diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 8d7c81fa..71ad8630 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -13,7 +13,7 @@ jobs: matrix: include: - { python: "3.13", os: "ubuntu-latest", session: "pre-commit" } - - { python: "3.13", os: "ubuntu-latest", session: "safety" } + # - { python: "3.13", os: "ubuntu-latest", session: "safety" } # - { python: "3.13", os: "ubuntu-latest", session: "mypy" } # - { python: "3.12", os: "ubuntu-latest", session: "mypy" } # - { python: "3.11", os: "ubuntu-latest", session: "mypy" } diff --git a/docs/conf.py b/docs/conf.py index 89990324..aeb59636 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -17,6 +17,7 @@ "sphinx.ext.napoleon", "sphinx.ext.intersphinx", "sphinx.ext.autosummary", + "sphinxcontrib.autodoc_pydantic", "sphinx.ext.autosectionlabel", "sphinx_click", "sphinx_copybutton", @@ -38,6 +39,7 @@ intersphinx_mapping = { "python": ("https://docs.python.org/3", None), "numpy": ("https://numpy.org/doc/stable/", None), + "pydantic": ("https://docs.pydantic.dev/latest/", None), "zarr": ("https://zarr.readthedocs.io/en/stable/", None), } @@ -50,6 +52,14 @@ autoclass_content = "class" autosectionlabel_prefix_document = True +autodoc_pydantic_field_list_validators = False +autodoc_pydantic_field_swap_name_and_alias = True +autodoc_pydantic_field_show_alias = False +autodoc_pydantic_model_show_config_summary = False +autodoc_pydantic_model_show_validator_summary = False +autodoc_pydantic_model_show_validator_members = False +autodoc_pydantic_model_show_field_summary = False + html_theme = "furo" myst_number_code_blocks = ["python"] diff --git a/docs/data_models/chunk_grids.md b/docs/data_models/chunk_grids.md new file mode 100644 index 00000000..5f178a04 --- /dev/null +++ b/docs/data_models/chunk_grids.md @@ -0,0 +1,154 @@ +```{eval-rst} +:tocdepth: 3 +``` + +```{currentModule} mdio.schemas.chunk_grid + +``` + +# Chunk Grid Models + +```{article-info} +:author: Altay Sansal +:date: "{sub-ref}`today`" +:read-time: "{sub-ref}`wordcount-minutes` min read" +:class-container: sd-p-0 sd-outline-muted sd-rounded-3 sd-font-weight-light +``` + +The variables in MDIO data model can represent different types of chunk grids. +These grids are essential for managing multi-dimensional data arrays efficiently. +In this breakdown, we will explore four distinct data models within the MDIO schema, +each serving a specific purpose in data handling and organization. + +MDIO implements data models following the guidelines of the Zarr v3 spec and ZEPs: + +- [Zarr core specification (version 3)](https://zarr-specs.readthedocs.io/en/latest/v3/core/v3.0.html) +- [ZEP 1 — Zarr specification version 3](https://zarr.dev/zeps/accepted/ZEP0001.html) +- [ZEP 3 — Variable chunking](https://zarr.dev/zeps/draft/ZEP0003.html) + +## Regular Grid + +The regular grid models are designed to represent a rectangular and regularly +paced chunk grid. + +```{eval-rst} +.. autosummary:: + RegularChunkGrid + RegularChunkShape +``` + +For 1D array with `size = 31`{l=python}, we can divide it into 5 equally sized +chunks. Note that the last chunk will be truncated to match the size of the array. + +`{ "name": "regular", "configuration": { "chunkShape": [7] } }`{l=json} + +Using the above schema resulting array chunks will look like this: + +```bash + ←─ 7 ─→ ←─ 7 ─→ ←─ 7 ─→ ←─ 7 ─→ ↔ 3 +┌───────┬───────┬───────┬───────┬───┐ +└───────┴───────┴───────┴───────┴───┘ +``` + +For 2D array with shape `rows, cols = (7, 17)`{l=python}, we can divide it into 9 +equally sized chunks. + +`{ "name": "regular", "configuration": { "chunkShape": [3, 7] } }`{l=json} + +Using the above schema, the resulting 2D array chunks will look like below. +Note that the rows and columns are conceptual and visually not to scale. + +```bash + ←─ 7 ─→ ←─ 7 ─→ ↔ 3 +┌───────┬───────┬───┐ +│ ╎ ╎ │ ↑ +│ ╎ ╎ │ 3 +│ ╎ ╎ │ ↓ +├╶╶╶╶╶╶╶┼╶╶╶╶╶╶╶┼╶╶╶┤ +│ ╎ ╎ │ ↑ +│ ╎ ╎ │ 3 +│ ╎ ╎ │ ↓ +├╶╶╶╶╶╶╶┼╶╶╶╶╶╶╶┼╶╶╶┤ +│ ╎ ╎ │ ↕ 1 +└───────┴───────┴───┘ +``` + +## Rectilinear Grid + +The [RectilinearChunkGrid](RectilinearChunkGrid) model extends +the concept of chunk grids to accommodate rectangular and irregularly spaced chunks. +This model is useful in data structures where non-uniform chunk sizes are necessary. +[RectilinearChunkShape](RectilinearChunkShape) specifies the chunk sizes for each +dimension as a list allowing for irregular intervals. + +```{eval-rst} +.. autosummary:: + RectilinearChunkGrid + RectilinearChunkShape +``` + +:::{note} +It's important to ensure that the sum of the irregular spacings specified +in the `chunkShape` matches the size of the respective array dimension. +::: + +For 1D array with `size = 39`{l=python}, we can divide it into 5 irregular sized +chunks. + +`{ "name": "rectilinear", "configuration": { "chunkShape": [[10, 7, 5, 7, 10]] } }`{l=json} + +Using the above schema resulting array chunks will look like this: + +```bash + ←── 10 ──→ ←─ 7 ─→ ← 5 → ←─ 7 ─→ ←── 10 ──→ +┌──────────┬───────┬─────┬───────┬──────────┐ +└──────────┴───────┴─────┴───────┴──────────┘ +``` + +For 2D array with shape `rows, cols = (7, 25)`{l=python}, we can divide it into 12 +rectilinear (rectangular bur irregular) chunks. Note that the rows and columns are +conceptual and visually not to scale. + +`{ "name": "rectilinear", "configuration": { "chunkShape": [[3, 1, 3], [10, 5, 7, 3]] } }`{l=json} + +```bash + ←── 10 ──→ ← 5 → ←─ 7 ─→ ↔ 3 +┌──────────┬─────┬───────┬───┐ +│ ╎ ╎ ╎ │ ↑ +│ ╎ ╎ ╎ │ 3 +│ ╎ ╎ ╎ │ ↓ +├╶╶╶╶╶╶╶╶╶╶┼╶╶╶╶╶┼╶╶╶╶╶╶╶┼╶╶╶┤ +│ ╎ ╎ ╎ │ ↕ 1 +├╶╶╶╶╶╶╶╶╶╶┼╶╶╶╶╶┼╶╶╶╶╶╶╶┼╶╶╶┤ +│ ╎ ╎ ╎ │ ↑ +│ ╎ ╎ ╎ │ 3 +│ ╎ ╎ ╎ │ ↓ +└──────────┴─────┴───────┴───┘ +``` + +## Model Reference + +:::{dropdown} RegularChunkGrid +:animate: fade-in-slide-down + +```{eval-rst} +.. autopydantic_model:: RegularChunkGrid + +---------- + +.. autopydantic_model:: RegularChunkShape +``` + +::: +:::{dropdown} RectilinearChunkGrid +:animate: fade-in-slide-down + +```{eval-rst} +.. autopydantic_model:: RectilinearChunkGrid + +---------- + +.. autopydantic_model:: RectilinearChunkShape +``` + +::: diff --git a/docs/data_models/compressors.md b/docs/data_models/compressors.md new file mode 100644 index 00000000..17a1afd8 --- /dev/null +++ b/docs/data_models/compressors.md @@ -0,0 +1,100 @@ +```{eval-rst} +:tocdepth: 3 +``` + +```{currentModule} mdio.schemas.compressors + +``` + +# Compressors + +```{article-info} +:author: Altay Sansal +:date: "{sub-ref}`today`" +:read-time: "{sub-ref}`wordcount-minutes` min read" +:class-container: sd-p-0 sd-outline-muted sd-rounded-3 sd-font-weight-light +``` + +## Dataset Compression + +MDIO relies on [numcodecs] for data compression. We provide good defaults based +on opinionated and limited heuristics for each compressor for various energy datasets. +However, using these data models, the compression can be customized. + +[Numcodecs] is a project that a convenient interface to different compression +libraries. We selected the [Blosc] and [ZFP] compressors for lossless and lossy +compression of energy data. + +## Blosc + +A high-performance compressor optimized for binary data, combining fast compression +with a byte-shuffle filter for enhanced efficiency, particularly effective with +numerical arrays in multi-threaded environments. + +For more details about compression modes, see [Blosc Documentation]. + +```{eval-rst} +.. autosummary:: + Blosc +``` + +## ZFP + +ZFP is a compression algorithm tailored for floating-point and integer arrays, offering +lossy and lossless compression with customizable precision, well-suited for large +scientific datasets with a focus on balancing data fidelity and compression ratio. + +For more details about compression modes, see [ZFP Documentation]. + +```{eval-rst} +.. autosummary:: + ZFP +``` + +[numcodecs]: https://github.com/zarr-developers/numcodecs +[blosc]: https://github.com/Blosc/c-blosc +[blosc documentation]: https://www.blosc.org/python-blosc/python-blosc.html +[zfp]: https://github.com/LLNL/zfp +[zfp documentation]: https://computing.llnl.gov/projects/zfp + +## Model Reference + +::: +:::{dropdown} Blosc +:animate: fade-in-slide-down + +```{eval-rst} +.. autopydantic_model:: Blosc + +---------- + +.. autoclass:: BloscAlgorithm() + :members: + :undoc-members: + :member-order: bysource + +---------- + +.. autoclass:: BloscShuffle() + :members: + :undoc-members: + :member-order: bysource +``` + +::: + +:::{dropdown} ZFP +:animate: fade-in-slide-down + +```{eval-rst} +.. autopydantic_model:: ZFP + +---------- + +.. autoclass:: ZFPMode() + :members: + :undoc-members: + :member-order: bysource +``` + +::: diff --git a/docs/data_models/data_types.md b/docs/data_models/data_types.md new file mode 100644 index 00000000..b58ff535 --- /dev/null +++ b/docs/data_models/data_types.md @@ -0,0 +1,259 @@ +```{eval-rst} +:tocdepth: 3 +``` + +```{currentModule} mdio.schemas.dtype + +``` + +# Data Types + +```{article-info} +:author: Altay Sansal +:date: "{sub-ref}`today`" +:read-time: "{sub-ref}`wordcount-minutes` min read" +:class-container: sd-p-0 sd-outline-muted sd-rounded-3 sd-font-weight-light +``` + +## Scalar Type + +Scalar types are used to represent numbers and boolean values in MDIO arrays. + +```{eval-rst} +.. autosummary:: + :nosignatures: + + ScalarType +``` + +These numbers can be integers (whole numbers without a decimal +point, like 1, -15, 204), floating-point numbers (numbers with a fractional part, +like 3.14, -0.001, 2.71828) in various 16-64 bit formats like `float32` etc. + +It is important to choose the right type for the content of the data for type safety, +memory efficiency, performance, and accuracy of the numbers represented. Most scientific +datasets are `float16`, `float32`, or `float64` values. However, there are many good +use cases for integer and complex values as well. + +The [`ScalarType`](#ScalarType)s MDIO supports can be viewed below with the tabs. + +:::::{tab-set} + +::::{tab-item} Boolean +:::{table} +:widths: auto +:align: center + +| Data Type | Options | Example Value | +| --------- | --------------- | ------------- | +| `bool` | `False`, `True` | `True` | + +::: +:::: + +::::{tab-item} Integers +:::{table} +:widths: auto +:align: center + +| Data Type | Range | Example Value | +| --------- | ----------------------------------------------------------- | ------------- | +| `int8` | `-128` to `127` | `45` | +| `int16` | `-32,768` to `32,767` | `1,234` | +| `int32` | `-2,147,483,648` to `2,147,483,647` | `2,024` | +| `int64` | `-9,223,372,036,854,775,808` to `9,223,372,036,854,775,807` | `987,654,321` | + +::: +:::: + +::::{tab-item} Unsigned Integers +:::{table} +:widths: auto +:align: center + +| Data Type | Range | Example Value | +| --------- | ----------------------------------- | --------------- | +| `uint8` | `0` to `255` | `200` | +| `uint16` | `0` to `65,535` | `50,000` | +| `uint32` | `0` to `4,294,967,295` | `3,000,000` | +| `uint64` | `0` to `18,446,744,073,709,551,615` | `5,000,000,000` | + +::: +:::: + +::::{tab-item} Floating Point +:::{table} +:widths: auto +:align: center + +| Data Type | Range | Example Value | +| --------- | ------------------------------------------------------- | -------------------- | +| `float16` | `-65,504` to `65,504` | `10.10` | +| `float32` | `-3.4028235e+38` to `3.4028235e+38` | `0.1234567` | +| `float64` | `-1.7976931348623157e+308` to `1.7976931348623157e+308` | `3.1415926535897932` | + +::: + +**Precision** + +- `float16`: 2 decimal places +- `float32`: 7 decimal places +- `float32`: 16 decimal places + +:::: + +::::{tab-item} Complex Numbers +:::{table} +:widths: auto +:align: center + +| Data Type | Range | Example Value | +| ------------ | ------------------------------------------------------- | ------------------ | +| `complex64` | `-3.4028235e+38` to `3.4028235e+38` | `3.14+2.71j` | +| `complex128` | `-1.7976931348623157e+308` to `1.7976931348623157e+308` | `2.71828+3.14159j` | + +::: +Ranges are for both real and imaginary parts. +:::: + +::::: + +## Structured Type + +Structured data type organizes and stores data in a fixed arrangement, allowing memory +efficient access and manipulation. + +```{eval-rst} +.. autosummary:: + :nosignatures: + + StructuredType + StructuredField +``` + +Structured data types are an essential component in handling complex data structures, +particularly in specialized domains like seismic data processing for subsurface +imaging applications. These data types allow for the organization of heterogeneous +data into a single, structured format. + +They are designed to be memory-efficient, which is vital for handling large seismic +datasets. Structured data types are adaptable, allowing for the addition or +modification of fields. + +A [`StructuredType`](#StructuredType) consists of [`StructuredField`](#StructuredField)s. +Fields can be different [numeric types](#numeric-types), and each represent a specific +attribute of the seismic data, like coordinate, line numbers, and time stamps. + +Each [`StructuredField`](#StructuredField) must specify a `name` and a data format +(`format`). + +All the structured fields will be packed and there will be no gaps between them. + +## Examples + +The table below illustrate [ScalarType](#ScalarType) ranges and shows an example each +type. + +Variable `foo` with type `float32`. + +```json +{ + "name": "foo", + "dataType": "float32", + "dimensions": ["x", "y"] +} +``` + +Variable `bar` with type `uint8`. + +```json +{ + "name": "bar", + "dataType": "uint8", + "dimensions": ["x", "y"] +} +``` + +Below are a couple examples of [StructuredType](#StructuredType) with varying lengths. + +We can specify a variable named `headers` that holds a 32-byte struct with +four `int32` values. + +```json +{ + "name": "headers", + "dataType": { + "fields": [ + { "name": "cdp-x", "format": "int32" }, + { "name": "cdp-y", "format": "int32" }, + { "name": "inline", "format": "int32" }, + { "name": "crossline", "format": "int32" } + ] + }, + "dimensions": ["inline", "crossline"] +} +``` + +This will yield an in-memory or on-disk struct that looks like this (for each element): + +```bash + ←─ 4 ─→ ←─ 4 ─→ ←─ 4 ─→ ←─ 4 ─→ = 16-bytes +┌───────┬───────┬───────┬───────┐ +│ int32 ╎ int32 ╎ int32 ╎ int32 │ ⋯ (next sample) +└───────┴───────┴───────┴───────┘ + └→ cdp-x └→ cdp-y └→ inline └→crossline +``` + +The below example shows mixing different data types. + +```json +{ + "name": "headers", + "dataType": { + "fields": [ + { "name": "cdp", "format": "uint32" }, + { "name": "offset", "format": "int16" }, + { "name": "cdp-x", "format": "float64" }, + { "name": "cdp-y", "format": "float64" } + ] + }, + "dimensions": ["inline", "crossline"] +} +``` + +This will yield an in-memory or on-disk struct that looks like this (for each element): + +```bash + ←── 4 ──→ ← 2 → ←─── 8 ───→ ←─── 8 ───→ = 24-bytes +┌─────────┬─────┬───────────┬───────────┐ +│ int32 ╎int16╎ float64 ╎ float64 │ ⋯ (next sample) +└─────────┴─────┴───────────┴───────────┘ + └→ cdp └→ offset └→ cdp-x └→ cdp-y +``` + +## Model Reference + +:::{dropdown} Scalar Types +:animate: fade-in-slide-down + +```{eval-rst} +.. autoclass:: ScalarType() + :members: + :undoc-members: + :member-order: bysource +``` + +::: + +:::{dropdown} Structured Type +:animate: fade-in-slide-down + +```{eval-rst} +.. autopydantic_model:: StructuredType + +---------- + +.. autopydantic_model:: StructuredField +``` + +::: diff --git a/docs/data_models/dimensions.md b/docs/data_models/dimensions.md new file mode 100644 index 00000000..c04b9206 --- /dev/null +++ b/docs/data_models/dimensions.md @@ -0,0 +1,33 @@ +```{eval-rst} +:tocdepth: 3 +``` + +```{currentModule} mdio.schemas.dimension + +``` + +# Dimensions + +```{article-info} +:author: Altay Sansal +:date: "{sub-ref}`today`" +:read-time: "{sub-ref}`wordcount-minutes` min read" +:class-container: sd-p-0 sd-outline-muted sd-rounded-3 sd-font-weight-light +``` + +## Intro + +```{eval-rst} +.. autosummary:: NamedDimension +``` + +## Reference + +:::{dropdown} Dimension +:open: + +```{eval-rst} +.. autopydantic_model:: NamedDimension +``` + +::: diff --git a/docs/data_models/index.md b/docs/data_models/index.md new file mode 100644 index 00000000..191f937b --- /dev/null +++ b/docs/data_models/index.md @@ -0,0 +1,10 @@ +# Dataset Models + +This section contains the data models for the MDIO format. + +```{toctree} +:maxdepth: 2 + +version_0 +version_1 +``` diff --git a/docs/data_models/version_0.md b/docs/data_models/version_0.md new file mode 100644 index 00000000..bac21ff6 --- /dev/null +++ b/docs/data_models/version_0.md @@ -0,0 +1,55 @@ +```{eval-rst} +:tocdepth: 3 +``` + +```{currentModule} mdio.schemas.v0.dataset + +``` + +# MDIO v0 + +```{article-info} +:author: Altay Sansal +:date: "{sub-ref}`today`" +:read-time: "{sub-ref}`wordcount-minutes` min read" +:class-container: sd-p-0 sd-outline-muted sd-rounded-3 sd-font-weight-light +``` + +## Intro + +```{eval-rst} +.. autosummary:: + + DatasetModelV0 + VariableModelV0 + DatasetMetadataModelV0 + DimensionModelV0 +``` + +## Reference + +:::{dropdown} Dataset +:open: + +```{eval-rst} +.. autopydantic_model:: DatasetModelV0 + :inherited-members: BaseModel + +.. autopydantic_model:: DatasetMetadataModelV0 + :inherited-members: BaseModel + +.. autopydantic_model:: DimensionModelV0 + :inherited-members: BaseModel +``` + +::: + +:::{dropdown} Variable +:open: + +```{eval-rst} +.. autopydantic_model:: VariableModelV0 + :inherited-members: BaseModel +``` + +::: diff --git a/docs/data_models/version_1.md b/docs/data_models/version_1.md new file mode 100644 index 00000000..2fd39753 --- /dev/null +++ b/docs/data_models/version_1.md @@ -0,0 +1,134 @@ +```{eval-rst} +:tocdepth: 3 +``` + +```{currentModule} mdio.schemas.v1.dataset + +``` + +# MDIO v1 + +```{article-info} +:author: Altay Sansal +:date: "{sub-ref}`today`" +:read-time: "{sub-ref}`wordcount-minutes` min read" +:class-container: sd-p-0 sd-outline-muted sd-rounded-3 sd-font-weight-light +``` + +## Intro + +```{eval-rst} +.. autosummary:: Dataset +.. autosummary:: DatasetMetadata +``` + +## Reference + +:::{dropdown} Dataset +:open: + +```{eval-rst} +.. autopydantic_model:: Dataset + :inherited-members: BaseModel + +.. autopydantic_model:: DatasetMetadata + :inherited-members: BaseModel +``` + +::: +:::{dropdown} Variable + +```{eval-rst} +.. autopydantic_model:: mdio.schemas.v1.variable.Variable + :inherited-members: BaseModel + +.. autopydantic_model:: mdio.schemas.v1.variable.Coordinate + :inherited-members: BaseModel + +.. autopydantic_model:: mdio.schemas.v1.variable.CoordinateMetadata + :inherited-members: BaseModel + +.. automodule:: mdio.schemas.metadata + :members: UserAttributes + +.. autopydantic_model:: mdio.schemas.v1.variable.VariableMetadata + :inherited-members: BaseModel +``` + +::: + +:::{dropdown} Units + +```{eval-rst} +.. autopydantic_model:: mdio.schemas.v1.units.AllUnits +``` + +```{eval-rst} +.. automodule:: mdio.schemas.v1.units + :members: LengthUnitModel, + TimeUnitModel, + AngleUnitModel, + DensityUnitModel, + SpeedUnitModel, + FrequencyUnitModel, + VoltageUnitModel +``` + +::: + +:::{dropdown} Stats + +```{eval-rst} +.. autopydantic_model:: mdio.schemas.v1.stats.StatisticsMetadata + +.. autopydantic_model:: mdio.schemas.v1.stats.SummaryStatistics + +.. autopydantic_model:: mdio.schemas.v1.stats.EdgeDefinedHistogram + :inherited-members: BaseModel + +.. autopydantic_model:: mdio.schemas.v1.stats.CenteredBinHistogram + :inherited-members: BaseModel +``` + +::: + +:::{dropdown} Enums + +```{eval-rst} +.. autoclass:: mdio.schemas.v1.units.AngleUnitEnum() + :members: + :undoc-members: + :member-order: bysource + +.. autoclass:: mdio.schemas.v1.units.DensityUnitEnum() + :members: + :undoc-members: + :member-order: bysource + +.. autoclass:: mdio.schemas.v1.units.FrequencyUnitEnum() + :members: + :undoc-members: + :member-order: bysource + +.. autoclass:: mdio.schemas.v1.units.LengthUnitEnum() + :members: + :undoc-members: + :member-order: bysource + +.. autoclass:: mdio.schemas.v1.units.SpeedUnitEnum() + :members: + :undoc-members: + :member-order: bysource + +.. autoclass:: mdio.schemas.v1.units.TimeUnitEnum() + :members: + :undoc-members: + :member-order: bysource + +.. autoclass:: mdio.schemas.v1.units.VoltageUnitEnum() + :members: + :undoc-members: + :member-order: bysource +``` + +::: diff --git a/docs/index.md b/docs/index.md index 9b674107..ba4db72d 100644 --- a/docs/index.md +++ b/docs/index.md @@ -26,6 +26,17 @@ tutorials/index api_reference ``` +```{toctree} +:hidden: +:caption: Core Concepts and Structures + +data_models/index +data_models/dimensions +data_models/chunk_grids +data_models/data_types +data_models/compressors +``` + ```{toctree} :hidden: :caption: Community and Contribution diff --git a/docs/requirements.txt b/docs/requirements.txt index c80a7d9d..cb04269c 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,4 @@ +autodoc-pydantic==2.2.0 furo==2024.8.6 linkify-it-py==2.0.3 myst-nb==1.2.0 diff --git a/noxfile.py b/noxfile.py index d78994a0..281ec525 100644 --- a/noxfile.py +++ b/noxfile.py @@ -257,6 +257,7 @@ def docs_build(session: Session) -> None: session_install_uv_package( session, [ + "autodoc-pydantic", "sphinx", "sphinx-click", "sphinx-copybutton", @@ -282,6 +283,7 @@ def docs(session: Session) -> None: session_install_uv_package( session, [ + "autodoc-pydantic", "sphinx", "sphinx-autobuild", "sphinx-click", diff --git a/pyproject.toml b/pyproject.toml index d059b876..e2f72190 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "multidimio" -version = "0.9.2" +version = "1.0.0-alpha.1" description = "Cloud-native, scalable, and user-friendly multi dimensional energy data!" authors = [{ name = "Altay Sansal", email = "altay.sansal@tgs.com" }] requires-python = ">=3.11,<3.14" @@ -23,26 +23,30 @@ classifiers = [ ] dependencies = [ - "click (>=8.1.7,<9.0.0)", + "click (>=8.2.1,<9.0.0)", "click-params (>=0.5.0,<0.6.0)", - "dask (>=2024.12.0)", - "fsspec (>=2024.10.0)", - "psutil (>=6.1.0,<7.0.0)", - "rich (>=13.9.4,<14.0.0)", - "segy (>=0.4.0,<0.5.0)", - "tqdm (>=4.67.0,<5.0.0)", - "zarr (>=3.0.8,<4.0.0)", + "dask (>=2025.5.1)", + "fsspec (>=2025.5.1)", + "pint>=0.24.4,<0.25", + "psutil (>=7.0.0,<8.0.0)", + "pydantic (>=2.11.7,<3.0.0)", + "pydantic-settings (>=2.10.1,<3.0.0)", + "rich (>=14.0.0,<15.0.0)", + "segy (>=0.4.1.post2,<0.5.0)", + "tqdm (>=4.67.1,<5.0.0)", + "xarray>=2025.6.1", + "zarr (>=3.0.10,<4.0.0)", ] [project.optional-dependencies] cloud = [ - "s3fs == 2024.12.0", - "gcsfs (>=2024.10.0)", - "adlfs (>=2024.7.0)", + "s3fs == 2025.5.1", + "gcsfs (>=2025.5.1)", + "adlfs (>=2024.12.0)", ] distributed = [ - "distributed (>=2024.12.0)", - "bokeh (>=3.4.2,<4.0.0)", + "distributed (>=2025.5.1)", + "bokeh (>=3.7.3,<4.0.0)", ] lossy = ["zfpy (>=1.0.1,<2.0.0)"] @@ -56,17 +60,17 @@ mdio = "mdio.__main__:main" [dependency-groups] dev = [ - "ruff (>=0.11.8)", - "coverage[toml] (>=7.6.7,<8)", - "mypy (>=1.13.0,<2)", - "pre-commit (>=4.0.1,<5)", + "ruff (>=0.12.1)", + "coverage[toml] (>=7.9.1,<8)", + "mypy (>=1.16.1,<2)", + "pre-commit (>=4.2.0,<5)", "pre-commit-hooks (>=5.0.0,<6)", - "pytest (>=8.3.3,<9)", + "pytest (>=8.4.1,<9)", "pytest-dependency (>=0.6.0,<0.7)", - "safety (>=3.2.3,<4)", - "typeguard (>=4.4.1,<5)", +# "safety (>=3.5.2,<4)", # too tight pydantic and psutil dependency + "typeguard (>=4.4.4,<5)", "xdoctest[colors] (>=1.2.0,<2)", - "Pygments (>=2.18.0,<3)", + "Pygments (>=2.19.2,<3)", ] docs = [ @@ -112,7 +116,7 @@ select = [ "RET", # return "SIM", # simplify "TID", # tidy-imports - "TCH", # type-checking + "TC", # type-checking "ARG", # unused-arguments "PTH", # use-pathlib "TD", # todos @@ -180,11 +184,16 @@ pretty = true show_column_numbers = true show_error_codes = true show_error_context = true -disallow_untyped_defs = true # for strict mypy: (this is the tricky one) -plugins = ["numpy.typing.mypy_plugin"] +disallow_untyped_defs = true # for strict mypy: (this is the tricky one) +plugins = ["pydantic.mypy", "numpy.typing.mypy_plugin"] + +[tool.pydantic-mypy] +init_forbid_extra = true +init_typed = true +warn_required_dynamic_aliases = true [tool.bumpversion] -current_version = "0.9.2" +current_version = "1.0.0-alpha.1" allow_dirty = true commit = false tag = false diff --git a/src/mdio/schemas/__init__.py b/src/mdio/schemas/__init__.py new file mode 100644 index 00000000..8d9d8b86 --- /dev/null +++ b/src/mdio/schemas/__init__.py @@ -0,0 +1,17 @@ +"""MDIO schemas for different data types.""" + +from mdio.schemas.compressors import ZFP +from mdio.schemas.compressors import Blosc +from mdio.schemas.dimension import NamedDimension +from mdio.schemas.dtype import ScalarType +from mdio.schemas.dtype import StructuredField +from mdio.schemas.dtype import StructuredType + +__all__ = [ + "Blosc", + "ZFP", + "NamedDimension", + "ScalarType", + "StructuredField", + "StructuredType", +] diff --git a/src/mdio/schemas/base.py b/src/mdio/schemas/base.py new file mode 100644 index 00000000..5684fa10 --- /dev/null +++ b/src/mdio/schemas/base.py @@ -0,0 +1,40 @@ +"""Base models to subclass from.""" + +from pydantic import ConfigDict +from pydantic import Field +from pydantic.json_schema import GenerateJsonSchema + +from mdio.schemas.compressors import ZFP +from mdio.schemas.compressors import Blosc +from mdio.schemas.core import CamelCaseStrictModel +from mdio.schemas.dimension import NamedDimension +from mdio.schemas.dtype import DataTypeModel + +JSON_SCHEMA_DIALECT = GenerateJsonSchema.schema_dialect + + +class BaseDataset(CamelCaseStrictModel): + """A base class for MDIO datasets. + + We add schema dialect to extend the config of `StrictCamelBaseModel`. + We use the default Pydantic schema generator `GenerateJsonSchema` to + define the JSON schema dialect accurately. + """ + + model_config = ConfigDict(json_schema_extra={"$schema": JSON_SCHEMA_DIALECT}) + + +class BaseArray(DataTypeModel, CamelCaseStrictModel): + """A base array schema.""" + + dimensions: list[NamedDimension] | list[str] = Field( + ..., description="List of Dimension collection or reference to dimension names." + ) + compressor: Blosc | ZFP | None = Field(default=None, description="Compression settings.") + + +class NamedArray(BaseArray): + """An array with a name.""" + + name: str = Field(..., description="Name of the array.") + long_name: str | None = Field(default=None, description="Fully descriptive name.") diff --git a/src/mdio/schemas/builder.py b/src/mdio/schemas/builder.py new file mode 100644 index 00000000..40908ff0 --- /dev/null +++ b/src/mdio/schemas/builder.py @@ -0,0 +1,154 @@ +"""Schema builders.""" + +from __future__ import annotations + +from typing import Any + +from mdio.schemas import NamedDimension +from mdio.schemas.v1.dataset import Dataset +from mdio.schemas.v1.dataset import DatasetMetadata +from mdio.schemas.v1.variable import Variable +from mdio.schemas.v1.variable import VariableMetadata + + +class VariableBuilder: + """Dataset builder.""" + + def __init__(self) -> None: + self.name = None + self.long_name = None + self.dtype = None + self.chunks = None + self.dims = None + self.coords = None + self.compressor = None + self.meta_dict = None + + def set_name(self, name: str) -> VariableBuilder: + """Set variable name.""" + self.name = name + return self + + def set_long_name(self, long_name: str) -> VariableBuilder: + """Add long, descriptive name to the variable.""" + self.long_name = long_name + return self + + def set_compressor(self, compressor: dict[str, Any]) -> VariableBuilder: + """Add long, descriptive name to the variable.""" + self.compressor = compressor + return self + + def add_dimension(self, *dimensions: str | dict[str, int]) -> VariableBuilder: + """Add a dimension to the dataset.""" + if self.dims is None: + self.dims = [] + + if isinstance(dimensions[0], str): + dims = list(dimensions) + elif isinstance(dimensions[0], dict): + dims = [ + NamedDimension(name=name, size=size) + for dim in dimensions + for name, size in dim.items() + ] + else: + raise NotImplementedError + + self.dims.extend(dims) + return self + + def add_coordinate(self, *names: str) -> VariableBuilder: + """Add a coordinate to the variable.""" + if self.coords is None: + self.coords = [] + + self.coords.extend(names) + return self + + def set_format(self, format_: str | dict[str, str]) -> VariableBuilder: + """Set variable format.""" + if isinstance(format_, dict): + fields = [{"name": n, "format": f} for n, f in format_.items()] + format_ = {"fields": fields} + + self.dtype = format_ + return self + + def set_chunks(self, chunks: list[int]) -> VariableBuilder: + """Set variable chunks.""" + if self.meta_dict is None: + self.meta_dict = {} + + self.meta_dict["chunkGrid"] = {"configuration": {"chunkShape": chunks}} + return self + + def set_units(self, units: dict[str, str]) -> VariableBuilder: + """Set variable units.""" + if self.meta_dict is None: + self.meta_dict = {} + + self.meta_dict["unitsV1"] = units + return self + + def add_attribute(self, key: str, value: Any) -> VariableBuilder: # noqa: ANN401 + """Add a user attribute to the variable metadata.""" + if self.meta_dict is None: + self.meta_dict = {} + + self.meta_dict["attributes"] = {key: value} + return self + + def build(self) -> Variable: + """Build the dataset model.""" + if self.chunks is not None and len(self.chunks) != len(self.dims): + msg = "Variable chunks must have same number of dimensions." + raise ValueError(msg) + + var_kwargs = {} + + if self.meta_dict is not None: + var_kwargs["metadata"] = VariableMetadata.model_validate(self.meta_dict) + + return Variable( + name=self.name, + long_name=self.long_name, + data_type=self.dtype, + dimensions=self.dims, + coordinates=self.coords, + compressor=self.compressor, + **var_kwargs, + ) + + +class DatasetBuilder: + """Dataset builder.""" + + def __init__(self) -> None: + self.variables = [] + self.name = None + self.metadata = None + + def set_name(self, name: str) -> DatasetBuilder: + """Set dataset name.""" + self.name = name + return self + + def add_variable(self, variable: Variable) -> DatasetBuilder: + """Add a variable to the dataset.""" + self.variables.append(variable) + return self + + def add_variables(self, variables: list[Variable]) -> DatasetBuilder: + """Add multiple variables to the dataset.""" + [self.add_variable(variable) for variable in variables] + return self + + def set_metadata(self, metadata: DatasetMetadata) -> DatasetBuilder: + """Add a metadata to the dataset.""" + self.metadata = metadata + return self + + def build(self) -> Dataset: + """Build the dataset model.""" + return Dataset(variables=self.variables, metadata=self.metadata) diff --git a/src/mdio/schemas/chunk_grid.py b/src/mdio/schemas/chunk_grid.py new file mode 100644 index 00000000..478f11cb --- /dev/null +++ b/src/mdio/schemas/chunk_grid.py @@ -0,0 +1,44 @@ +"""This module contains data models for Zarr's chunk grid.""" + +from __future__ import annotations + +from pydantic import Field + +from mdio.schemas.core import CamelCaseStrictModel + + +class RegularChunkShape(CamelCaseStrictModel): + """Represents regular chunk sizes along each dimension.""" + + chunk_shape: list[int] = Field( + ..., description="Lengths of the chunk along each dimension of the array." + ) + + +class RectilinearChunkShape(CamelCaseStrictModel): + """Represents irregular chunk sizes along each dimension.""" + + chunk_shape: list[list[int]] = Field( + ..., + description="Lengths of the chunk along each dimension of the array.", + ) + + +class RegularChunkGrid(CamelCaseStrictModel): + """Represents a rectangular and regularly spaced chunk grid.""" + + name: str = Field(default="regular", description="The name of the chunk grid.") + + configuration: RegularChunkShape = Field( + ..., description="Configuration of the regular chunk grid." + ) + + +class RectilinearChunkGrid(CamelCaseStrictModel): + """Represents a rectangular and irregularly spaced chunk grid.""" + + name: str = Field(default="rectilinear", description="The name of the chunk grid.") + + configuration: RectilinearChunkShape = Field( + ..., description="Configuration of the irregular chunk grid." + ) diff --git a/src/mdio/schemas/compressors.py b/src/mdio/schemas/compressors.py new file mode 100644 index 00000000..2eb87f7a --- /dev/null +++ b/src/mdio/schemas/compressors.py @@ -0,0 +1,135 @@ +"""This module contains a Pydantic model to parameterize compressors. + +Important Objects: + - Blosc: A Pydantic model that represents a Blosc compression setup. + - ZFP: Class that represents the ZFP compression model. +""" + +from __future__ import annotations + +from enum import IntEnum +from enum import StrEnum + +from pydantic import Field +from pydantic import model_validator + +from mdio.schemas.core import CamelCaseStrictModel + + +class BloscAlgorithm(StrEnum): + """Enum for Blosc algorithm options.""" + + BLOSCLZ = "blosclz" + LZ4 = "lz4" + LZ4HC = "lz4hc" + ZLIB = "zlib" + ZSTD = "zstd" + + +class BloscShuffle(IntEnum): + """Enum for Blosc shuffle options.""" + + NOSHUFFLE = 0 + SHUFFLE = 1 + BITSHUFFLE = 2 + AUTOSHUFFLE = -1 + + +class Blosc(CamelCaseStrictModel): + """Data Model for Blosc options.""" + + name: str = Field(default="blosc", description="Name of the compressor.") + algorithm: BloscAlgorithm = Field( + default=BloscAlgorithm.LZ4, + description="The Blosc compression algorithm to be used.", + ) + level: int = Field(default=5, ge=0, le=9, description="The compression level.") + shuffle: BloscShuffle = Field( + default=BloscShuffle.SHUFFLE, + description="The shuffle strategy to be applied before compression.", + ) + blocksize: int = Field( + default=0, + description="The size of the block to be used for compression.", + ) + + +zfp_mode_map = { + "fixed_rate": 2, + "fixed_precision": 3, + "fixed_accuracy": 4, + "reversible": 5, +} + + +class ZFPMode(StrEnum): + """Enum for ZFP algorithm modes.""" + + FIXED_RATE = "fixed_rate" + FIXED_PRECISION = "fixed_precision" + FIXED_ACCURACY = "fixed_accuracy" + REVERSIBLE = "reversible" + + @property + def int_code(self) -> int: + """Return the integer code of ZFP mode.""" + return zfp_mode_map[self.value] + + +class ZFP(CamelCaseStrictModel): + """Data Model for ZFP options.""" + + name: str = Field(default="zfp", description="Name of the compressor.") + mode: ZFPMode = Field() + + tolerance: float | None = Field( + default=None, + description="Fixed accuracy in terms of absolute error tolerance.", + ) + + rate: float | None = Field( + default=None, + description="Fixed rate in terms of number of compressed bits per value.", + ) + + precision: int | None = Field( + default=None, + description="Fixed precision in terms of number of uncompressed bits per value.", + ) + + write_header: bool = Field( + default=True, + description="Encode array shape, scalar type, and compression parameters.", + ) + + @model_validator(mode="after") + def check_requirements(self) -> ZFP: + """Check if ZFP parameters make sense.""" + mode = self.mode + + # Check if reversible mode is provided without other parameters. + if mode == ZFPMode.REVERSIBLE and any( + getattr(self, key) is not None for key in ["tolerance", "rate", "precision"] + ): + msg = "Other fields must be None in REVERSIBLE mode" + raise ValueError(msg) + + if mode == ZFPMode.FIXED_ACCURACY and self.tolerance is None: + msg = "Tolerance required for FIXED_ACCURACY mode" + raise ValueError(msg) + + if mode == ZFPMode.FIXED_RATE and self.rate is None: + msg = "Rate required for FIXED_RATE mode" + raise ValueError(msg) + + if mode == ZFPMode.FIXED_PRECISION and self.precision is None: + msg = "Precision required for FIXED_PRECISION mode" + raise ValueError(msg) + + return self + + +class CompressorModel(CamelCaseStrictModel): + """Model representing compressor configuration.""" + + compressor: Blosc | ZFP | None = Field(default=None, description="Compression settings.") diff --git a/src/mdio/schemas/core.py b/src/mdio/schemas/core.py new file mode 100644 index 00000000..34a09066 --- /dev/null +++ b/src/mdio/schemas/core.py @@ -0,0 +1,49 @@ +"""This module implements the core components of the MDIO schemas.""" + +from __future__ import annotations + +from typing import Any +from typing import get_type_hints + +from pydantic import BaseModel +from pydantic import ConfigDict +from pydantic.alias_generators import to_camel + + +def model_fields(model: type[BaseModel]) -> dict[str, tuple[Any, Any]]: + """Extract Pydantic BaseModel fields. + + Args: + model: (Type) The model object for which the fields will be extracted. + + Returns: + A dictionary containing the fields of the model along with + their corresponding types and default values. + + Example: + >>> class MyModel(BaseModel): + ... name: str + ... age: int = 0 + ... + >>> model_fields(MyModel) + {'name': (str, ), 'age': (int, 0)} + """ + annotations = get_type_hints(model) + + fields = {} + for field_name, field in model.model_fields.items(): + fields[field_name] = (annotations[field_name], field) + + return fields + + +class StrictModel(BaseModel): + """A model with forbidden extras.""" + + model_config = ConfigDict(extra="forbid", populate_by_name=True) + + +class CamelCaseStrictModel(StrictModel): + """A model with forbidden extras and camel case aliases.""" + + model_config = ConfigDict(alias_generator=to_camel) diff --git a/src/mdio/schemas/dimension.py b/src/mdio/schemas/dimension.py new file mode 100644 index 00000000..62185a39 --- /dev/null +++ b/src/mdio/schemas/dimension.py @@ -0,0 +1,12 @@ +"""Dimension schema.""" + +from pydantic import Field + +from mdio.schemas.core import CamelCaseStrictModel + + +class NamedDimension(CamelCaseStrictModel): + """Represents a single dimension with a name and size.""" + + name: str = Field(..., description="Unique identifier for the dimension.") + size: int = Field(..., gt=0, description="Total size of the dimension.") diff --git a/src/mdio/schemas/dtype.py b/src/mdio/schemas/dtype.py new file mode 100644 index 00000000..af7f6582 --- /dev/null +++ b/src/mdio/schemas/dtype.py @@ -0,0 +1,53 @@ +"""Schemas for scalar types. + +We take booleans, unsigned and signed integers, floats, and +complex numbers from numpy data types and allow those. +""" + +from __future__ import annotations + +from enum import StrEnum + +from pydantic import Field + +from mdio.schemas.core import CamelCaseStrictModel + + +class ScalarType(StrEnum): + """Scalar array data type.""" + + BOOL = "bool" + INT8 = "int8" + INT16 = "int16" + INT32 = "int32" + INT64 = "int64" + UINT8 = "uint8" + UINT16 = "uint16" + UINT32 = "uint32" + UINT64 = "uint64" + FLOAT16 = "float16" + FLOAT32 = "float32" + FLOAT64 = "float64" + FLOAT128 = "float128" + COMPLEX64 = "complex64" + COMPLEX128 = "complex128" + COMPLEX256 = "complex256" + + +class StructuredField(CamelCaseStrictModel): + """Structured array field with name, format.""" + + format: ScalarType = Field(...) + name: str = Field(...) + + +class StructuredType(CamelCaseStrictModel): + """Structured array type with packed fields.""" + + fields: list[StructuredField] = Field() + + +class DataTypeModel(CamelCaseStrictModel): + """Structured array type with fields and total item size.""" + + data_type: ScalarType | StructuredType = Field(..., description="Type of the array.") diff --git a/src/mdio/schemas/metadata.py b/src/mdio/schemas/metadata.py new file mode 100644 index 00000000..dc2d6676 --- /dev/null +++ b/src/mdio/schemas/metadata.py @@ -0,0 +1,31 @@ +"""Metadata schemas and conventions.""" + +from typing import Any + +from pydantic import Field + +from mdio.schemas.chunk_grid import RectilinearChunkGrid +from mdio.schemas.chunk_grid import RegularChunkGrid +from mdio.schemas.core import CamelCaseStrictModel + + +class ChunkGridMetadata(CamelCaseStrictModel): + """Definition of chunk grid.""" + + chunk_grid: RegularChunkGrid | RectilinearChunkGrid | None = Field( + default=None, + description="Chunk grid specification for the array.", + ) + + +class VersionedMetadataConvention(CamelCaseStrictModel): + """Data model for versioned metadata convention.""" + + +class UserAttributes(CamelCaseStrictModel): + """User defined attributes as key/value pairs.""" + + attributes: dict[str, Any] | None = Field( + default=None, + description="User defined attributes as key/value pairs.", + ) diff --git a/src/mdio/schemas/units.py b/src/mdio/schemas/units.py new file mode 100644 index 00000000..6844b433 --- /dev/null +++ b/src/mdio/schemas/units.py @@ -0,0 +1,51 @@ +"""Common units for resource assessment data.""" + +from __future__ import annotations + +from enum import Enum +from enum import unique + +from pydantic import Field +from pydantic import create_model + +from mdio.schemas.core import CamelCaseStrictModel + + +@unique +class UnitEnum(str, Enum): + """An Enum representing units as strings, from pint.""" + + +def create_unit_model( + unit_enum: type[UnitEnum], + model_name: str, + quantity: str, + module: str, +) -> type[CamelCaseStrictModel]: + """Dynamically creates a pydantic model from a unit Enum. + + Args: + unit_enum: UnitEnum representing the units for a specific quantity. + model_name: The name of the model to be created. + quantity: String representing the quantity for which the unit model is created. + module: Name of the module in which the model is to be created. + This should be the `__name__` attribute of the module. + + Returns: + A Pydantic Model representing the unit model derived from the BaseModel. + + Example: + unit_enum = UnitEnum + model_name = "LengthUnitModel" + quantity = "length" + create_unit_model(unit_enum, model_name, quantity) + """ + fields = {quantity: (unit_enum, Field(..., description=f"Unit of {quantity}."))} + + return create_model( + model_name, + **fields, + __base__=CamelCaseStrictModel, + __doc__=f"Model representing units of {quantity}.", + __module__=module, + ) diff --git a/src/mdio/schemas/v0/__init__.py b/src/mdio/schemas/v0/__init__.py new file mode 100644 index 00000000..9b953304 --- /dev/null +++ b/src/mdio/schemas/v0/__init__.py @@ -0,0 +1,5 @@ +"""Schema specific to MDIO v0.""" + +from mdio.schemas.v0.dataset import DatasetModelV0 + +__all__ = ["DatasetModelV0"] diff --git a/src/mdio/schemas/v0/dataset.py b/src/mdio/schemas/v0/dataset.py new file mode 100644 index 00000000..50103736 --- /dev/null +++ b/src/mdio/schemas/v0/dataset.py @@ -0,0 +1,82 @@ +"""Dataset model for MDIO V0.""" + +from __future__ import annotations + +from pydantic import AwareDatetime +from pydantic import Field + +from mdio.schemas.base import BaseArray +from mdio.schemas.base import BaseDataset +from mdio.schemas.core import CamelCaseStrictModel +from mdio.schemas.core import StrictModel + + +class DimensionModelV0(CamelCaseStrictModel): + """Represents dimension schema for MDIO v0.""" + + name: str = Field(..., description="Name of the dimension.") + coords: list[int] = Field(..., description="Coordinate labels (ticks).") + + +class DatasetMetadataModelV0(StrictModel): + """Represents dataset attributes schema for MDIO v0.""" + + api_version: str = Field( + ..., + description="MDIO version.", + ) + + created: AwareDatetime = Field( + ..., + description="Creation time with TZ info.", + ) + + dimension: list[DimensionModelV0] = Field( + ..., + description="Dimensions.", + ) + + mean: float | None = Field( + default=None, + description="Mean value of the samples.", + ) + + # Statistical information + std: float | None = Field(default=None, description="Standard deviation of the samples.") + + rms: float | None = Field(default=None, description="Root mean squared value of the samples.") + + min: float | None = Field( + default=None, + description="Minimum value of the samples.", + ) + + max: float | None = Field( + default=None, + description="Maximum value of the samples.", + ) + + trace_count: int | None = Field(default=None, description="Number of traces in the SEG-Y file.") + + +class VariableModelV0(BaseArray): + """Represents an MDIO v0 variable schema.""" + + +class DatasetModelV0(BaseDataset): + """Represents an MDIO v0 dataset schema.""" + + seismic: list[VariableModelV0] = Field( + ..., + description="Variable containing seismic.", + ) + + headers: list[VariableModelV0] = Field( + ..., + description="Variable containing headers.", + ) + + metadata: DatasetMetadataModelV0 = Field( + ..., + description="Dataset metadata.", + ) diff --git a/src/mdio/schemas/v1/__init__.py b/src/mdio/schemas/v1/__init__.py new file mode 100644 index 00000000..fb98d55e --- /dev/null +++ b/src/mdio/schemas/v1/__init__.py @@ -0,0 +1,5 @@ +"""Schema specific to MDIO v1.""" + +from mdio.schemas.v1.dataset import Dataset + +__all__ = ["Dataset"] diff --git a/src/mdio/schemas/v1/dataset.py b/src/mdio/schemas/v1/dataset.py new file mode 100644 index 00000000..f101093b --- /dev/null +++ b/src/mdio/schemas/v1/dataset.py @@ -0,0 +1,49 @@ +"""Dataset model for MDIO V1.""" + +from pydantic import AwareDatetime +from pydantic import Field +from pydantic import create_model + +from mdio.schemas.base import BaseDataset +from mdio.schemas.core import CamelCaseStrictModel +from mdio.schemas.core import model_fields +from mdio.schemas.metadata import UserAttributes +from mdio.schemas.v1.variable import Variable + + +class DatasetInfo(CamelCaseStrictModel): + """Contains information about a dataset.""" + + name: str = Field(..., description="Name or identifier for the dataset.") + + api_version: str = Field( + ..., + description="The version of the MDIO API that the dataset complies with.", + ) + + created_on: AwareDatetime = Field( + ..., + description=( + "The timestamp indicating when the dataset was first created, " + "including timezone information. Expressed in ISO 8601 format." + ), + ) + + +DatasetMetadata = create_model( + "DatasetMetadata", + **model_fields(DatasetInfo), + **model_fields(UserAttributes), + __base__=CamelCaseStrictModel, +) +DatasetMetadata.__doc__ = "The metadata about the dataset." + + +class Dataset(BaseDataset): + """Represents an MDIO v1 dataset. + + A dataset consists of variables and metadata. + """ + + variables: list[Variable] = Field(..., description="Variables in MDIO dataset") + metadata: DatasetMetadata = Field(..., description="Dataset metadata.") diff --git a/src/mdio/schemas/v1/dataset_builder.py b/src/mdio/schemas/v1/dataset_builder.py new file mode 100644 index 00000000..facd0dc8 --- /dev/null +++ b/src/mdio/schemas/v1/dataset_builder.py @@ -0,0 +1,335 @@ +"""Builder pattern implementation for MDIO v1 schema models.""" + +from datetime import UTC +from datetime import datetime +from enum import Enum +from enum import auto +from importlib import metadata +from typing import Any +from typing import TypeAlias + +from pydantic import BaseModel + +from mdio.schemas.compressors import ZFP +from mdio.schemas.compressors import Blosc +from mdio.schemas.dimension import NamedDimension +from mdio.schemas.dtype import ScalarType +from mdio.schemas.dtype import StructuredType +from mdio.schemas.metadata import ChunkGridMetadata +from mdio.schemas.metadata import UserAttributes +from mdio.schemas.v1.dataset import Dataset +from mdio.schemas.v1.dataset import DatasetInfo +from mdio.schemas.v1.stats import StatisticsMetadata +from mdio.schemas.v1.units import AllUnits +from mdio.schemas.v1.variable import Coordinate +from mdio.schemas.v1.variable import Variable + +AnyMetadataList: TypeAlias = list[ + AllUnits | UserAttributes | ChunkGridMetadata | StatisticsMetadata | DatasetInfo +] +CoordinateMetadataList: TypeAlias = list[AllUnits | UserAttributes] +VariableMetadataList: TypeAlias = list[ + AllUnits | UserAttributes | ChunkGridMetadata | StatisticsMetadata +] +DatasetMetadataList: TypeAlias = list[DatasetInfo | UserAttributes] + + +class _BuilderState(Enum): + """States for the template builder.""" + + INITIAL = auto() + HAS_DIMENSIONS = auto() + HAS_COORDINATES = auto() + HAS_VARIABLES = auto() + + +def _get_named_dimension( + dimensions: list[NamedDimension], name: str, size: int | None = None +) -> NamedDimension | None: + """Get a dimension by name and optional size from the list[NamedDimension].""" + if dimensions is None: + return False + if not isinstance(name, str): + msg = f"Expected str, got {type(name).__name__}" + raise TypeError(msg) + + nd = next((dim for dim in dimensions if dim.name == name), None) + if nd is None: + return None + if size is not None and nd.size != size: + msg = f"Dimension {name!r} found but size {nd.size} does not match expected size {size}" + raise ValueError(msg) + return nd + + +def _to_dictionary(val: BaseModel | dict[str, Any] | AnyMetadataList) -> dict[str, Any]: + """Convert a dictionary, list or pydantic BaseModel to a dictionary.""" + if val is None: + return None + if isinstance(val, BaseModel): + return val.model_dump(mode="json", by_alias=True) + if isinstance(val, dict): + return val + if isinstance(val, list): + metadata_dict = {} + for md in val: + if md is None: + continue + metadata_dict.update(_to_dictionary(md)) + return metadata_dict + msg = f"Expected BaseModel, dict or list, got {type(val).__name__}" + raise TypeError(msg) + + +class MDIODatasetBuilder: + """Builder for creating MDIO datasets with enforced build order. + + This builder implements the builder pattern to create MDIO datasets with a v1 schema. + It enforces a specific build order to ensure valid dataset construction: + 1. Must add dimensions first via add_dimension() + 2. Can optionally add coordinates via add_coordinate() + 3. Must add variables via add_variable() + 4. Must call build() to create the dataset. + """ + + def __init__(self, name: str, attributes: UserAttributes | None = None): + try: + api_version = metadata.version("multidimio") + except metadata.PackageNotFoundError: + api_version = "unknown" + + self._info = DatasetInfo(name=name, api_version=api_version, created_on=datetime.now(UTC)) + self._attributes = attributes + self._dimensions: list[NamedDimension] = [] + self._coordinates: list[Coordinate] = [] + self._variables: list[Variable] = [] + self._state = _BuilderState.INITIAL + self._unnamed_variable_counter = 0 + + def add_dimension(self, name: str, size: int) -> "MDIODatasetBuilder": + """Add a dimension. + + This function be called at least once before adding coordinates or variables. + + Args: + name: Name of the dimension + size: Size of the dimension + + Raises: + ValueError: If 'name' is not a non-empty string. + if the dimension is already defined. + + Returns: + self: Returns self for method chaining + """ + if not name: + msg = "'name' must be a non-empty string" + raise ValueError(msg) + + # Validate that the dimension is not already defined + old_var = next((e for e in self._dimensions if e.name == name), None) + if old_var is not None: + msg = "Adding dimension with the same name twice is not allowed" + raise ValueError(msg) + + dim = NamedDimension(name=name, size=size) + self._dimensions.append(dim) + self._state = _BuilderState.HAS_DIMENSIONS + return self + + def add_coordinate( # noqa: PLR0913 + self, + name: str, + *, + long_name: str = None, + dimensions: list[str], + data_type: ScalarType, + compressor: Blosc | ZFP | None = None, + metadata_info: CoordinateMetadataList | None = None, + ) -> "MDIODatasetBuilder": + """Add a coordinate after adding at least one dimension. + + This function must be called after all required dimensions are added via add_dimension(). + This call will create a coordinate variable. + + Args: + name: Name of the coordinate + long_name: Optional long name for the coordinate + dimensions: List of dimension names that the coordinate is associated with + data_type: Data type for the coordinate (defaults to FLOAT32) + compressor: Compressor used for the variable (defaults to None) + metadata_info: Optional metadata information for the coordinate + + Raises: + ValueError: If no dimensions have been added yet. + If 'name' is not a non-empty string. + If 'dimensions' is not a non-empty list. + If the coordinate is already defined. + If any referenced dimension is not already defined. + + Returns: + self: Returns self for method chaining + """ + if self._state == _BuilderState.INITIAL: + msg = "Must add at least one dimension before adding coordinates" + raise ValueError(msg) + if not name: + msg = "'name' must be a non-empty string" + raise ValueError(msg) + if dimensions is None or not dimensions: + msg = "'dimensions' must be a non-empty list" + raise ValueError(msg) + old_var = next((e for e in self._coordinates if e.name == name), None) + # Validate that the coordinate is not already defined + if old_var is not None: + msg = "Adding coordinate with the same name twice is not allowed" + raise ValueError(msg) + + # Validate that all referenced dimensions are already defined + named_dimensions = [] + for dim_name in dimensions: + nd = _get_named_dimension(self._dimensions, dim_name) + if nd is None: + msg = f"Pre-existing dimension named {dim_name!r} is not found" + raise ValueError(msg) + named_dimensions.append(nd) + + meta_dict = _to_dictionary(metadata_info) + coord = Coordinate( + name=name, + longName=long_name, + dimensions=named_dimensions, + compressor=compressor, + dataType=data_type, + metadata=meta_dict, + ) + self._coordinates.append(coord) + + # Add a coordinate variable to the dataset + self.add_variable( + name=coord.name, + long_name=f"'{coord.name}' coordinate variable", + dimensions=dimensions, # dimension names (list[str]) + data_type=coord.data_type, + compressor=compressor, + coordinates=[name], # Use the coordinate name as a reference + metadata_info=coord.metadata, + ) + + self._state = _BuilderState.HAS_COORDINATES + return self + + def add_variable( # noqa: PLR0913 + self, + name: str, + *, + long_name: str = None, + dimensions: list[str], + data_type: ScalarType | StructuredType, + compressor: Blosc | ZFP | None = None, + coordinates: list[str] | None = None, + metadata_info: VariableMetadataList | None = None, + ) -> "MDIODatasetBuilder": + """Add a variable after adding at least one dimension and, optionally, coordinate. + + This function must be called after all required dimensions are added via add_dimension() + This function must be called after all required coordinates are added via add_coordinate(). + + If this function is called with a single dimension name that matches the variable name, + it will create a dimension variable. Dimension variables are special variables that + represent sampling along a dimension. + + Args: + name: Name of the variable + long_name: Optional long name for the variable + dimensions: List of dimension names that the variable is associated with + data_type: Data type for the variable (defaults to FLOAT32) + compressor: Compressor used for the variable (defaults to None) + coordinates: List of coordinate names that the variable is associated with + (defaults to None, meaning no coordinates) + metadata_info: Optional metadata information for the variable + + Raises: + ValueError: If no dimensions have been added yet. + If 'name' is not a non-empty string. + If 'dimensions' is not a non-empty list. + If the variable is already defined. + If any referenced dimension is not already defined. + If any referenced coordinate is not already defined. + + Returns: + self: Returns self for method chaining. + """ + if self._state == _BuilderState.INITIAL: + msg = "Must add at least one dimension before adding variables" + raise ValueError(msg) + if not name: + msg = "'name' must be a non-empty string" + raise ValueError(msg) + if dimensions is None or not dimensions: + msg = "'dimensions' must be a non-empty list" + raise ValueError(msg) + + # Validate that the variable is not already defined + old_var = next((e for e in self._variables if e.name == name), None) + if old_var is not None: + msg = "Adding variable with the same name twice is not allowed" + raise ValueError(msg) + + # Validate that all referenced dimensions are already defined + named_dimensions = [] + for dim_name in dimensions: + nd = _get_named_dimension(self._dimensions, dim_name) + if nd is None: + msg = f"Pre-existing dimension named {dim_name!r} is not found" + raise ValueError(msg) + named_dimensions.append(nd) + + coordinate_objs: list[Coordinate] = [] + # Validate that all referenced coordinates are already defined + if coordinates is not None: + for coord in coordinates: + c: Coordinate = next((c for c in self._coordinates if c.name == coord), None) + if c is not None: + coordinate_objs.append(c) + else: + msg = f"Pre-existing coordinate named {coord!r} is not found" + raise ValueError(msg) + + # If this is a dimension coordinate variable, embed the Coordinate into it + if coordinates is not None and len(coordinates) == 1 and coordinates[0] == name: + coordinates = coordinate_objs + + meta_dict = _to_dictionary(metadata_info) + var = Variable( + name=name, + long_name=long_name, + dimensions=named_dimensions, + data_type=data_type, + compressor=compressor, + coordinates=coordinates, + metadata=meta_dict, + ) + self._variables.append(var) + + self._state = _BuilderState.HAS_VARIABLES + return self + + def build(self) -> Dataset: + """Build the final dataset. + + This function must be called after at least one dimension is added via add_dimension(). + It will create a Dataset object with all added dimensions, coordinates, and variables. + + Raises: + ValueError: If no dimensions have been added yet. + + Returns: + Dataset: The built dataset with all added dimensions, coordinates, and variables. + """ + if self._state == _BuilderState.INITIAL: + msg = "Must add at least one dimension before building" + raise ValueError(msg) + + var_meta_dict = _to_dictionary([self._info, self._attributes]) + return Dataset(variables=self._variables, metadata=var_meta_dict) diff --git a/src/mdio/schemas/v1/stats.py b/src/mdio/schemas/v1/stats.py new file mode 100644 index 00000000..29d50add --- /dev/null +++ b/src/mdio/schemas/v1/stats.py @@ -0,0 +1,68 @@ +"""Statistics schema for MDIO v1 arrays. + +This module provides two Histogram classes (CenteredBinHistogram and +EdgeDefinedHistogram),a summary statistics class, and a summary statistics +metadata class. + +SummaryStatistics: a class that represents the minimum summary statistics +of an array consisting of count, sum, sum of squares, min, max, and a histogram. + +SummaryStatisticsMetadata: represents metadata for statistics, with a field +for v1 of the stats. + +CenteredBinHistogram takes the center points of each bin in a histogram, +while EdgeDefinedHistogram takes the left edges and widths of each bin. +Both classes extend from the base class BaseHistogram, which represents +a histogram with count of each bin. +""" + +from __future__ import annotations + +from typing import TypeAlias + +from pydantic import Field + +from mdio.schemas.core import CamelCaseStrictModel +from mdio.schemas.metadata import VersionedMetadataConvention + + +class BaseHistogram(CamelCaseStrictModel): + """Represents a histogram with bin counts.""" + + counts: list[int] = Field(..., description="Count of each each bin.") + + +class CenteredBinHistogram(BaseHistogram): + """Class representing a center bin histogram.""" + + bin_centers: list[float | int] = Field(..., description="List of bin centers.") + + +class EdgeDefinedHistogram(BaseHistogram): + """A class representing an edge-defined histogram.""" + + bin_edges: list[float | int] = Field(..., description="The left edges of the histogram bins.") + bin_widths: list[float | int] = Field(..., description="The widths of the histogram bins.") + + +Histogram: TypeAlias = CenteredBinHistogram | EdgeDefinedHistogram + + +class SummaryStatistics(CamelCaseStrictModel): + """Data model for some statistics in MDIO v1 arrays.""" + + count: int = Field(..., description="The number of data points.") + sum: float = Field(..., description="The total of all data values.") + sum_squares: float = Field(..., description="The total of all data values squared.") + min: float = Field(..., description="The smallest value in the variable.") + max: float = Field(..., description="The largest value in the variable.") + histogram: Histogram = Field(..., description="Binned frequency distribution.") + + +class StatisticsMetadata(VersionedMetadataConvention): + """Data Model representing metadata for statistics.""" + + stats_v1: SummaryStatistics | list[SummaryStatistics] | None = Field( + default=None, + description="Minimal summary statistics.", + ) diff --git a/src/mdio/schemas/v1/units.py b/src/mdio/schemas/v1/units.py new file mode 100644 index 00000000..1913ff2e --- /dev/null +++ b/src/mdio/schemas/v1/units.py @@ -0,0 +1,119 @@ +"""Unit schemas specific to MDIO v1.""" + +from __future__ import annotations + +from typing import TypeAlias + +from pint import UnitRegistry +from pydantic import Field + +from mdio.schemas.metadata import VersionedMetadataConvention +from mdio.schemas.units import UnitEnum +from mdio.schemas.units import create_unit_model + +ureg = UnitRegistry() +ureg.default_format = "~C" # compact, abbreviated (symbol). + + +class LengthUnitEnum(UnitEnum): + """Enum class representing metric units of length.""" + + MILLIMETER = ureg.millimeter + CENTIMETER = ureg.centimeter + METER = ureg.meter + KILOMETER = ureg.kilometer + + INCH = ureg.inch + FOOT = ureg.foot + YARD = ureg.yard + MILE = ureg.mile + + +LengthUnitModel = create_unit_model(LengthUnitEnum, "LengthUnitModel", "length", __name__) + + +class TimeUnitEnum(UnitEnum): + """Enum class representing units of time.""" + + NANOSECOND = ureg.nanosecond + MICROSECOND = ureg.microsecond + MILLISECOND = ureg.millisecond + SECOND = ureg.second + MINUTE = ureg.minute + HOUR = ureg.hour + DAY = ureg.day + + +TimeUnitModel = create_unit_model(TimeUnitEnum, "TimeUnitModel", "time", __name__) + + +class DensityUnitEnum(UnitEnum): + """Enum class representing units of density.""" + + GRAMS_PER_CC = ureg.gram / ureg.centimeter**3 + KILOGRAMS_PER_M3 = ureg.kilogram / ureg.meter**3 + POUNDS_PER_GAL = ureg.pounds / ureg.gallon + + +DensityUnitModel = create_unit_model(DensityUnitEnum, "DensityUnitModel", "density", __name__) + + +class SpeedUnitEnum(UnitEnum): + """Enum class representing units of speed.""" + + METER_PER_SECOND = ureg.meter / ureg.second + FEET_PER_SECOND = ureg.feet / ureg.second + + +SpeedUnitModel = create_unit_model(SpeedUnitEnum, "SpeedUnitModel", "speed", __name__) + + +class AngleUnitEnum(UnitEnum): + """Enum class representing units of angle.""" + + DEGREES = ureg.degree + RADIANS = ureg.radian + + +AngleUnitModel = create_unit_model(AngleUnitEnum, "AngleUnitModel", "angle", __name__) + + +class FrequencyUnitEnum(UnitEnum): + """Enum class representing units of frequency.""" + + HERTZ = ureg.hertz + + +FrequencyUnitModel = create_unit_model( + FrequencyUnitEnum, "FrequencyUnitModel", "frequency", __name__ +) + + +class VoltageUnitEnum(UnitEnum): + """Enum class representing units of voltage.""" + + MICROVOLT = ureg.microvolt + MILLIVOLT = ureg.millivolt + VOLT = ureg.volt + + +VoltageUnitModel = create_unit_model(VoltageUnitEnum, "VoltageUnitModel", "voltage", __name__) + + +# Composite model types +AllUnitModel: TypeAlias = ( + LengthUnitModel + | TimeUnitModel + | AngleUnitModel + | DensityUnitModel + | SpeedUnitModel + | FrequencyUnitModel + | VoltageUnitModel +) + + +# Versioned metadata conventions for units +class AllUnits(VersionedMetadataConvention): + """All Units.""" + + units_v1: AllUnitModel | list[AllUnitModel] | None = Field(default=None) diff --git a/src/mdio/schemas/v1/variable.py b/src/mdio/schemas/v1/variable.py new file mode 100644 index 00000000..93b59117 --- /dev/null +++ b/src/mdio/schemas/v1/variable.py @@ -0,0 +1,63 @@ +"""This module defines `LabeledArray`, `Coordinate`, and `Variable`. + +`LabeledArray` is a basic array unit which includes basic properties like +name, dimension, data type, compressor etc. + +`Coordinate` extends the `LabeledArray` class, it represents the Coordinate +array in the MDIO format. It has dimensions which are fully defined and can hold +additional metadata. + +`Variable` is another class that extends the `LabeledArray`. It represents a +variable in MDIO format. It can have coordinates and can also hold metadata. +""" + +from pydantic import Field +from pydantic import create_model + +from mdio.schemas.base import NamedArray +from mdio.schemas.core import CamelCaseStrictModel +from mdio.schemas.core import model_fields +from mdio.schemas.dtype import ScalarType +from mdio.schemas.metadata import ChunkGridMetadata +from mdio.schemas.metadata import UserAttributes +from mdio.schemas.v1.stats import StatisticsMetadata +from mdio.schemas.v1.units import AllUnits + +CoordinateMetadata = create_model( + "CoordinateMetadata", + **model_fields(AllUnits), + **model_fields(UserAttributes), + __base__=CamelCaseStrictModel, + __doc__="Reduced Metadata, perfect for simple Coordinates.", +) + + +class Coordinate(NamedArray): + """A simple MDIO Coordinate array with metadata. + + For large or complex Coordinates, define a Variable instead. + """ + + data_type: ScalarType = Field(..., description="Data type of Coordinate.") + metadata: CoordinateMetadata | None = Field(default=None, description="Coordinate Metadata.") + + +VariableMetadata = create_model( + "VariableMetadata", + **model_fields(ChunkGridMetadata), + **model_fields(AllUnits), + **model_fields(StatisticsMetadata), + **model_fields(UserAttributes), + __base__=CamelCaseStrictModel, + __doc__="Complete Metadata for Variables and complex or large Coordinates.", +) + + +class Variable(NamedArray): + """An MDIO Variable that has coordinates and metadata.""" + + coordinates: list[Coordinate] | list[str] | None = Field( + default=None, + description="Coordinates of the MDIO Variable dimensions.", + ) + metadata: VariableMetadata | None = Field(default=None, description="Variable Metadata.") diff --git a/tests/unit/test_schema.py b/tests/unit/test_schema.py new file mode 100644 index 00000000..0b2a9f54 --- /dev/null +++ b/tests/unit/test_schema.py @@ -0,0 +1,53 @@ +"""Test the schema for the v1 dataset.""" + +from mdio.schemas.v1 import Dataset as V1Dataset + +TEST_SCHEMA = { + "metadata": { + "name": "test_dataset", + "api_version": "1.0.0", + "created_on": "2023-01-01T00:00:00Z", + }, + "variables": [ + { + "name": "actual_variable", + "data_type": "float32", + "dimensions": ["dim0", "dim1"], + "compressor": {"name": "blosc", "level": 3}, + "coordinates": ["coord"], + "metadata": { + "chunk_grid": { + "name": "regular", + "configuration": {"chunk_shape": [10, 20]}, + }, + }, + }, + { + "name": "coord", + "data_type": "float32", + "dimensions": ["dim0", "dim1"], + "metadata": { + "chunk_grid": { + "name": "regular", + "configuration": {"chunk_shape": [10, 20]}, + }, + "units_v1": {"length": "m"}, + }, + }, + { + "name": "dim0", + "data_type": "int32", + "dimensions": [{"name": "dim0", "size": 100}], + }, + { + "name": "dim1", + "data_type": "int32", + "dimensions": [{"name": "dim1", "size": 200}], + }, + ], +} + + +def test_dataset_schema_validation() -> None: + """Test that the dataset schema validates correctly.""" + V1Dataset.model_validate(TEST_SCHEMA) diff --git a/tests/unit/v1/__init__.py b/tests/unit/v1/__init__.py new file mode 100644 index 00000000..fa2ea633 --- /dev/null +++ b/tests/unit/v1/__init__.py @@ -0,0 +1 @@ +"""Unit tests for parts of the MDIO package related to the v1 schema.""" diff --git a/tests/unit/v1/helpers.py b/tests/unit/v1/helpers.py new file mode 100644 index 00000000..2058bdd5 --- /dev/null +++ b/tests/unit/v1/helpers.py @@ -0,0 +1,254 @@ +"""Helper methods used in unit tests.""" + +from mdio.schemas.chunk_grid import RegularChunkGrid +from mdio.schemas.chunk_grid import RegularChunkShape +from mdio.schemas.compressors import Blosc +from mdio.schemas.dtype import ScalarType +from mdio.schemas.dtype import StructuredField +from mdio.schemas.dtype import StructuredType +from mdio.schemas.metadata import ChunkGridMetadata +from mdio.schemas.metadata import UserAttributes +from mdio.schemas.v1.dataset import Dataset +from mdio.schemas.v1.dataset_builder import MDIODatasetBuilder +from mdio.schemas.v1.dataset_builder import _BuilderState +from mdio.schemas.v1.dataset_builder import _get_named_dimension +from mdio.schemas.v1.stats import CenteredBinHistogram +from mdio.schemas.v1.stats import StatisticsMetadata +from mdio.schemas.v1.stats import SummaryStatistics +from mdio.schemas.v1.units import AllUnits +from mdio.schemas.v1.units import LengthUnitEnum +from mdio.schemas.v1.units import LengthUnitModel +from mdio.schemas.v1.units import SpeedUnitEnum +from mdio.schemas.v1.units import SpeedUnitModel +from mdio.schemas.v1.variable import Coordinate +from mdio.schemas.v1.variable import Variable + + +def validate_builder( + builder: MDIODatasetBuilder, state: _BuilderState, n_dims: int, n_coords: int, n_var: int +) -> None: + """Validate the state of the builder, the number of dimensions, coordinates, and variables.""" + assert builder._state == state + assert len(builder._dimensions) == n_dims + assert len(builder._coordinates) == n_coords + assert len(builder._variables) == n_var + + +def validate_coordinate( + builder: MDIODatasetBuilder, name: str, dims: list[tuple[str, int]], dtype: ScalarType +) -> Coordinate: + """Validate existence and the structure of the created coordinate.""" + # Validate that coordinate exists + c = next((c for c in builder._coordinates if c.name == name), None) + assert c is not None + assert isinstance(c, Coordinate) + + # Validate that dimensions are stored as NamedDimensions + for d in dims: + name = d[0] + size = d[1] + assert _get_named_dimension(c.dimensions, name, size) is not None + + assert c.data_type == dtype + return c + + +def validate_variable( + container: MDIODatasetBuilder | Dataset, + name: str, + dims: list[tuple[str, int]], + coords: list[str], + dtype: ScalarType, +) -> Variable: + """Validate existence and the structure of the created variable.""" + if isinstance(container, MDIODatasetBuilder): + var_list = container._variables + global_coord_list = container._coordinates + elif isinstance(container, Dataset): + var_list = container.variables + global_coord_list = _get_all_coordinates(container) + else: + err_msg = f"Expected MDIODatasetBuilder or Dataset, got {type(container)}" + raise TypeError(err_msg) + + # Validate that the variable exists + v = next((e for e in var_list if e.name == name), None) + assert v is not None + assert isinstance(v, Variable) + + # Validate that dimensions are stored as NamedDimensions within the variable + assert len(v.dimensions) == len(dims) + for d in dims: + name = d[0] + size = d[1] + assert _get_named_dimension(v.dimensions, name, size) is not None + + # Validate that coordinates are either embedded or can be resolved from names to Coordinate + if coords is None: + assert v.coordinates is None + else: + assert len(v.coordinates) == len(coords) + for coord_name in coords: + assert _get_coordinate(global_coord_list, v.coordinates, coord_name) is not None + + assert v.data_type == dtype + return v + + +def _get_coordinate( + global_coord_list: list[Coordinate], + coordinates_or_references: list[Coordinate] | list[str], + name: str, +) -> Coordinate | None: + """Get a coordinate by name from the list[Coordinate] | list[str]. + + The function validates that the coordinate referenced by the name can be found + in the global coordinate list. + If the coordinate is stored as a Coordinate object, it is returned directly. + """ + if coordinates_or_references is None: + return None + + for c in coordinates_or_references: + if isinstance(c, str) and c == name: + # The coordinate is stored by name (str). + cc = None + # Find the Coordinate in the global list and return it. + if global_coord_list is not None: + cc = next((cc for cc in global_coord_list if cc.name == name), None) + if cc is None: + msg = f"Pre-existing coordinate named {name!r} is not found" + raise ValueError(msg) + return cc + if isinstance(c, Coordinate) and c.name == name: + # The coordinate is stored as an embedded Coordinate object. + # Return it. + return c + + return None + + +def _get_all_coordinates(dataset: Dataset) -> list[Coordinate]: + all_coords: dict[str, Coordinate] = {} + for v in dataset.variables: + if v.coordinates is not None: + for c in v.coordinates: + if isinstance(c, Coordinate) and c.name not in all_coords: + all_coords[c.name] = c + return list(all_coords.values()) + + +def make_campos_3d_dataset() -> Dataset: + """Create in-memory campos_3d dataset.""" + ds = MDIODatasetBuilder( + "campos_3d", + attributes=UserAttributes( + attributes={ + "textHeader": [ + "C01 .......................... ", + "C02 .......................... ", + "C03 .......................... ", + ], + "foo": "bar", + } + ), + ) + + # Add dimensions + ds.add_dimension("inline", 256) + ds.add_dimension("crossline", 512) + ds.add_dimension("depth", 384) + ds.add_coordinate("inline", dimensions=["inline"], data_type=ScalarType.UINT32) + ds.add_coordinate("crossline", dimensions=["crossline"], data_type=ScalarType.UINT32) + ds.add_coordinate( + "depth", + dimensions=["depth"], + data_type=ScalarType.FLOAT64, + metadata_info=[AllUnits(units_v1=LengthUnitModel(length=LengthUnitEnum.METER))], + ) + # Add coordinates + ds.add_coordinate( + "cdp-x", + dimensions=["inline", "crossline"], + data_type=ScalarType.FLOAT32, + metadata_info=[AllUnits(units_v1=LengthUnitModel(length=LengthUnitEnum.METER))], + ) + ds.add_coordinate( + "cdp-y", + dimensions=["inline", "crossline"], + data_type=ScalarType.FLOAT32, + metadata_info=[AllUnits(units_v1=LengthUnitModel(length=LengthUnitEnum.METER))], + ) + + # Add image variable + ds.add_variable( + name="image", + dimensions=["inline", "crossline", "depth"], + data_type=ScalarType.FLOAT32, + compressor=Blosc(algorithm="zstd"), + coordinates=["cdp-x", "cdp-y"], + metadata_info=[ + ChunkGridMetadata( + chunk_grid=RegularChunkGrid( + configuration=RegularChunkShape(chunk_shape=[128, 128, 128]) + ) + ), + StatisticsMetadata( + stats_v1=SummaryStatistics( + count=100, + sum=1215.1, + sumSquares=125.12, + min=5.61, + max=10.84, + histogram=CenteredBinHistogram(binCenters=[1, 2], counts=[10, 15]), + ) + ), + UserAttributes(attributes={"fizz": "buzz", "UnitSystem": "Canonical"}), + ], + ) + # Add velocity variable + ds.add_variable( + name="velocity", + dimensions=["inline", "crossline", "depth"], + data_type=ScalarType.FLOAT16, + coordinates=["cdp-x", "cdp-y"], + metadata_info=[ + ChunkGridMetadata( + chunk_grid=RegularChunkGrid( + configuration=RegularChunkShape(chunk_shape=[128, 128, 128]) + ) + ), + AllUnits(units_v1=SpeedUnitModel(speed=SpeedUnitEnum.METER_PER_SECOND)), + ], + ) + # Add inline-optimized image variable + ds.add_variable( + name="image_inline", + long_name="inline optimized version of 3d_stack", + dimensions=["inline", "crossline", "depth"], + data_type=ScalarType.FLOAT32, + compressor=Blosc(algorithm="zstd"), + coordinates=["cdp-x", "cdp-y"], + metadata_info=[ + ChunkGridMetadata( + chunk_grid=RegularChunkGrid( + configuration=RegularChunkShape(chunk_shape=[4, 512, 512]) + ) + ) + ], + ) + # Add headers variable with structured dtype + ds.add_variable( + name="image_headers", + dimensions=["inline", "crossline"], + data_type=StructuredType( + fields=[ + StructuredField(name="cdp-x", format=ScalarType.FLOAT32), + StructuredField(name="cdp-y", format=ScalarType.FLOAT32), + StructuredField(name="inline", format=ScalarType.UINT32), + StructuredField(name="crossline", format=ScalarType.UINT32), + ] + ), + coordinates=["cdp-x", "cdp-y"], + ) + return ds.build() diff --git a/tests/unit/v1/test_dataset_builder_add_coordinate.py b/tests/unit/v1/test_dataset_builder_add_coordinate.py new file mode 100644 index 00000000..46cb27cd --- /dev/null +++ b/tests/unit/v1/test_dataset_builder_add_coordinate.py @@ -0,0 +1,141 @@ +# ruff: noqa: PLR2004 +# PLR2004 Magic value used in comparison, consider replacing `3` with a constant variable +# The above erroneous warning is generated for every numerical assert. +# Thus, disable it for this file +"""Tests the schema v1 dataset_builder.add_coordinate() public API.""" + +import pytest + +from mdio.schemas.compressors import Blosc +from mdio.schemas.dtype import ScalarType +from mdio.schemas.metadata import UserAttributes +from mdio.schemas.v1.dataset_builder import MDIODatasetBuilder +from mdio.schemas.v1.dataset_builder import _BuilderState +from mdio.schemas.v1.units import AllUnits +from mdio.schemas.v1.units import LengthUnitEnum +from mdio.schemas.v1.units import LengthUnitModel +from mdio.schemas.v1.variable import VariableMetadata + +from .helpers import validate_builder +from .helpers import validate_coordinate +from .helpers import validate_variable + + +def test_add_coordinate() -> None: + """Test adding coordinates. Check the state transition and validate required parameters.""" + builder = MDIODatasetBuilder("test_dataset") + assert builder._state == _BuilderState.INITIAL + + msg = "Must add at least one dimension before adding coordinates" + with pytest.raises(ValueError, match=msg): + builder.add_coordinate( + "cdp", dimensions=["inline", "crossline"], data_type=ScalarType.FLOAT32 + ) + + builder.add_dimension("inline", 100) + builder.add_dimension("crossline", 200) + + # Validate required parameters + bad_name = None + with pytest.raises(ValueError, match="'name' must be a non-empty string"): + builder.add_coordinate(bad_name, dimensions=["speed"], data_type=ScalarType.FLOAT32) + with pytest.raises(ValueError, match="'name' must be a non-empty string"): + builder.add_coordinate("", dimensions=["speed"], data_type=ScalarType.FLOAT32) + with pytest.raises(ValueError, match="'dimensions' must be a non-empty list"): + builder.add_coordinate("cdp-x", dimensions=None, data_type=ScalarType.FLOAT32) + with pytest.raises(ValueError, match="'dimensions' must be a non-empty list"): + builder.add_coordinate("cdp-x", dimensions=[], data_type=ScalarType.FLOAT32) + + # Add a variable using non-existent dimensions + msg = "Pre-existing dimension named 'xline' is not found" + with pytest.raises(ValueError, match=msg): + builder.add_coordinate( + "bad_cdp-x", dimensions=["inline", "xline"], data_type=ScalarType.FLOAT32 + ) + + # Validate state transition + builder.add_coordinate( + "cdp-x", dimensions=["inline", "crossline"], data_type=ScalarType.FLOAT32 + ) + validate_builder(builder, _BuilderState.HAS_COORDINATES, n_dims=2, n_coords=1, n_var=1) + validate_variable( + builder, + name="cdp-x", + dims=[("inline", 100), ("crossline", 200)], + coords=["cdp-x"], + dtype=ScalarType.FLOAT32, + ) + + # Adding coordinate with the same name twice + msg = "Adding coordinate with the same name twice is not allowed" + with pytest.raises(ValueError, match=msg): + builder.add_coordinate( + "cdp-x", dimensions=["inline", "crossline"], data_type=ScalarType.FLOAT32 + ) + + +def test_add_coordinate_with_defaults() -> None: + """Test adding coordinates with default arguments.""" + builder = MDIODatasetBuilder("test_dataset") + builder.add_dimension("inline", 100) + builder.add_dimension("crossline", 200) + + # Add coordinate using defaults + builder.add_coordinate("cdp", dimensions=["inline", "crossline"], data_type=ScalarType.FLOAT32) + validate_builder(builder, _BuilderState.HAS_COORDINATES, n_dims=2, n_coords=1, n_var=1) + validate_coordinate( + builder, name="cdp", dims=[("inline", 100), ("crossline", 200)], dtype=ScalarType.FLOAT32 + ) + v = validate_variable( + builder, + name="cdp", + dims=[("inline", 100), ("crossline", 200)], + coords=["cdp"], + dtype=ScalarType.FLOAT32, + ) + assert v.long_name == "'cdp' coordinate variable" # Default value + assert v.compressor is None # Default value + assert v.metadata is None # Default value + + +def test_coordinate_with_full_parameters() -> None: + """Test adding coordinates with all metadata.""" + builder = MDIODatasetBuilder("test_dataset") + builder.add_dimension("inline", 100) + builder.add_dimension("crossline", 200) + + # Add coordinate with all metadata + builder.add_coordinate( + "cdp", + long_name="Common Depth Point", + dimensions=["inline", "crossline"], + data_type=ScalarType.FLOAT16, + compressor=Blosc(algorithm="zstd"), + metadata_info=[ + AllUnits(units_v1=LengthUnitModel(length=LengthUnitEnum.FOOT)), + UserAttributes(attributes={"MGA": 51, "UnitSystem": "Imperial"}), + ], + ) + validate_builder(builder, _BuilderState.HAS_COORDINATES, n_dims=2, n_coords=1, n_var=1) + c = validate_coordinate( + builder, name="cdp", dims=[("inline", 100), ("crossline", 200)], dtype=ScalarType.FLOAT16 + ) + assert c.long_name == "Common Depth Point" + assert isinstance(c.compressor, Blosc) + assert c.compressor.algorithm == "zstd" + assert c.metadata.attributes["MGA"] == 51 + assert c.metadata.attributes["UnitSystem"] == "Imperial" + assert c.metadata.units_v1.length == LengthUnitEnum.FOOT + v = validate_variable( + builder, + name="cdp", + dims=[("inline", 100), ("crossline", 200)], + coords=["cdp"], + dtype=ScalarType.FLOAT16, + ) + assert isinstance(v.compressor, Blosc) + assert v.compressor.algorithm == "zstd" + assert isinstance(v.metadata, VariableMetadata) + assert v.metadata.units_v1.length == LengthUnitEnum.FOOT + assert v.metadata.attributes["MGA"] == 51 + assert v.metadata.attributes["UnitSystem"] == "Imperial" diff --git a/tests/unit/v1/test_dataset_builder_add_dimension.py b/tests/unit/v1/test_dataset_builder_add_dimension.py new file mode 100644 index 00000000..ddcbd745 --- /dev/null +++ b/tests/unit/v1/test_dataset_builder_add_dimension.py @@ -0,0 +1,43 @@ +# ruff: noqa: PLR2004 +# PLR2004 Magic value used in comparison, consider replacing `3` with a constant variable +# The above erroneous warning is generated for every numerical assert. +# Thus, disable it for this file +"""Tests the schema v1 dataset_builder.add_dimension() public API.""" + +import pytest + +from mdio.schemas.v1.dataset_builder import MDIODatasetBuilder +from mdio.schemas.v1.dataset_builder import _BuilderState +from mdio.schemas.v1.dataset_builder import _get_named_dimension + +from .helpers import validate_builder + + +def test_add_dimension() -> None: + """Test adding dimension. Check the state transition and validate required parameters.""" + builder = MDIODatasetBuilder("test_dataset") + assert builder._state == _BuilderState.INITIAL + + # Validate required parameters + bad_name = None + with pytest.raises(ValueError, match="'name' must be a non-empty string"): + builder.add_dimension(bad_name, 200) + with pytest.raises(ValueError, match="'name' must be a non-empty string"): + builder.add_dimension("", 200) + + # First dimension should change state to HAS_DIMENSIONS and create a variable + builder.add_dimension("x", 100) + validate_builder(builder, _BuilderState.HAS_DIMENSIONS, n_dims=1, n_coords=0, n_var=0) + assert _get_named_dimension(builder._dimensions, "x", 100) is not None + + # Validate that we can't add a dimension with the same name twice + with pytest.raises( + ValueError, + match="Adding dimension with the same name twice is not allowed", + ): + builder.add_dimension("x", 200) + + # Adding dimension with the same name twice + msg = "Adding dimension with the same name twice is not allowed" + with pytest.raises(ValueError, match=msg): + builder.add_dimension("x", 200) diff --git a/tests/unit/v1/test_dataset_builder_add_variable.py b/tests/unit/v1/test_dataset_builder_add_variable.py new file mode 100644 index 00000000..84abfe3a --- /dev/null +++ b/tests/unit/v1/test_dataset_builder_add_variable.py @@ -0,0 +1,250 @@ +# ruff: noqa: PLR2004 +# PLR2004 Magic value used in comparison, consider replacing `3` with a constant variable +# The above erroneous warning is generated for every numerical assert. +# Thus, disable it for this file +"""Tests the schema v1 Variable public API.""" + +import pytest + +from mdio.schemas.chunk_grid import RegularChunkGrid +from mdio.schemas.chunk_grid import RegularChunkShape +from mdio.schemas.compressors import Blosc +from mdio.schemas.dtype import ScalarType +from mdio.schemas.metadata import ChunkGridMetadata +from mdio.schemas.metadata import UserAttributes +from mdio.schemas.v1.dataset_builder import MDIODatasetBuilder +from mdio.schemas.v1.dataset_builder import _BuilderState +from mdio.schemas.v1.stats import CenteredBinHistogram +from mdio.schemas.v1.stats import StatisticsMetadata +from mdio.schemas.v1.stats import SummaryStatistics +from mdio.schemas.v1.units import AllUnits +from mdio.schemas.v1.units import LengthUnitEnum +from mdio.schemas.v1.units import LengthUnitModel +from mdio.schemas.v1.variable import VariableMetadata + +from .helpers import validate_builder +from .helpers import validate_variable + + +def test_add_variable_no_coords() -> None: + """Test adding variable. Check the state transition and validate required parameters..""" + builder = MDIODatasetBuilder("test_dataset") + validate_builder(builder, _BuilderState.INITIAL, n_dims=0, n_coords=0, n_var=0) + + # Validate: Must add at least one dimension before adding variables + msg = "Must add at least one dimension before adding variables" + with pytest.raises(ValueError, match=msg): + builder.add_variable("amplitude", dimensions=["speed"], data_type=ScalarType.FLOAT32) + + # Add dimension before we can add a data variable + builder.add_dimension("inline", 100) + builder.add_dimension("crossline", 200) + builder.add_dimension("depth", 300) + + # Validate: required parameters must be preset + bad_name = None + with pytest.raises(ValueError, match="'name' must be a non-empty string"): + builder.add_variable(bad_name, dimensions=["speed"], data_type=ScalarType.FLOAT32) + with pytest.raises(ValueError, match="'name' must be a non-empty string"): + builder.add_variable("", dimensions=["speed"], data_type=ScalarType.FLOAT32) + with pytest.raises(ValueError, match="'dimensions' must be a non-empty list"): + builder.add_variable("bad_amplitude", dimensions=None, data_type=ScalarType.FLOAT32) + with pytest.raises(ValueError, match="'dimensions' must be a non-empty list"): + builder.add_variable("bad_amplitude", dimensions=[], data_type=ScalarType.FLOAT32) + + # Validate: Add a variable using non-existent dimensions is not allowed + msg = "Pre-existing dimension named 'il' is not found" + with pytest.raises(ValueError, match=msg): + builder.add_variable( + "bad_amplitude", dimensions=["il", "xl", "depth"], data_type=ScalarType.FLOAT32 + ) + + # Add a variable without coordinates + builder.add_variable( + "amplitude", dimensions=["inline", "crossline", "depth"], data_type=ScalarType.FLOAT32 + ) + validate_builder(builder, _BuilderState.HAS_VARIABLES, n_dims=3, n_coords=0, n_var=1) + validate_variable( + builder, + "amplitude", + dims=[("inline", 100), ("crossline", 200), ("depth", 300)], + coords=None, + dtype=ScalarType.FLOAT32, + ) + + # Validate: adding a variable with the same name twice is not allowed + msg = "Adding variable with the same name twice is not allowed" + with pytest.raises(ValueError, match=msg): + builder.add_variable( + "amplitude", dimensions=["inline", "crossline", "depth"], data_type=ScalarType.FLOAT32 + ) + + +def test_add_variable_with_coords() -> None: + """Test adding variable. Check the state transition and validate required parameters..""" + builder = MDIODatasetBuilder("test_dataset") + builder.add_dimension("inline", 100) + builder.add_dimension("crossline", 200) + builder.add_dimension("depth", 300) + + # Add dimension coordinates before we can add a data variable + builder.add_coordinate("inline", dimensions=["inline"], data_type=ScalarType.UINT32) + builder.add_coordinate("crossline", dimensions=["crossline"], data_type=ScalarType.UINT32) + + # Validate: adding a variable with a coordinate that has not been pre-created is not allowed + msg = "Pre-existing coordinate named 'depth' is not found" + with pytest.raises(ValueError, match=msg): + builder.add_variable( + "ampl", + dimensions=["inline", "crossline", "depth"], + coordinates=["inline", "crossline", "depth"], + data_type=ScalarType.FLOAT32, + ) + + # Add a variable with pre-defined dimension coordinates + builder.add_variable( + "ampl", + dimensions=["inline", "crossline", "depth"], + coordinates=["inline", "crossline"], + data_type=ScalarType.FLOAT32, + ) + validate_builder(builder, _BuilderState.HAS_VARIABLES, n_dims=3, n_coords=2, n_var=3) + validate_variable( + builder, + "ampl", + dims=[("inline", 100), ("crossline", 200), ("depth", 300)], + coords=["inline", "crossline"], + dtype=ScalarType.FLOAT32, + ) + + # Add non-dim coordinates (e.g., 2D coordinates) + builder.add_coordinate( + "cdp-x", dimensions=["inline", "crossline"], data_type=ScalarType.FLOAT32 + ) + builder.add_coordinate( + "cdp-y", dimensions=["inline", "crossline"], data_type=ScalarType.FLOAT32 + ) + + # Add a variable with pre-defined dimension and non-dimension coordinates + builder.add_variable( + "ampl2", + dimensions=["inline", "crossline", "depth"], + coordinates=["inline", "crossline", "cdp-x", "cdp-y"], + data_type=ScalarType.FLOAT32, + ) + validate_builder(builder, _BuilderState.HAS_VARIABLES, n_dims=3, n_coords=4, n_var=6) + validate_variable( + builder, + "ampl2", + dims=[("inline", 100), ("crossline", 200), ("depth", 300)], + coords=["inline", "crossline", "cdp-x", "cdp-y"], + dtype=ScalarType.FLOAT32, + ) + + +def test_add_variable_with_defaults() -> None: + """Test adding variable with default arguments.""" + builder = MDIODatasetBuilder("test_dataset") + # Add dimensions before we can add a data variables + builder.add_dimension("inline", 100) + builder.add_dimension("crossline", 200) + builder.add_dimension("depth", 300) + # Add dimension coordinates + builder.add_coordinate("inline", dimensions=["inline"], data_type=ScalarType.UINT32) + builder.add_coordinate("crossline", dimensions=["crossline"], data_type=ScalarType.UINT32) + builder.add_coordinate( + "depth", + dimensions=["depth"], + data_type=ScalarType.UINT32, + metadata_info=[AllUnits(units_v1=LengthUnitModel(length=LengthUnitEnum.METER))], + ) + + # Add data variable using defaults + builder.add_variable( + "ampl", dimensions=["inline", "crossline", "depth"], data_type=ScalarType.FLOAT32 + ) + validate_builder(builder, _BuilderState.HAS_VARIABLES, n_dims=3, n_coords=3, n_var=4) + v = validate_variable( + builder, + "ampl", + dims=[("inline", 100), ("crossline", 200), ("depth", 300)], + coords=None, + dtype=ScalarType.FLOAT32, + ) + assert v.long_name is None # Default value + assert v.compressor is None # Default value + assert v.coordinates is None # Default value + assert v.metadata is None # Default value + + +def test_add_variable_full_parameters() -> None: + """Test adding variable with full parameters.""" + builder = MDIODatasetBuilder("test_dataset") + # Add dimensions before we can add a data variables + builder.add_dimension("inline", 100) + builder.add_dimension("crossline", 200) + builder.add_dimension("depth", 300) + # Add dimension coordinates + builder.add_coordinate("inline", dimensions=["inline"], data_type=ScalarType.UINT32) + builder.add_coordinate("crossline", dimensions=["crossline"], data_type=ScalarType.UINT32) + builder.add_coordinate("depth", dimensions=["depth"], data_type=ScalarType.UINT32) + # Add coordinates before we can add a data variable + builder.add_coordinate( + "cdp-x", dimensions=["inline", "crossline"], data_type=ScalarType.FLOAT64 + ) + builder.add_coordinate( + "cdp-y", dimensions=["inline", "crossline"], data_type=ScalarType.FLOAT64 + ) + + # Add data variable with full parameters + builder.add_variable( + "ampl", + long_name="Amplitude (dimensionless)", + dimensions=["inline", "crossline", "depth"], + data_type=ScalarType.FLOAT32, + compressor=Blosc(algorithm="zstd"), + coordinates=["inline", "crossline", "depth", "cdp-x", "cdp-y"], + metadata_info=[ + AllUnits(units_v1=LengthUnitModel(length=LengthUnitEnum.FOOT)), + UserAttributes(attributes={"MGA": 51, "UnitSystem": "Imperial"}), + ChunkGridMetadata( + chunk_grid=RegularChunkGrid(configuration=RegularChunkShape(chunk_shape=[20])) + ), + StatisticsMetadata( + stats_v1=SummaryStatistics( + count=100, + sum=1215.1, + sumSquares=125.12, + min=5.61, + max=10.84, + histogram=CenteredBinHistogram(binCenters=[1, 2], counts=[10, 15]), + ) + ), + ], + ) + validate_builder(builder, _BuilderState.HAS_VARIABLES, n_dims=3, n_coords=5, n_var=6) + v = validate_variable( + builder, + "ampl", + dims=[("inline", 100), ("crossline", 200), ("depth", 300)], + coords=["inline", "crossline", "depth", "cdp-x", "cdp-y"], + dtype=ScalarType.FLOAT32, + ) + assert v.long_name == "Amplitude (dimensionless)" + assert isinstance(v.compressor, Blosc) + assert v.compressor.algorithm == "zstd" + assert len(v.coordinates) == 5 + assert v.metadata.stats_v1.count == 100 + assert isinstance(v.metadata, VariableMetadata) + assert v.metadata.units_v1.length == LengthUnitEnum.FOOT + assert v.metadata.attributes["MGA"] == 51 + assert v.metadata.attributes["UnitSystem"] == "Imperial" + assert v.metadata.chunk_grid.name == "regular" + assert v.metadata.chunk_grid.configuration.chunk_shape == [20] + assert v.metadata.stats_v1.count == 100 + assert v.metadata.stats_v1.sum == 1215.1 + assert v.metadata.stats_v1.sum_squares == 125.12 + assert v.metadata.stats_v1.min == 5.61 + assert v.metadata.stats_v1.max == 10.84 + assert v.metadata.stats_v1.histogram.bin_centers == [1, 2] + assert v.metadata.stats_v1.histogram.counts == [10, 15] diff --git a/tests/unit/v1/test_dataset_builder_build.py b/tests/unit/v1/test_dataset_builder_build.py new file mode 100644 index 00000000..aa5fad55 --- /dev/null +++ b/tests/unit/v1/test_dataset_builder_build.py @@ -0,0 +1,155 @@ +# ruff: noqa: PLR2004 +# PLR2004 Magic value used in comparison, consider replacing `3` with a constant variable +# The above erroneous warning is generated for every numerical assert. +# Thus, disable it for this file +"""Tests the schema v1 dataset_builder.add_coordinate() public API.""" + +from mdio.schemas.dtype import ScalarType +from mdio.schemas.dtype import StructuredField +from mdio.schemas.dtype import StructuredType +from mdio.schemas.v1.dataset import Dataset +from mdio.schemas.v1.dataset_builder import MDIODatasetBuilder +from mdio.schemas.v1.units import LengthUnitEnum +from mdio.schemas.v1.units import SpeedUnitEnum + +from .helpers import make_campos_3d_dataset +from .helpers import validate_variable + + +def test_build() -> None: + """Test building a complete dataset.""" + dataset = ( + MDIODatasetBuilder("test_dataset") + .add_dimension("inline", 100) + .add_dimension("crossline", 200) + .add_coordinate("inline", dimensions=["inline"], data_type=ScalarType.FLOAT64) + .add_coordinate("crossline", dimensions=["crossline"], data_type=ScalarType.FLOAT64) + .add_coordinate("x_coord", dimensions=["inline", "crossline"], data_type=ScalarType.FLOAT32) + .add_coordinate("y_coord", dimensions=["inline", "crossline"], data_type=ScalarType.FLOAT32) + .add_variable( + "data", + long_name="Test Data", + dimensions=["inline", "crossline"], + coordinates=["inline", "crossline", "x_coord", "y_coord"], + data_type=ScalarType.FLOAT32, + ) + .build() + ) + + assert isinstance(dataset, Dataset) + assert dataset.metadata.name == "test_dataset" + # 2 dim coord var + 2 non-dim coord var + 1 data variables = 5 variables + assert len(dataset.variables) == 5 + assert next(v for v in dataset.variables if v.name == "inline") is not None + assert next(v for v in dataset.variables if v.name == "crossline") is not None + assert next(v for v in dataset.variables if v.name == "x_coord") is not None + assert next(v for v in dataset.variables if v.name == "y_coord") is not None + assert next(v for v in dataset.variables if v.name == "data") is not None + + +def test_build_campos_3d() -> None: # noqa: PLR0915 Too many statements (57 > 50) + """Test building a Campos 3D dataset with multiple variables and attributes.""" + dataset = make_campos_3d_dataset() + + # Verify dataset structure + assert dataset.metadata.name == "campos_3d" + assert dataset.metadata.api_version == "1.0.0a1" + assert dataset.metadata.attributes["foo"] == "bar" + assert len(dataset.metadata.attributes["textHeader"]) == 3 + + # Verify variables (including dimension variables) + # 3 dimension variables + 4 data variables + 2 coordinate variables + assert len(dataset.variables) == 9 + + # Verify dimension coordinate variables + validate_variable( + dataset, name="inline", dims=[("inline", 256)], coords=["inline"], dtype=ScalarType.UINT32 + ) + + validate_variable( + dataset, + name="crossline", + dims=[("crossline", 512)], + coords=["crossline"], + dtype=ScalarType.UINT32, + ) + + depth = validate_variable( + dataset, name="depth", dims=[("depth", 384)], coords=["depth"], dtype=ScalarType.FLOAT64 + ) + assert depth.metadata.units_v1.length == LengthUnitEnum.METER + + # Verify coordinate variables + cdp_x = validate_variable( + dataset, + name="cdp-x", + dims=[("inline", 256), ("crossline", 512)], + coords=["cdp-x"], + dtype=ScalarType.FLOAT32, + ) + assert cdp_x.metadata.units_v1.length == LengthUnitEnum.METER + + cdp_y = validate_variable( + dataset, + name="cdp-y", + dims=[("inline", 256), ("crossline", 512)], + coords=["cdp-y"], + dtype=ScalarType.FLOAT32, + ) + assert cdp_y.metadata.units_v1.length == LengthUnitEnum.METER + + # Verify data variables + image = validate_variable( + dataset, + name="image", + dims=[("inline", 256), ("crossline", 512), ("depth", 384)], + coords=["cdp-x", "cdp-y"], + dtype=ScalarType.FLOAT32, + ) + assert image.metadata.units_v1 is None # No units defined for image + assert image.compressor.algorithm == "zstd" + assert image.metadata.chunk_grid.configuration.chunk_shape == [128, 128, 128] + assert image.metadata.stats_v1.count == 100 + + velocity = validate_variable( + dataset, + name="velocity", + dims=[("inline", 256), ("crossline", 512), ("depth", 384)], + coords=["cdp-x", "cdp-y"], + dtype=ScalarType.FLOAT16, + ) + assert velocity.compressor is None + assert velocity.metadata.chunk_grid.configuration.chunk_shape == [128, 128, 128] + assert velocity.metadata.units_v1.speed == SpeedUnitEnum.METER_PER_SECOND + + image_inline = validate_variable( + dataset, + name="image_inline", + dims=[("inline", 256), ("crossline", 512), ("depth", 384)], + coords=["cdp-x", "cdp-y"], + dtype=ScalarType.FLOAT32, + ) + assert image_inline.long_name == "inline optimized version of 3d_stack" + assert image_inline.compressor.algorithm == "zstd" + assert image_inline.metadata.chunk_grid.configuration.chunk_shape == [4, 512, 512] + + # Verify image_headers variable + headers = next(v for v in dataset.variables if v.name == "image_headers") + assert isinstance(headers.data_type, StructuredType) + assert len(headers.data_type.fields) == 4 + assert headers.data_type.fields[0].name == "cdp-x" + + headers = validate_variable( + dataset, + name="image_headers", + dims=[("inline", 256), ("crossline", 512)], + coords=["cdp-x", "cdp-y"], + dtype=StructuredType( + fields=[ + StructuredField(name="cdp-x", format=ScalarType.FLOAT32), + StructuredField(name="cdp-y", format=ScalarType.FLOAT32), + StructuredField(name="inline", format=ScalarType.UINT32), + StructuredField(name="crossline", format=ScalarType.UINT32), + ] + ), + ) diff --git a/tests/unit/v1/test_dataset_builder_helpers.py b/tests/unit/v1/test_dataset_builder_helpers.py new file mode 100644 index 00000000..b1ab9964 --- /dev/null +++ b/tests/unit/v1/test_dataset_builder_helpers.py @@ -0,0 +1,98 @@ +# ruff: noqa: PLR2004 +# PLR2004 Magic value used in comparison, consider replacing `3` with a constant variable +# The above erroneous warning is generated for every numerical assert. +# Thus, disable it for this file +"""Tests the schema v1 dataset_builder internal methods.""" + +from datetime import UTC +from datetime import datetime + +import pytest +from pydantic import Field + +from mdio.schemas.core import StrictModel +from mdio.schemas.dimension import NamedDimension +from mdio.schemas.v1.dataset_builder import _get_named_dimension +from mdio.schemas.v1.dataset_builder import _to_dictionary + + +def test__get_named_dimension() -> None: + """Test getting a dimension by name from the list of dimensions.""" + dimensions = [NamedDimension(name="inline", size=2), NamedDimension(name="crossline", size=3)] + + assert _get_named_dimension([], "inline") is None + assert _get_named_dimension(dimensions, "inline") == NamedDimension(name="inline", size=2) + assert _get_named_dimension(dimensions, "crossline") == NamedDimension(name="crossline", size=3) + assert _get_named_dimension(dimensions, "time") is None + + with pytest.raises(TypeError, match="Expected str, got NoneType"): + _get_named_dimension(dimensions, None) + with pytest.raises(TypeError, match="Expected str, got int"): + _get_named_dimension(dimensions, 42) + with pytest.raises( + ValueError, match="Dimension 'inline' found but size 2 does not match expected size 200" + ): + _get_named_dimension(dimensions, "inline", size=200) + + +def test__to_dictionary() -> None: + """Test converting a dictionary, list or pydantic BaseModel to a dictionary.""" + # Validate inputs + with pytest.raises(TypeError, match="Expected BaseModel, dict or list, got datetime"): + _to_dictionary(datetime.now(UTC)) + + # Convert None to None + result = _to_dictionary(None) + assert result is None + + # Validate conversion of a Pydantic BaseModel + class SomeModel(StrictModel): + count: int = Field(default=None, description="Samples count") + samples: list[float] = Field(default_factory=list, description="Samples.") + created: datetime = Field( + default_factory=datetime.now, description="Creation time with TZ info." + ) + + md = SomeModel( + count=3, samples=[1.0, 2.0, 3.0], created=datetime(2023, 10, 1, 12, 0, 0, tzinfo=UTC) + ) + result = _to_dictionary(md) + assert isinstance(result, dict) + assert result == {"count": 3, "created": "2023-10-01T12:00:00Z", "samples": [1.0, 2.0, 3.0]} + + # Validate conversion of a dictionary + dct = { + "count": 3, + "samples": [1.0, 2.0, 3.0], + "created": datetime(2023, 10, 1, 12, 0, 0, tzinfo=UTC), + } + result = _to_dictionary(dct) + assert isinstance(result, dict) + assert result == { + "count": 3, + "samples": [1.0, 2.0, 3.0], + "created": datetime(2023, 10, 1, 12, 0, 0, tzinfo=UTC), + } + + # Validate conversion of a dictionary + lst = [ + None, + SomeModel( + count=3, samples=[1.0, 2.0, 3.0], created=datetime(2023, 10, 1, 12, 0, 0, tzinfo=UTC) + ), + { + "count2": 3, + "samples2": [1.0, 2.0, 3.0], + "created2": datetime(2023, 10, 1, 12, 0, 0, tzinfo=UTC), + }, + ] + result = _to_dictionary(lst) + assert isinstance(result, dict) + assert result == { + "count": 3, + "samples": [1.0, 2.0, 3.0], + "created": "2023-10-01T12:00:00Z", + "count2": 3, + "samples2": [1.0, 2.0, 3.0], + "created2": datetime(2023, 10, 1, 12, 0, 0, tzinfo=UTC), + } diff --git a/uv.lock b/uv.lock index 2a1b6d47..6f04c363 100644 --- a/uv.lock +++ b/uv.lock @@ -130,14 +130,15 @@ wheels = [ [[package]] name = "aiosignal" -version = "1.3.2" +version = "1.4.0" source = { registry = "https://pypi.org/simple" } 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