PySeis-IO: Stable Seismic Dataset Layout & Implementation

Hey guys, let's talk about something super important for anyone working with seismic data and PySeis: establishing a stable, versioned, and forward-compatible directory layout for the PySeis internal format. This isn't just about organizing files; it's about creating a robust foundation that ensures our data is reliable, predictable, and ready for future innovations. Think of it as the bedrock upon which all your awesome seismic processing and analysis will stand. We're essentially defining a contract – a clear set of rules for how our seismic datasets are structured on disk. This layout will support cutting-edge data management techniques like normalized metadata using Parquet, chunked trace storage with Zarr, and the crucial lazy-loading infrastructure powered by Dask. This initiative is a big step forward, formalizing stability guarantees and ensuring that every internal writer must produce this layout, and every internal reader must rely on it. This commitment means that even as our schemas and formats evolve, compatibility will be preserved, making your life a whole lot easier and your data workflows incredibly robust. We're moving beyond ad-hoc arrangements to a systematically designed structure that empowers developers and users alike, ensuring seamless integration and long-term usability for all your seismic projects.

Why a Stable Layout Matters: The Heart of PySeis Data

A stable directory layout is not just a nice-to-have; it's absolutely critical for the PySeis internal format, acting as the beating heart of how our seismic data is organized and accessed. Imagine trying to build a complex processing pipeline if you couldn't trust where your sources.parquet file or your traces.zarr group would be located – it would be a nightmare! This stability eliminates all that ambiguity, creating a predictable, long-term internal format that both humans and machines can understand without a hitch. By establishing this clear contract between InternalFormatWriter and InternalFormatReader components, we unlock a world of possibilities. No more guessing games about file paths or data relationships; everything is explicitly defined. This certainty is a game-changer for multi-writer, multi-reader workflows, allowing different tools and processes to interact with the same dataset confidently, knowing that the structure they expect will always be there. Furthermore, a stable layout ensures cross-version compatibility for our entire v1.x series, meaning your existing data won't suddenly become unreadable with a minor software update. This is huge for protecting your investments in data and code! Beyond just PySeis itself, this robust structure enables external tooling – from users developing custom scripts to major cloud platforms and advanced pipelines – to interact seamlessly with our datasets. When the layout is predictable, integrations become straightforward, and the adoption of cloud-native Zarr workflows for large-scale, distributed computing becomes not just possible, but efficient and reliable. This foundational work also lays the groundwork for lazy loading and Dask integration, allowing us to work with massive datasets without loading everything into memory at once, which is essential for modern seismic data volumes. Ultimately, this stable layout isn't just about folders and files; it's about building trust in our data and empowering everyone in the PySeis ecosystem to work more effectively, collaboratively, and confidently with seismic information. It’s an investment in future efficiency and reliability, guys.

Diving Deep into the SeismicDatasetLayout Class: Your Data's Best Friend

At the core of our stable directory layout initiative is the SeismicDatasetLayout class, designed to be your ultimate data manager and guarantor of the internal-format directory hierarchy. This isn't just a utility class; it's the guardian of your data's structure, ensuring everything is where it should be, every single time. Let's break down the awesome capabilities it brings to the table, making your life as a seismic data enthusiast much easier. First up, we have the Creation / Mutation Operations, which are like your data's personal construction crew. The create(path) method is your starting point; it effortlessly spins up a new dataset directory, complete with all the necessary subdirectories and foundational files, setting up your project perfectly from the get-go. But what if you're handed an existing dataset? That's where ensure_structure() comes in, acting as a diligent validator, checking that your dataset conforms to the expected layout and version. If something's off, it'll let you know, ensuring data integrity. Need to move things around? rename(src, dst) handles atomic directory renames, so you can reorganize without a hitch. And for those times you need a duplicate, copy(src, dst) performs a deep copy of the entire internal dataset, meticulously duplicating both your Parquet metadata and your Zarr groups, preserving every detail. Finally, when it's time to clean up, delete(path) provides a safe recursive delete with built-in validation, preventing accidental data loss. Beyond these operations, the SeismicDatasetLayout class also exposes a suite of Path Properties, providing stable, canonical paths that you can always rely on. These include sources_path, receivers_path, trace_headers_path, and the critical traces_path (which points to your Zarr group). We also have global_metadata_path, along with future-proofed paths for survey, instrument, and job metadata. The beauty here is that all these paths are computed deterministically from the root, meaning you never have to hardcode paths or worry about them changing unexpectedly. This consistency is vital for building robust, maintainable, and scalable seismic data applications, truly making SeismicDatasetLayout your data's best friend in the PySeis ecosystem.

The Normalized Layout: A Blueprint for Seismic Data Storage

Now, let's dive into the nitty-gritty of The Normalized / Relational Layout, which is the agreed-upon blueprint for our seismic storage model within PySeis, following the principles outlined in #1. This structure isn't just arbitrarily chosen; it's a meticulously designed hierarchy that leverages the strengths of modern data formats like Parquet for metadata and Zarr for trace data, all while enabling powerful features like lazy loading and Dask integration. Guys, this layout is designed for clarity, efficiency, and scalability, making your seismic data workflows genuinely robust. At the top level, everything lives under a dataset/ directory. Inside, you'll find a series of key Parquet files that store your normalized metadata. We have sources.parquet, which holds all the detailed information about your seismic sources. Then there's receivers.parquet, containing equally comprehensive data for your receivers. And critically, trace_headers.parquet stores all the per-trace header information, but in a normalized way, avoiding redundancy and enabling flexible querying. These Parquet files are fantastic because they're column-oriented, highly efficient for analytical queries, and easily integrated with big data tools. Moving on, the heart of our waveform data is housed in the traces.zarr/ directory. Zarr is a game-changer for large, chunked array storage, perfect for seismic traces. Inside traces.zarr/, you'll find data/, which is where your actual waveform samples reside (think n_traces × n_samples). But it's not just raw data; to maintain our relational model, we also have source_id/ and receiver_id/, which act as foreign keys pointing back to sources.parquet and receivers.parquet, respectively. This linkage ensures data integrity and allows for powerful joins between your metadata and waveform data. There's also an optional cdp_id/ foreign key, allowing for integration with CDP (Common Depth Point) groups or tables when applicable. Finally, we have a dedicated metadata/ directory, which houses crucial configuration and descriptive files, such as survey.yaml, instrument.yaml, and job.yaml. These YAML files provide human-readable, machine-parseable context for your entire dataset. A critical aspect of this structure is its Stability requirement: this exact directory and file arrangement must remain valid for the entire v1.x series of PySeis. This commitment ensures that once you create a dataset using this layout, you can trust it to be readable and usable across all minor versions, providing unparalleled reliability for your seismic data management efforts.

Versioning Your Data: Future-Proofing with layout.yaml

Alright, folks, let's talk about versioning – because in the world of data, things change, but your ability to read old data shouldn't be compromised! Our approach to Layout Versioning & Backwards Compatibility is meticulously designed to future-proof your PySeis internal format datasets. The secret sauce here lies in the metadata/layout.yaml file. This isn't just another configuration file; it's the Rosetta Stone for your dataset's structure, embedding vital layout metadata right where it's needed. Inside this YAML file, you'll find key fields: layout_version: "1.0", which tells you precisely which iteration of our directory structure is in play; schema_version: "1.0", linking directly to the specific header/trace schema used; created: <timestamp>, giving you a clear record of when the dataset was born; and generator: pyseis-io <version>, indicating exactly which tool and version created it. This comprehensive metadata is crucial for understanding and interpreting any PySeis dataset. Now, let's get serious about the Versioning Policy (Strict). This isn't a suggestion; it's a firm rulebook. We believe in additive changes for minor versions (think 1.1, 1.2). This means new files or subdirectories may be added without breaking existing readers. However, and this is super important, existing file names, paths, or structural relationships must never change within the entire v1.x series. Any fundamental shifts – like deletions, renames of core components, or major structural reorganizations – are reserved for a major version bump (v2.0). This strict policy ensures that any v1.x reader can always understand any v1.x dataset. Speaking of readers, we have strict Reader Requirements. A valid PySeis internal reader must first read and parse the layout.yaml file. It will then reject datasets missing this layout metadata because, without it, we can't guarantee anything. Readers must accept additive layout extensions (new files/dirs in minor versions), allowing for flexibility. If an optional component is absent, the reader will issue a warning (not a failure), giving you flexibility without critical errors. However, if required components are missing or malformed – we're talking about sources.parquet, receivers.parquet, trace_headers.parquet, or the core traces.zarr/ structure – then the reader must fail. This robust validation ensures data integrity and prevents silent data corruption or misinterpretation. This meticulous approach to versioning with layout.yaml provides the long-term stability and compatibility that truly future-proofs your seismic data assets, giving you peace of mind that your efforts today will be readable and usable for years to come.

The Unbreakable Promise: Stability Guarantees for PySeis v1.x

When we talk about Stability Guarantees for the PySeis internal format, we're not just throwing around fancy words; we're establishing a long-term contract. This contract binds our InternalFormatWriter(s), our InternalFormatReader(s), and critically, all the external tooling – that means you, the users, your custom pipelines, and even cloud platforms – to a consistent and predictable data structure. Think of it as a handshake deal: we promise that within the entire v1.x series, certain things will simply not change, providing you with a rock-solid foundation for all your seismic data endeavors. First off, and this is fundamental, directory names remain unchanged. No surprise renames of metadata/ or traces.zarr/ that break your scripts overnight. Secondly, required files remain present and consistent. This means sources.parquet, receivers.parquet, trace_headers.parquet, and the core traces.zarr/ structure will always be there, in their expected places, and in the expected format. You can count on them. Thirdly, all paths derived via SeismicDatasetLayout remain stable. This is crucial because it means that even if the internal implementation of path computation changes slightly, the canonical paths exposed to you will be consistent, ensuring your code that uses these properties won't suddenly fail. Next up, the Zarr group structure itself, specifically data/, source_id/, and receiver_id/, remains stable. This is a huge win for anyone integrating Dask or other Zarr-aware tools, as they can rely on the internal organization of the trace data. Fifth, the metadata/ directory remains central and versioned, ensuring that critical information about your dataset is always accessible and interpretable through layout.yaml. Finally, and this is a testament to our forward-thinking approach, additions must not break older readers. This means we can evolve the format by adding new files or optional components in minor versions without invalidating existing data or requiring a complete overhaul of your reading infrastructure. These guarantees are not just technical specifications; they are a promise of reliability and longevity for your seismic data. They empower you to build robust, scalable, and maintainable data workflows with confidence, knowing that the foundation beneath your feet is truly unbreakable within the PySeis v1.x series. This commitment allows for the seamless integration of PySeis into diverse computational environments, from local workstations to high-performance computing clusters and cloud-based platforms, without fear of architectural surprises.

Bringing It All Together: Implementation & Integration

So, guys, how do we make all these awesome stability guarantees and the SeismicDatasetLayout a reality? It boils down to a set of concrete Implementation Tasks that bring this vision to life. First, the core task is to implement SeismicDatasetLayout itself, equipping it with all those critical path properties and robust validation logic we discussed. This includes crafting methods for creation, renaming, copying, and deleting datasets, all while ensuring strong type hints and comprehensive docstrings are in place – because clear code is happy code! A huge part of this implementation involves setting up the layout metadata (YAML) creation and validation, ensuring every dataset correctly embeds its versioning information from the start. Alongside this, rigorous Validation Logic is being built. We're talking checks to ensure that required Parquet files like sources.parquet actually exist and have the correct extensions. We're also making sure the Zarr group (traces.zarr/) contains its required arrays (data, source_id, receiver_id), confirming that our trace storage is correctly formed. And where appropriate, we'll implement lightweight checks for foreign key alignment rules to maintain relational integrity. These validation steps are crucial for catching issues early and maintaining data quality. But implementation isn't just about building new things; it's also about smart Integration Points. The InternalFormatWriter will be updated to rely solely on layout for all path management, ensuring every write operation adheres to the defined structure. Similarly, the InternalFormatReader will depend on layout metadata for interpretation, ensuring it correctly understands and accesses data, regardless of minor version additions. And, of course, the entire layout contract will be thoroughly documented in docs/architecture.md, providing a clear reference for everyone in the PySeis community. Now, every great design has its Trade-offs. We acknowledge that this brings more initial implementation complexity – building robust, versioned systems takes effort! It also imposes stronger constraints on future directory design, meaning we can't just make arbitrary changes. This also requires version-management discipline from developers to adhere to the strict versioning policy. And naturally, we must maintain robust validation logic to keep everything in check. While these are challenges, the Benefits far outweigh them: we get a predictable, stable, long-term internal format, which eliminates ambiguity, enables multi-writer/reader workflows, ensures cross-version compatibility, and supports lazy loading, Dask, and cloud-native Zarr. For those interested in the deeper technical context, this work is related to seisdata/seisdata.py (our normalized schema predecessor), seisdata/seisdata_schema.yaml (our canonical data model), and will update docs/architecture.md. We'll also deprecate models.py as this new system takes over. This holistic approach ensures that PySeis continues to be a powerful, reliable, and future-ready tool for seismic data processing.

This initiative to establish a stable, versioned SeismicDatasetLayout for PySeis is a game-changer, folks. It's about laying down a robust, predictable foundation for all your seismic data, making it easier to manage, share, and process, both now and well into the future. By committing to this clear contract and leveraging powerful tools like Parquet and Zarr, we're building a PySeis ecosystem that is not just functional, but truly resilient and ready for anything you throw at it. Keep an eye out as these exciting developments unfold!