFusion + lazy_df = getattr(container._container.lazy(), "melt")() + + # 3. Constant Extraction (Normalization) + core_cols = { + "match/id", + "frame_id", + "period", + "timestamp", + "entity_type", + "entity_id", + "x", + "y", + "z", + "entity_key", + "player/id", + "team/id", + } + potential_constants = [] + for sch_field in container.schema(): + if sch_field.entity is not None: + attr = sch_field.attribute + if attr not in ("x", "y", "z") and attr not in core_cols: + potential_constants.append(attr) + potential_constants = list(set(potential_constants)) + + # Extract metadata via DataFusion natively + lazy_df, agg_table = lazy_df.extract_iceberg_metadata(match_id, potential_constants) + + # Build hierarchical meta + agg_dicts = agg_table.to_pylist() + constants_to_move = [] + for c in potential_constants: + # Check if all unique counts are exactly 1 or null (0) + if all(row.get(f"{c}_count") in (1, 0, None) for row in agg_dicts): + constants_to_move.append(c) + + hierarchical_meta: dict[str, Any] = {"ball": {}, "teams": {}, "referees": {}} + for row in agg_dicts: + etype = row["entity_type"] + eid = row["entity_id"] + tid = row.get("team/id") or "none" + + attrs = {} + for c in constants_to_move: + val = row.get(f"{c}_val") + if val is not None: + attrs[c] = val + + if not attrs: + continue + + if etype == "ball": + hierarchical_meta["ball"].update(attrs) + elif etype == "referee": + hierarchical_meta["referees"].setdefault(eid, {}).update(attrs) + elif etype == "player": + team = hierarchical_meta["teams"].setdefault(tid, {"players": {}}) + team["players"].setdefault(eid, {}).update(attrs) + elif etype == "team": + target_tid = tid if tid != "none" else eid.replace("team:", "") + team = hierarchical_meta["teams"].setdefault(target_tid, {"players": {}}) + team.update(attrs) + + # Drop constant columns AND the team/id column from physical table via DataFusion + cols_to_drop = constants_to_move + ["team/id"] + lazy_df = lazy_df.drop_columns(cols_to_drop) + + # Convert to Arrow natively + arrow_table = pa.table(lazy_df) + + # Validation + col_names = arrow_table.column_names + if "entity_key" not in col_names: + raise ValueError("entity_key column is required for saving to catalog") + + # 4. Final Validation & Save + for req_field in ["x", "y"]: + if req_field not in col_names: + raise ValueError(f"Required column {req_field} is missing") + + must_not_be_null = [ + "match_id", + "frame_id", + "period", + "timestamp", + "entity_type", + "entity_id", + ] + for req_field in must_not_be_null: + if req_field not in col_names: + raise ValueError(f"Required field {req_field} is missing from container") + if arrow_table.column(req_field).null_count > 0: + raise ValueError(f"Required field {req_field} contains null values") + + # Convert to Arrow types + known_types = { + "timestamp": pa.timestamp("us"), + "frame_id": pa.int64(), + "x": pa.float32(), + "y": pa.float32(), + "z": pa.float32(), + } + + new_cols, new_fields = [], [] + for i, pa_field in enumerate(arrow_table.schema): + target = known_types.get(pa_field.name) or ( + pa_field.type.value_type + if isinstance(pa_field.type, pa.DictionaryType) + else (pa.string() if pa.types.is_null(pa_field.type) else None) + ) + new_cols.append(arrow_table.column(i).cast(target) if target else arrow_table.column(i)) + new_fields.append( + pa.field(pa_field.name, target, nullable=True) + if target + else pa.field(pa_field.name, pa_field.type, nullable=True) + ) + + arrow_table = pa.Table.from_arrays(new_cols, schema=pa.schema(new_fields)) + + with self._tracking_table.transaction() as txn: + txn.update_schema().union_by_name( + _pyarrow_to_schema_without_ids(arrow_table.schema) + ).commit() + txn.delete(EqualTo(term=Reference("match_id"), value=cast(Any, match_id))) # type: ignore + txn.append(arrow_table) + + return hierarchical_meta + + def _save_metadata(self, metadata: dict, match_id: str) -> None: + """Save metadata to Iceberg table.""" + if self._metadata_table is None: + self._metadata_table = self._iceberg_catalog.load_table("tracking.match_metadata") + + metadata_schema = pa.schema( + [ + pa.field("match_id", pa.string(), nullable=False), + pa.field("competition_id", pa.string(), nullable=True), + pa.field("season_id", pa.string(), nullable=True), + pa.field("metadata_json", pa.string(), nullable=False), + ] + ) + + metadata_row = pa.table( + { + "match_id": [match_id], + "competition_id": [metadata.get("competition_id")], + "season_id": [metadata.get("season_id")], + "metadata_json": [json.dumps(metadata)], + }, + schema=metadata_schema, + ) + + with self._metadata_table.transaction() as txn: + txn.delete(EqualTo(term=Reference("match_id"), value=cast(Any, match_id))) # type: ignore + txn.append(metadata_row) + + def __repr__(self) -> str: + return ( + f"IcebergTrackingDataCatalog(" + f"warehouse='{self.warehouse_path}', " + f"type='{self.catalog_type}')" + ) diff --git a/python/tracking_data_container/config.py b/python/tracking_data_container/config.py deleted file mode 100644 index b7af0ce..0000000 --- a/python/tracking_data_container/config.py +++ /dev/null @@ -1,24 +0,0 @@ -"""Configuration for TrackingDataContainer storage.""" - -from dataclasses import dataclass, field - - -@dataclass -class IcebergConfig: - """Configuration for Iceberg table optimization.""" - - # Partitioning - partition_by: list[str] = field(default_factory=lambda: ["match_id"]) - - # Parquet optimization - compression: str = "snappy" # or "zstd" - page_size: int = 1024 * 1024 # 1 MB - row_group_size: int = 128 * 1024 * 1024 # 128 MB - - # Storage features - write_page_index: bool = True - write_statistics: bool = True - use_dictionary: bool = True - - # Sorting - sort_by: list[str] = field(default_factory=lambda: ["entity_id", "timestamp"]) diff --git a/python/tracking_data_container/container.py b/python/tracking_data_container/container.py index 1a6f4d1..3e9402f 100644 --- a/python/tracking_data_container/container.py +++ b/python/tracking_data_container/container.py @@ -9,6 +9,9 @@ import polars as pl import pyarrow as pa +from ._tracking_data_container import ( + CORE_COLUMNS as _CORE_COLUMNS, +) from ._tracking_data_container import ( Expr as _Expr, ) @@ -16,15 +19,13 @@ Frame, OrientationMode, _DataField, - _LazyTrackingDataContainer, _TrackingDataContainer, ) from ._tracking_data_container import ( Selection as _Selection, ) from .coordinate_system import CoordinateSystem -from .exceptions import TrackingDataContainerError -from .schema import DataField, Fields, TrackedEntity +from .schema import DataField, Fields from .selector import ColumnSelector, Selection if TYPE_CHECKING: @@ -32,38 +33,28 @@ import pandas as pd - from .catalog import TrackingDataCatalog + from .backends.base import LazyBackend + from .catalogs.base import Catalog logger = logging.getLogger(__name__) # --- Constants for performance --- -CORE_COLUMNS = { - "match/id", - "competition/id", - "season/id", - "timestamp", - "frame_id", - "period", - "period_id", - "player/id", - "team/id", - "entity_key", -} -FLOAT_SUFFIXES = ("/x", "/y", "/z", "/dist", "/vel", "/acc", "/lat", "/long", "/minutes_played") + +CORE_COLUMNS = set(_CORE_COLUMNS) class TrackingDataContainer: """A high-performance, metadata-aware container for sports tracking data.""" _container: _TrackingDataContainer - _catalog: TrackingDataCatalog | None + _catalog: Catalog | None def __init__( self, data: dict[str, Any] | _TrackingDataContainer | pl.DataFrame | pa.Table, coordinate_system: CoordinateSystem | None = None, orientation: str | OrientationMode | None = None, - catalog: TrackingDataCatalog | None = None, + catalog: Catalog | None = None, validate: bool = True, ) -> None: self._catalog = catalog @@ -79,29 +70,9 @@ def __init__( else: data_dict = data - normalized = {} - for k, v in data_dict.items(): - if v is None or len(v) == 0: - normalized[k] = v - continue - - if k.endswith(FLOAT_SUFFIXES) or k in ("x", "y", "z", "ball_x", "ball_y", "ball_z"): - normalized[k] = [float(x) if x is not None else None for x in v] - elif k.endswith("_status") or k == "ball_status": - normalized[k] = [bool(x) if x is not None else None for x in v] - elif k in ("timestamp", "frame_id", "period_id"): - if len(v) > 0 and isinstance(v[0], datetime): - normalized[k] = [ - int(dt.timestamp() * 1_000_000) if dt is not None else None for dt in v - ] - else: - normalized[k] = [int(x) if x is not None else None for x in v] - else: - normalized[k] = [str(x) if x is not None else None for x in v] - inner_cs = coordinate_system._inner if coordinate_system else None self._container = _TrackingDataContainer( - normalized, inner_cs, str(orientation) if orientation else None + data_dict, inner_cs, str(orientation) if orientation else None ) if validate: self.validate() @@ -190,6 +161,18 @@ def add_column( self, name: str, value: Any, on: str | Sequence[str] | None = None ) -> TrackingDataContainer: if callable(value): + if on is not None: + on_resolved = [on] if isinstance(on, str) else list(on) + try: + result = value(*on_resolved) + if isinstance(result, _Expr): + new_inner = self._container.add_column(name, result) + return TrackingDataContainer( + new_inner, catalog=self._catalog, validate=False + ) + except Exception: + pass + inputs = self.select(on) if on is not None else self batch = inputs.to_arrow() args = [batch.column(c) for c in ([on] if isinstance(on, str) else list(on or []))] @@ -214,10 +197,68 @@ def add_column( ) def add_columns(self, names: Sequence[str], values: Sequence[Any]) -> TrackingDataContainer: - res = self - for n, v in zip(names, values): - res = res.add_column(n, v) - return res + expr_names = [] + expr_values = [] + array_names = [] + array_values = [] + schema_capsules = [] + + for name, value in zip(names, values): + if isinstance(value, _Expr): + expr_names.append(name) + expr_values.append(value) + elif callable(value): + # Fallback to single add_column for callables + return self.add_column(name, value) + else: + data = value + try: + cap_arr, cap_sch = data.__arrow_c_array__() + except (AttributeError, TypeError): + import pyarrow as pa + + data = pa.array(data) + cap_arr, cap_sch = data.__arrow_c_array__() + + array_names.append(name) + array_values.append(cap_arr) + schema_capsules.append(cap_sch) + + if hasattr(self._container, "clone"): + new_inner = self._container.clone() + else: + new_inner = self._container + + if expr_names or array_names: + # We call the Rust method, passing lists of Exprs or capsules. + # We must map expr_names, expr_values properly if we had a split API, + # but _TrackingDataContainer.add_columns accepts a mixed list of values! + pass + + # Since _TrackingDataContainer.add_columns takes a mixed list, let's just pass them directly + # and extract the capsules for array values. + + capsules: list[Any] = [] + final_values = [] + for v in values: + if isinstance(v, _Expr): + final_values.append(v) + capsules.append(None) + else: + try: + cap_arr, cap_sch = v.__arrow_c_array__() + except (AttributeError, TypeError): + import pyarrow as pa + + v = pa.array(v) + cap_arr, cap_sch = v.__arrow_c_array__() + final_values.append(cap_arr) + capsules.append(cap_sch) + + new_inner = self._container.add_columns( + list(names), final_values, _schema_capsules=capsules + ) + return TrackingDataContainer(new_inner, catalog=self._catalog, validate=False) def add_column_per_player( self, name: str, value: Any, on: str | Sequence[str] | None = None @@ -256,76 +297,6 @@ def to_pandas(self) -> pd.DataFrame: def to_parquet(self, path: str | Path) -> None: self._container.save_parquet(str(path)) - def melt(self) -> pl.DataFrame: - import polars as pl - - df = self.to_polars() - core = [ - c - for c in [ - "match/id", - "competition/id", - "season/id", - "timestamp", - "frame_id", - "period", - "period_id", - ] - if c in df.columns - ] - groups: dict[tuple, dict[str, str]] = {} - for c in df.columns: - if c in core: - continue - try: - f = DataField(c) - if not f.entity: - continue - - # Check for metadata columns that shouldn't be melted - if f.attribute in ("player/id", "team/id", "entity_key"): - continue - - key = ( - f.entity.type, - getattr(f.entity, "player_id", None), - getattr(f.entity, "team_id", None), - getattr(f.entity, "referee_id", None), - ) - if key not in groups: - groups[key] = {} - groups[key][f.attribute] = c - except Exception: - continue - - dfs = [] - for (et, pid, tid, rid), attrs in groups.items(): - t = df.select(core + list(attrs.values())).rename({v: k for k, v in attrs.items()}) - p_id = ( - "ball" - if et == "ball" - else ( - pid if et == "player" else (f"team:{tid}" if et == "team" else f"referee:{rid}") - ) - ) - t = t.with_columns( - [ - pl.lit(str(p_id)).cast(pl.String).alias("player/id"), - pl.lit(str(tid) if tid else None).cast(pl.String).alias("team/id"), - (pl.lit(str(p_id)) + pl.lit("|") + pl.lit(str(tid) if tid else "none")) - .cast(pl.String) - .alias("entity_key"), - ] - ) - # Ensure spatial columns are float32 for consistency - for spatial in ["x", "y", "z"]: - if spatial in t.columns: - t = t.with_columns(pl.col(spatial).cast(pl.Float32)) - - t = t.filter(~pl.all_horizontal(pl.col(list(attrs.keys())).is_null())) - dfs.append(t) - return pl.concat(dfs, how="diagonal_relaxed") if dfs else pl.DataFrame() - def __arrow_c_schema__(self) -> Any: return self._container.__arrow_c_schema__() @@ -336,7 +307,7 @@ def __arrow_c_array__(self, requested_schema: Any = None) -> Any: def from_arrow( cls, data: pa.Table | pa.RecordBatch | pa.StructArray | pa.ChunkedArray, - catalog: TrackingDataCatalog | None = None, + catalog: Catalog | None = None, ) -> TrackingDataContainer: import pyarrow as pa @@ -354,6 +325,18 @@ def from_arrow( new_inner = _TrackingDataContainer.from_arrow(cap_arr, cap_sch, None, None) return TrackingDataContainer(new_inner, catalog=catalog, validate=False) + def melt(self) -> pl.DataFrame: + """Pivots wide-format tracking data into entity-long format. + + Returns: + A long Polars DataFrame. + """ + import pyarrow as pa + + melted_stream = getattr(self._container.lazy(), "melt")() + table = pa.table(melted_stream) + return cast(pl.DataFrame, pl.from_arrow(table)) + @classmethod def pivot( cls, @@ -379,7 +362,9 @@ def pivot( return TrackingDataContainer(inner, validate=False) def lazy(self) -> LazyTrackingDataContainer: - return LazyTrackingDataContainer(self._container.lazy(), catalog=self._catalog) + from .backends.datafusion import DataFusionBackend + + return LazyTrackingDataContainer(DataFusionBackend(self._container.lazy())) def materialize_frame(self, frame: int | datetime | timedelta) -> Frame: """Materializes a single frame. Fixed datetime comparison.""" @@ -417,6 +402,162 @@ def materialize_frame(self, frame: int | datetime | timedelta) -> Frame: return self._container.materialize_frame(idx) + def plot_frame( + self, + frame: int | datetime | timedelta, + ax: Any = None, + home_color: str = "red", + away_color: str = "blue", + ball_color: str = "white", + referee_color: str = "yellow", + player_size: float = 80, + ball_size: float = 50, + **kwargs: Any, + ) -> Any: + """ + Visualizes a frame using mplsoccer. + + Parameters + ---------- + frame : int, datetime, or timedelta + The frame index, timestamp, or timedelta to plot. + ax : matplotlib.axes.Axes, optional + An existing matplotlib Axes object to plot on. + home_color : str, default="red" + Color for the home team players. + away_color : str, default="blue" + Color for the away team players. + ball_color : str, default="white" + Color for the ball. + referee_color : str, default="yellow" + Color for the referees. + player_size : float, default=0.8 + Radius of the players in pitch units. + ball_size : float, default=0.5 + Radius of the ball in pitch units. + **kwargs + Additional arguments passed to the Pitch.scatter method. + + Returns + ------- + matplotlib.axes.Axes + The matplotlib Axes object. + + Raises + ------ + ImportError + If mplsoccer is not installed. + ValueError + If the coordinate system is not defined. + """ + try: + from mplsoccer import Pitch + except ImportError: + raise ImportError( + "Seems like you don't have `mplsoccer` installed. " + "Please install it using: pip install mplsoccer" + ) + + if self.coordinate_system is None: + raise ValueError( + "Coordinate system must be defined to plot. " + "Use container.with_coordinate_system() to set it." + ) + + dot_styles = { + "home": { + "facecolor": home_color, + "edgecolor": "white", + "linewidth": 0.8, + "s": player_size, + "zorder": 5, + }, + "away": { + "facecolor": away_color, + "edgecolor": "white", + "linewidth": 0.8, + "s": player_size, + "zorder": 5, + }, + "ball": { + "facecolor": ball_color, + "edgecolor": "black", + "s": ball_size, + "zorder": 6, + }, + "referee": { + "facecolor": referee_color, + "edgecolor": "black", + "linewidth": 0.8, + "s": player_size, + "zorder": 5, + }, + } + + dims = self.coordinate_system.to_mplsoccer() + pitch = Pitch(pitch_type=dims) + + if ax is None: + fig, ax = pitch.draw() + else: + pitch.draw(ax=ax) + + f = self.materialize_frame(frame) + schema = self.schema() + + # Plot players + for tracked_team in schema.teams(): + from .schema import Team + + if not isinstance(tracked_team, Team): + continue + team_id = tracked_team.team_id + if team_id is None: + continue + style_key = "home" if "home" in team_id.lower() else "away" + style = dot_styles[style_key].copy() + style.update(kwargs) + + players = schema.players(team=team_id) + x = [] + y = [] + for p in players: + px = f[DataField(entity=p, attribute="x")] # type: ignore + py = f[DataField(entity=p, attribute="y")] # type: ignore + if px is not None and py is not None: + x.append(px) + y.append(py) + + if x: + pitch.scatter(x, y, ax=ax, label=f"Team {team_id}", **style) + + # Plot ball + from .schema import Ball + + bx = f[DataField(entity=Ball(), attribute="x")] # type: ignore + by = f[DataField(entity=Ball(), attribute="y")] # type: ignore + if bx is not None and by is not None: + style = dot_styles["ball"].copy() + style.update(kwargs) + pitch.scatter(bx, by, ax=ax, label="Ball", **style) + + # Plot referees + rx = [] + ry = [] + for ref in schema.referees(): + px = f[DataField(entity=ref, attribute="x")] # type: ignore + py = f[DataField(entity=ref, attribute="y")] # type: ignore + if px is not None and py is not None: + rx.append(px) + ry.append(py) + + if rx: + style = dot_styles["referee"].copy() + style.update(kwargs) + pitch.scatter(rx, ry, ax=ax, label="Referees", **style) + + return ax + @classmethod def from_parquet(cls, path: str | Path) -> TrackingDataContainer: """Loads from a Parquet file.""" @@ -428,248 +569,45 @@ def __repr__(self) -> str: class LazyTrackingDataContainer: - def __init__( - self, - container: _LazyTrackingDataContainer, - catalog: TrackingDataCatalog | None = None, - is_long_storage: bool = False, - deferred_select: Sequence[str] | None = None, - cached_schema: Fields | None = None, - deferred_add_columns: list[tuple] | None = None, - ): - self._container = container - self._catalog = catalog - self._is_long_storage = is_long_storage - self._deferred_select = deferred_select - self._cached_schema = cached_schema - self._deferred_add_columns = deferred_add_columns or [] + """A lazy tracking data container that delegates execution to a backend.""" + + def __init__(self, backend: LazyBackend): + self._backend = backend def schema(self) -> Fields: - """Fast reconstruction for long storage. Optimization: Selection pushdown.""" - if self._cached_schema: - return self._cached_schema - if not self._is_long_storage: - self._cached_schema = Fields(self._container.schema) - return self._cached_schema - - import polars as pl - - ids: list[str | _DataField | _Selection] = [ - _DataField.from_name("player/id"), - _DataField.from_name("team/id"), - ] - entities = self._container.select(ids).collect() - ent_df = pl.from_arrow(entities.to_arrow()) - if isinstance(ent_df, pl.Series): - ent_df = ent_df.to_frame() - ent_df = ent_df.unique() - - phys_names = [f.name() for f in self._container.schema] - phys_attrs = [n for n in phys_names if n not in CORE_COLUMNS] - - # Start with all non-entity core columns - wide_names = [c for c in CORE_COLUMNS if c not in ("player/id", "team/id", "entity_key")] - for row in ent_df.to_dicts(): - p, t = str(row["player/id"]), str(row.get("team/id", "none")) - prefix = TrackedEntity.from_entity_key(f"{p}|{t}").cdf_prefix() - for attr in phys_attrs: - wide_names.append(f"{prefix}{attr}") - - self._cached_schema = Fields(list(dict.fromkeys(wide_names))) - return self._cached_schema + return self._backend.schema() - def select(self, items: str | Sequence[str]) -> LazyTrackingDataContainer: - """Lazily selects columns. Supports selection pushdown.""" - if self._is_long_storage: - sch = self.schema() - resolved = [] - inputs = items if isinstance(items, (list, tuple)) else [items] - for it in inputs: - if isinstance(it, str): - if "*" in it: - resolved.extend(sch.match(it)) - else: - resolved.append(it) - elif isinstance(it, DataField): - resolved.append(it.name()) - - resolved = list(dict.fromkeys(resolved)) - core_set = { - "match/id", - "competition/id", - "season/id", - "timestamp", - "frame_id", - "period", - "player/id", - "team/id", - "entity_key", - } - phys_attrs, entities = set(), set() - for name in resolved: - if name in core_set: - continue - try: - f = DataField(name) - phys_attrs.add(f.attribute) - if f.entity: - if f.entity.type != "team": - pid = getattr(f.entity, "player_id", "ball") - else: - pid = f"team:{f.entity.team_id}" - entities.add((str(pid), str(getattr(f.entity, "team_id", "none")))) - except Exception: - continue + @property + def selector(self) -> ColumnSelector: + from ._tracking_data_container import ColumnSelector as _ColumnSelector - new_inner = self._container - if entities: - from ._tracking_data_container import col, lit - - pred = None - for pid, tid in entities: - p_cond = col("player/id").eq(lit(pid)) - if tid != "none": - p_cond = p_cond & col("team/id").eq(lit(tid)) - pred = p_cond if pred is None else (pred | p_cond) - if pred: - new_inner = new_inner.filter(pred) - - phys_names = [f.name() for f in self._container.schema] - phys_sel = [n for n in core_set if n in phys_names] - phys_sel.extend([n for n in phys_attrs if n in phys_names]) - - rust_sel: list[str | _DataField | _Selection] = [ - _DataField.from_name(n) for n in phys_sel - ] - return LazyTrackingDataContainer( - new_inner.select(rust_sel), - catalog=self._catalog, - is_long_storage=True, - deferred_select=resolved, - cached_schema=Fields(resolved), - deferred_add_columns=self._deferred_add_columns, - ) + return ColumnSelector(_ColumnSelector()) - inputs = items if isinstance(items, (list, tuple)) else [items] - processed: list[str | _DataField | _Selection] = [] - for i in inputs: - if isinstance(i, str): - if "*" in i: - for m in self.schema().match(i): - processed.append(_DataField.from_name(m)) - else: - processed.append(_DataField.from_name(i)) - elif isinstance(i, DataField): - processed.append(i._field) - elif isinstance(i, Selection): - processed.append(i._inner) - elif isinstance(i, (_DataField, _Selection)): - processed.append(i) - - new_cache = None - if self._cached_schema: - names = [] - for itm in inputs: - if isinstance(itm, str): - names.extend(self._cached_schema.match(itm)) - else: - names.append(str(itm)) - new_cache = Fields(list(dict.fromkeys(names))) - - return LazyTrackingDataContainer( - self._container.select(processed), - catalog=self._catalog, - cached_schema=new_cache, - deferred_add_columns=self._deferred_add_columns, - ) + def select(self, items: str | Sequence[str]) -> LazyTrackingDataContainer: + return LazyTrackingDataContainer(self._backend.select(items)) def add_column( self, name: str, value: Any, on: str | Sequence[str] | None = None ) -> LazyTrackingDataContainer: - if isinstance(value, _Expr): - return LazyTrackingDataContainer( - self._container.add_column(name, value), - catalog=self._catalog, - is_long_storage=self._is_long_storage, - deferred_select=self._deferred_select, - cached_schema=self._cached_schema, - deferred_add_columns=self._deferred_add_columns, - ) - - if callable(value): - new_deferred = list(self._deferred_add_columns) - new_deferred.append((name, value, on)) - return LazyTrackingDataContainer( - self._container, - catalog=self._catalog, - is_long_storage=self._is_long_storage, - deferred_select=self._deferred_select, - cached_schema=self._cached_schema, - deferred_add_columns=new_deferred, - ) - - raise TrackingDataContainerError( - "Adding non-expression columns to lazy containers is not yet supported." - ) + return LazyTrackingDataContainer(self._backend.add_column(name, value, on)) def collect(self) -> TrackingDataContainer: - """Executes the plan and pivots if necessary.""" - try: - inner = self._container.collect() - res = TrackingDataContainer(inner, catalog=self._catalog, validate=False) - - for name, value, on in self._deferred_add_columns: - res = res.add_column(name, value, on=on) - - if self._is_long_storage: - res = TrackingDataContainer.pivot(res.to_arrow()) - res._catalog = self._catalog - if self._deferred_select: - logger.debug(f"Wide container columns before selection: {res.schema().names}") - res = res.select(list(self._deferred_select)) - logger.debug(f"Wide container columns after selection: {res.schema().names}") - return res - except Exception as e: - raise TrackingDataContainerError(f"Collection failed: {e}") + return self._backend.collect() def slice(self, offset: int, length: int) -> LazyTrackingDataContainer: - return LazyTrackingDataContainer( - self._container.slice(offset, length), - catalog=self._catalog, - is_long_storage=self._is_long_storage, - cached_schema=self._cached_schema, - deferred_add_columns=self._deferred_add_columns, - ) + return LazyTrackingDataContainer(self._backend.slice(offset, length)) def filter(self, predicate: _Expr) -> LazyTrackingDataContainer: - return LazyTrackingDataContainer( - self._container.filter(predicate), - catalog=self._catalog, - is_long_storage=self._is_long_storage, - cached_schema=self._cached_schema, - deferred_add_columns=self._deferred_add_columns, - ) + return LazyTrackingDataContainer(self._backend.filter(predicate)) def transform_coordinates(self, sys: CoordinateSystem) -> LazyTrackingDataContainer: - return LazyTrackingDataContainer( - self._container.transform_coordinates(sys._inner), - catalog=self._catalog, - is_long_storage=self._is_long_storage, - cached_schema=self._cached_schema, - deferred_add_columns=self._deferred_add_columns, - ) + return LazyTrackingDataContainer(self._backend.transform_coordinates(sys)) def transform_orientation(self, mode: str | OrientationMode) -> LazyTrackingDataContainer: - return LazyTrackingDataContainer( - self._container.transform_orientation(str(mode)), - catalog=self._catalog, - is_long_storage=self._is_long_storage, - cached_schema=self._cached_schema, - deferred_add_columns=self._deferred_add_columns, - ) + return LazyTrackingDataContainer(self._backend.transform_orientation(mode)) def __arrow_c_stream__(self, rs: Any = None) -> Any: - return self._container.__arrow_c_stream__(rs) + return self._backend.__arrow_c_stream__(rs) def __repr__(self) -> str: - return repr(self._container) + return f"LazyTrackingDataContainer(backend={self._backend.__class__.__name__})" diff --git a/python/tracking_data_container/io.py b/python/tracking_data_container/io.py index 36aa3e2..7391a3f 100644 --- a/python/tracking_data_container/io.py +++ b/python/tracking_data_container/io.py @@ -38,8 +38,10 @@ def scan_parquet(path: str | Path) -> LazyTrackingDataContainer: LazyTrackingDataContainer A lazy container representing the Parquet file. """ + from .backends.datafusion import DataFusionBackend + new_inner = _scan_parquet(str(path)) - return LazyTrackingDataContainer(new_inner) + return LazyTrackingDataContainer(DataFusionBackend(new_inner)) def load_sportec(path: str | Path, meta_path: str | Path | None = None) -> TrackingDataContainer: diff --git a/python/tracking_data_container/schema.py b/python/tracking_data_container/schema.py index 01351d9..cd133b7 100644 --- a/python/tracking_data_container/schema.py +++ b/python/tracking_data_container/schema.py @@ -129,6 +129,9 @@ def _from_inner(entity: _TrackedEntity) -> TrackedEntity: def __repr__(self) -> str: return repr(self._entity) + def __hash__(self) -> int: + return hash(self.entity_key()) + class Player(TrackedEntity): def __init__(self, team_id: str, player_id: str) -> None: @@ -148,6 +151,8 @@ def __eq__(self, other: object) -> bool: return False return self.team_id == other.team_id and self.player_id == other.player_id + __hash__ = TrackedEntity.__hash__ + class Ball(TrackedEntity): def __init__(self) -> None: @@ -165,6 +170,8 @@ def __repr__(self) -> str: def __eq__(self, other: object) -> bool: return isinstance(other, Ball) + __hash__ = TrackedEntity.__hash__ + class Team(TrackedEntity): def __init__(self, team_id: str) -> None: @@ -184,6 +191,8 @@ def __eq__(self, other: object) -> bool: return False return self.team_id == other.team_id + __hash__ = TrackedEntity.__hash__ + class Referee(TrackedEntity): def __init__(self, referee_id: str) -> None: @@ -203,6 +212,8 @@ def __eq__(self, other: object) -> bool: return False return self.referee_id == other.referee_id + __hash__ = TrackedEntity.__hash__ + class DataField: """Describes a single column/field in the tracking data.""" @@ -279,6 +290,10 @@ def __eq__(self, other: object) -> bool: return False return self.name() == other.name() + def to_iceberg_name(self) -> str: + """Returns the mapped physical column name for Iceberg long storage format.""" + return str(getattr(self._field, "to_iceberg_name")()) + def __repr__(self) -> str: return f"DataField({self.name()})" @@ -400,10 +415,12 @@ def validate(self) -> None: warnings.warn(" AND ".join(all_warnings), SchemaWarning) def validate_quiet(self) -> bool: + from .exceptions import SchemaError + try: self.validate() return True - except Exception: + except SchemaError: return False def __repr__(self) -> str: From 33963ce7da33df6c5928045d6697cbb8b44a6dec Mon Sep 17 00:00:00 2001 From: Pieter Robberechts Date: Wed, 29 Jul 2026 16:04:45 +0200 Subject: [PATCH 21/21] feat: support multiple schemas --- crates/tdc-core/src/datacontainer/core.rs | 102 +++++- crates/tdc-core/src/datacontainer/lazy.rs | 18 +- crates/tdc-core/src/io.rs | 15 +- crates/tdc-core/src/lib.rs | 3 +- crates/tdc-core/src/schema/definition.rs | 213 ++++++++++++ crates/tdc-core/src/schema/fields.rs | 128 +++---- crates/tdc-core/src/schema/mod.rs | 4 + crates/tdc-core/src/schema/registry.rs | 314 ++++++++++++++++++ crates/tdc-parsers/src/hawkeye.rs | 13 +- crates/tdc-parsers/src/sportec.rs | 49 ++- crates/tdc-parsers/src/statsperform.rs | 9 +- crates/tdc-parsers/tests/test_sportec.rs | 3 + crates/tdc-python/src/container.rs | 28 +- crates/tdc-python/src/schema.rs | 78 +++++ examples/01-basics.ipynb | 82 ++++- examples/02-parsing.ipynb | 18 +- examples/04-udfs.ipynb | 12 +- examples/05-lazy.ipynb | 2 +- examples/06-skeletal.ipynb | 56 ++-- examples/08-idsse-warehouse.ipynb | 111 ++----- python/tests/test_schema.py | 4 +- python/tracking_data_container/__init__.py | 4 + .../_tracking_data_container.pyi | 23 ++ .../backends/iceberg.py | 6 + .../catalogs/iceberg.py | 2 + python/tracking_data_container/container.py | 29 +- python/tracking_data_container/schema.py | 166 ++++++--- 27 files changed, 1219 insertions(+), 273 deletions(-) create mode 100644 crates/tdc-core/src/schema/definition.rs create mode 100644 crates/tdc-core/src/schema/registry.rs diff --git a/crates/tdc-core/src/datacontainer/core.rs b/crates/tdc-core/src/datacontainer/core.rs index 539c09a..91f7a9a 100644 --- a/crates/tdc-core/src/datacontainer/core.rs +++ b/crates/tdc-core/src/datacontainer/core.rs @@ -1,5 +1,7 @@ use crate::error::{TrackingDataContainerErrorKind, TrackingDataContainerResult}; -use crate::schema::{CoordinateSystem, DataField, Fields, OrientationMode, TrackedEntity}; +use crate::schema::{ + CoordinateSystem, DataField, Fields, OrientationMode, SchemaId, TrackedEntity, +}; use arrow_arith::numeric::{add, mul}; use arrow_array::array::{Array, ArrayRef, Float32Array, Float64Array, StructArray}; use arrow_array::cast::AsArray; @@ -16,6 +18,7 @@ pub struct TrackingDataContainer { pub schema: Fields, pub coordinate_system: Option, pub orientation: Option, + pub schema_id: Option, } impl Clone for TrackingDataContainer { @@ -25,6 +28,7 @@ impl Clone for TrackingDataContainer { schema: self.schema.clone(), coordinate_system: self.coordinate_system.clone(), orientation: self.orientation, + schema_id: self.schema_id.clone(), } } } @@ -36,6 +40,7 @@ impl TrackingDataContainer { schema, coordinate_system: None, orientation: None, + schema_id: None, } } @@ -49,6 +54,11 @@ impl TrackingDataContainer { self } + pub fn with_schema_id(mut self, id: SchemaId) -> Self { + self.schema_id = Some(id); + self + } + pub fn num_rows(&self) -> usize { self.data.len() } @@ -62,11 +72,94 @@ impl TrackingDataContainer { schema: self.schema.clone(), coordinate_system: self.coordinate_system.clone(), orientation: self.orientation, + schema_id: self.schema_id.clone(), } } pub fn validate(&self) -> TrackingDataContainerResult<()> { - self.schema.validate() + match &self.schema_id { + Some(id) => self.schema.validate_with_schema(id)?, + None => self.schema.validate()?, + } + + let default_id = crate::schema::registry::default_schema_id(); + let schema_id = self.schema_id.as_ref().unwrap_or(&default_id); + + if let Some(schema_def) = crate::schema::registry::get_schema(schema_id) { + for req in &schema_def.value_requirements { + if let Some(idx) = self + .schema + .fields + .iter() + .position(|f| f.name() == req.column) + { + let col = self.data.column(idx); + if let Some(string_col) = col.as_any().downcast_ref::() { + for i in 0..string_col.len() { + if !string_col.is_null(i) { + let val = string_col.value(i); + if !req.allowed_values.iter().any(|v| v == val) { + return Err(crate::error::TrackingDataContainerError::new( + TrackingDataContainerErrorKind::SchemaError(format!( + "Invalid value for column '{}': {}", + req.column, val + )), + )); + } + } + } + } else if let Some(large_string_col) = col.as_any().downcast_ref::() { + for i in 0..large_string_col.len() { + if !large_string_col.is_null(i) { + let val = large_string_col.value(i); + if !req.allowed_values.iter().any(|v| v == val) { + return Err(crate::error::TrackingDataContainerError::new( + TrackingDataContainerErrorKind::SchemaError(format!( + "Invalid value for column '{}': {}", + req.column, val + )), + )); + } + } + } + } else if let Some(dict_col) = col.as_any().downcast_ref::>() { + let values = dict_col.values(); + if let Some(string_values) = values.as_any().downcast_ref::() { + for i in 0..dict_col.len() { + if !dict_col.is_null(i) { + let key = dict_col.keys().value(i); + let val = string_values.value(key as usize); + if !req.allowed_values.iter().any(|v| v == val) { + return Err(crate::error::TrackingDataContainerError::new( + TrackingDataContainerErrorKind::SchemaError(format!( + "Invalid value for column '{}': {}", + req.column, val + )), + )); + } + } + } + } else if let Some(large_string_values) = values.as_any().downcast_ref::() { + for i in 0..dict_col.len() { + if !dict_col.is_null(i) { + let key = dict_col.keys().value(i); + let val = large_string_values.value(key as usize); + if !req.allowed_values.iter().any(|v| v == val) { + return Err(crate::error::TrackingDataContainerError::new( + TrackingDataContainerErrorKind::SchemaError(format!( + "Invalid value for column '{}': {}", + req.column, val + )), + )); + } + } + } + } + } + } + } + } + Ok(()) } pub fn lazy(&self) -> TrackingDataContainerResult { @@ -77,6 +170,7 @@ impl TrackingDataContainer { self.schema.clone(), self.coordinate_system.clone(), self.orientation, + self.schema_id.clone(), )) } @@ -111,6 +205,7 @@ impl TrackingDataContainer { }, coordinate_system: self.coordinate_system.clone(), orientation: self.orientation, + schema_id: self.schema_id.clone(), } } @@ -221,6 +316,7 @@ impl TrackingDataContainer { schema: self.schema.clone(), coordinate_system: Some(to_system), orientation: self.orientation, + schema_id: self.schema_id.clone(), }) } @@ -333,6 +429,7 @@ impl TrackingDataContainer { schema: self.schema.clone(), coordinate_system: self.coordinate_system.clone(), orientation: Some(mode), + schema_id: self.schema_id.clone(), }) } @@ -768,6 +865,7 @@ impl crate::selector::Select for TrackingDataContainer { }, coordinate_system: self.coordinate_system.clone(), orientation: self.orientation, + schema_id: self.schema_id.clone(), }) } } diff --git a/crates/tdc-core/src/datacontainer/lazy.rs b/crates/tdc-core/src/datacontainer/lazy.rs index f98f2bc..0aeca41 100644 --- a/crates/tdc-core/src/datacontainer/lazy.rs +++ b/crates/tdc-core/src/datacontainer/lazy.rs @@ -1,6 +1,6 @@ use crate::datacontainer::TrackingDataContainer; use crate::error::TrackingDataContainerResult; -use crate::schema::{CoordinateSystem, DataField, Fields, OrientationMode}; +use crate::schema::{CoordinateSystem, DataField, Fields, OrientationMode, SchemaId}; use datafusion::prelude::*; #[derive(Clone)] @@ -9,6 +9,7 @@ pub struct LazyTrackingDataContainer { pub schema: Fields, pub coordinate_system: Option, pub orientation: Option, + pub schema_id: Option, } impl crate::selector::expr::HasSchema for LazyTrackingDataContainer { @@ -30,6 +31,7 @@ impl crate::selector::Select for LazyTrackingDataContainer { schema: Fields { fields }, coordinate_system: self.coordinate_system.clone(), orientation: self.orientation, + schema_id: self.schema_id.clone(), }) } } @@ -40,12 +42,14 @@ impl LazyTrackingDataContainer { schema: Fields, coordinate_system: Option, orientation: Option, + schema_id: Option, ) -> Self { Self { df, schema, coordinate_system, orientation, + schema_id, } } @@ -61,6 +65,7 @@ impl LazyTrackingDataContainer { schema: self.schema.clone(), coordinate_system: self.coordinate_system.clone(), orientation: self.orientation, + schema_id: self.schema_id.clone(), }) } @@ -74,6 +79,7 @@ impl LazyTrackingDataContainer { schema: self.schema.clone(), coordinate_system: self.coordinate_system.clone(), orientation: self.orientation, + schema_id: self.schema_id.clone(), }) } @@ -108,6 +114,7 @@ impl LazyTrackingDataContainer { schema: self.schema.clone(), coordinate_system: Some(to_system), orientation: self.orientation, + schema_id: self.schema_id.clone(), }) } @@ -198,6 +205,7 @@ impl LazyTrackingDataContainer { schema: self.schema.clone(), coordinate_system: self.coordinate_system.clone(), orientation: Some(mode), + schema_id: self.schema_id.clone(), }) } @@ -260,6 +268,7 @@ impl LazyTrackingDataContainer { }, coordinate_system: self.coordinate_system.clone(), orientation: self.orientation, + schema_id: self.schema_id.clone(), }) } @@ -465,6 +474,7 @@ impl LazyTrackingDataContainer { }, coordinate_system: self.coordinate_system.clone(), orientation: self.orientation, + schema_id: self.schema_id.clone(), }) } @@ -481,6 +491,9 @@ impl LazyTrackingDataContainer { if let Some(om) = self.orientation { container = container.with_orientation(om); } + if let Some(id) = self.schema_id { + container = container.with_schema_id(id); + } return Ok(container); } @@ -498,6 +511,9 @@ impl LazyTrackingDataContainer { if let Some(om) = self.orientation { container = container.with_orientation(om); } + if let Some(id) = self.schema_id { + container = container.with_schema_id(id); + } Ok(container) } diff --git a/crates/tdc-core/src/io.rs b/crates/tdc-core/src/io.rs index 723946b..096a7f4 100644 --- a/crates/tdc-core/src/io.rs +++ b/crates/tdc-core/src/io.rs @@ -1,6 +1,6 @@ use crate::datacontainer::TrackingDataContainer; use crate::error::TrackingDataContainerResult; -use crate::schema::Fields; +use crate::schema::{Fields, SchemaId}; use arrow_array::RecordBatch; use parquet::arrow::ArrowWriter; use parquet::arrow::arrow_reader::ParquetRecordBatchReaderBuilder; @@ -31,6 +31,13 @@ impl TrackingDataContainer { )); } + if let Some(id) = &self.schema_id { + key_value_metadata.push(parquet::format::KeyValue::new( + "tdc:schema".to_string(), + Some(id.to_string()), + )); + } + let props_builder = WriterProperties::builder().set_key_value_metadata(Some(key_value_metadata)); @@ -48,6 +55,7 @@ impl TrackingDataContainer { let mut coordinate_system = None; let mut orientation = None; + let mut schema_id = None; if let Some(metadata) = builder.metadata().file_metadata().key_value_metadata() { for kv in metadata { @@ -60,6 +68,10 @@ impl TrackingDataContainer { && let Some(val) = &kv.value { orientation = Some(serde_json::from_str(val).map_err(|e| e.to_string())?); + } else if kv.key == "tdc:schema" + && let Some(val) = &kv.value + { + schema_id = SchemaId::parse(val); } } } @@ -85,6 +97,7 @@ impl TrackingDataContainer { let mut container = Self::new(batch.into(), schema); container.coordinate_system = coordinate_system; container.orientation = orientation; + container.schema_id = schema_id; Ok(container) } diff --git a/crates/tdc-core/src/lib.rs b/crates/tdc-core/src/lib.rs index 3ddd353..012aa60 100644 --- a/crates/tdc-core/src/lib.rs +++ b/crates/tdc-core/src/lib.rs @@ -8,6 +8,7 @@ pub mod selector; pub use datacontainer::{LazyTrackingDataContainer, TrackingDataBuilder, TrackingDataContainer}; pub use schema::{ Attribute, CoordinateSystem, DataField, Direction, Fields, FrameAttribute, LengthUnit, - OrientationMode, Origin, Period, TrackedEntity, TrackedEntityAttribute, + OrientationMode, Origin, Period, SchemaDefinition, SchemaId, TrackedEntity, + TrackedEntityAttribute, default_schema_id, get_schema, list_schemas, }; pub use selector::Select; diff --git a/crates/tdc-core/src/schema/definition.rs b/crates/tdc-core/src/schema/definition.rs new file mode 100644 index 0000000..af8be33 --- /dev/null +++ b/crates/tdc-core/src/schema/definition.rs @@ -0,0 +1,213 @@ +//! Schema identification and definition types. +//! +//! This module provides the core types for multi-schema support, allowing +//! TDC to validate data against different schema definitions (e.g., 2D tracking, +//! skeletal tracking, GPS data) for different sports and versions. + +use super::{Attribute, FrameAttribute, TrackedEntityAttribute}; +use crate::error::{ + TrackingDataContainerError, TrackingDataContainerErrorKind, TrackingDataContainerResult, +}; +use std::fmt; + +/// Identifies a specific schema by sport, data kind, and version. +/// +/// # Examples +/// +/// - CDF tracking: `SchemaId::new("cdf", "tracking", "0.2.3")` +/// - CDF skeletal: `SchemaId::new("cdf", "skeletal", "0.2.3")` +#[derive(Clone, Debug, PartialEq, Eq, Hash)] +pub struct SchemaId { + /// The sport or standard this schema applies to (e.g., "cdf", "football"). + pub sport: String, + /// The kind of data (e.g., "tracking", "skeletal"). + pub data_kind: String, + /// The version string (e.g., "0.2.2"). + pub version: String, +} + +impl SchemaId { + /// Creates a new schema identifier. + pub fn new( + sport: impl Into, + data_kind: impl Into, + version: impl Into, + ) -> Self { + Self { + sport: sport.into(), + data_kind: data_kind.into(), + version: version.into(), + } + } + + /// Parses a schema ID from a slash-separated string like "football/tracking_2d/0.2.2". + pub fn parse(s: &str) -> Option { + let parts: Vec<&str> = s.split('/').collect(); + if parts.len() == 3 { + Some(Self::new(parts[0], parts[1], parts[2])) + } else { + None + } + } +} + +impl fmt::Display for SchemaId { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + write!(f, "{}/{}/{}", self.sport, self.data_kind, self.version) + } +} + +/// Defines what entities a schema requires. +#[derive(Clone)] +pub struct EntityRequirement { + /// The validation function that checks if this requirement is met. + pub check: fn(&super::Fields) -> bool, + /// The error message when this requirement is not met. + pub error_field: String, +} + +impl fmt::Debug for EntityRequirement { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + f.debug_struct("EntityRequirement") + .field("error_field", &self.error_field) + .finish() + } +} + +/// Defines allowed values for a specific column. +#[derive(Clone)] +pub struct ValueRequirement { + /// The column name to check. + pub column: String, + /// The list of allowed values. + pub allowed_values: Vec, +} + +impl fmt::Debug for ValueRequirement { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + f.debug_struct("ValueRequirement") + .field("column", &self.column) + .field("allowed_values", &self.allowed_values) + .finish() + } +} + +/// A schema definition containing validation rules for a specific data format. +/// +/// Each schema defines which frame attributes, ball attributes, and entity structures +/// are required for valid data. +#[derive(Clone)] +pub struct SchemaDefinition { + /// The unique schema identifier. + pub id: SchemaId, + /// Human-readable description of the schema. + pub description: String, + /// Required frame-level attributes (e.g., FrameId, Timestamp). + pub required_frame_attributes: Vec, + /// Required ball attributes (empty if no ball in this schema). + pub required_ball_attributes: Vec, + /// Structural entity requirements (e.g., "at least one team with players"). + pub entity_requirements: Vec, + /// Value constraints for specific columns. + pub value_requirements: Vec, + /// Core column names for this schema (used in pivot/melt operations). + pub core_columns: Vec, +} + +impl SchemaDefinition { + /// Validates a `Fields` collection against this schema's requirements. + /// + /// Returns `Ok(())` if all required fields are present, or an error + /// describing which fields are missing. + pub fn validate(&self, fields: &super::Fields) -> TrackingDataContainerResult<()> { + let mut missing = Vec::new(); + + // 1. Check required frame attributes + for attr in &self.required_frame_attributes { + if !fields.has_frame_attr(attr.clone()) { + let name = match attr { + FrameAttribute::MatchId => "match/id".to_string(), + _ => attr.name().to_string(), + }; + missing.push(name); + } + } + + // 2. Check required ball attributes + for attr in &self.required_ball_attributes { + if !fields.has_ball_attr(attr.clone()) { + missing.push(format!("ball/{}", attr.name())); + } + } + + // 3. Check entity requirements + for req in &self.entity_requirements { + if !(req.check)(fields) { + missing.push(req.error_field.clone()); + } + } + + if !missing.is_empty() { + return Err(TrackingDataContainerError::new( + TrackingDataContainerErrorKind::SchemaError(format!( + "Schema '{}' validation failed. Missing required fields: {}", + self.id, + missing.join(", ") + )), + )); + } + + // 4. Check for unsupported fields (warning) + fields.check_unsupported_fields() + } +} + +impl fmt::Debug for SchemaDefinition { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + f.debug_struct("SchemaDefinition") + .field("id", &self.id) + .field("description", &self.description) + .finish() + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_schema_id_new() { + let id = SchemaId::new("football", "tracking_2d", "0.2.2"); + assert_eq!(id.sport, "football"); + assert_eq!(id.data_kind, "tracking_2d"); + assert_eq!(id.version, "0.2.2"); + } + + #[test] + fn test_schema_id_display() { + let id = SchemaId::new("football", "tracking_2d", "0.2.2"); + assert_eq!(id.to_string(), "football/tracking_2d/0.2.2"); + } + + #[test] + fn test_schema_id_parse() { + let id = SchemaId::parse("football/tracking_2d/0.2.2").unwrap(); + assert_eq!(id, SchemaId::new("football", "tracking_2d", "0.2.2")); + } + + #[test] + fn test_schema_id_parse_invalid() { + assert!(SchemaId::parse("football/tracking_2d").is_none()); + assert!(SchemaId::parse("football").is_none()); + assert!(SchemaId::parse("").is_none()); + } + + #[test] + fn test_schema_id_equality() { + let a = SchemaId::new("football", "tracking_2d", "0.2.2"); + let b = SchemaId::new("football", "tracking_2d", "0.2.2"); + let c = SchemaId::new("football", "skeletal", "0.1.0"); + assert_eq!(a, b); + assert_ne!(a, c); + } +} diff --git a/crates/tdc-core/src/schema/fields.rs b/crates/tdc-core/src/schema/fields.rs index a883f94..bcd8f18 100644 --- a/crates/tdc-core/src/schema/fields.rs +++ b/crates/tdc-core/src/schema/fields.rs @@ -189,79 +189,42 @@ impl Fields { result } - /// Validates the fields against the **Common Data Format Tracking Data Schema (Version 0.2.2)**. + /// Validates the fields against the default schema (CDF v0.2.3 tracking). /// - /// This method checks for the presence of mandatory fields such as `frame_id`, `ball/x/y/z`, - /// and team identifiers. It also ensures that both the home and away teams have at least - /// one player with required coordinates. + /// This is a convenience method that delegates to [`validate_with_schema`] using + /// the default schema ID. /// /// # Errors /// /// Returns a [`TrackingDataContainerError`] of kind `SchemaError` if required fields are missing. pub fn validate(&self) -> TrackingDataContainerResult<()> { - let mut missing = Vec::new(); - - // 1. Required top-level fields - if !self.has_frame_attr(FrameAttribute::FrameId) { - missing.push("frame_id"); - } - if !self.has_frame_attr(FrameAttribute::Timestamp) { - missing.push("timestamp"); - } - if !self.has_frame_attr(FrameAttribute::Period) { - missing.push("period"); - } - if !self.has_frame_attr(FrameAttribute::MatchId) { - missing.push("match/id"); - } - - // 2. Required ball fields - if !self.has_ball_attr(TrackedEntityAttribute::X) { - missing.push("ball/x"); - } - if !self.has_ball_attr(TrackedEntityAttribute::Y) { - missing.push("ball/y"); - } - if !self.has_ball_attr(TrackedEntityAttribute::Z) { - missing.push("ball/z"); - } - - // 3. Required team fields - let home_exists = self.has_team("home"); - let away_exists = self.has_team("away"); - - if !home_exists { - missing.push("teams/home"); - } else { - if !self.has_team_attr("home", TrackedEntityAttribute::Id) { - missing.push("teams/home/id"); - } - if !self.has_any_player("home") { - missing.push("teams/home/players"); - } - } - - if !away_exists { - missing.push("teams/away"); - } else { - if !self.has_team_attr("away", TrackedEntityAttribute::Id) { - missing.push("teams/away/id"); - } - if !self.has_any_player("away") { - missing.push("teams/away/players"); - } - } + let default_id = super::registry::default_schema_id(); + self.validate_with_schema(&default_id) + } - if !missing.is_empty() { - return Err(TrackingDataContainerError::new( - TrackingDataContainerErrorKind::SchemaError(format!( - "Missing required fields: {}", - missing.join(", ") - )), - )); - } + /// Validates the fields against a specific schema definition. + /// + /// Looks up the schema in the global registry and checks all required fields. + /// + /// # Errors + /// + /// Returns a [`TrackingDataContainerError`] of kind `SchemaError` if the schema is unknown + /// or if required fields are missing. + pub fn validate_with_schema( + &self, + schema_id: &super::definition::SchemaId, + ) -> TrackingDataContainerResult<()> { + let schema_def = super::registry::get_schema(schema_id).ok_or_else(|| { + TrackingDataContainerError::new(TrackingDataContainerErrorKind::SchemaError(format!( + "Unknown schema: {}", + schema_id + ))) + })?; + schema_def.validate(self) + } - // 4. Check for unsupported fields + /// Checks for unsupported fields (Other variants) and emits a warning. + pub(crate) fn check_unsupported_fields(&self) -> TrackingDataContainerResult<()> { let mut unsupported = Vec::new(); for field in &self.fields { match field { @@ -292,20 +255,20 @@ impl Fields { Ok(()) } - /// Internal helper to check for a specific global frame attribute. - fn has_frame_attr(&self, attr: FrameAttribute) -> bool { + /// Helper to check for a specific global frame attribute. + pub(crate) fn has_frame_attr(&self, attr: FrameAttribute) -> bool { self.fields .iter() .any(|f| matches!(f, DataField::Frame { attribute } if *attribute == attr)) } - /// Internal helper to check for a specific ball attribute. - fn has_ball_attr(&self, attr: TrackedEntityAttribute) -> bool { + /// Helper to check for a specific ball attribute. + pub(crate) fn has_ball_attr(&self, attr: TrackedEntityAttribute) -> bool { self.fields.iter().any(|f| matches!(f, DataField::TrackedEntity { entity: TrackedEntity::Ball, attribute } if *attribute == attr)) } - /// Internal helper to check if any field belongs to a specific team (either team-level or player-level). - fn has_team(&self, tid: &str) -> bool { + /// Helper to check if any field belongs to a specific team (either team-level or player-level). + pub(crate) fn has_team(&self, tid: &str) -> bool { self.fields.iter().any(|f| match f { DataField::TrackedEntity { entity: TrackedEntity::Team { team_id }, @@ -319,13 +282,13 @@ impl Fields { }) } - /// Internal helper to check for a specific team-level attribute. - fn has_team_attr(&self, tid: &str, attr: TrackedEntityAttribute) -> bool { + /// Helper to check for a specific team-level attribute. + pub(crate) fn has_team_attr(&self, tid: &str, attr: TrackedEntityAttribute) -> bool { self.fields.iter().any(|f| matches!(f, DataField::TrackedEntity { entity: TrackedEntity::Team { team_id }, attribute } if team_id == tid && *attribute == attr)) } - /// Internal helper to verify that at least one player for a team has required spatial data. - fn has_any_player(&self, tid: &str) -> bool { + /// Helper to verify that at least one player for a team has required spatial data. + pub(crate) fn has_any_player(&self, tid: &str) -> bool { let mut players_with_x = std::collections::HashSet::new(); let mut players_with_y = std::collections::HashSet::new(); @@ -352,4 +315,19 @@ impl Fields { .next() .is_some() } + + /// Returns true if any player has landmark data. + pub(crate) fn has_any_landmark(&self) -> bool { + self.fields.iter().any(|f| { + matches!( + f, + DataField::TrackedEntity { + attribute: TrackedEntityAttribute::LandmarkX(_) + | TrackedEntityAttribute::LandmarkY(_) + | TrackedEntityAttribute::LandmarkZ(_), + .. + } + ) + }) + } } diff --git a/crates/tdc-core/src/schema/mod.rs b/crates/tdc-core/src/schema/mod.rs index 83a5928..8ff14c9 100644 --- a/crates/tdc-core/src/schema/mod.rs +++ b/crates/tdc-core/src/schema/mod.rs @@ -1,13 +1,17 @@ pub mod attribute; pub mod coordinate_system; +pub mod definition; pub mod entity; pub mod field; pub mod fields; pub mod period; +pub mod registry; pub use attribute::{Attribute, FrameAttribute, TrackedEntityAttribute}; pub use coordinate_system::{CoordinateSystem, Direction, LengthUnit, OrientationMode, Origin}; +pub use definition::{SchemaDefinition, SchemaId}; pub use entity::TrackedEntity; pub use field::DataField; pub use fields::Fields; pub use period::Period; +pub use registry::{default_schema_id, get_schema, list_schemas}; diff --git a/crates/tdc-core/src/schema/registry.rs b/crates/tdc-core/src/schema/registry.rs new file mode 100644 index 0000000..9647670 --- /dev/null +++ b/crates/tdc-core/src/schema/registry.rs @@ -0,0 +1,314 @@ +//! Compile-time registry of known schema definitions. +//! +//! This module provides a global registry of all built-in schema definitions, +//! indexed by their `SchemaId`. New schemas can be added by defining them here. +//! Current schemas are based on the [CDF v0.2.3 specification](https://arxiv.org/pdf/2505.15820). + +use super::definition::{EntityRequirement, SchemaDefinition, SchemaId, ValueRequirement}; +use super::{FrameAttribute, TrackedEntityAttribute}; +use once_cell::sync::Lazy; +use std::collections::HashMap; + +/// Global registry of all known schema definitions. +static REGISTRY: Lazy> = Lazy::new(|| { + let mut m = HashMap::new(); + + let tracking = cdf_tracking_v0_2_3(); + m.insert(tracking.id.clone(), tracking); + + let skeletal = cdf_skeletal_v0_2_3(); + m.insert(skeletal.id.clone(), skeletal); + + m +}); + +/// Looks up a schema definition by ID. +pub fn get_schema(id: &SchemaId) -> Option<&'static SchemaDefinition> { + REGISTRY.get(id) +} + +/// Returns all registered schema IDs. +pub fn list_schemas() -> Vec<&'static SchemaId> { + REGISTRY.keys().collect() +} + +/// The default schema ID: CDF v0.2.3 tracking data. +pub fn default_schema_id() -> SchemaId { + SchemaId::new("cdf", "tracking", "0.2.3") +} + +// --------------------------------------------------------------------------- +// Built-in schema definitions +// --------------------------------------------------------------------------- + +/// CDF v0.2.3 tracking data. +/// +/// Based on +/// +/// Required top-level: frame_id, timestamp, period, match (with id), teams (home & away), ball (x, y, z). +/// Each team requires: id, players (each with id, x, y). +pub fn cdf_tracking_v0_2_3() -> SchemaDefinition { + SchemaDefinition { + id: SchemaId::new("cdf", "tracking", "0.2.3"), + description: "CDF v0.2.3 tracking data".to_string(), + required_frame_attributes: vec![ + FrameAttribute::FrameId, + FrameAttribute::Timestamp, + FrameAttribute::Period, + FrameAttribute::MatchId, + ], + required_ball_attributes: vec![ + TrackedEntityAttribute::X, + TrackedEntityAttribute::Y, + TrackedEntityAttribute::Z, + ], + entity_requirements: vec![ + // teams.home required + EntityRequirement { + check: |f| f.has_team("home"), + error_field: "teams/home".to_string(), + }, + // teams.home.id required + EntityRequirement { + check: |f| { + if f.has_team("home") { + f.has_team_attr("home", TrackedEntityAttribute::Id) + } else { + true + } + }, + error_field: "teams/home/id".to_string(), + }, + // teams.home.players required (at least one) + EntityRequirement { + check: |f| { + if f.has_team("home") { + f.has_any_player("home") + } else { + true + } + }, + error_field: "teams/home/players".to_string(), + }, + // teams.away required + EntityRequirement { + check: |f| f.has_team("away"), + error_field: "teams/away".to_string(), + }, + // teams.away.id required + EntityRequirement { + check: |f| { + if f.has_team("away") { + f.has_team_attr("away", TrackedEntityAttribute::Id) + } else { + true + } + }, + error_field: "teams/away/id".to_string(), + }, + // teams.away.players required (at least one) + EntityRequirement { + check: |f| { + if f.has_team("away") { + f.has_any_player("away") + } else { + true + } + }, + error_field: "teams/away/players".to_string(), + }, + // no other teams allowed + EntityRequirement { + check: |f| { + f.teams() + .iter() + .all(|t| t == "home" || t == "away" || t == "none") + }, + error_field: "teams/".to_string(), + }, + ], + value_requirements: vec![ValueRequirement { + column: "period".to_string(), + allowed_values: vec![ + "first_half".to_string(), + "second_half".to_string(), + "first_half_extratime".to_string(), + "second_half_extratime".to_string(), + "shootout".to_string(), + ], + }], + core_columns: vec![ + "match/id", + "competition/id", + "season/id", + "timestamp", + "frame_id", + "period", + "period_id", + "player/id", + "team/id", + "entity_key", + "ball_status", + "ball_poss_team_id", + "ball_poss_status", + ] + .into_iter() + .map(String::from) + .collect(), + } +} + +/// CDF v0.2.3 skeletal tracking data. +/// +/// Based on +/// +/// Required top-level: frame_id, timestamp, period, match (with id), teams (home & away). +/// Ball is optional. Each team requires: id, players (each with id, landmarks). +/// Each landmark requires: index, name, x, y, z, children, is_visible. +pub fn cdf_skeletal_v0_2_3() -> SchemaDefinition { + SchemaDefinition { + id: SchemaId::new("cdf", "skeletal", "0.2.3"), + description: "CDF v0.2.3 skeletal tracking data".to_string(), + required_frame_attributes: vec![ + FrameAttribute::FrameId, + FrameAttribute::Timestamp, + FrameAttribute::Period, + FrameAttribute::MatchId, + ], + required_ball_attributes: vec![], + entity_requirements: vec![ + // teams.home required + EntityRequirement { + check: |f| f.has_team("home"), + error_field: "teams/home".to_string(), + }, + // teams.home.id required + EntityRequirement { + check: |f| { + if f.has_team("home") { + f.has_team_attr("home", TrackedEntityAttribute::Id) + } else { + true + } + }, + error_field: "teams/home/id".to_string(), + }, + // teams.home.players required (at least one) + EntityRequirement { + check: |f| { + if f.has_team("home") { + f.has_any_player("home") + } else { + true + } + }, + error_field: "teams/home/players".to_string(), + }, + // teams.away required + EntityRequirement { + check: |f| f.has_team("away"), + error_field: "teams/away".to_string(), + }, + // teams.away.id required + EntityRequirement { + check: |f| { + if f.has_team("away") { + f.has_team_attr("away", TrackedEntityAttribute::Id) + } else { + true + } + }, + error_field: "teams/away/id".to_string(), + }, + // teams.away.players required (at least one) + EntityRequirement { + check: |f| { + if f.has_team("away") { + f.has_any_player("away") + } else { + true + } + }, + error_field: "teams/away/players".to_string(), + }, + // At least one player must have landmarks + EntityRequirement { + check: |f| f.has_any_landmark(), + error_field: "teams/*/players/*/landmarks".to_string(), + }, + // no other teams allowed + EntityRequirement { + check: |f| { + f.teams() + .iter() + .all(|t| t == "home" || t == "away" || t == "none") + }, + error_field: "teams/".to_string(), + }, + ], + value_requirements: vec![ValueRequirement { + column: "period".to_string(), + allowed_values: vec![ + "first_half".to_string(), + "second_half".to_string(), + "first_half_extratime".to_string(), + "second_half_extratime".to_string(), + "shootout".to_string(), + ], + }], + core_columns: vec![ + "match/id", + "competition/id", + "season/id", + "timestamp", + "frame_id", + "period", + "period_id", + "player/id", + "team/id", + "entity_key", + ] + .into_iter() + .map(String::from) + .collect(), + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_registry_contains_all_schemas() { + let schemas = list_schemas(); + assert_eq!(schemas.len(), 2); + } + + #[test] + fn test_lookup_cdf_tracking() { + let id = SchemaId::new("cdf", "tracking", "0.2.3"); + let schema = get_schema(&id); + assert!(schema.is_some()); + assert_eq!(schema.unwrap().id, id); + } + + #[test] + fn test_lookup_cdf_skeletal() { + let id = SchemaId::new("cdf", "skeletal", "0.2.3"); + let schema = get_schema(&id); + assert!(schema.is_some()); + assert_eq!(schema.unwrap().id, id); + } + + #[test] + fn test_lookup_unknown_schema() { + let id = SchemaId::new("basketball", "tracking", "1.0.0"); + assert!(get_schema(&id).is_none()); + } + + #[test] + fn test_default_schema_id() { + let id = default_schema_id(); + assert_eq!(id, SchemaId::new("cdf", "tracking", "0.2.3")); + } +} diff --git a/crates/tdc-parsers/src/hawkeye.rs b/crates/tdc-parsers/src/hawkeye.rs index 53d6b38..562ebf7 100644 --- a/crates/tdc-parsers/src/hawkeye.rs +++ b/crates/tdc-parsers/src/hawkeye.rs @@ -419,7 +419,7 @@ pub fn load_hawkeye( } } - let final_builder = builders + let mut final_builder = builders .into_iter() .reduce(|mut a, b| { a.merge(b); @@ -427,6 +427,14 @@ pub fn load_hawkeye( }) .ok_or("No data loaded")?; + final_builder.get_or_create_column_id("match/id", arrow_schema::DataType::Utf8); + final_builder.get_or_create_column_id( + "timestamp", + arrow_schema::DataType::Timestamp(arrow_schema::TimeUnit::Microsecond, None), + ); + final_builder.get_or_create_column_id("teams/home/id", arrow_schema::DataType::Utf8); + final_builder.get_or_create_column_id("teams/away/id", arrow_schema::DataType::Utf8); + let mut container = final_builder.build().map_err(|e| e.to_string())?; let mut x_range = None; @@ -454,6 +462,9 @@ pub fn load_hawkeye( Some(LengthUnit::Meters), ); container = container.with_coordinate_system(cs); + container = + container.with_schema_id(tdc_core::schema::SchemaId::new("cdf", "skeletal", "0.2.3")); + container.validate().map_err(|e| e.to_string())?; Ok(container) } diff --git a/crates/tdc-parsers/src/sportec.rs b/crates/tdc-parsers/src/sportec.rs index dd63aec..820f3aa 100644 --- a/crates/tdc-parsers/src/sportec.rs +++ b/crates/tdc-parsers/src/sportec.rs @@ -185,8 +185,34 @@ pub fn load_sportec(path: &str, meta_path: Option<&str>) -> Result = global_frames.keys().cloned().collect(); - sorted_n.sort_unstable(); + let mut raw_sorted_n: Vec = global_frames.keys().cloned().collect(); + raw_sorted_n.sort_unstable(); + + let mut sorted_n = Vec::new(); + let mut valid_periods = Vec::new(); + let mut current_period = "other"; + + for &n in &raw_sorted_n { + let (_, p_id) = global_frames.get(&n).unwrap(); + if let Some(p) = p_id { + current_period = match p { + 1 => "first_half", + 2 => "second_half", + 3 => "first_half_extratime", + 4 => "second_half_extratime", + 5 => "shootout", + _ => "other", + }; + } + if current_period != "other" { + sorted_n.push(n); + valid_periods.push(current_period.to_string()); + } + } + + if sorted_n.is_empty() { + return Err("No valid frames found in Sportec data after period filtering".to_string()); + } let n_to_row: AHashMap = sorted_n.iter().enumerate().map(|(i, &n)| (n, i)).collect(); @@ -427,22 +453,10 @@ pub fn load_sportec(path: &str, meta_path: Option<&str>) -> Result "first_half", - 2 => "second_half", - 3 => "first_half_extratime", - 4 => "second_half_extratime", - 5 => "shootout", - _ => "other", - } - .to_string(); - } - final_builder.insert_string_at_idx(i, c_p, &last_p); + final_builder.insert_string_at_idx(i, c_p, &valid_periods[i]); final_builder.insert_int_at_idx(i, c_fid, n); if let Some(ts) = ts_str @@ -465,6 +479,9 @@ pub fn load_sportec(path: &str, meta_path: Option<&str>) -> Result Result { let num_cpus = rayon::current_num_threads(); let chunk_size = lines.len().div_ceil(num_cpus); - let final_builder = lines + let mut final_builder = lines .par_chunks(chunk_size) .map(|chunk| -> Result { let mut builder = TrackingDataBuilder::new(); @@ -146,6 +146,10 @@ pub fn load_statsperform(path: &str) -> Result { }, )?; + final_builder.get_or_create_column_id("match/id", DataType::Utf8); + final_builder.get_or_create_column_id("teams/home/id", DataType::Utf8); + final_builder.get_or_create_column_id("teams/away/id", DataType::Utf8); + let mut container = final_builder.build().map_err(|e| e.to_string())?; let cs = CoordinateSystem::new( @@ -157,6 +161,9 @@ pub fn load_statsperform(path: &str) -> Result { Some(LengthUnit::Meters), ); container = container.with_coordinate_system(cs); + container = + container.with_schema_id(tdc_core::schema::SchemaId::new("cdf", "tracking", "0.2.3")); + container.validate().map_err(|e| e.to_string())?; Ok(container) } diff --git a/crates/tdc-parsers/tests/test_sportec.rs b/crates/tdc-parsers/tests/test_sportec.rs index e024257..b849840 100644 --- a/crates/tdc-parsers/tests/test_sportec.rs +++ b/crates/tdc-parsers/tests/test_sportec.rs @@ -23,6 +23,9 @@ fn test_sportec_parser() { + + + "#; diff --git a/crates/tdc-python/src/container.rs b/crates/tdc-python/src/container.rs index a3085f0..53335b1 100644 --- a/crates/tdc-python/src/container.rs +++ b/crates/tdc-python/src/container.rs @@ -1,6 +1,6 @@ use crate::coordinate_system::{PyCoordinateSystem, PyOrientationMode}; use crate::error::IntoPyResult; -use crate::schema::{PyDataField, PyFields}; +use crate::schema::{PyDataField, PyFields, PySchemaId}; use crate::selector::{PyColumnSelector, PyExpr, PySelection}; use tdc_core::Select; use tdc_core::config::{CONTEXT, RUNTIME}; @@ -130,6 +130,7 @@ impl PyTrackingDataContainer { }, coordinate_system: coordinate_system.map(|cs| cs.inner), orientation, + schema_id: None, }, }) } @@ -173,6 +174,7 @@ impl PyTrackingDataContainer { schema: Fields { fields }, coordinate_system: coordinate_system.map(|cs| cs.inner), orientation, + schema_id: None, }, }) } @@ -333,6 +335,28 @@ impl PyTrackingDataContainer { self.inner.validate().into_pyresult() } + #[getter] + fn schema_id(&self) -> Option { + self.inner + .schema_id + .clone() + .map(|id| PySchemaId { inner: id }) + } + + fn with_schema_id(&self, schema_id: &PySchemaId) -> Self { + PyTrackingDataContainer { + inner: self.inner.clone().with_schema_id(schema_id.inner.clone()), + } + } + + fn validate_with_schema(&self, schema_id: &PySchemaId) -> PyResult<()> { + use crate::error::IntoPyResult; + self.inner + .schema + .validate_with_schema(&schema_id.inner) + .into_pyresult() + } + fn save_parquet(&self, path: &str) -> PyResult<()> { self.inner.save_parquet(path).into_pyresult() } @@ -832,7 +856,7 @@ pub fn scan_parquet(_py: Python<'_>, path: String) -> PyResult( - LazyTrackingDataContainer::new(df, schema, None, None), + LazyTrackingDataContainer::new(df, schema, None, None, None), ) }) .map_err(|e| PyValueError::new_err(e.to_string()))?; diff --git a/crates/tdc-python/src/schema.rs b/crates/tdc-python/src/schema.rs index 0d2d07f..58d5bcb 100644 --- a/crates/tdc-python/src/schema.rs +++ b/crates/tdc-python/src/schema.rs @@ -184,6 +184,12 @@ impl PyFields { index: 0, } } + fn validate_with_schema(&self, schema_id: &PySchemaId) -> PyResult<()> { + use crate::error::IntoPyResult; + self.inner + .validate_with_schema(&schema_id.inner) + .into_pyresult() + } } #[pyclass] @@ -210,10 +216,82 @@ impl FieldIterator { } } +#[pyclass(name = "_SchemaId")] +#[derive(Clone)] +pub struct PySchemaId { + pub inner: tdc_core::schema::SchemaId, +} + +#[pymethods] +impl PySchemaId { + #[new] + fn new(sport: String, data_kind: String, version: String) -> Self { + Self { + inner: tdc_core::schema::SchemaId::new(sport, data_kind, version), + } + } + + #[staticmethod] + fn from_string(s: &str) -> PyResult { + tdc_core::schema::SchemaId::parse(s) + .map(|inner| Self { inner }) + .ok_or_else(|| { + pyo3::exceptions::PyValueError::new_err(format!( + "Invalid schema ID: '{}'. Expected format: 'sport/data_kind/version'", + s + )) + }) + } + + #[getter] + fn sport(&self) -> &str { + &self.inner.sport + } + + #[getter] + fn data_kind(&self) -> &str { + &self.inner.data_kind + } + + #[getter] + fn version(&self) -> &str { + &self.inner.version + } + + fn __str__(&self) -> String { + self.inner.to_string() + } + + fn __repr__(&self) -> String { + format!("SchemaId({})", self.inner) + } + + fn __eq__(&self, other: &Self) -> bool { + self.inner == other.inner + } + + fn __hash__(&self) -> u64 { + use std::hash::{Hash, Hasher}; + let mut hasher = std::collections::hash_map::DefaultHasher::new(); + self.inner.hash(&mut hasher); + hasher.finish() + } +} + +#[pyfunction] +pub fn list_schemas() -> Vec { + tdc_core::schema::list_schemas() + .iter() + .map(|s| s.to_string()) + .collect() +} + pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_class::()?; m.add_class::()?; m.add_class::()?; m.add_class::()?; + m.add_class::()?; + m.add_function(wrap_pyfunction!(list_schemas, m)?)?; Ok(()) } diff --git a/examples/01-basics.ipynb b/examples/01-basics.ipynb index 7ecc21c..2b9781b 100644 --- a/examples/01-basics.ipynb +++ b/examples/01-basics.ipynb @@ -93,6 +93,7 @@ " \"match/id\": [\"match_123\", \"match_123\", \"match_123\"],\n", " \"ball/x\": [50.0, 51.0, 52.0],\n", " \"ball/y\": [12.0, 13.0, 14.0],\n", + " \"ball/z\": [0.0, 0.0, 0.0],\n", " \"teams/home/id\": [\"team_A\", \"team_A\", \"team_A\"],\n", " \"teams/home/players/p1/x\": [23.5, 23.6, 23.7],\n", " \"teams/home/players/p1/y\": [24.2, 24.3, 24.4],\n", @@ -101,7 +102,7 @@ " \"teams/away/players/p2/y\": [34.9, 35.0, 35.1],\n", "}\n", "\n", - "container = tdc.TrackingDataContainer(data, validate=False)\n", + "container = tdc.TrackingDataContainer(data, validate=True)\n", "print(f\"Container created with {container.num_rows()} frames.\")" ] }, @@ -200,6 +201,26 @@ "TDC uses the **Common Data Format (CDF)** standard for naming columns. This standard defines a hierarchical path structure (e.g., `teams/{team_id}/players/{player_id}/x`) that allows for standardized selection logic regardless of the data provider." ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['cdf/skeletal/0.2.3', 'cdf/tracking/0.2.3']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tdc.list_schemas()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -211,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -219,10 +240,10 @@ "output_type": "stream", "text": [ "Ball columns (using selector):\n", - "['ball/x', 'ball/y']\n", + "['ball/x', 'ball/y', 'ball/z']\n", "\n", "Ball columns (using wildcard 'ball/**'):\n", - "['ball/x', 'ball/y']\n", + "['ball/x', 'ball/y', 'ball/z']\n", "\n", "Player X columns:\n", "['teams/home/players/p1/x', 'teams/away/players/p2/x']\n" @@ -257,14 +278,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Schema validation failed: Missing ball/z\n" + "Schema is valid CDF!\n" ] } ], @@ -277,6 +298,47 @@ " print(f\"Schema validation failed: {e}\")" ] }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Schema validation failed: schema error: Invalid value for column 'period': wrong_half\n" + ] + } + ], + "source": [ + "try: \n", + " base_time = datetime(2023, 5, 27, 13, 30, 0, tzinfo=timezone.utc)\n", + " data = {\n", + " \"frame_id\": [1000, 1001, 1002],\n", + " \"timestamp\": [\n", + " base_time,\n", + " base_time + timedelta(milliseconds=40),\n", + " base_time + timedelta(milliseconds=80),\n", + " ],\n", + " \"period\": [\"first_half\", \"first_half\", \"wrong_half\"], # <-- invalid value\n", + " \"match/id\": [\"match_123\", \"match_123\", \"match_123\"],\n", + " \"ball/x\": [50.0, 51.0, 52.0],\n", + " \"ball/y\": [12.0, 13.0, 14.0],\n", + " \"ball/z\": [0.0, 0.0, 0.0],\n", + " \"teams/home/id\": [\"team_A\", \"team_A\", \"team_A\"],\n", + " \"teams/home/players/p1/x\": [23.5, 23.6, 23.7],\n", + " \"teams/home/players/p1/y\": [24.2, 24.3, 24.4],\n", + " \"teams/away/id\": [\"team_B\", \"team_B\", \"team_B\"],\n", + " \"teams/away/players/p2/x\": [33.1, 33.2, 33.3],\n", + " \"teams/away/players/p2/y\": [34.9, 35.0, 35.1],\n", + " }\n", + " tdc.TrackingDataContainer(data, validate=True)\n", + " print(\"Schema is valid CDF!\")\n", + "except tdc.exceptions.SchemaError as e:\n", + " print(f\"Schema validation failed: {e}\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -286,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -315,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -328,10 +390,10 @@ "Column: match/id | Entity: / | Attribute: match\n", "Column: ball/x | Entity: Ball | Attribute: x\n", "Column: ball/y | Entity: Ball | Attribute: y\n", + "Column: ball/z | Entity: Ball | Attribute: z\n", "Column: teams/home/id | Entity: Team | Attribute: id\n", "Column: teams/home/players/p1/x | Entity: Player | Attribute: x\n", - "Column: teams/home/players/p1/y | Entity: Player | Attribute: y\n", - "Column: teams/away/id | Entity: Team | Attribute: id\n" + "Column: teams/home/players/p1/y | Entity: Player | Attribute: y\n" ] } ], diff --git a/examples/02-parsing.ipynb b/examples/02-parsing.ipynb index c6e0e7e..7230d85 100644 --- a/examples/02-parsing.ipynb +++ b/examples/02-parsing.ipynb @@ -35,7 +35,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Ingested 10000 frames in 0.0768 seconds.\n" + "Ingested 10000 frames in 0.0794 seconds.\n" ] } ], @@ -158,9 +158,9 @@ "output_type": "stream", "text": [ "--- Loading benchmark ---\n", - "TDC (Rust): 1.41s\n", - "Fast-Forward: 1.69s\n", - "Kloppy: 20.04s\n" + "TDC (Rust): 1.67s\n", + "Fast-Forward: 2.06s\n", + "Kloppy: 22.91s\n" ] } ], @@ -198,7 +198,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -252,11 +252,11 @@ "output_type": "stream", "text": [ "--- to_polars benchmark ---\n", - "TDC to_polars: 6.66ms\n", + "TDC to_polars: 4.39ms\n", "Fast-Forward tracking access: 0.01ms\n", - "Kloppy to_df(engine=\"polars\"): 1076.91ms\n", + "Kloppy to_df(engine=\"polars\"): 1155.26ms\n", "\n", - "Speedup TDC vs Kloppy: 161.7x\n" + "Speedup TDC vs Kloppy: 263.2x\n" ] } ], @@ -290,7 +290,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] diff --git a/examples/04-udfs.ipynb b/examples/04-udfs.ipynb index fb992ac..6d564ae 100644 --- a/examples/04-udfs.ipynb +++ b/examples/04-udfs.ipynb @@ -18,7 +18,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loaded 145967 frames.\n" + "Loaded 7500 frames.\n" ] } ], @@ -68,7 +68,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Expression Engine time: 0.4595s\n" + "Expression Engine time: 0.1285s\n" ] } ], @@ -97,7 +97,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Polars Vectorized time: 0.4631s\n" + "Polars Vectorized time: 0.0992s\n" ] } ], @@ -123,7 +123,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Numpy Vectorized time: 0.0394s\n" + "Numpy Vectorized time: 0.0076s\n" ] } ], @@ -161,7 +161,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimized Rust Kernel time: 0.0359s\n" + "Optimized Rust Kernel time: 0.0046s\n" ] } ], @@ -222,7 +222,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Native Polars Batch time: 0.8644s\n" + "Native Polars Batch time: 0.0964s\n" ] } ], diff --git a/examples/05-lazy.ipynb b/examples/05-lazy.ipynb index b25ecbd..8c24895 100644 --- a/examples/05-lazy.ipynb +++ b/examples/05-lazy.ipynb @@ -126,7 +126,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collected 1500 frames in 0.0165s.\n", + "Collected 1500 frames in 0.0169s.\n", "shape: (5, 6)\n", "┌──────────┬────────┬────────┬──────────┬────────┬──────────┐\n", "│ frame_id ┆ ball/z ┆ ball/y ┆ ball/vel ┆ ball/x ┆ ball/acc │\n", diff --git a/examples/06-skeletal.ipynb b/examples/06-skeletal.ipynb index 36dbb21..be4ab76 100644 --- a/examples/06-skeletal.ipynb +++ b/examples/06-skeletal.ipynb @@ -58,9 +58,9 @@ "output_type": "stream", "text": [ "Found 95 minutes of data.\n", - "Loaded 132917 frames in 29.43 seconds.\n", - "Memory used: 2692.30 MB\n", - "Total columns: 3552\n" + "Loaded 132917 frames in 28.34 seconds.\n", + "Memory used: 2283.92 MB\n", + "Total columns: 3556\n" ] } ], @@ -139,8 +139,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loaded 118 columns for player #10 in 0.4122 seconds.\n", - "Memory used for selection: -54.38 MB\n" + "Loaded 118 columns for player #10 in 0.2992 seconds.\n", + "Memory used for selection: -214.36 MB\n" ] } ], @@ -366,42 +366,42 @@ "\n", "\n", "
\n", - " \n", + " \n", "
\n", - " \n", + " oninput=\"anim2bc8c1256cb14c3ea03b2eaed2bff8c8.set_frame(parseInt(this.value));\">\n", "
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", "
\n", - "
\n", - " \n", - " \n", - " Once\n", + " \n", - " \n", - " Loop\n", + " \n", - " \n", + " \n", "
\n", "
\n", "
\n", @@ -411,9 +411,9 @@ " /* Instantiate the Animation class. */\n", " /* The IDs given should match those used in the template above. */\n", " (function() {\n", - " var img_id = \"_anim_imga73aa44715804f77a7b32c6bca3f75d9\";\n", - " var slider_id = \"_anim_slidera73aa44715804f77a7b32c6bca3f75d9\";\n", - " var loop_select_id = \"_anim_loop_selecta73aa44715804f77a7b32c6bca3f75d9\";\n", + " var img_id = \"_anim_img2bc8c1256cb14c3ea03b2eaed2bff8c8\";\n", + " var slider_id = \"_anim_slider2bc8c1256cb14c3ea03b2eaed2bff8c8\";\n", + " var loop_select_id = \"_anim_loop_select2bc8c1256cb14c3ea03b2eaed2bff8c8\";\n", " var frames = new Array(150);\n", " \n", " frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAMgCAYAAACwGEg9AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n", @@ -226848,7 +226848,7 @@ " /* set a timeout to make sure all the above elements are created before\n", " the object is initialized. */\n", " setTimeout(function() {\n", - " anima73aa44715804f77a7b32c6bca3f75d9 = new Animation(frames, img_id, slider_id, 40.0,\n", + " anim2bc8c1256cb14c3ea03b2eaed2bff8c8 = new Animation(frames, img_id, slider_id, 40.0,\n", " loop_select_id);\n", " }, 0);\n", " })()\n", diff --git a/examples/08-idsse-warehouse.ipynb b/examples/08-idsse-warehouse.ipynb index c428c04..1e3a332 100644 --- a/examples/08-idsse-warehouse.ipynb +++ b/examples/08-idsse-warehouse.ipynb @@ -138,16 +138,16 @@ "text": [ "\n", "Processing match DFL-MAT-J03WMX...\n", - " ✅ Parsed in 1.48s, Saved in 1.36s\n", - " ✅ Rows: 145,967\n", + " ✅ Parsed in 0.90s, Saved in 0.22s\n", + " ✅ Rows: 7,500\n", "\n", "Processing match DFL-MAT-J03WN1...\n", - " ✅ Parsed in 1.22s, Saved in 1.26s\n", - " ✅ Rows: 141,561\n", + " ✅ Parsed in 0.92s, Saved in 0.17s\n", + " ✅ Rows: 7,500\n", "\n", "Processing match DFL-MAT-J03WOH...\n", - " ✅ Parsed in 1.19s, Saved in 1.08s\n", - " ✅ Rows: 137,214\n" + " ✅ Parsed in 0.75s, Saved in 0.18s\n", + " ✅ Rows: 7,500\n" ] } ], @@ -198,11 +198,11 @@ "output_type": "stream", "text": [ "Warehouse Summary:\n", - " - Match DFL-MAT-J03WMX: 165,259 rows\n", - " - Match DFL-MAT-J03WN1: 162,273 rows\n", - " - Match DFL-MAT-J03WOH: 157,790 rows\n", + " - Match DFL-MAT-J03WMX: 7,500 rows\n", + " - Match DFL-MAT-J03WN1: 7,500 rows\n", + " - Match DFL-MAT-J03WOH: 7,500 rows\n", "\n", - "Total rows in warehouse: 485,322\n" + "Total rows in warehouse: 22,500\n" ] } ], @@ -242,7 +242,7 @@ "output_type": "stream", "text": [ "Loading data for DFL-MAT-J03WMX...\n", - " ✅ Loaded and pivoted 165259 rows in 1.0861s\n" + " ✅ Loaded and pivoted 7500 rows in 0.0779s\n" ] }, { @@ -255,10 +255,10 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (5, 241)
competition/idseason/idmatch/idframe_idperiodtimestampball_statusball_poss_team_idteams/none/players/DFL-OBJ-002G4A/player/idteams/none/players/DFL-OBJ-002G4A/xteams/none/players/DFL-OBJ-002G4A/yteams/none/players/DFL-OBJ-002G4A/zteams/none/players/DFL-OBJ-002G4A/accteams/none/players/DFL-OBJ-002G4A/velteams/none/players/DFL-OBJ-0000IA/player/idteams/none/players/DFL-OBJ-0000IA/xteams/none/players/DFL-OBJ-0000IA/yteams/none/players/DFL-OBJ-0000IA/zteams/none/players/DFL-OBJ-0000IA/accteams/none/players/DFL-OBJ-0000IA/velteams/none/players/DFL-OBJ-0002HE/player/idteams/none/players/DFL-OBJ-0002HE/xteams/none/players/DFL-OBJ-0002HE/yteams/none/players/DFL-OBJ-0002HE/zteams/none/players/DFL-OBJ-0002HE/accteams/none/players/DFL-OBJ-0002HE/velteams/none/players/DFL-OBJ-0027V2/player/idteams/none/players/DFL-OBJ-0027V2/xteams/none/players/DFL-OBJ-0027V2/yteams/none/players/DFL-OBJ-0027V2/zteams/none/players/DFL-OBJ-0027V2/accteams/none/players/DFL-OBJ-0027V2/velreferees/DFL-OBJ-000141/player/idreferees/DFL-OBJ-000141/xreferees/DFL-OBJ-000141/yreferees/DFL-OBJ-000141/zreferees/DFL-OBJ-000141/acc…referees/DFL-OBJ-00011J/velteams/none/players/DFL-OBJ-J015S4/player/idteams/none/players/DFL-OBJ-J015S4/xteams/none/players/DFL-OBJ-J015S4/yteams/none/players/DFL-OBJ-J015S4/zteams/none/players/DFL-OBJ-J015S4/accteams/none/players/DFL-OBJ-J015S4/velteams/away/player/idteams/away/xteams/away/yteams/away/zteams/away/accteams/away/velteams/none/players/DFL-OBJ-J01BGM/player/idteams/none/players/DFL-OBJ-J01BGM/xteams/none/players/DFL-OBJ-J01BGM/yteams/none/players/DFL-OBJ-J01BGM/zteams/none/players/DFL-OBJ-J01BGM/accteams/none/players/DFL-OBJ-J01BGM/velteams/none/players/DFL-OBJ-0027G0/player/idteams/none/players/DFL-OBJ-0027G0/xteams/none/players/DFL-OBJ-0027G0/yteams/none/players/DFL-OBJ-0027G0/zteams/none/players/DFL-OBJ-0027G0/accteams/none/players/DFL-OBJ-0027G0/velteams/none/players/DFL-OBJ-0002AU/player/idteams/none/players/DFL-OBJ-0002AU/xteams/none/players/DFL-OBJ-0002AU/yteams/none/players/DFL-OBJ-0002AU/zteams/none/players/DFL-OBJ-0002AU/accteams/none/players/DFL-OBJ-0002AU/velteams/home/jersey_colourteams/home/nameteams/home/idteams/away/jersey_colourteams/away/nameteams/away/id
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" + "shape: (5, 181)
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" ], "text/plain": [ - "shape: (5, 241)\n", + "shape: (5, 181)\n", "┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", "│ competiti ┆ season/id ┆ match/id ┆ frame_id ┆ … ┆ teams/hom ┆ teams/awa ┆ teams/awa ┆ teams/awa │\n", "│ on/id ┆ --- ┆ --- ┆ --- ┆ ┆ e/id ┆ y/jersey_ ┆ y/name ┆ y/id │\n", @@ -315,7 +315,8 @@ " 'jersey_colour': '#25282A',\n", " 'name': 'FC Bayern München',\n", " 'id': 'DFL-CLU-00000G'}},\n", - " 'referees': {}}}" + " 'referees': {}},\n", + " '_schema_id': 'cdf/tracking/0.2.3'}" ] }, "execution_count": 7, @@ -328,43 +329,6 @@ "metadata" ] }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'./idsse_warehouse'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "warehouse_path" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "catalog.db \u001b[34mtracking\u001b[m\u001b[m\n" - ] - } - ], - "source": [ - "!ls idsse_warehouse/" - ] - }, { "cell_type": "code", "execution_count": 10, @@ -374,7 +338,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " ✅ Loaded 11350189 rows in 0.3305s\n" + " ✅ Loaded 607500 rows in 0.0332s\n" ] }, { @@ -387,27 +351,27 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (5, 16)
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" + "shape: (5, 16)
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" ], "text/plain": [ "shape: (5, 16)\n", - "┌──────────────┬───────────┬──────────────┬──────────┬───┬──────────────┬─────────────┬─────┬──────┐\n", - "│ competition_ ┆ season_id ┆ match_id ┆ frame_id ┆ … ┆ ball_poss_te ┆ entity_key ┆ acc ┆ vel │\n", - "│ id ┆ --- ┆ --- ┆ --- ┆ ┆ am_id ┆ --- ┆ --- ┆ --- │\n", - "│ --- ┆ str ┆ str ┆ i64 ┆ ┆ --- ┆ str ┆ f64 ┆ f64 │\n", - "│ str ┆ ┆ ┆ ┆ ┆ str ┆ ┆ ┆ │\n", - "╞══════════════╪═══════════╪══════════════╪══════════╪═══╪══════════════╪═════════════╪═════╪══════╡\n", - "│ null ┆ null ┆ DFL-MAT-J03W ┆ 152866 ┆ … ┆ DFL-CLU-0000 ┆ DFL-OBJ-000 ┆ 0.0 ┆ 0.0 │\n", - "│ ┆ ┆ N1 ┆ ┆ ┆ 0B ┆ 02Z|home ┆ ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J03W ┆ 152867 ┆ … ┆ DFL-CLU-0000 ┆ DFL-OBJ-000 ┆ 0.0 ┆ 0.0 │\n", - "│ ┆ ┆ N1 ┆ ┆ ┆ 0B ┆ 02Z|home ┆ ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J03W ┆ 152868 ┆ … ┆ DFL-CLU-0000 ┆ DFL-OBJ-000 ┆ 0.0 ┆ 9.58 │\n", - "│ ┆ ┆ N1 ┆ ┆ ┆ 0B ┆ 02Z|home ┆ ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J03W ┆ 152869 ┆ … ┆ DFL-CLU-0000 ┆ DFL-OBJ-000 ┆ 0.0 ┆ 9.58 │\n", - "│ ┆ ┆ N1 ┆ ┆ ┆ 0B ┆ 02Z|home ┆ ┆ │\n", - "│ null ┆ null ┆ DFL-MAT-J03W ┆ 152870 ┆ … ┆ DFL-CLU-0000 ┆ DFL-OBJ-000 ┆ 0.0 ┆ 9.58 │\n", - "│ ┆ ┆ N1 ┆ ┆ ┆ 0B ┆ 02Z|home ┆ ┆ │\n", - "└──────────────┴───────────┴──────────────┴──────────┴───┴──────────────┴─────────────┴─────┴──────┘" + "┌───────────────┬───────────┬──────────────┬──────────┬───┬─────────────┬────────────┬──────┬──────┐\n", + "│ competition_i ┆ season_id ┆ match_id ┆ frame_id ┆ … ┆ ball_status ┆ entity_key ┆ acc ┆ vel │\n", + "│ d ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ --- ┆ str ┆ str ┆ i64 ┆ ┆ bool ┆ str ┆ f64 ┆ f64 │\n", + "│ str ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "╞═══════════════╪═══════════╪══════════════╪══════════╪═══╪═════════════╪════════════╪══════╪══════╡\n", + "│ null ┆ null ┆ DFL-MAT-J03W ┆ 10000 ┆ … ┆ true ┆ team:away ┆ null ┆ null │\n", + "│ ┆ ┆ N1 ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J03W ┆ 10001 ┆ … ┆ true ┆ team:away ┆ null ┆ null │\n", + "│ ┆ ┆ N1 ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J03W ┆ 10002 ┆ … ┆ true ┆ team:away ┆ null ┆ null │\n", + "│ ┆ ┆ N1 ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J03W ┆ 10003 ┆ … ┆ true ┆ team:away ┆ null ┆ null │\n", + "│ ┆ ┆ N1 ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ null ┆ null ┆ DFL-MAT-J03W ┆ 10004 ┆ … ┆ true ┆ team:away ┆ null ┆ null │\n", + "│ ┆ ┆ N1 ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "└───────────────┴───────────┴──────────────┴──────────┴───┴─────────────┴────────────┴──────┴──────┘" ] }, "execution_count": 10, @@ -455,13 +419,6 @@ " shutil.rmtree(warehouse_path)\n", "print(\"Warehouse cleaned up.\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/python/tests/test_schema.py b/python/tests/test_schema.py index fdd5491..3830b3f 100644 --- a/python/tests/test_schema.py +++ b/python/tests/test_schema.py @@ -231,8 +231,8 @@ def test_validate_missing_required(self) -> None: with pytest.raises(tdc.exceptions.SchemaError) as excinfo: fields.validate() - assert "Missing ball/z" in str(excinfo.value) - assert "Missing match/id" in str(excinfo.value) + assert "ball/z" in str(excinfo.value) + assert "match/id" in str(excinfo.value) assert fields.validate_quiet() is False def test_validate_no_players(self) -> None: diff --git a/python/tracking_data_container/__init__.py b/python/tracking_data_container/__init__.py index 530fb1b..c77bf8f 100644 --- a/python/tracking_data_container/__init__.py +++ b/python/tracking_data_container/__init__.py @@ -31,8 +31,10 @@ FrameDataField, Player, Referee, + SchemaId, Team, TrackedEntity, + list_schemas, ) from .selector import ColumnSelector, Selection @@ -71,6 +73,8 @@ "Referee", "FrameDataField", "EntityDataField", + "SchemaId", + "list_schemas", "Expr", "Attr", "Fields", diff --git a/python/tracking_data_container/_tracking_data_container.pyi b/python/tracking_data_container/_tracking_data_container.pyi index 850672d..6b46ae2 100644 --- a/python/tracking_data_container/_tracking_data_container.pyi +++ b/python/tracking_data_container/_tracking_data_container.pyi @@ -78,6 +78,25 @@ class _Fields: def referee_entities(self) -> list[_TrackedEntity]: ... def validate(self) -> None: ... def validate_quiet(self) -> bool: ... + def validate_with_schema(self, schema_id: _SchemaId) -> None: ... + +class _SchemaId: + """Internal representation of a schema identifier.""" + def __init__(self, sport: str, data_kind: str, version: str) -> None: ... + @staticmethod + def from_string(s: str) -> _SchemaId: ... + @property + def sport(self) -> str: ... + @property + def data_kind(self) -> str: ... + @property + def version(self) -> str: ... + def __str__(self) -> str: ... + def __repr__(self) -> str: ... + def __eq__(self, other: object) -> bool: ... + def __hash__(self) -> int: ... + +def list_schemas() -> list[str]: ... class Origin: """Possible origin locations for a coordinate system.""" @@ -202,6 +221,10 @@ class _TrackingDataContainer: def num_rows(self) -> int: ... def slice(self, offset: int, length: int) -> _TrackingDataContainer: ... def validate(self) -> None: ... + def validate_with_schema(self, schema_id: _SchemaId) -> None: ... + @property + def schema_id(self) -> _SchemaId | None: ... + def with_schema_id(self, schema_id: _SchemaId) -> _TrackingDataContainer: ... def save_parquet(self, path: str) -> None: ... @staticmethod def load_parquet(path: str) -> _TrackingDataContainer: ... diff --git a/python/tracking_data_container/backends/iceberg.py b/python/tracking_data_container/backends/iceberg.py index d9522e5..b9940d5 100644 --- a/python/tracking_data_container/backends/iceberg.py +++ b/python/tracking_data_container/backends/iceberg.py @@ -272,6 +272,12 @@ def collect(self) -> TrackingDataContainer: if self._limit: wide_container = wide_container.slice(0, self._limit) + if "_schema_id" in metadata: + from ..schema import SchemaId + + schema_id = SchemaId.from_string(metadata["_schema_id"]) + wide_container._container = wide_container._container.with_schema_id(schema_id._inner) + return wide_container def add_column( diff --git a/python/tracking_data_container/catalogs/iceberg.py b/python/tracking_data_container/catalogs/iceberg.py index ef6e8d2..9ad25a6 100644 --- a/python/tracking_data_container/catalogs/iceberg.py +++ b/python/tracking_data_container/catalogs/iceberg.py @@ -240,6 +240,8 @@ def save_container( final_metadata = metadata.copy() if metadata else {} final_metadata["_entities"] = entities + if container.schema_id is not None: + final_metadata["_schema_id"] = str(container.schema_id) self._save_metadata(final_metadata, match_id) def commit_container(self, container: TrackingDataContainer) -> None: diff --git a/python/tracking_data_container/container.py b/python/tracking_data_container/container.py index 3e9402f..350494f 100644 --- a/python/tracking_data_container/container.py +++ b/python/tracking_data_container/container.py @@ -25,7 +25,7 @@ Selection as _Selection, ) from .coordinate_system import CoordinateSystem -from .schema import DataField, Fields +from .schema import DataField, Fields, SchemaId from .selector import ColumnSelector, Selection if TYPE_CHECKING: @@ -56,10 +56,15 @@ def __init__( orientation: str | OrientationMode | None = None, catalog: Catalog | None = None, validate: bool = True, + schema: str | SchemaId | None = None, ) -> None: self._catalog = catalog if isinstance(data, _TrackingDataContainer): self._container = data + if schema is not None: + if isinstance(schema, str): + schema = SchemaId.from_string(schema) + self._container = self._container.with_schema_id(schema._inner) return data_dict: dict[str, Any] @@ -74,6 +79,10 @@ def __init__( self._container = _TrackingDataContainer( data_dict, inner_cs, str(orientation) if orientation else None ) + if schema is not None: + if isinstance(schema, str): + schema = SchemaId.from_string(schema) + self._container = self._container.with_schema_id(schema._inner) if validate: self.validate() @@ -83,8 +92,24 @@ def num_rows(self) -> int: def schema(self) -> Fields: return Fields(self._container.schema) + @property + def schema_id(self) -> SchemaId | None: + """Returns the schema identifier, if set.""" + inner = self._container.schema_id + if inner is None: + return None + return SchemaId._from_inner(inner) + def validate(self) -> None: - self._container.validate() + """Validate the container against its schema. + + If a ``schema_id`` is set, validates against that schema. + Otherwise, validates against the default schema (CDF v0.2.3 tracking). + """ + if self._container.schema_id is not None: + self._container.validate_with_schema(self._container.schema_id) + else: + self._container.validate() def is_bound(self) -> bool: return self._catalog is not None diff --git a/python/tracking_data_container/schema.py b/python/tracking_data_container/schema.py index cd133b7..19855fc 100644 --- a/python/tracking_data_container/schema.py +++ b/python/tracking_data_container/schema.py @@ -6,8 +6,12 @@ from ._tracking_data_container import ( _DataField, _Fields, + _SchemaId, _TrackedEntity, ) +from ._tracking_data_container import ( + list_schemas as _list_schemas, +) class TrackedEntity: @@ -306,6 +310,83 @@ class EntityDataField(DataField): pass +class SchemaId: + """Identifies a schema by (sport, data_kind, version). + + A schema is uniquely identified by a triple of (sport, data_kind, version). + For example, ``SchemaId("cdf", "tracking", "0.2.3")`` identifies the + CDF v0.2.3 standard tracking schema. + """ + + def __init__(self, sport: str, data_kind: str, version: str) -> None: + self._inner = _SchemaId(sport, data_kind, version) + + @staticmethod + def from_string(s: str) -> SchemaId: + """Parse a schema ID from a slash-separated string. + + Parameters + ---------- + s : str + A string in the format ``"sport/data_kind/version"``. + + Returns + ------- + SchemaId + """ + instance = SchemaId.__new__(SchemaId) + instance._inner = _SchemaId.from_string(s) + return instance + + @staticmethod + def _from_inner(inner: _SchemaId) -> SchemaId: + instance = SchemaId.__new__(SchemaId) + instance._inner = inner + return instance + + @property + def sport(self) -> str: + """The sport this schema applies to.""" + return self._inner.sport + + @property + def data_kind(self) -> str: + """The kind of data (e.g., 'tracking_2d', 'skeletal', 'gps').""" + return self._inner.data_kind + + @property + def version(self) -> str: + """The version string.""" + return self._inner.version + + def __str__(self) -> str: + return str(self._inner) + + def __repr__(self) -> str: + return f"SchemaId({self})" + + def __eq__(self, other: object) -> bool: + if isinstance(other, str): + return str(self) == other + if not isinstance(other, SchemaId): + return False + return str(self) == str(other) + + def __hash__(self) -> int: + return hash(str(self)) + + +def list_schemas() -> list[str]: + """Returns a list of all registered schema identifiers. + + Returns + ------- + list[str] + A list of schema ID strings in the format ``"sport/data_kind/version"``. + """ + return _list_schemas() + + class Fields: """A collection of DataFields representing the schema of tracking data.""" @@ -374,51 +455,60 @@ def match(self, pattern: str) -> list[str]: # Standard fnmatch for simple patterns return fnmatch.filter(all_names, pattern) - def validate(self) -> None: + def validate(self, schema: str | SchemaId | None = None) -> None: + """Validate the fields against a schema. + + Parameters + ---------- + schema : str | SchemaId | None, optional + The schema to validate against. If ``None``, validates against the + default schema (CDF v0.2.3 tracking). Can be a string in + the format ``"sport/data_kind/version"`` or a :class:`SchemaId`. + + Raises + ------ + SchemaError + If required fields are missing. + SchemaWarning + If unsupported fields are present (emitted as a warning). + """ import warnings from .exceptions import SchemaError, SchemaWarning - names = self.names() - errors = [] - - # Required errors - if "ball/x" in names and "ball/z" not in names: - errors.append("Missing ball/z") - if "period" in names and "match/id" not in names: - errors.append("Missing match/id") - if any("teams/" in n for n in names) and not any("/players/" in n for n in names): - errors.append("teams/home/players missing") - - player_prefixes = set() - for n in names: - if "/players/" in n: - player_prefixes.add(n.rsplit("/", 1)[0] + "/") - for p in player_prefixes: - if f"{p}x" in names and f"{p}y" not in names: - errors.append(f"{p} missing y") - - if errors: - raise SchemaError(" AND ".join(errors)) - - # Order warnings specifically for TestFields.test_unsupported_field - # The test checks for xGoal THEN dummy in the same validate call message? - # Actually it checks record[0].message for BOTH. - all_warnings = [] - for n in names: - if n == "teams/home/xGoal": - all_warnings.append("unsupported entity attribute: teams/home/xGoal") - elif n == "dummy": - all_warnings.append("unsupported frame attribute: dummy") - - if all_warnings: - warnings.warn(" AND ".join(all_warnings), SchemaWarning) - - def validate_quiet(self) -> bool: + try: + if schema is not None: + if isinstance(schema, str): + schema = SchemaId.from_string(schema) + self._fields.validate_with_schema(schema._inner) + else: + self._fields.validate_with_schema(_SchemaId("cdf", "tracking", "0.2.3")) + except SchemaError: + raise + except Exception as e: # noqa: BLE001 + msg = str(e) + if "SchemaWarning" in type(e).__name__ or "unsupported" in msg.lower(): + warnings.warn(msg, SchemaWarning) + else: + raise SchemaError(msg) from e + + def validate_quiet(self, schema: str | SchemaId | None = None) -> bool: + """Validate the fields without raising errors. + + Parameters + ---------- + schema : str | SchemaId | None, optional + The schema to validate against. See :meth:`validate`. + + Returns + ------- + bool + ``True`` if valid, ``False`` otherwise. + """ from .exceptions import SchemaError try: - self.validate() + self.validate(schema=schema) return True except SchemaError: return False

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