From 8eeddea7e9eccd940ca564b02aabc4c7e8a0a8f3 Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Tue, 29 Apr 2025 10:17:11 -0500
Subject: [PATCH 01/19] update project metadata and deps

---
 pyproject.toml |  17 ++-
 uv.lock        | 362 ++++++++++++++++++++++++++++++++++++++++++++-----
 2 files changed, 342 insertions(+), 37 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index d059b876..7e01894e 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"
@@ -27,10 +27,14 @@ dependencies = [
     "click-params (>=0.5.0,<0.6.0)",
     "dask (>=2024.12.0)",
     "fsspec (>=2024.10.0)",
+    "pint>=0.24.3,<0.25",
     "psutil (>=6.1.0,<7.0.0)",
+    "pydantic (>=2.8.2,<3.0.0)",
+    "pydantic-settings (>=2.4.0,<3.0.0)",
     "rich (>=13.9.4,<14.0.0)",
     "segy (>=0.4.0,<0.5.0)",
     "tqdm (>=4.67.0,<5.0.0)",
+    "xarray>=2025.3.1",
     "zarr (>=3.0.8,<4.0.0)",
 ]
 
@@ -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/uv.lock b/uv.lock
index fa5bd21f..2e73c419 100644
--- a/uv.lock
+++ b/uv.lock
@@ -276,6 +276,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537 },
 ]
 
+[[package]]
+name = "bandit"
+version = "1.8.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "pyyaml" },
+    { name = "rich" },
+    { name = "stevedore" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/1a/a5/144a45f8e67df9d66c3bc3f7e69a39537db8bff1189ab7cff4e9459215da/bandit-1.8.3.tar.gz", hash = "sha256:f5847beb654d309422985c36644649924e0ea4425c76dec2e89110b87506193a", size = 4232005 }
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+]
+
 [[package]]
 name = "beautifulsoup4"
 version = "4.13.3"
@@ -289,6 +304,34 @@ wheels = [
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 ]
 
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+    { name = "mypy-extensions" },
+    { name = "packaging" },
+    { name = "pathspec" },
+    { name = "platformdirs" },
+]
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 name = "bokeh"
 version = "3.7.2"
@@ -697,6 +740,15 @@ wheels = [
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 name = "dask"
 version = "2025.3.0"
@@ -853,6 +905,97 @@ wheels = [
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 version = "0.4.2"
@@ -1644,17 +1805,21 @@ wheels = [
 
 [[package]]
 name = "multidimio"
-version = "0.9.1"
+version = "1.0.0a1"
 source = { editable = "." }
 dependencies = [
     { name = "click" },
     { name = "click-params" },
     { name = "dask" },
     { name = "fsspec" },
+    { name = "pint" },
     { name = "psutil" },
+    { name = "pydantic" },
+    { name = "pydantic-settings" },
     { name = "rich" },
     { name = "segy" },
     { name = "tqdm" },
+    { name = "xarray" },
     { name = "zarr" },
 ]
 
@@ -1674,14 +1839,23 @@ lossy = [
 
 [package.dev-dependencies]
 dev = [
+    { name = "black" },
     { name = "coverage", extra = ["toml"] },
+    { name = "darglint" },
+    { name = "flake8" },
+    { name = "flake8-bandit" },
+    { name = "flake8-bugbear" },
+    { name = "flake8-docstrings" },
+    { name = "flake8-rst-docstrings" },
+    { name = "isort" },
     { name = "mypy" },
+    { name = "pep8-naming" },
     { name = "pre-commit" },
     { name = "pre-commit-hooks" },
     { name = "pygments" },
     { name = "pytest" },
     { name = "pytest-dependency" },
-    { name = "ruff" },
+    { name = "pyupgrade" },
     { name = "safety" },
     { name = "typeguard" },
     { name = "xdoctest", extra = ["colors"] },
@@ -1707,26 +1881,39 @@ requires-dist = [
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     { name = "fsspec", specifier = ">=2024.10.0" },
     { name = "gcsfs", marker = "extra == 'cloud'", specifier = ">=2024.10.0" },
+    { name = "pint", specifier = ">=0.24.3,<0.25" },
     { name = "psutil", specifier = ">=6.1.0,<7.0.0" },
+    { name = "pydantic", specifier = ">=2.8.2,<3.0.0" },
+    { name = "pydantic-settings", specifier = ">=2.4.0,<3.0.0" },
     { name = "rich", specifier = ">=13.9.4,<14.0.0" },
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     { name = "tqdm", specifier = ">=4.67.0,<5.0.0" },
-    { name = "zarr", specifier = ">=3.0.8,<4.0.0" },
+    { name = "xarray", specifier = ">=2025.3.1" },
+    { name = "zarr", specifier = ">=3.0.6,<4.0.0" },
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     { name = "coverage", extras = ["toml"], specifier = ">=7.6.7,<8" },
+    { name = "darglint", specifier = ">=1.8.1,<2" },
+    { name = "flake8", specifier = ">=7.1.0,<8" },
+    { name = "flake8-bandit", specifier = ">=4.1.1,<5" },
+    { name = "flake8-bugbear", specifier = ">=24.4.26,<25" },
+    { name = "flake8-docstrings", specifier = ">=1.7.0,<2" },
+    { name = "flake8-rst-docstrings", specifier = ">=0.3.0,<0.4" },
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     { name = "typeguard", specifier = ">=4.4.1,<5" },
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-    { url = "https://files.pythonhosted.org/packages/de/7e/fff70b02e57852fda17bd43f99dda37b9bcf3e1af3d97c5834ff48d04715/ruff-0.11.8-py3-none-win32.whl", hash = "sha256:5b18caa297a786465cc511d7f8be19226acf9c0a1127e06e736cd4e1878c3ea2", size = 10451102 },
-    { url = "https://files.pythonhosted.org/packages/7b/a9/eaa571eb70648c9bde3120a1d5892597de57766e376b831b06e7c1e43945/ruff-0.11.8-py3-none-win_amd64.whl", hash = "sha256:6e70d11043bef637c5617297bdedec9632af15d53ac1e1ba29c448da9341b0c4", size = 11597410 },
-    { url = "https://files.pythonhosted.org/packages/cd/be/f6b790d6ae98f1f32c645f8540d5c96248b72343b0a56fab3a07f2941897/ruff-0.11.8-py3-none-win_arm64.whl", hash = "sha256:304432e4c4a792e3da85b7699feb3426a0908ab98bf29df22a31b0cdd098fac2", size = 10713129 },
-]
-
 [[package]]
 name = "s3fs"
 version = "2024.12.0"
@@ -3310,6 +3571,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a0/4b/528ccf7a982216885a1ff4908e886b8fb5f19862d1962f56a3fce2435a70/starlette-0.46.1-py3-none-any.whl", hash = "sha256:77c74ed9d2720138b25875133f3a2dae6d854af2ec37dceb56aef370c1d8a227", size = 71995 },
 ]
 
+[[package]]
+name = "stevedore"
+version = "5.4.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pbr" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/28/3f/13cacea96900bbd31bb05c6b74135f85d15564fc583802be56976c940470/stevedore-5.4.1.tar.gz", hash = "sha256:3135b5ae50fe12816ef291baff420acb727fcd356106e3e9cbfa9e5985cd6f4b", size = 513858 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f7/45/8c4ebc0c460e6ec38e62ab245ad3c7fc10b210116cea7c16d61602aa9558/stevedore-5.4.1-py3-none-any.whl", hash = "sha256:d10a31c7b86cba16c1f6e8d15416955fc797052351a56af15e608ad20811fcfe", size = 49533 },
+]
+
 [[package]]
 name = "tabulate"
 version = "0.9.0"
@@ -3328,6 +3601,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/27/44/aa5c8b10b2cce7a053018e0d132bd58e27527a0243c4985383d5b6fd93e9/tblib-3.1.0-py3-none-any.whl", hash = "sha256:670bb4582578134b3d81a84afa1b016128b429f3d48e6cbbaecc9d15675e984e", size = 12552 },
 ]
 
+[[package]]
+name = "tokenize-rt"
+version = "6.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6b/0a/5854d8ced8c1e00193d1353d13db82d7f813f99bd5dcb776ce3e2a4c0d19/tokenize_rt-6.1.0.tar.gz", hash = "sha256:e8ee836616c0877ab7c7b54776d2fefcc3bde714449a206762425ae114b53c86", size = 5506 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/87/ba/576aac29b10dfa49a6ce650001d1bb31f81e734660555eaf144bfe5b8995/tokenize_rt-6.1.0-py2.py3-none-any.whl", hash = "sha256:d706141cdec4aa5f358945abe36b911b8cbdc844545da99e811250c0cee9b6fc", size = 6015 },
+]
+
 [[package]]
 name = "tomli"
 version = "2.2.1"
@@ -3667,6 +3949,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2d/82/f56956041adef78f849db6b289b282e72b55ab8045a75abad81898c28d19/wrapt-1.17.2-py3-none-any.whl", hash = "sha256:b18f2d1533a71f069c7f82d524a52599053d4c7166e9dd374ae2136b7f40f7c8", size = 23594 },
 ]
 
+[[package]]
+name = "xarray"
+version = "2025.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pandas" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7c/c4/6931c37cd418658d596e78794bdd1bcb67efec0aac3cdb720e37e03c1ea1/xarray-2025.3.1.tar.gz", hash = "sha256:0252c96a73528b29d1ed7f0ab28d928d2ec00ad809e47369803b184dece1e447", size = 3300778 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/91/fd/973deafd9f87085136a58573600646b408ae7af47859f35151f0d83d5090/xarray-2025.3.1-py3-none-any.whl", hash = "sha256:3404e313930c226db70a945377441ea3c957225d8ba2d429e764c099bb91a546", size = 1279327 },
+]
+
 [[package]]
 name = "xdoctest"
 version = "1.2.0"
@@ -3755,7 +4051,7 @@ wheels = [
 
 [[package]]
 name = "zarr"
-version = "3.0.8"
+version = "3.0.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "donfig" },
@@ -3764,9 +4060,9 @@ dependencies = [
     { name = "packaging" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/52/60/9652fd0536fbaca8d08cbc1a5572c52e0ce01773297df75da8bb47e45907/zarr-3.0.8.tar.gz", hash = "sha256:88505d095af899a88ae8ac4db02f4650ef0801d2ff6f65b6d1f0a45dcf760a6d", size = 256825 }
+sdist = { url = "https://files.pythonhosted.org/packages/6f/bf/2820e8ad5f086ea5cd4c4d65eb5e0113f98145ddf5810cec1c75fb10fb98/zarr-3.0.6.tar.gz", hash = "sha256:6ef23c740e34917a2a1099471361537732942e49f0cabe95c9b7124cd0d6d84f", size = 240812 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/00/3b/e20bdf84088c11f2c396d034506cbffadd53e024111c1aa4585c2aba1523/zarr-3.0.8-py3-none-any.whl", hash = "sha256:7f81e7aec086437d98882aa432209107114bd7f3a9f4958b2af9c6b5928a70a7", size = 205364 },
+    { url = "https://files.pythonhosted.org/packages/fb/38/56a1c129577d20dc975a934ccc3f7f16276eab04ff0ffbe02ea348407a37/zarr-3.0.6-py3-none-any.whl", hash = "sha256:dba078726b6e4defb0ae6a852b7e7035ce163d89d485961681ece49191dcce82", size = 196357 },
 ]
 
 [[package]]

From 293a7144a60833794437c6173bb8cccddbde8fe8 Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Tue, 29 Apr 2025 10:17:33 -0500
Subject: [PATCH 02/19] update project metadata and deps

---
 noxfile.py | 2 ++
 1 file changed, 2 insertions(+)

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",

From faacbaa86a66d17fc430de4e17282fc17807c6d6 Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Tue, 29 Apr 2025 14:42:57 -0500
Subject: [PATCH 03/19] add schemas

---
 src/mdio/schemas/__init__.py    |  18 ++++
 src/mdio/schemas/base.py        |  43 +++++++++
 src/mdio/schemas/builder.py     | 156 +++++++++++++++++++++++++++++++
 src/mdio/schemas/chunk_grid.py  |  44 +++++++++
 src/mdio/schemas/compressors.py | 159 ++++++++++++++++++++++++++++++++
 src/mdio/schemas/core.py        |  49 ++++++++++
 src/mdio/schemas/dimension.py   |  12 +++
 src/mdio/schemas/dtype.py       |  55 +++++++++++
 src/mdio/schemas/metadata.py    |  31 +++++++
 src/mdio/schemas/units.py       |  51 ++++++++++
 src/mdio/schemas/v0/__init__.py |   6 ++
 src/mdio/schemas/v0/dataset.py  |  88 ++++++++++++++++++
 src/mdio/schemas/v1/__init__.py |   6 ++
 src/mdio/schemas/v1/dataset.py  |  49 ++++++++++
 src/mdio/schemas/v1/stats.py    |  72 +++++++++++++++
 src/mdio/schemas/v1/units.py    | 126 +++++++++++++++++++++++++
 src/mdio/schemas/v1/variable.py |  55 +++++++++++
 17 files changed, 1020 insertions(+)
 create mode 100644 src/mdio/schemas/__init__.py
 create mode 100644 src/mdio/schemas/base.py
 create mode 100644 src/mdio/schemas/builder.py
 create mode 100644 src/mdio/schemas/chunk_grid.py
 create mode 100644 src/mdio/schemas/compressors.py
 create mode 100644 src/mdio/schemas/core.py
 create mode 100644 src/mdio/schemas/dimension.py
 create mode 100644 src/mdio/schemas/dtype.py
 create mode 100644 src/mdio/schemas/metadata.py
 create mode 100644 src/mdio/schemas/units.py
 create mode 100644 src/mdio/schemas/v0/__init__.py
 create mode 100644 src/mdio/schemas/v0/dataset.py
 create mode 100644 src/mdio/schemas/v1/__init__.py
 create mode 100644 src/mdio/schemas/v1/dataset.py
 create mode 100644 src/mdio/schemas/v1/stats.py
 create mode 100644 src/mdio/schemas/v1/units.py
 create mode 100644 src/mdio/schemas/v1/variable.py

diff --git a/src/mdio/schemas/__init__.py b/src/mdio/schemas/__init__.py
new file mode 100644
index 00000000..17ae4dd3
--- /dev/null
+++ b/src/mdio/schemas/__init__.py
@@ -0,0 +1,18 @@
+"""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..d4bdfd48
--- /dev/null
+++ b/src/mdio/schemas/base.py
@@ -0,0 +1,43 @@
+"""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..9a0e6cbb
--- /dev/null
+++ b/src/mdio/schemas/builder.py
@@ -0,0 +1,156 @@
+"""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:
+        """Initialize the builder."""
+        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:
+        """Initialize the builder."""
+        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..b550b98d
--- /dev/null
+++ b/src/mdio/schemas/compressors.py
@@ -0,0 +1,159 @@
+"""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.",
+    )
+
+    def make_instance(self):  # noqa: ANN201
+        """Translate parameters to compressor kwargs.."""
+        from zarr.codecs import Blosc as _Blosc
+
+        return _Blosc(
+            cname=self.algorithm,
+            clevel=self.level,
+            shuffle=self.shuffle,
+            blocksize=self.blocksize,
+        )
+
+
+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
+
+    def make_instance(self):  # noqa: ANN201
+        """Translate parameters to compressor kwargs.."""
+        from zarr.codecs import ZFPY as _ZFPY
+
+        return _ZFPY(
+            mode=self.mode.int_code,
+            tolerance=self.tolerance,
+            rate=self.rate,
+            precision=self.precision,
+        )
+
+
+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, <default_value>), '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..5c2427b0
--- /dev/null
+++ b/src/mdio/schemas/dtype.py
@@ -0,0 +1,55 @@
+"""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..e74f484f
--- /dev/null
+++ b/src/mdio/schemas/v0/__init__.py
@@ -0,0 +1,6 @@
+"""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..58c3ae72
--- /dev/null
+++ b/src/mdio/schemas/v0/dataset.py
@@ -0,0 +1,88 @@
+"""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..4f9bcdf3
--- /dev/null
+++ b/src/mdio/schemas/v1/__init__.py
@@ -0,0 +1,6 @@
+"""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/stats.py b/src/mdio/schemas/v1/stats.py
new file mode 100644
index 00000000..a8a319c0
--- /dev/null
+++ b/src/mdio/schemas/v1/stats.py
@@ -0,0 +1,72 @@
+"""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..dd46296c
--- /dev/null
+++ b/src/mdio/schemas/v1/units.py
@@ -0,0 +1,126 @@
+"""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..22ad0bf3
--- /dev/null
+++ b/src/mdio/schemas/v1/variable.py
@@ -0,0 +1,55 @@
+"""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
+
+
+class Coordinate(NamedArray):
+    """An MDIO coordinate array with metadata."""
+
+    data_type: ScalarType = Field(..., description="Data type of coordinate.")
+    metadata: list[AllUnits | UserAttributes] | 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,
+)
+
+
+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."
+    )

From 1a1e7436b96041df944b058c976da7b753f1db73 Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Tue, 29 Apr 2025 14:43:21 -0500
Subject: [PATCH 04/19] Relocate quickstart notebook to tutorials directory

---
 docs/tutorials/creation.ipynb | 1225 ++++++++++++++++++++++-----------
 1 file changed, 838 insertions(+), 387 deletions(-)

diff --git a/docs/tutorials/creation.ipynb b/docs/tutorials/creation.ipynb
index af4d3b65..d154c63f 100644
--- a/docs/tutorials/creation.ipynb
+++ b/docs/tutorials/creation.ipynb
@@ -1,396 +1,847 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "# Create an Empty MDIO\n",
-    "\n",
-    "```{article-info}\n",
-    ":author: Altay Sansal\n",
-    ":date: \"{sub-ref}`today`\"\n",
-    ":read-time: \"{sub-ref}`wordcount-minutes` min read\"\n",
-    ":class-container: sd-p-0 sd-outline-muted sd-rounded-3 sd-font-weight-light\n",
-    "```\n",
-    "\n",
-    "Here we will create an empty MDIO file and populate its fields.\n",
-    "\n",
-    "```{warning}\n",
-    "For plotting, the notebook requires [Matplotlib](https://matplotlib.org/) as a dependency. Please install it before executing using\n",
-    "`pip install matplotlib` or `conda install matplotlib`.\n",
-    "```\n",
-    "\n",
-    "We first need to import the configuration classes and create function to be able to create the empty MDIO."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-16T14:50:04.371422Z",
-     "start_time": "2025-04-16T14:50:04.368107Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from mdio import Dimension\n",
-    "from mdio import Grid\n",
-    "from mdio import MDIOCreateConfig\n",
-    "from mdio import MDIOReader\n",
-    "from mdio import MDIOVariableConfig\n",
-    "from mdio import MDIOWriter\n",
-    "from mdio import create_empty\n",
-    "from mdio.converters.segy import get_compressor\n",
-    "from mdio.segy.compat import mdio_segy_spec"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Setting Up Grid Dimensions and Variable(s)\n",
-    "\n",
-    "Let's try to create an MDIO file in 3D with known `inline`, `crossline`, and `sample`\n",
-    "dimensions. This is exactly like a 3D Post-Stack dataset.\n",
-    "\n",
-    "We will use the standard `chunked_012` access pattern. All variable names must start\n",
-    "with `chunked_*` for `MDIOReader` to be able to open them with any access pattern.\n",
-    "\n",
-    "We will also use an MDIO private utility function to get the compressor easily. The\n",
-    "compressor is fully customizable, but this function gives us ones available in SEG-Y\n",
-    "ingestion.\n",
-    "\n",
-    "If we want headers, we can also provide `header_dtype` which is a Numpy `dtype`. Again,\n",
-    "we will be using the standard one MDIO uses for ingesting SEG-Ys. (Similar to SEG-Y Rev1).\n",
-    "\n",
-    "At the end we create empty MDIO on local disk or cloud, based on the path string."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-16T14:50:06.121450Z",
-     "start_time": "2025-04-16T14:50:06.002634Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "grid = Grid(\n",
-    "    dims=[\n",
-    "        Dimension(name=\"inline\", coords=range(100, 300, 1)),  # 100-300 with step 1\n",
-    "        Dimension(name=\"crossline\", coords=range(1000, 1600, 2)),  # 1000-1600 with step 2\n",
-    "        Dimension(name=\"sample\", coords=range(0, 3000, 4)),  # 0-3 seconds 4ms sample rate\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "compressor = get_compressor(lossless=True)\n",
-    "header_dtype = mdio_segy_spec().trace.header.dtype\n",
-    "variable = MDIOVariableConfig(\n",
-    "    name=\"chunked_012\",\n",
-    "    dtype=\"float32\",\n",
-    "    chunks=(128, 128, 128),\n",
-    "    compressors=compressor,\n",
-    "    header_dtype=header_dtype,\n",
-    ")\n",
-    "\n",
-    "create_conf = MDIOCreateConfig(path=\"demo.mdio\", grid=grid, variables=[variable])\n",
-    "create_empty(config=create_conf);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Populating the MDIO\n",
-    "\n",
-    "Once empty MDIO is created, you can populate it with sample data, header data,\n",
-    "metadata, and statistics. The `live_mask` is automatically flipped to 1 after\n",
-    "every write. However, the consistency of the `live_mask` is up to the user. If\n",
-    "not populated correctly, at read time, traces may be skipped.\n",
-    "\n",
-    "We will read the usual stats and as you can see they're all empty. Text header\n",
-    "and binary header are only required if we are going to export this back to SEG-Y,\n",
-    "and can be modified. We can edit these if necessary using the writer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-16T14:50:09.974442Z",
-     "start_time": "2025-04-16T14:50:09.951257Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(['C00                                                                             ',\n",
-       "  'C01                                                                             ',\n",
-       "  'C02                                                                             ',\n",
-       "  'C03                                                                             ',\n",
-       "  'C04                                                                             '],\n",
-       " {},\n",
-       " {'rms': 0, 'min': 0, 'mean': 0, 'max': 0, 'std': 0})"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "writer = MDIOWriter(create_conf.path)\n",
-    "\n",
-    "# Truncate text header for display\n",
-    "writer.text_header[:5], writer.binary_header, writer.stats"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Updating File and Checking with `MDIOReader`.\n",
-    "\n",
-    "We write traces here, the headers are still empty. If you need the headers\n",
-    "populated, this is where they would be written to file as well.\n",
-    "\n",
-    "All the extra metadata we are writing is so that we can export back to SEG-Y\n",
-    "in the future. If that's not a requirement most of the metadata can be\n",
-    "omitted.\n",
-    "\n",
-    "If you already have an MDIO prior to this, all the metadata can be copied\n",
-    "over from the previous version."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-16T14:50:12.498287Z",
-     "start_time": "2025-04-16T14:50:11.670230Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({'data_sample_format': 1,\n",
-       "  'sample_interval': 4,\n",
-       "  'samples_per_trace': 750,\n",
-       "  'segy_revision_major': 1,\n",
-       "  'segy_revision_minor': 0},\n",
-       " {'rms': 3, 'min': -4, 'mean': 12, 'max': 4, 'std': 5})"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample_dim = writer.grid.select_dim(\"sample\")\n",
-    "sample_interval = int(sample_dim.coords[1] - sample_dim.coords[0])\n",
-    "num_samples = sample_dim.size\n",
-    "writer.binary_header = {\n",
-    "    \"data_sample_format\": 1,\n",
-    "    \"sample_interval\": sample_interval,\n",
-    "    \"samples_per_trace\": num_samples,\n",
-    "    \"segy_revision_major\": 1,\n",
-    "    \"segy_revision_minor\": 0,\n",
-    "}\n",
-    "\n",
-    "# Write some values to the file\n",
-    "writer[:5] = 1\n",
-    "writer[5:10] = 2\n",
-    "writer[50:100] = 3\n",
-    "writer[150:175] = -1\n",
-    "\n",
-    "# Write some made up stats\n",
-    "writer.stats = {\"mean\": 12, \"std\": 5, \"rms\": 3, \"min\": -4, \"max\": 4}\n",
-    "\n",
-    "reader = MDIOReader(create_conf.path)\n",
-    "reader.binary_header, reader.stats"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": "Plot some of the data we populated:"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-16T14:50:14.256953Z",
-     "start_time": "2025-04-16T14:50:14.036094Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(1, 2, sharex=\"all\")\n",
-    "ax[0].imshow(writer.live_mask[:].T, aspect=\"auto\")\n",
-    "ax[1].imshow(reader[:, 150].T, aspect=\"auto\")\n",
-    "\n",
-    "ax[0].set_xlabel(\"inline\")\n",
-    "ax[0].set_ylabel(\"crossline\")\n",
-    "ax[1].set_xlabel(\"inline\")\n",
-    "ax[1].set_ylabel(\"sample\")\n",
-    "ax[0].set_title(\"Live Mask\")\n",
-    "ax[1].set_title(\"Middle Crossline\")\n",
-    "fig.tight_layout();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Write to SEG-Y\n",
-    "\n",
-    "Because we populated all the fields necessary for SEG-Y output, we can call the\n",
-    "MDIO to SEGY converter and get a file out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-16T14:50:15.788812Z",
-     "start_time": "2025-04-16T14:50:15.009889Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Unwrapping MDIO Blocks: 100%|██████████| 7/7 [00:00<00:00, 10.55it/s]\n",
-      "Merging lines: 100%|██████████| 1/1 [00:00<00:00, 17.26it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(0, 0, 0, 0, 0, 4, 4000, 750, 750, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0)]\n",
-      "[[1. 1. 1. ... 1. 1. 1.]\n",
-      " [1. 1. 1. ... 1. 1. 1.]\n",
-      " [1. 1. 1. ... 1. 1. 1.]\n",
-      " ...\n",
-      " [1. 1. 1. ... 1. 1. 1.]\n",
-      " [1. 1. 1. ... 1. 1. 1.]\n",
-      " [1. 1. 1. ... 1. 1. 1.]]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from segy import SegyFile\n",
-    "\n",
-    "from mdio import mdio_to_segy\n",
-    "\n",
-    "mdio_to_segy(\"demo.mdio\", \"demo.segy\")\n",
-    "segy = SegyFile(\"demo.segy\")\n",
-    "print(segy.binary_header)\n",
-    "print(segy.sample[:100])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "raw",
    "source": [
-    "## Copying an Existing MDIO without Data and Headers\n",
-    "\n",
-    "We also provide a `create_empty_like` function which allows us to create an MDIO file\n",
-    "with the same structure and basic metadata as an existing one.\n",
-    "\n",
-    "This will carry over:\n",
-    "* All access patterns in source MDIO\n",
-    "* Grid (shape, coordinates, dimensions, etc)\n",
-    "* Text Header\n",
-    "* Binary Header\n",
-    "\n",
-    "It will **NOT** carry over:\n",
-    "* Trace data\n",
-    "* Header data\n",
-    "* Statistics\n",
-    "* Live Mask"
+    "{\n",
+    " \"cells\": [\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"metadata\": {\n",
+    "    \"pycharm\": {\n",
+    "     \"name\": \"#%% md\\n\"\n",
+    "    }\n",
+    "   },\n",
+    "   \"source\": [\n",
+    "    \"# Create an Empty MDIO\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"```{article-info}\\n\",\n",
+    "    \":author: Altay Sansal\\n\",\n",
+    "    \":date: \\\"{sub-ref}`today`\\\"\\n\",\n",
+    "    \":read-time: \\\"{sub-ref}`wordcount-minutes` min read\\\"\\n\",\n",
+    "    \":class-container: sd-p-0 sd-outline-muted sd-rounded-3 sd-font-weight-light\\n\",\n",
+    "    \"```\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"Here we will create an empty MDIO file and populate its fields.\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"```{warning}\\n\",\n",
+    "    \"For plotting, the notebook requires [Matplotlib](https://matplotlib.org/) as a dependency. Please install it before executing using\\n\",\n",
+    "    \"`pip install matplotlib` or `conda install matplotlib`.\\n\",\n",
+    "    \"```\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"We first need to import the configuration classes and create function to be able to create the empty MDIO.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 2,\n",
+    "   \"metadata\": {\n",
+    "    \"ExecuteTime\": {\n",
+    "     \"end_time\": \"2025-04-16T14:50:04.371422Z\",\n",
+    "     \"start_time\": \"2025-04-16T14:50:04.368107Z\"\n",
+    "    },\n",
+    "    \"pycharm\": {\n",
+    "     \"name\": \"#%%\\n\"\n",
+    "    }\n",
+    "   },\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"import matplotlib.pyplot as plt\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"from mdio import Dimension\\n\",\n",
+    "    \"from mdio import Grid\\n\",\n",
+    "    \"from mdio import MDIOCreateConfig\\n\",\n",
+    "    \"from mdio import MDIOReader\\n\",\n",
+    "    \"from mdio import MDIOVariableConfig\\n\",\n",
+    "    \"from mdio import MDIOWriter\\n\",\n",
+    "    \"from mdio import create_empty\\n\",\n",
+    "    \"from mdio.converters.segy import get_compressor\\n\",\n",
+    "    \"from mdio.segy.compat import mdio_segy_spec\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"metadata\": {\n",
+    "    \"pycharm\": {\n",
+    "     \"name\": \"#%% md\\n\"\n",
+    "    }\n",
+    "   },\n",
+    "   \"source\": [\n",
+    "    \"## Setting Up Grid Dimensions and Variable(s)\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"Let's try to create an MDIO file in 3D with known `inline`, `crossline`, and `sample`\\n\",\n",
+    "    \"dimensions. This is exactly like a 3D Post-Stack dataset.\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"We will use the standard `chunked_012` access pattern. All variable names must start\\n\",\n",
+    "    \"with `chunked_*` for `MDIOReader` to be able to open them with any access pattern.\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"We will also use an MDIO private utility function to get the compressor easily. The\\n\",\n",
+    "    \"compressor is fully customizable, but this function gives us ones available in SEG-Y\\n\",\n",
+    "    \"ingestion.\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"If we want headers, we can also provide `header_dtype` which is a Numpy `dtype`. Again,\\n\",\n",
+    "    \"we will be using the standard one MDIO uses for ingesting SEG-Ys. (Similar to SEG-Y Rev1).\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"At the end we create empty MDIO on local disk or cloud, based on the path string.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 3,\n",
+    "   \"metadata\": {\n",
+    "    \"ExecuteTime\": {\n",
+    "     \"end_time\": \"2025-04-16T14:50:06.121450Z\",\n",
+    "     \"start_time\": \"2025-04-16T14:50:06.002634Z\"\n",
+    "    },\n",
+    "    \"pycharm\": {\n",
+    "     \"name\": \"#%%\\n\"\n",
+    "    }\n",
+    "   },\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"grid = Grid(\\n\",\n",
+    "    \"    dims=[\\n\",\n",
+    "    \"        Dimension(name=\\\"inline\\\", coords=range(100, 300, 1)),  # 100-300 with step 1\\n\",\n",
+    "    \"        Dimension(name=\\\"crossline\\\", coords=range(1000, 1600, 2)),  # 1000-1600 with step 2\\n\",\n",
+    "    \"        Dimension(name=\\\"sample\\\", coords=range(0, 3000, 4)),  # 0-3 seconds 4ms sample rate\\n\",\n",
+    "    \"    ]\\n\",\n",
+    "    \")\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"compressor = get_compressor(lossless=True)\\n\",\n",
+    "    \"header_dtype = mdio_segy_spec().trace.header.dtype\\n\",\n",
+    "    \"variable = MDIOVariableConfig(\\n\",\n",
+    "    \"    name=\\\"chunked_012\\\",\\n\",\n",
+    "    \"    dtype=\\\"float32\\\",\\n\",\n",
+    "    \"    chunks=(128, 128, 128),\\n\",\n",
+    "    \"    compressors=compressor,\\n\",\n",
+    "    \"    header_dtype=header_dtype,\\n\",\n",
+    "    \")\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"create_conf = MDIOCreateConfig(path=\\\"demo.mdio\\\", grid=grid, variables=[variable])\\n\",\n",
+    "    \"create_empty(config=create_conf);\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"## Populating the MDIO\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"Once empty MDIO is created, you can populate it with sample data, header data,\\n\",\n",
+    "    \"metadata, and statistics. The `live_mask` is automatically flipped to 1 after\\n\",\n",
+    "    \"every write. However, the consistency of the `live_mask` is up to the user. If\\n\",\n",
+    "    \"not populated correctly, at read time, traces may be skipped.\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"We will read the usual stats and as you can see they're all empty. Text header\\n\",\n",
+    "    \"and binary header are only required if we are going to export this back to SEG-Y,\\n\",\n",
+    "    \"and can be modified. We can edit these if necessary using the writer.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 4,\n",
+    "   \"metadata\": {\n",
+    "    \"ExecuteTime\": {\n",
+    "     \"end_time\": \"2025-04-16T14:50:09.974442Z\",\n",
+    "     \"start_time\": \"2025-04-16T14:50:09.951257Z\"\n",
+    "    },\n",
+    "    \"pycharm\": {\n",
+    "     \"name\": \"#%%\\n\"\n",
+    "    }\n",
+    "   },\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/plain\": [\n",
+    "       \"(['C00                                                                             ',\\n\",\n",
+    "       \"  'C01                                                                             ',\\n\",\n",
+    "       \"  'C02                                                                             ',\\n\",\n",
+    "       \"  'C03                                                                             ',\\n\",\n",
+    "       \"  'C04                                                                             '],\\n\",\n",
+    "       \" {},\\n\",\n",
+    "       \" {'rms': 0, 'min': 0, 'mean': 0, 'max': 0, 'std': 0})\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"execution_count\": 4,\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"execute_result\"\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"writer = MDIOWriter(create_conf.path)\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"# Truncate text header for display\\n\",\n",
+    "    \"writer.text_header[:5], writer.binary_header, writer.stats\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"## Updating File and Checking with `MDIOReader`.\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"We write traces here, the headers are still empty. If you need the headers\\n\",\n",
+    "    \"populated, this is where they would be written to file as well.\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"All the extra metadata we are writing is so that we can export back to SEG-Y\\n\",\n",
+    "    \"in the future. If that's not a requirement most of the metadata can be\\n\",\n",
+    "    \"omitted.\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"If you already have an MDIO prior to this, all the metadata can be copied\\n\",\n",
+    "    \"over from the previous version.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 5,\n",
+    "   \"metadata\": {\n",
+    "    \"ExecuteTime\": {\n",
+    "     \"end_time\": \"2025-04-16T14:50:12.498287Z\",\n",
+    "     \"start_time\": \"2025-04-16T14:50:11.670230Z\"\n",
+    "    },\n",
+    "    \"pycharm\": {\n",
+    "     \"name\": \"#%%\\n\"\n",
+    "    }\n",
+    "   },\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/plain\": [\n",
+    "       \"({'data_sample_format': 1,\\n\",\n",
+    "       \"  'sample_interval': 4,\\n\",\n",
+    "       \"  'samples_per_trace': 750,\\n\",\n",
+    "       \"  'segy_revision_major': 1,\\n\",\n",
+    "       \"  'segy_revision_minor': 0},\\n\",\n",
+    "       \" {'rms': 3, 'min': -4, 'mean': 12, 'max': 4, 'std': 5})\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"execution_count\": 5,\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"execute_result\"\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"sample_dim = writer.grid.select_dim(\\\"sample\\\")\\n\",\n",
+    "    \"sample_interval = int(sample_dim.coords[1] - sample_dim.coords[0])\\n\",\n",
+    "    \"num_samples = sample_dim.size\\n\",\n",
+    "    \"writer.binary_header = {\\n\",\n",
+    "    \"    \\\"data_sample_format\\\": 1,\\n\",\n",
+    "    \"    \\\"sample_interval\\\": sample_interval,\\n\",\n",
+    "    \"    \\\"samples_per_trace\\\": num_samples,\\n\",\n",
+    "    \"    \\\"segy_revision_major\\\": 1,\\n\",\n",
+    "    \"    \\\"segy_revision_minor\\\": 0,\\n\",\n",
+    "    \"}\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"# Write some values to the file\\n\",\n",
+    "    \"writer[:5] = 1\\n\",\n",
+    "    \"writer[5:10] = 2\\n\",\n",
+    "    \"writer[50:100] = 3\\n\",\n",
+    "    \"writer[150:175] = -1\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"# Write some made up stats\\n\",\n",
+    "    \"writer.stats = {\\\"mean\\\": 12, \\\"std\\\": 5, \\\"rms\\\": 3, \\\"min\\\": -4, \\\"max\\\": 4}\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"reader = MDIOReader(create_conf.path)\\n\",\n",
+    "    \"reader.binary_header, reader.stats\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": \"Plot some of the data we populated:\"\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 6,\n",
+    "   \"metadata\": {\n",
+    "    \"ExecuteTime\": {\n",
+    "     \"end_time\": \"2025-04-16T14:50:14.256953Z\",\n",
+    "     \"start_time\": \"2025-04-16T14:50:14.036094Z\"\n",
+    "    }\n",
+    "   },\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"image/png\": 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\",\n",
+    "      \"text/plain\": [\n",
+    "       \"<Figure size 640x480 with 2 Axes>\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"fig, ax = plt.subplots(1, 2, sharex=\\\"all\\\")\\n\",\n",
+    "    \"ax[0].imshow(writer.live_mask[:].T, aspect=\\\"auto\\\")\\n\",\n",
+    "    \"ax[1].imshow(reader[:, 150].T, aspect=\\\"auto\\\")\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"ax[0].set_xlabel(\\\"inline\\\")\\n\",\n",
+    "    \"ax[0].set_ylabel(\\\"crossline\\\")\\n\",\n",
+    "    \"ax[1].set_xlabel(\\\"inline\\\")\\n\",\n",
+    "    \"ax[1].set_ylabel(\\\"sample\\\")\\n\",\n",
+    "    \"ax[0].set_title(\\\"Live Mask\\\")\\n\",\n",
+    "    \"ax[1].set_title(\\\"Middle Crossline\\\")\\n\",\n",
+    "    \"fig.tight_layout();\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"## Write to SEG-Y\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"Because we populated all the fields necessary for SEG-Y output, we can call the\\n\",\n",
+    "    \"MDIO to SEGY converter and get a file out.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 7,\n",
+    "   \"metadata\": {\n",
+    "    \"ExecuteTime\": {\n",
+    "     \"end_time\": \"2025-04-16T14:50:15.788812Z\",\n",
+    "     \"start_time\": \"2025-04-16T14:50:15.009889Z\"\n",
+    "    }\n",
+    "   },\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"Unwrapping MDIO Blocks: 100%|██████████| 7/7 [00:00<00:00, 10.55it/s]\\n\",\n",
+    "      \"Merging lines: 100%|██████████| 1/1 [00:00<00:00, 17.26it/s]\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stdout\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"[(0, 0, 0, 0, 0, 4, 4000, 750, 750, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0)]\\n\",\n",
+    "      \"[[1. 1. 1. ... 1. 1. 1.]\\n\",\n",
+    "      \" [1. 1. 1. ... 1. 1. 1.]\\n\",\n",
+    "      \" [1. 1. 1. ... 1. 1. 1.]\\n\",\n",
+    "      \" ...\\n\",\n",
+    "      \" [1. 1. 1. ... 1. 1. 1.]\\n\",\n",
+    "      \" [1. 1. 1. ... 1. 1. 1.]\\n\",\n",
+    "      \" [1. 1. 1. ... 1. 1. 1.]]\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"\\n\"\n",
+    "     ]\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"from segy import SegyFile\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"from mdio import mdio_to_segy\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"mdio_to_segy(\\\"demo.mdio\\\", \\\"demo.segy\\\")\\n\",\n",
+    "    \"segy = SegyFile(\\\"demo.segy\\\")\\n\",\n",
+    "    \"print(segy.binary_header)\\n\",\n",
+    "    \"print(segy.sample[:100])\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"## Copying an Existing MDIO without Data and Headers\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"We also provide a `create_empty_like` function which allows us to create an MDIO file\\n\",\n",
+    "    \"with the same structure and basic metadata as an existing one.\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"This will carry over:\\n\",\n",
+    "    \"* All access patterns in source MDIO\\n\",\n",
+    "    \"* Grid (shape, coordinates, dimensions, etc)\\n\",\n",
+    "    \"* Text Header\\n\",\n",
+    "    \"* Binary Header\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"It will **NOT** carry over:\\n\",\n",
+    "    \"* Trace data\\n\",\n",
+    "    \"* Header data\\n\",\n",
+    "    \"* Statistics\\n\",\n",
+    "    \"* Live Mask\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 8,\n",
+    "   \"metadata\": {\n",
+    "    \"ExecuteTime\": {\n",
+    "     \"end_time\": \"2025-04-16T14:50:17.246513Z\",\n",
+    "     \"start_time\": \"2025-04-16T14:50:17.111779Z\"\n",
+    "    }\n",
+    "   },\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"from mdio import create_empty_like\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"copy_path = \\\"copy_of_existing.mdio\\\"\\n\",\n",
+    "    \"create_empty_like(create_conf.path, copy_path)\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"copy_reader = MDIOReader(copy_path)\\n\",\n",
+    "    \"assert copy_reader.shape == reader.shape\\n\",\n",
+    "    \"assert copy_reader.binary_header == reader.binary_header\\n\",\n",
+    "    \"assert copy_reader.stats != reader.stats\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": null,\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": []\n",
+    "  }\n",
+    " ],\n",
+    " \"metadata\": {\n",
+    "  \"kernelspec\": {\n",
+    "   \"display_name\": \"Python 3 (ipykernel)\",\n",
+    "   \"language\": \"python\",\n",
+    "   \"name\": \"python3\"\n",
+    "  },\n",
+    "  \"language_info\": {\n",
+    "   \"codemirror_mode\": {\n",
+    "    \"name\": \"ipython\",\n",
+    "    \"version\": 3\n",
+    "   },\n",
+    "   \"file_extension\": \".py\",\n",
+    "   \"mimetype\": \"text/x-python\",\n",
+    "   \"name\": \"python\",\n",
+    "   \"nbconvert_exporter\": \"python\",\n",
+    "   \"pygments_lexer\": \"ipython3\",\n",
+    "   \"version\": \"3.12.3\"\n",
+    "  },\n",
+    "  \"widgets\": {\n",
+    "   \"application/vnd.jupyter.widget-state+json\": {\n",
+    "    \"state\": {\n",
+    "     \"104c0d4e90244ebc959e51a58794dcf4\": {\n",
+    "      \"model_module\": \"@jupyter-widgets/base\",\n",
+    "      \"model_module_version\": \"2.0.0\",\n",
+    "      \"model_name\": \"LayoutModel\",\n",
+    "      \"state\": {\n",
+    "       \"display\": \"inline-flex\",\n",
+    "       \"flex_flow\": \"row wrap\",\n",
+    "       \"width\": \"100%\"\n",
+    "      }\n",
+    "     },\n",
+    "     \"1c0f5b458ee1401eaa8de63c65802073\": {\n",
+    "      \"model_module\": \"@jupyter-widgets/controls\",\n",
+    "      \"model_module_version\": \"2.0.0\",\n",
+    "      \"model_name\": \"HTMLStyleModel\",\n",
+    "      \"state\": {\n",
+    "       \"description_width\": \"\",\n",
+    "       \"font_size\": null,\n",
+    "       \"text_color\": null\n",
+    "      }\n",
+    "     },\n",
+    "     \"21b25dd6080d42368afc06770d5e1755\": {\n",
+    "      \"model_module\": \"@jupyter-widgets/controls\",\n",
+    "      \"model_module_version\": \"2.0.0\",\n",
+    "      \"model_name\": \"HTMLModel\",\n",
+    "      \"state\": {\n",
+    "       \"layout\": \"IPY_MODEL_35fb49026dac479eba87217c2f6bcb9a\",\n",
+    "       \"style\": \"IPY_MODEL_3ab3e27c83aa423d9ecdac94e576f0fe\",\n",
+    "       \"value\": \"Ingesting SEG-Y in 6 chunks: 100%\"\n",
+    "      }\n",
+    "     },\n",
+    "     \"2693aae410f04e7587f0400b5eef1079\": {\n",
+    "      \"model_module\": \"@jupyter-widgets/controls\",\n",
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+    "       \"description_width\": \"\"\n",
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+    "       \"description_width\": \"\"\n",
+    "      }\n",
+    "     },\n",
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+    "      \"model_name\": \"LayoutModel\",\n",
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+    "       \"layout\": \"IPY_MODEL_9fe5348964cd4f7880bcd33204bbc7a2\",\n",
+    "       \"style\": \"IPY_MODEL_4df685a95de542ffab6f265df89b8949\",\n",
+    "       \"value\": \"Step 2 / 2 Concat Blocks: \"\n",
+    "      }\n",
+    "     },\n",
+    "     \"e66368cda8124001b16459a0634029e2\": {\n",
+    "      \"model_module\": \"@jupyter-widgets/controls\",\n",
+    "      \"model_module_version\": \"2.0.0\",\n",
+    "      \"model_name\": \"HTMLStyleModel\",\n",
+    "      \"state\": {\n",
+    "       \"description_width\": \"\",\n",
+    "       \"font_size\": null,\n",
+    "       \"text_color\": null\n",
+    "      }\n",
+    "     },\n",
+    "     \"eab4b060672b4dd5a73ca7bb8f782cd2\": {\n",
+    "      \"model_module\": \"@jupyter-widgets/base\",\n",
+    "      \"model_module_version\": \"2.0.0\",\n",
+    "      \"model_name\": \"LayoutModel\",\n",
+    "      \"state\": {\n",
+    "       \"display\": \"inline-flex\",\n",
+    "       \"flex_flow\": \"row wrap\",\n",
+    "       \"width\": \"100%\"\n",
+    "      }\n",
+    "     },\n",
+    "     \"f65266f861cf40ef84e9ec74a8e0b44c\": {\n",
+    "      \"model_module\": \"@jupyter-widgets/controls\",\n",
+    "      \"model_module_version\": \"2.0.0\",\n",
+    "      \"model_name\": \"HTMLModel\",\n",
+    "      \"state\": {\n",
+    "       \"layout\": \"IPY_MODEL_fff4199774534d9f982f03738fbb08e7\",\n",
+    "       \"style\": \"IPY_MODEL_e66368cda8124001b16459a0634029e2\",\n",
+    "       \"value\": \"Scanning SEG-Y for geometry attributes: 100%\"\n",
+    "      }\n",
+    "     },\n",
+    "     \"fdcea609ee7349629604217b625371d3\": {\n",
+    "      \"model_module\": \"@jupyter-widgets/controls\",\n",
+    "      \"model_module_version\": \"2.0.0\",\n",
+    "      \"model_name\": \"HTMLStyleModel\",\n",
+    "      \"state\": {\n",
+    "       \"description_width\": \"\",\n",
+    "       \"font_size\": null,\n",
+    "       \"text_color\": null\n",
+    "      }\n",
+    "     },\n",
+    "     \"fff4199774534d9f982f03738fbb08e7\": {\n",
+    "      \"model_module\": \"@jupyter-widgets/base\",\n",
+    "      \"model_module_version\": \"2.0.0\",\n",
+    "      \"model_name\": \"LayoutModel\",\n",
+    "      \"state\": {}\n",
+    "     }\n",
+    "    },\n",
+    "    \"version_major\": 2,\n",
+    "    \"version_minor\": 0\n",
+    "   }\n",
+    "  }\n",
+    " },\n",
+    " \"nbformat\": 4,\n",
+    " \"nbformat_minor\": 4\n",
+    "}\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-16T14:50:17.246513Z",
-     "start_time": "2025-04-16T14:50:17.111779Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from mdio import create_empty_like\n",
-    "\n",
-    "copy_path = \"copy_of_existing.mdio\"\n",
-    "create_empty_like(create_conf.path, copy_path)\n",
-    "\n",
-    "copy_reader = MDIOReader(copy_path)\n",
-    "assert copy_reader.shape == reader.shape\n",
-    "assert copy_reader.binary_header == reader.binary_header\n",
-    "assert copy_reader.stats != reader.stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 3c8ea4dac89b261ea1002c1874b09e130ba06080 Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Tue, 29 Apr 2025 14:43:41 -0500
Subject: [PATCH 05/19] update docs dependencies

---
 docs/requirements.txt | 1 +
 uv.lock               | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

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/uv.lock b/uv.lock
index 2e73c419..92ce6cdc 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1922,7 +1922,7 @@ docs = [
     { name = "furo", specifier = ">=2024.8.6" },
     { name = "linkify-it-py", specifier = ">=2.0.3" },
     { name = "myst-nb", specifier = ">=1.2.0" },
-    { name = "sphinx", specifier = ">=8.1.3,<9" },
+    { name = "sphinx", specifier = ">=8.2.3,<9" },
     { name = "sphinx-autobuild", specifier = ">=2024.10.3" },
     { name = "sphinx-click", specifier = ">=6.0.0,<7" },
     { name = "sphinx-copybutton", specifier = ">=0.5.2,<0.6" },

From 53ecaa9346b2be03e08ec4b986b28865a429a31d Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Tue, 29 Apr 2025 14:43:58 -0500
Subject: [PATCH 06/19] add new docs

---
 docs/api_reference.md           |   1 +
 docs/conf.py                    |  10 ++
 docs/data_models/chunk_grids.md | 154 +++++++++++++++++++
 docs/data_models/compressors.md | 100 ++++++++++++
 docs/data_models/data_types.md  | 259 ++++++++++++++++++++++++++++++++
 docs/data_models/dimensions.md  |  33 ++++
 docs/data_models/index.md       |  10 ++
 docs/data_models/version_0.md   |  55 +++++++
 docs/data_models/version_1.md   | 131 ++++++++++++++++
 docs/index.md                   |  11 ++
 10 files changed, 764 insertions(+)
 create mode 100644 docs/data_models/chunk_grids.md
 create mode 100644 docs/data_models/compressors.md
 create mode 100644 docs/data_models/data_types.md
 create mode 100644 docs/data_models/dimensions.md
 create mode 100644 docs/data_models/index.md
 create mode 100644 docs/data_models/version_0.md
 create mode 100644 docs/data_models/version_1.md

diff --git a/docs/api_reference.md b/docs/api_reference.md
index 0ceba4f5..f2ce3639 100644
--- a/docs/api_reference.md
+++ b/docs/api_reference.md
@@ -71,4 +71,5 @@ and
 ```{eval-rst}
 .. automodule:: mdio.core.serialization
    :members:
+   :exclude-members: create_rechunk_plan, write_rechunked_values
 ```
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..caf0ed5c
--- /dev/null
+++ b/docs/data_models/version_1.md
@@ -0,0 +1,131 @@
+```{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
+
+.. 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

From da09c4b8f7c216b098ee4925b1eb86fc5b251806 Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Tue, 29 Apr 2025 14:55:26 -0500
Subject: [PATCH 07/19] remove incorrect exclude

---
 docs/api_reference.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/docs/api_reference.md b/docs/api_reference.md
index f2ce3639..0ceba4f5 100644
--- a/docs/api_reference.md
+++ b/docs/api_reference.md
@@ -71,5 +71,4 @@ and
 ```{eval-rst}
 .. automodule:: mdio.core.serialization
    :members:
-   :exclude-members: create_rechunk_plan, write_rechunked_values
 ```

From 0d59c9554f2b124fee50105fc20cbb31092d60ce Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Tue, 29 Apr 2025 15:33:52 -0500
Subject: [PATCH 08/19] remove duplicate doc directive

---
 docs/tutorials/creation.ipynb | 1225 ++++++++++++++++++++++-----------
 1 file changed, 838 insertions(+), 387 deletions(-)

diff --git a/docs/tutorials/creation.ipynb b/docs/tutorials/creation.ipynb
index d154c63f..25a67c4f 100644
--- a/docs/tutorials/creation.ipynb
+++ b/docs/tutorials/creation.ipynb
@@ -7,396 +7,847 @@
     "{\n",
     " \"cells\": [\n",
     "  {\n",
-    "   \"cell_type\": \"markdown\",\n",
-    "   \"metadata\": {\n",
-    "    \"pycharm\": {\n",
-    "     \"name\": \"#%% md\\n\"\n",
-    "    }\n",
-    "   },\n",
-    "   \"source\": [\n",
-    "    \"# Create an Empty MDIO\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"```{article-info}\\n\",\n",
-    "    \":author: Altay Sansal\\n\",\n",
-    "    \":date: \\\"{sub-ref}`today`\\\"\\n\",\n",
-    "    \":read-time: \\\"{sub-ref}`wordcount-minutes` min read\\\"\\n\",\n",
-    "    \":class-container: sd-p-0 sd-outline-muted sd-rounded-3 sd-font-weight-light\\n\",\n",
-    "    \"```\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"Here we will create an empty MDIO file and populate its fields.\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"```{warning}\\n\",\n",
-    "    \"For plotting, the notebook requires [Matplotlib](https://matplotlib.org/) as a dependency. Please install it before executing using\\n\",\n",
-    "    \"`pip install matplotlib` or `conda install matplotlib`.\\n\",\n",
-    "    \"```\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"We first need to import the configuration classes and create function to be able to create the empty MDIO.\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 2,\n",
-    "   \"metadata\": {\n",
-    "    \"ExecuteTime\": {\n",
-    "     \"end_time\": \"2025-04-16T14:50:04.371422Z\",\n",
-    "     \"start_time\": \"2025-04-16T14:50:04.368107Z\"\n",
-    "    },\n",
-    "    \"pycharm\": {\n",
-    "     \"name\": \"#%%\\n\"\n",
-    "    }\n",
-    "   },\n",
-    "   \"outputs\": [],\n",
-    "   \"source\": [\n",
-    "    \"import matplotlib.pyplot as plt\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"from mdio import Dimension\\n\",\n",
-    "    \"from mdio import Grid\\n\",\n",
-    "    \"from mdio import MDIOCreateConfig\\n\",\n",
-    "    \"from mdio import MDIOReader\\n\",\n",
-    "    \"from mdio import MDIOVariableConfig\\n\",\n",
-    "    \"from mdio import MDIOWriter\\n\",\n",
-    "    \"from mdio import create_empty\\n\",\n",
-    "    \"from mdio.converters.segy import get_compressor\\n\",\n",
-    "    \"from mdio.segy.compat import mdio_segy_spec\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"markdown\",\n",
-    "   \"metadata\": {\n",
-    "    \"pycharm\": {\n",
-    "     \"name\": \"#%% md\\n\"\n",
-    "    }\n",
-    "   },\n",
-    "   \"source\": [\n",
-    "    \"## Setting Up Grid Dimensions and Variable(s)\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"Let's try to create an MDIO file in 3D with known `inline`, `crossline`, and `sample`\\n\",\n",
-    "    \"dimensions. This is exactly like a 3D Post-Stack dataset.\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"We will use the standard `chunked_012` access pattern. All variable names must start\\n\",\n",
-    "    \"with `chunked_*` for `MDIOReader` to be able to open them with any access pattern.\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"We will also use an MDIO private utility function to get the compressor easily. The\\n\",\n",
-    "    \"compressor is fully customizable, but this function gives us ones available in SEG-Y\\n\",\n",
-    "    \"ingestion.\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"If we want headers, we can also provide `header_dtype` which is a Numpy `dtype`. Again,\\n\",\n",
-    "    \"we will be using the standard one MDIO uses for ingesting SEG-Ys. (Similar to SEG-Y Rev1).\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"At the end we create empty MDIO on local disk or cloud, based on the path string.\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 3,\n",
-    "   \"metadata\": {\n",
-    "    \"ExecuteTime\": {\n",
-    "     \"end_time\": \"2025-04-16T14:50:06.121450Z\",\n",
-    "     \"start_time\": \"2025-04-16T14:50:06.002634Z\"\n",
-    "    },\n",
-    "    \"pycharm\": {\n",
-    "     \"name\": \"#%%\\n\"\n",
-    "    }\n",
-    "   },\n",
-    "   \"outputs\": [],\n",
-    "   \"source\": [\n",
-    "    \"grid = Grid(\\n\",\n",
-    "    \"    dims=[\\n\",\n",
-    "    \"        Dimension(name=\\\"inline\\\", coords=range(100, 300, 1)),  # 100-300 with step 1\\n\",\n",
-    "    \"        Dimension(name=\\\"crossline\\\", coords=range(1000, 1600, 2)),  # 1000-1600 with step 2\\n\",\n",
-    "    \"        Dimension(name=\\\"sample\\\", coords=range(0, 3000, 4)),  # 0-3 seconds 4ms sample rate\\n\",\n",
-    "    \"    ]\\n\",\n",
-    "    \")\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"compressor = get_compressor(lossless=True)\\n\",\n",
-    "    \"header_dtype = mdio_segy_spec().trace.header.dtype\\n\",\n",
-    "    \"variable = MDIOVariableConfig(\\n\",\n",
-    "    \"    name=\\\"chunked_012\\\",\\n\",\n",
-    "    \"    dtype=\\\"float32\\\",\\n\",\n",
-    "    \"    chunks=(128, 128, 128),\\n\",\n",
-    "    \"    compressors=compressor,\\n\",\n",
-    "    \"    header_dtype=header_dtype,\\n\",\n",
-    "    \")\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"create_conf = MDIOCreateConfig(path=\\\"demo.mdio\\\", grid=grid, variables=[variable])\\n\",\n",
-    "    \"create_empty(config=create_conf);\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"markdown\",\n",
     "   \"metadata\": {},\n",
+    "   \"cell_type\": \"raw\",\n",
     "   \"source\": [\n",
-    "    \"## Populating the MDIO\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"Once empty MDIO is created, you can populate it with sample data, header data,\\n\",\n",
-    "    \"metadata, and statistics. The `live_mask` is automatically flipped to 1 after\\n\",\n",
-    "    \"every write. However, the consistency of the `live_mask` is up to the user. If\\n\",\n",
-    "    \"not populated correctly, at read time, traces may be skipped.\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"We will read the usual stats and as you can see they're all empty. Text header\\n\",\n",
-    "    \"and binary header are only required if we are going to export this back to SEG-Y,\\n\",\n",
-    "    \"and can be modified. We can edit these if necessary using the writer.\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 4,\n",
-    "   \"metadata\": {\n",
-    "    \"ExecuteTime\": {\n",
-    "     \"end_time\": \"2025-04-16T14:50:09.974442Z\",\n",
-    "     \"start_time\": \"2025-04-16T14:50:09.951257Z\"\n",
-    "    },\n",
-    "    \"pycharm\": {\n",
-    "     \"name\": \"#%%\\n\"\n",
-    "    }\n",
-    "   },\n",
-    "   \"outputs\": [\n",
-    "    {\n",
-    "     \"data\": {\n",
-    "      \"text/plain\": [\n",
-    "       \"(['C00                                                                             ',\\n\",\n",
-    "       \"  'C01                                                                             ',\\n\",\n",
-    "       \"  'C02                                                                             ',\\n\",\n",
-    "       \"  'C03                                                                             ',\\n\",\n",
-    "       \"  'C04                                                                             '],\\n\",\n",
-    "       \" {},\\n\",\n",
-    "       \" {'rms': 0, 'min': 0, 'mean': 0, 'max': 0, 'std': 0})\"\n",
-    "      ]\n",
-    "     },\n",
-    "     \"execution_count\": 4,\n",
-    "     \"metadata\": {},\n",
-    "     \"output_type\": \"execute_result\"\n",
-    "    }\n",
-    "   ],\n",
-    "   \"source\": [\n",
-    "    \"writer = MDIOWriter(create_conf.path)\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"# Truncate text header for display\\n\",\n",
-    "    \"writer.text_header[:5], writer.binary_header, writer.stats\"\n",
+    "    \"{\\n\",\n",
+    "    \" \\\"cells\\\": [\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"markdown\\\",\\n\",\n",
+    "    \"   \\\"metadata\\\": {\\n\",\n",
+    "    \"    \\\"pycharm\\\": {\\n\",\n",
+    "    \"     \\\"name\\\": \\\"#%% md\\\\n\\\"\\n\",\n",
+    "    \"    }\\n\",\n",
+    "    \"   },\\n\",\n",
+    "    \"   \\\"source\\\": [\\n\",\n",
+    "    \"    \\\"# Create an Empty MDIO\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"```{article-info}\\\\n\\\",\\n\",\n",
+    "    \"    \\\":author: Altay Sansal\\\\n\\\",\\n\",\n",
+    "    \"    \\\":date: \\\\\\\"{sub-ref}`today`\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\":read-time: \\\\\\\"{sub-ref}`wordcount-minutes` min read\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\":class-container: sd-p-0 sd-outline-muted sd-rounded-3 sd-font-weight-light\\\\n\\\",\\n\",\n",
+    "    \"    \\\"```\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"Here we will create an empty MDIO file and populate its fields.\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"```{warning}\\\\n\\\",\\n\",\n",
+    "    \"    \\\"For plotting, the notebook requires [Matplotlib](https://matplotlib.org/) as a dependency. Please install it before executing using\\\\n\\\",\\n\",\n",
+    "    \"    \\\"`pip install matplotlib` or `conda install matplotlib`.\\\\n\\\",\\n\",\n",
+    "    \"    \\\"```\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"We first need to import the configuration classes and create function to be able to create the empty MDIO.\\\"\\n\",\n",
+    "    \"   ]\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
+    "    \"   \\\"execution_count\\\": 2,\\n\",\n",
+    "    \"   \\\"metadata\\\": {\\n\",\n",
+    "    \"    \\\"ExecuteTime\\\": {\\n\",\n",
+    "    \"     \\\"end_time\\\": \\\"2025-04-16T14:50:04.371422Z\\\",\\n\",\n",
+    "    \"     \\\"start_time\\\": \\\"2025-04-16T14:50:04.368107Z\\\"\\n\",\n",
+    "    \"    },\\n\",\n",
+    "    \"    \\\"pycharm\\\": {\\n\",\n",
+    "    \"     \\\"name\\\": \\\"#%%\\\\n\\\"\\n\",\n",
+    "    \"    }\\n\",\n",
+    "    \"   },\\n\",\n",
+    "    \"   \\\"outputs\\\": [],\\n\",\n",
+    "    \"   \\\"source\\\": [\\n\",\n",
+    "    \"    \\\"import matplotlib.pyplot as plt\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"from mdio import Dimension\\\\n\\\",\\n\",\n",
+    "    \"    \\\"from mdio import Grid\\\\n\\\",\\n\",\n",
+    "    \"    \\\"from mdio import MDIOCreateConfig\\\\n\\\",\\n\",\n",
+    "    \"    \\\"from mdio import MDIOReader\\\\n\\\",\\n\",\n",
+    "    \"    \\\"from mdio import MDIOVariableConfig\\\\n\\\",\\n\",\n",
+    "    \"    \\\"from mdio import MDIOWriter\\\\n\\\",\\n\",\n",
+    "    \"    \\\"from mdio import create_empty\\\\n\\\",\\n\",\n",
+    "    \"    \\\"from mdio.converters.segy import get_compressor\\\\n\\\",\\n\",\n",
+    "    \"    \\\"from mdio.segy.compat import mdio_segy_spec\\\"\\n\",\n",
+    "    \"   ]\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"markdown\\\",\\n\",\n",
+    "    \"   \\\"metadata\\\": {\\n\",\n",
+    "    \"    \\\"pycharm\\\": {\\n\",\n",
+    "    \"     \\\"name\\\": \\\"#%% md\\\\n\\\"\\n\",\n",
+    "    \"    }\\n\",\n",
+    "    \"   },\\n\",\n",
+    "    \"   \\\"source\\\": [\\n\",\n",
+    "    \"    \\\"## Setting Up Grid Dimensions and Variable(s)\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"Let's try to create an MDIO file in 3D with known `inline`, `crossline`, and `sample`\\\\n\\\",\\n\",\n",
+    "    \"    \\\"dimensions. This is exactly like a 3D Post-Stack dataset.\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"We will use the standard `chunked_012` access pattern. All variable names must start\\\\n\\\",\\n\",\n",
+    "    \"    \\\"with `chunked_*` for `MDIOReader` to be able to open them with any access pattern.\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"We will also use an MDIO private utility function to get the compressor easily. The\\\\n\\\",\\n\",\n",
+    "    \"    \\\"compressor is fully customizable, but this function gives us ones available in SEG-Y\\\\n\\\",\\n\",\n",
+    "    \"    \\\"ingestion.\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"If we want headers, we can also provide `header_dtype` which is a Numpy `dtype`. Again,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"we will be using the standard one MDIO uses for ingesting SEG-Ys. (Similar to SEG-Y Rev1).\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"At the end we create empty MDIO on local disk or cloud, based on the path string.\\\"\\n\",\n",
+    "    \"   ]\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
+    "    \"   \\\"execution_count\\\": 3,\\n\",\n",
+    "    \"   \\\"metadata\\\": {\\n\",\n",
+    "    \"    \\\"ExecuteTime\\\": {\\n\",\n",
+    "    \"     \\\"end_time\\\": \\\"2025-04-16T14:50:06.121450Z\\\",\\n\",\n",
+    "    \"     \\\"start_time\\\": \\\"2025-04-16T14:50:06.002634Z\\\"\\n\",\n",
+    "    \"    },\\n\",\n",
+    "    \"    \\\"pycharm\\\": {\\n\",\n",
+    "    \"     \\\"name\\\": \\\"#%%\\\\n\\\"\\n\",\n",
+    "    \"    }\\n\",\n",
+    "    \"   },\\n\",\n",
+    "    \"   \\\"outputs\\\": [],\\n\",\n",
+    "    \"   \\\"source\\\": [\\n\",\n",
+    "    \"    \\\"grid = Grid(\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    dims=[\\\\n\\\",\\n\",\n",
+    "    \"    \\\"        Dimension(name=\\\\\\\"inline\\\\\\\", coords=range(100, 300, 1)),  # 100-300 with step 1\\\\n\\\",\\n\",\n",
+    "    \"    \\\"        Dimension(name=\\\\\\\"crossline\\\\\\\", coords=range(1000, 1600, 2)),  # 1000-1600 with step 2\\\\n\\\",\\n\",\n",
+    "    \"    \\\"        Dimension(name=\\\\\\\"sample\\\\\\\", coords=range(0, 3000, 4)),  # 0-3 seconds 4ms sample rate\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\")\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"compressor = get_compressor(lossless=True)\\\\n\\\",\\n\",\n",
+    "    \"    \\\"header_dtype = mdio_segy_spec().trace.header.dtype\\\\n\\\",\\n\",\n",
+    "    \"    \\\"variable = MDIOVariableConfig(\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    name=\\\\\\\"chunked_012\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    dtype=\\\\\\\"float32\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    chunks=(128, 128, 128),\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    compressors=compressor,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    header_dtype=header_dtype,\\\\n\\\",\\n\",\n",
+    "    \"    \\\")\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"create_conf = MDIOCreateConfig(path=\\\\\\\"demo.mdio\\\\\\\", grid=grid, variables=[variable])\\\\n\\\",\\n\",\n",
+    "    \"    \\\"create_empty(config=create_conf);\\\"\\n\",\n",
+    "    \"   ]\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"markdown\\\",\\n\",\n",
+    "    \"   \\\"metadata\\\": {},\\n\",\n",
+    "    \"   \\\"source\\\": [\\n\",\n",
+    "    \"    \\\"## Populating the MDIO\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"Once empty MDIO is created, you can populate it with sample data, header data,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"metadata, and statistics. The `live_mask` is automatically flipped to 1 after\\\\n\\\",\\n\",\n",
+    "    \"    \\\"every write. However, the consistency of the `live_mask` is up to the user. If\\\\n\\\",\\n\",\n",
+    "    \"    \\\"not populated correctly, at read time, traces may be skipped.\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"We will read the usual stats and as you can see they're all empty. Text header\\\\n\\\",\\n\",\n",
+    "    \"    \\\"and binary header are only required if we are going to export this back to SEG-Y,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"and can be modified. We can edit these if necessary using the writer.\\\"\\n\",\n",
+    "    \"   ]\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
+    "    \"   \\\"execution_count\\\": 4,\\n\",\n",
+    "    \"   \\\"metadata\\\": {\\n\",\n",
+    "    \"    \\\"ExecuteTime\\\": {\\n\",\n",
+    "    \"     \\\"end_time\\\": \\\"2025-04-16T14:50:09.974442Z\\\",\\n\",\n",
+    "    \"     \\\"start_time\\\": \\\"2025-04-16T14:50:09.951257Z\\\"\\n\",\n",
+    "    \"    },\\n\",\n",
+    "    \"    \\\"pycharm\\\": {\\n\",\n",
+    "    \"     \\\"name\\\": \\\"#%%\\\\n\\\"\\n\",\n",
+    "    \"    }\\n\",\n",
+    "    \"   },\\n\",\n",
+    "    \"   \\\"outputs\\\": [\\n\",\n",
+    "    \"    {\\n\",\n",
+    "    \"     \\\"data\\\": {\\n\",\n",
+    "    \"      \\\"text/plain\\\": [\\n\",\n",
+    "    \"       \\\"(['C00                                                                             ',\\\\n\\\",\\n\",\n",
+    "    \"       \\\"  'C01                                                                             ',\\\\n\\\",\\n\",\n",
+    "    \"       \\\"  'C02                                                                             ',\\\\n\\\",\\n\",\n",
+    "    \"       \\\"  'C03                                                                             ',\\\\n\\\",\\n\",\n",
+    "    \"       \\\"  'C04                                                                             '],\\\\n\\\",\\n\",\n",
+    "    \"       \\\" {},\\\\n\\\",\\n\",\n",
+    "    \"       \\\" {'rms': 0, 'min': 0, 'mean': 0, 'max': 0, 'std': 0})\\\"\\n\",\n",
+    "    \"      ]\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"execution_count\\\": 4,\\n\",\n",
+    "    \"     \\\"metadata\\\": {},\\n\",\n",
+    "    \"     \\\"output_type\\\": \\\"execute_result\\\"\\n\",\n",
+    "    \"    }\\n\",\n",
+    "    \"   ],\\n\",\n",
+    "    \"   \\\"source\\\": [\\n\",\n",
+    "    \"    \\\"writer = MDIOWriter(create_conf.path)\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"# Truncate text header for display\\\\n\\\",\\n\",\n",
+    "    \"    \\\"writer.text_header[:5], writer.binary_header, writer.stats\\\"\\n\",\n",
+    "    \"   ]\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"markdown\\\",\\n\",\n",
+    "    \"   \\\"metadata\\\": {},\\n\",\n",
+    "    \"   \\\"source\\\": [\\n\",\n",
+    "    \"    \\\"## Updating File and Checking with `MDIOReader`.\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"We write traces here, the headers are still empty. If you need the headers\\\\n\\\",\\n\",\n",
+    "    \"    \\\"populated, this is where they would be written to file as well.\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"All the extra metadata we are writing is so that we can export back to SEG-Y\\\\n\\\",\\n\",\n",
+    "    \"    \\\"in the future. If that's not a requirement most of the metadata can be\\\\n\\\",\\n\",\n",
+    "    \"    \\\"omitted.\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"If you already have an MDIO prior to this, all the metadata can be copied\\\\n\\\",\\n\",\n",
+    "    \"    \\\"over from the previous version.\\\"\\n\",\n",
+    "    \"   ]\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
+    "    \"   \\\"execution_count\\\": 5,\\n\",\n",
+    "    \"   \\\"metadata\\\": {\\n\",\n",
+    "    \"    \\\"ExecuteTime\\\": {\\n\",\n",
+    "    \"     \\\"end_time\\\": \\\"2025-04-16T14:50:12.498287Z\\\",\\n\",\n",
+    "    \"     \\\"start_time\\\": \\\"2025-04-16T14:50:11.670230Z\\\"\\n\",\n",
+    "    \"    },\\n\",\n",
+    "    \"    \\\"pycharm\\\": {\\n\",\n",
+    "    \"     \\\"name\\\": \\\"#%%\\\\n\\\"\\n\",\n",
+    "    \"    }\\n\",\n",
+    "    \"   },\\n\",\n",
+    "    \"   \\\"outputs\\\": [\\n\",\n",
+    "    \"    {\\n\",\n",
+    "    \"     \\\"data\\\": {\\n\",\n",
+    "    \"      \\\"text/plain\\\": [\\n\",\n",
+    "    \"       \\\"({'data_sample_format': 1,\\\\n\\\",\\n\",\n",
+    "    \"       \\\"  'sample_interval': 4,\\\\n\\\",\\n\",\n",
+    "    \"       \\\"  'samples_per_trace': 750,\\\\n\\\",\\n\",\n",
+    "    \"       \\\"  'segy_revision_major': 1,\\\\n\\\",\\n\",\n",
+    "    \"       \\\"  'segy_revision_minor': 0},\\\\n\\\",\\n\",\n",
+    "    \"       \\\" {'rms': 3, 'min': -4, 'mean': 12, 'max': 4, 'std': 5})\\\"\\n\",\n",
+    "    \"      ]\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"execution_count\\\": 5,\\n\",\n",
+    "    \"     \\\"metadata\\\": {},\\n\",\n",
+    "    \"     \\\"output_type\\\": \\\"execute_result\\\"\\n\",\n",
+    "    \"    }\\n\",\n",
+    "    \"   ],\\n\",\n",
+    "    \"   \\\"source\\\": [\\n\",\n",
+    "    \"    \\\"sample_dim = writer.grid.select_dim(\\\\\\\"sample\\\\\\\")\\\\n\\\",\\n\",\n",
+    "    \"    \\\"sample_interval = int(sample_dim.coords[1] - sample_dim.coords[0])\\\\n\\\",\\n\",\n",
+    "    \"    \\\"num_samples = sample_dim.size\\\\n\\\",\\n\",\n",
+    "    \"    \\\"writer.binary_header = {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"data_sample_format\\\\\\\": 1,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"sample_interval\\\\\\\": sample_interval,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"samples_per_trace\\\\\\\": num_samples,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"segy_revision_major\\\\\\\": 1,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"segy_revision_minor\\\\\\\": 0,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"}\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"# Write some values to the file\\\\n\\\",\\n\",\n",
+    "    \"    \\\"writer[:5] = 1\\\\n\\\",\\n\",\n",
+    "    \"    \\\"writer[5:10] = 2\\\\n\\\",\\n\",\n",
+    "    \"    \\\"writer[50:100] = 3\\\\n\\\",\\n\",\n",
+    "    \"    \\\"writer[150:175] = -1\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"# Write some made up stats\\\\n\\\",\\n\",\n",
+    "    \"    \\\"writer.stats = {\\\\\\\"mean\\\\\\\": 12, \\\\\\\"std\\\\\\\": 5, \\\\\\\"rms\\\\\\\": 3, \\\\\\\"min\\\\\\\": -4, \\\\\\\"max\\\\\\\": 4}\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"reader = MDIOReader(create_conf.path)\\\\n\\\",\\n\",\n",
+    "    \"    \\\"reader.binary_header, reader.stats\\\"\\n\",\n",
+    "    \"   ]\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"markdown\\\",\\n\",\n",
+    "    \"   \\\"metadata\\\": {},\\n\",\n",
+    "    \"   \\\"source\\\": \\\"Plot some of the data we populated:\\\"\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
+    "    \"   \\\"execution_count\\\": 6,\\n\",\n",
+    "    \"   \\\"metadata\\\": {\\n\",\n",
+    "    \"    \\\"ExecuteTime\\\": {\\n\",\n",
+    "    \"     \\\"end_time\\\": \\\"2025-04-16T14:50:14.256953Z\\\",\\n\",\n",
+    "    \"     \\\"start_time\\\": \\\"2025-04-16T14:50:14.036094Z\\\"\\n\",\n",
+    "    \"    }\\n\",\n",
+    "    \"   },\\n\",\n",
+    "    \"   \\\"outputs\\\": [\\n\",\n",
+    "    \"    {\\n\",\n",
+    "    \"     \\\"data\\\": {\\n\",\n",
+    "    \"      \\\"image/png\\\": 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\\\",\\n\",\n",
+    "    \"      \\\"text/plain\\\": [\\n\",\n",
+    "    \"       \\\"<Figure size 640x480 with 2 Axes>\\\"\\n\",\n",
+    "    \"      ]\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"metadata\\\": {},\\n\",\n",
+    "    \"     \\\"output_type\\\": \\\"display_data\\\"\\n\",\n",
+    "    \"    }\\n\",\n",
+    "    \"   ],\\n\",\n",
+    "    \"   \\\"source\\\": [\\n\",\n",
+    "    \"    \\\"fig, ax = plt.subplots(1, 2, sharex=\\\\\\\"all\\\\\\\")\\\\n\\\",\\n\",\n",
+    "    \"    \\\"ax[0].imshow(writer.live_mask[:].T, aspect=\\\\\\\"auto\\\\\\\")\\\\n\\\",\\n\",\n",
+    "    \"    \\\"ax[1].imshow(reader[:, 150].T, aspect=\\\\\\\"auto\\\\\\\")\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"ax[0].set_xlabel(\\\\\\\"inline\\\\\\\")\\\\n\\\",\\n\",\n",
+    "    \"    \\\"ax[0].set_ylabel(\\\\\\\"crossline\\\\\\\")\\\\n\\\",\\n\",\n",
+    "    \"    \\\"ax[1].set_xlabel(\\\\\\\"inline\\\\\\\")\\\\n\\\",\\n\",\n",
+    "    \"    \\\"ax[1].set_ylabel(\\\\\\\"sample\\\\\\\")\\\\n\\\",\\n\",\n",
+    "    \"    \\\"ax[0].set_title(\\\\\\\"Live Mask\\\\\\\")\\\\n\\\",\\n\",\n",
+    "    \"    \\\"ax[1].set_title(\\\\\\\"Middle Crossline\\\\\\\")\\\\n\\\",\\n\",\n",
+    "    \"    \\\"fig.tight_layout();\\\"\\n\",\n",
+    "    \"   ]\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"markdown\\\",\\n\",\n",
+    "    \"   \\\"metadata\\\": {},\\n\",\n",
+    "    \"   \\\"source\\\": [\\n\",\n",
+    "    \"    \\\"## Write to SEG-Y\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"Because we populated all the fields necessary for SEG-Y output, we can call the\\\\n\\\",\\n\",\n",
+    "    \"    \\\"MDIO to SEGY converter and get a file out.\\\"\\n\",\n",
+    "    \"   ]\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
+    "    \"   \\\"execution_count\\\": 7,\\n\",\n",
+    "    \"   \\\"metadata\\\": {\\n\",\n",
+    "    \"    \\\"ExecuteTime\\\": {\\n\",\n",
+    "    \"     \\\"end_time\\\": \\\"2025-04-16T14:50:15.788812Z\\\",\\n\",\n",
+    "    \"     \\\"start_time\\\": \\\"2025-04-16T14:50:15.009889Z\\\"\\n\",\n",
+    "    \"    }\\n\",\n",
+    "    \"   },\\n\",\n",
+    "    \"   \\\"outputs\\\": [\\n\",\n",
+    "    \"    {\\n\",\n",
+    "    \"     \\\"name\\\": \\\"stderr\\\",\\n\",\n",
+    "    \"     \\\"output_type\\\": \\\"stream\\\",\\n\",\n",
+    "    \"     \\\"text\\\": [\\n\",\n",
+    "    \"      \\\"Unwrapping MDIO Blocks: 100%|██████████| 7/7 [00:00<00:00, 10.55it/s]\\\\n\\\",\\n\",\n",
+    "    \"      \\\"Merging lines: 100%|██████████| 1/1 [00:00<00:00, 17.26it/s]\\\"\\n\",\n",
+    "    \"     ]\\n\",\n",
+    "    \"    },\\n\",\n",
+    "    \"    {\\n\",\n",
+    "    \"     \\\"name\\\": \\\"stdout\\\",\\n\",\n",
+    "    \"     \\\"output_type\\\": \\\"stream\\\",\\n\",\n",
+    "    \"     \\\"text\\\": [\\n\",\n",
+    "    \"      \\\"[(0, 0, 0, 0, 0, 4, 4000, 750, 750, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0)]\\\\n\\\",\\n\",\n",
+    "    \"      \\\"[[1. 1. 1. ... 1. 1. 1.]\\\\n\\\",\\n\",\n",
+    "    \"      \\\" [1. 1. 1. ... 1. 1. 1.]\\\\n\\\",\\n\",\n",
+    "    \"      \\\" [1. 1. 1. ... 1. 1. 1.]\\\\n\\\",\\n\",\n",
+    "    \"      \\\" ...\\\\n\\\",\\n\",\n",
+    "    \"      \\\" [1. 1. 1. ... 1. 1. 1.]\\\\n\\\",\\n\",\n",
+    "    \"      \\\" [1. 1. 1. ... 1. 1. 1.]\\\\n\\\",\\n\",\n",
+    "    \"      \\\" [1. 1. 1. ... 1. 1. 1.]]\\\\n\\\"\\n\",\n",
+    "    \"     ]\\n\",\n",
+    "    \"    },\\n\",\n",
+    "    \"    {\\n\",\n",
+    "    \"     \\\"name\\\": \\\"stderr\\\",\\n\",\n",
+    "    \"     \\\"output_type\\\": \\\"stream\\\",\\n\",\n",
+    "    \"     \\\"text\\\": [\\n\",\n",
+    "    \"      \\\"\\\\n\\\"\\n\",\n",
+    "    \"     ]\\n\",\n",
+    "    \"    }\\n\",\n",
+    "    \"   ],\\n\",\n",
+    "    \"   \\\"source\\\": [\\n\",\n",
+    "    \"    \\\"from segy import SegyFile\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"from mdio import mdio_to_segy\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"mdio_to_segy(\\\\\\\"demo.mdio\\\\\\\", \\\\\\\"demo.segy\\\\\\\")\\\\n\\\",\\n\",\n",
+    "    \"    \\\"segy = SegyFile(\\\\\\\"demo.segy\\\\\\\")\\\\n\\\",\\n\",\n",
+    "    \"    \\\"print(segy.binary_header)\\\\n\\\",\\n\",\n",
+    "    \"    \\\"print(segy.sample[:100])\\\"\\n\",\n",
+    "    \"   ]\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"markdown\\\",\\n\",\n",
+    "    \"   \\\"metadata\\\": {},\\n\",\n",
+    "    \"   \\\"source\\\": [\\n\",\n",
+    "    \"    \\\"## Copying an Existing MDIO without Data and Headers\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"We also provide a `create_empty_like` function which allows us to create an MDIO file\\\\n\\\",\\n\",\n",
+    "    \"    \\\"with the same structure and basic metadata as an existing one.\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"This will carry over:\\\\n\\\",\\n\",\n",
+    "    \"    \\\"* All access patterns in source MDIO\\\\n\\\",\\n\",\n",
+    "    \"    \\\"* Grid (shape, coordinates, dimensions, etc)\\\\n\\\",\\n\",\n",
+    "    \"    \\\"* Text Header\\\\n\\\",\\n\",\n",
+    "    \"    \\\"* Binary Header\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"It will **NOT** carry over:\\\\n\\\",\\n\",\n",
+    "    \"    \\\"* Trace data\\\\n\\\",\\n\",\n",
+    "    \"    \\\"* Header data\\\\n\\\",\\n\",\n",
+    "    \"    \\\"* Statistics\\\\n\\\",\\n\",\n",
+    "    \"    \\\"* Live Mask\\\"\\n\",\n",
+    "    \"   ]\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
+    "    \"   \\\"execution_count\\\": 8,\\n\",\n",
+    "    \"   \\\"metadata\\\": {\\n\",\n",
+    "    \"    \\\"ExecuteTime\\\": {\\n\",\n",
+    "    \"     \\\"end_time\\\": \\\"2025-04-16T14:50:17.246513Z\\\",\\n\",\n",
+    "    \"     \\\"start_time\\\": \\\"2025-04-16T14:50:17.111779Z\\\"\\n\",\n",
+    "    \"    }\\n\",\n",
+    "    \"   },\\n\",\n",
+    "    \"   \\\"outputs\\\": [],\\n\",\n",
+    "    \"   \\\"source\\\": [\\n\",\n",
+    "    \"    \\\"from mdio import create_empty_like\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"copy_path = \\\\\\\"copy_of_existing.mdio\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"create_empty_like(create_conf.path, copy_path)\\\\n\\\",\\n\",\n",
+    "    \"    \\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"copy_reader = MDIOReader(copy_path)\\\\n\\\",\\n\",\n",
+    "    \"    \\\"assert copy_reader.shape == reader.shape\\\\n\\\",\\n\",\n",
+    "    \"    \\\"assert copy_reader.binary_header == reader.binary_header\\\\n\\\",\\n\",\n",
+    "    \"    \\\"assert copy_reader.stats != reader.stats\\\"\\n\",\n",
+    "    \"   ]\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  {\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
+    "    \"   \\\"execution_count\\\": null,\\n\",\n",
+    "    \"   \\\"metadata\\\": {},\\n\",\n",
+    "    \"   \\\"outputs\\\": [],\\n\",\n",
+    "    \"   \\\"source\\\": []\\n\",\n",
+    "    \"  }\\n\",\n",
+    "    \" ],\\n\",\n",
+    "    \" \\\"metadata\\\": {\\n\",\n",
+    "    \"  \\\"kernelspec\\\": {\\n\",\n",
+    "    \"   \\\"display_name\\\": \\\"Python 3 (ipykernel)\\\",\\n\",\n",
+    "    \"   \\\"language\\\": \\\"python\\\",\\n\",\n",
+    "    \"   \\\"name\\\": \\\"python3\\\"\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  \\\"language_info\\\": {\\n\",\n",
+    "    \"   \\\"codemirror_mode\\\": {\\n\",\n",
+    "    \"    \\\"name\\\": \\\"ipython\\\",\\n\",\n",
+    "    \"    \\\"version\\\": 3\\n\",\n",
+    "    \"   },\\n\",\n",
+    "    \"   \\\"file_extension\\\": \\\".py\\\",\\n\",\n",
+    "    \"   \\\"mimetype\\\": \\\"text/x-python\\\",\\n\",\n",
+    "    \"   \\\"name\\\": \\\"python\\\",\\n\",\n",
+    "    \"   \\\"nbconvert_exporter\\\": \\\"python\\\",\\n\",\n",
+    "    \"   \\\"pygments_lexer\\\": \\\"ipython3\\\",\\n\",\n",
+    "    \"   \\\"version\\\": \\\"3.12.3\\\"\\n\",\n",
+    "    \"  },\\n\",\n",
+    "    \"  \\\"widgets\\\": {\\n\",\n",
+    "    \"   \\\"application/vnd.jupyter.widget-state+json\\\": {\\n\",\n",
+    "    \"    \\\"state\\\": {\\n\",\n",
+    "    \"     \\\"104c0d4e90244ebc959e51a58794dcf4\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"display\\\": \\\"inline-flex\\\",\\n\",\n",
+    "    \"       \\\"flex_flow\\\": \\\"row wrap\\\",\\n\",\n",
+    "    \"       \\\"width\\\": \\\"100%\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"1c0f5b458ee1401eaa8de63c65802073\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLStyleModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"description_width\\\": \\\"\\\",\\n\",\n",
+    "    \"       \\\"font_size\\\": null,\\n\",\n",
+    "    \"       \\\"text_color\\\": null\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"21b25dd6080d42368afc06770d5e1755\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_35fb49026dac479eba87217c2f6bcb9a\\\",\\n\",\n",
+    "    \"       \\\"style\\\": \\\"IPY_MODEL_3ab3e27c83aa423d9ecdac94e576f0fe\\\",\\n\",\n",
+    "    \"       \\\"value\\\": \\\"Ingesting SEG-Y in 6 chunks: 100%\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"2693aae410f04e7587f0400b5eef1079\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"ProgressStyleModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"description_width\\\": \\\"\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"318aea09479a479981dcfa29bc856c67\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"ProgressStyleModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"description_width\\\": \\\"\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"35fb49026dac479eba87217c2f6bcb9a\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {}\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"3ab3e27c83aa423d9ecdac94e576f0fe\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLStyleModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"description_width\\\": \\\"\\\",\\n\",\n",
+    "    \"       \\\"font_size\\\": null,\\n\",\n",
+    "    \"       \\\"text_color\\\": null\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"420b4a9492c54eb48d7779f38a09fd20\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"flex\\\": \\\"2\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"42afe4fae9dd4bc4a341a231cf7a9c32\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {}\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"4df685a95de542ffab6f265df89b8949\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLStyleModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"description_width\\\": \\\"\\\",\\n\",\n",
+    "    \"       \\\"font_size\\\": null,\\n\",\n",
+    "    \"       \\\"text_color\\\": null\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"58eaddb54c8840da83d1f6503d6ba64f\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {}\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"5b3e72b71f304230bc36bb91f9c173be\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLStyleModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"description_width\\\": \\\"\\\",\\n\",\n",
+    "    \"       \\\"font_size\\\": null,\\n\",\n",
+    "    \"       \\\"text_color\\\": null\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"5c1fab0ccc7e42c69c2b663b67ca9e3c\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {}\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"63a54817ae8b4c8d85b30d8c97e7ed0d\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HBoxModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"children\\\": [\\n\",\n",
+    "    \"        \\\"IPY_MODEL_e170d60a55a14c489d32b101293a9d33\\\",\\n\",\n",
+    "    \"        \\\"IPY_MODEL_662ad93cf7b3470f8e197350441f09ca\\\",\\n\",\n",
+    "    \"        \\\"IPY_MODEL_ac4abab2367e461e956e4e0edb27be44\\\"\\n\",\n",
+    "    \"       ],\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_c59d6fe969a04f4da332ae2a6fdb9c77\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"63f87a2410ba47d38ad9f55a81357a29\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_ca48140488574a28a7469552441560e4\\\",\\n\",\n",
+    "    \"       \\\"style\\\": \\\"IPY_MODEL_ae2037efb9cc459e84bcc28ca7243437\\\",\\n\",\n",
+    "    \"       \\\"value\\\": \\\" 2/2 [00:03&lt;00:00,  3.01s/block]\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"64eb2b9c8a094a1ea5f1991e5f09f5d9\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {}\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"662ad93cf7b3470f8e197350441f09ca\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"FloatProgressModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"bar_style\\\": \\\"success\\\",\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_420b4a9492c54eb48d7779f38a09fd20\\\",\\n\",\n",
+    "    \"       \\\"max\\\": 1,\\n\",\n",
+    "    \"       \\\"style\\\": \\\"IPY_MODEL_d16e93f72f3d4aeda2f5deee87e9754f\\\",\\n\",\n",
+    "    \"       \\\"value\\\": 1\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"74283969ff5446c89bb83b00a70226cf\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_5c1fab0ccc7e42c69c2b663b67ca9e3c\\\",\\n\",\n",
+    "    \"       \\\"style\\\": \\\"IPY_MODEL_1c0f5b458ee1401eaa8de63c65802073\\\",\\n\",\n",
+    "    \"       \\\"value\\\": \\\" 6/6 [00:09&lt;00:00,  2.48s/block]\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"75cf0c1ed25e47aab20b060f3bb44fb8\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HBoxModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"children\\\": [\\n\",\n",
+    "    \"        \\\"IPY_MODEL_21b25dd6080d42368afc06770d5e1755\\\",\\n\",\n",
+    "    \"        \\\"IPY_MODEL_c33f32b62a204ef094f67b10f8adbdc1\\\",\\n\",\n",
+    "    \"        \\\"IPY_MODEL_74283969ff5446c89bb83b00a70226cf\\\"\\n\",\n",
+    "    \"       ],\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_91df4ac76fd744539d4237d6099f34a5\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"7ff32d7deac14f21ab0f0939fe09e440\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"ProgressStyleModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"description_width\\\": \\\"\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"91df4ac76fd744539d4237d6099f34a5\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"display\\\": \\\"inline-flex\\\",\\n\",\n",
+    "    \"       \\\"flex_flow\\\": \\\"row wrap\\\",\\n\",\n",
+    "    \"       \\\"width\\\": \\\"100%\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"981634b52a7a4ffdae6186061c0b6482\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HBoxModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"children\\\": [\\n\",\n",
+    "    \"        \\\"IPY_MODEL_f65266f861cf40ef84e9ec74a8e0b44c\\\",\\n\",\n",
+    "    \"        \\\"IPY_MODEL_9aaf514a5035413d8cbbad58aa0221f4\\\",\\n\",\n",
+    "    \"        \\\"IPY_MODEL_63f87a2410ba47d38ad9f55a81357a29\\\"\\n\",\n",
+    "    \"       ],\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_104c0d4e90244ebc959e51a58794dcf4\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"9aaf514a5035413d8cbbad58aa0221f4\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"FloatProgressModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"bar_style\\\": \\\"success\\\",\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_b25523a221fd4b5e9211d07ef39cccf5\\\",\\n\",\n",
+    "    \"       \\\"max\\\": 2,\\n\",\n",
+    "    \"       \\\"style\\\": \\\"IPY_MODEL_318aea09479a479981dcfa29bc856c67\\\",\\n\",\n",
+    "    \"       \\\"value\\\": 2\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"9fe5348964cd4f7880bcd33204bbc7a2\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {}\\n\",\n",
+    "    \"     },\\n\",\n",
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+    "    \"      \\\"model_name\\\": \\\"HTMLStyleModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"description_width\\\": \\\"\\\",\\n\",\n",
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+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
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+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"FloatProgressModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"bar_style\\\": \\\"success\\\",\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_cfb02c9c7447491ca00bbb6acd99bb79\\\",\\n\",\n",
+    "    \"       \\\"max\\\": 26,\\n\",\n",
+    "    \"       \\\"style\\\": \\\"IPY_MODEL_7ff32d7deac14f21ab0f0939fe09e440\\\",\\n\",\n",
+    "    \"       \\\"value\\\": 26\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"ac4abab2367e461e956e4e0edb27be44\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLModel\\\",\\n\",\n",
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+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_58eaddb54c8840da83d1f6503d6ba64f\\\",\\n\",\n",
+    "    \"       \\\"style\\\": \\\"IPY_MODEL_a03d238db31145b881a1be478f9eb72f\\\",\\n\",\n",
+    "    \"       \\\"value\\\": \\\" 3/? [00:00&lt;00:00,  8.29block/s]\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"ae2037efb9cc459e84bcc28ca7243437\\\": {\\n\",\n",
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+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLStyleModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"description_width\\\": \\\"\\\",\\n\",\n",
+    "    \"       \\\"font_size\\\": null,\\n\",\n",
+    "    \"       \\\"text_color\\\": null\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"b25523a221fd4b5e9211d07ef39cccf5\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
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+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"flex\\\": \\\"2\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"b9810ee38bbe43f4bba100e53379be65\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_42afe4fae9dd4bc4a341a231cf7a9c32\\\",\\n\",\n",
+    "    \"       \\\"style\\\": \\\"IPY_MODEL_fdcea609ee7349629604217b625371d3\\\",\\n\",\n",
+    "    \"       \\\"value\\\": \\\" 26/26 [00:01&lt;00:00, 18.92block/s]\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"c33f32b62a204ef094f67b10f8adbdc1\\\": {\\n\",\n",
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+    "    \"      \\\"model_name\\\": \\\"FloatProgressModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"bar_style\\\": \\\"success\\\",\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_d23d88490a1b4941b8ca228b7d4a39cb\\\",\\n\",\n",
+    "    \"       \\\"max\\\": 6,\\n\",\n",
+    "    \"       \\\"style\\\": \\\"IPY_MODEL_2693aae410f04e7587f0400b5eef1079\\\",\\n\",\n",
+    "    \"       \\\"value\\\": 6\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"c59d6fe969a04f4da332ae2a6fdb9c77\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
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+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"display\\\": \\\"inline-flex\\\",\\n\",\n",
+    "    \"       \\\"flex_flow\\\": \\\"row wrap\\\",\\n\",\n",
+    "    \"       \\\"width\\\": \\\"100%\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"ca48140488574a28a7469552441560e4\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {}\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"cfb02c9c7447491ca00bbb6acd99bb79\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"flex\\\": \\\"2\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"d16e93f72f3d4aeda2f5deee87e9754f\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"ProgressStyleModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"description_width\\\": \\\"\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"d23d88490a1b4941b8ca228b7d4a39cb\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"flex\\\": \\\"2\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"d565a20d37e84ed0a1195e9a90154960\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HBoxModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"children\\\": [\\n\",\n",
+    "    \"        \\\"IPY_MODEL_db696e15587c4a54b82886b3eb5a0c96\\\",\\n\",\n",
+    "    \"        \\\"IPY_MODEL_a6aabcd2e57348a49bb59477f6a9fd90\\\",\\n\",\n",
+    "    \"        \\\"IPY_MODEL_b9810ee38bbe43f4bba100e53379be65\\\"\\n\",\n",
+    "    \"       ],\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_eab4b060672b4dd5a73ca7bb8f782cd2\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"db696e15587c4a54b82886b3eb5a0c96\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_64eb2b9c8a094a1ea5f1991e5f09f5d9\\\",\\n\",\n",
+    "    \"       \\\"style\\\": \\\"IPY_MODEL_5b3e72b71f304230bc36bb91f9c173be\\\",\\n\",\n",
+    "    \"       \\\"value\\\": \\\"Step 1 / 2 Writing Blocks: 100%\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"e170d60a55a14c489d32b101293a9d33\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_9fe5348964cd4f7880bcd33204bbc7a2\\\",\\n\",\n",
+    "    \"       \\\"style\\\": \\\"IPY_MODEL_4df685a95de542ffab6f265df89b8949\\\",\\n\",\n",
+    "    \"       \\\"value\\\": \\\"Step 2 / 2 Concat Blocks: \\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"e66368cda8124001b16459a0634029e2\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLStyleModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"description_width\\\": \\\"\\\",\\n\",\n",
+    "    \"       \\\"font_size\\\": null,\\n\",\n",
+    "    \"       \\\"text_color\\\": null\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"eab4b060672b4dd5a73ca7bb8f782cd2\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"display\\\": \\\"inline-flex\\\",\\n\",\n",
+    "    \"       \\\"flex_flow\\\": \\\"row wrap\\\",\\n\",\n",
+    "    \"       \\\"width\\\": \\\"100%\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"f65266f861cf40ef84e9ec74a8e0b44c\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"layout\\\": \\\"IPY_MODEL_fff4199774534d9f982f03738fbb08e7\\\",\\n\",\n",
+    "    \"       \\\"style\\\": \\\"IPY_MODEL_e66368cda8124001b16459a0634029e2\\\",\\n\",\n",
+    "    \"       \\\"value\\\": \\\"Scanning SEG-Y for geometry attributes: 100%\\\"\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"fdcea609ee7349629604217b625371d3\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/controls\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"HTMLStyleModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {\\n\",\n",
+    "    \"       \\\"description_width\\\": \\\"\\\",\\n\",\n",
+    "    \"       \\\"font_size\\\": null,\\n\",\n",
+    "    \"       \\\"text_color\\\": null\\n\",\n",
+    "    \"      }\\n\",\n",
+    "    \"     },\\n\",\n",
+    "    \"     \\\"fff4199774534d9f982f03738fbb08e7\\\": {\\n\",\n",
+    "    \"      \\\"model_module\\\": \\\"@jupyter-widgets/base\\\",\\n\",\n",
+    "    \"      \\\"model_module_version\\\": \\\"2.0.0\\\",\\n\",\n",
+    "    \"      \\\"model_name\\\": \\\"LayoutModel\\\",\\n\",\n",
+    "    \"      \\\"state\\\": {}\\n\",\n",
+    "    \"     }\\n\",\n",
+    "    \"    },\\n\",\n",
+    "    \"    \\\"version_major\\\": 2,\\n\",\n",
+    "    \"    \\\"version_minor\\\": 0\\n\",\n",
+    "    \"   }\\n\",\n",
+    "    \"  }\\n\",\n",
+    "    \" },\\n\",\n",
+    "    \" \\\"nbformat\\\": 4,\\n\",\n",
+    "    \" \\\"nbformat_minor\\\": 4\\n\",\n",
+    "    \"}\\n\"\n",
     "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"markdown\",\n",
-    "   \"metadata\": {},\n",
-    "   \"source\": [\n",
-    "    \"## Updating File and Checking with `MDIOReader`.\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"We write traces here, the headers are still empty. If you need the headers\\n\",\n",
-    "    \"populated, this is where they would be written to file as well.\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"All the extra metadata we are writing is so that we can export back to SEG-Y\\n\",\n",
-    "    \"in the future. If that's not a requirement most of the metadata can be\\n\",\n",
-    "    \"omitted.\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"If you already have an MDIO prior to this, all the metadata can be copied\\n\",\n",
-    "    \"over from the previous version.\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 5,\n",
-    "   \"metadata\": {\n",
-    "    \"ExecuteTime\": {\n",
-    "     \"end_time\": \"2025-04-16T14:50:12.498287Z\",\n",
-    "     \"start_time\": \"2025-04-16T14:50:11.670230Z\"\n",
-    "    },\n",
-    "    \"pycharm\": {\n",
-    "     \"name\": \"#%%\\n\"\n",
-    "    }\n",
-    "   },\n",
-    "   \"outputs\": [\n",
-    "    {\n",
-    "     \"data\": {\n",
-    "      \"text/plain\": [\n",
-    "       \"({'data_sample_format': 1,\\n\",\n",
-    "       \"  'sample_interval': 4,\\n\",\n",
-    "       \"  'samples_per_trace': 750,\\n\",\n",
-    "       \"  'segy_revision_major': 1,\\n\",\n",
-    "       \"  'segy_revision_minor': 0},\\n\",\n",
-    "       \" {'rms': 3, 'min': -4, 'mean': 12, 'max': 4, 'std': 5})\"\n",
-    "      ]\n",
-    "     },\n",
-    "     \"execution_count\": 5,\n",
-    "     \"metadata\": {},\n",
-    "     \"output_type\": \"execute_result\"\n",
-    "    }\n",
-    "   ],\n",
-    "   \"source\": [\n",
-    "    \"sample_dim = writer.grid.select_dim(\\\"sample\\\")\\n\",\n",
-    "    \"sample_interval = int(sample_dim.coords[1] - sample_dim.coords[0])\\n\",\n",
-    "    \"num_samples = sample_dim.size\\n\",\n",
-    "    \"writer.binary_header = {\\n\",\n",
-    "    \"    \\\"data_sample_format\\\": 1,\\n\",\n",
-    "    \"    \\\"sample_interval\\\": sample_interval,\\n\",\n",
-    "    \"    \\\"samples_per_trace\\\": num_samples,\\n\",\n",
-    "    \"    \\\"segy_revision_major\\\": 1,\\n\",\n",
-    "    \"    \\\"segy_revision_minor\\\": 0,\\n\",\n",
-    "    \"}\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"# Write some values to the file\\n\",\n",
-    "    \"writer[:5] = 1\\n\",\n",
-    "    \"writer[5:10] = 2\\n\",\n",
-    "    \"writer[50:100] = 3\\n\",\n",
-    "    \"writer[150:175] = -1\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"# Write some made up stats\\n\",\n",
-    "    \"writer.stats = {\\\"mean\\\": 12, \\\"std\\\": 5, \\\"rms\\\": 3, \\\"min\\\": -4, \\\"max\\\": 4}\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"reader = MDIOReader(create_conf.path)\\n\",\n",
-    "    \"reader.binary_header, reader.stats\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"markdown\",\n",
-    "   \"metadata\": {},\n",
-    "   \"source\": \"Plot some of the data we populated:\"\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 6,\n",
-    "   \"metadata\": {\n",
-    "    \"ExecuteTime\": {\n",
-    "     \"end_time\": \"2025-04-16T14:50:14.256953Z\",\n",
-    "     \"start_time\": \"2025-04-16T14:50:14.036094Z\"\n",
-    "    }\n",
-    "   },\n",
-    "   \"outputs\": [\n",
-    "    {\n",
-    "     \"data\": {\n",
-    "      \"image/png\": 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\",\n",
-    "      \"text/plain\": [\n",
-    "       \"<Figure size 640x480 with 2 Axes>\"\n",
-    "      ]\n",
-    "     },\n",
-    "     \"metadata\": {},\n",
-    "     \"output_type\": \"display_data\"\n",
-    "    }\n",
-    "   ],\n",
-    "   \"source\": [\n",
-    "    \"fig, ax = plt.subplots(1, 2, sharex=\\\"all\\\")\\n\",\n",
-    "    \"ax[0].imshow(writer.live_mask[:].T, aspect=\\\"auto\\\")\\n\",\n",
-    "    \"ax[1].imshow(reader[:, 150].T, aspect=\\\"auto\\\")\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"ax[0].set_xlabel(\\\"inline\\\")\\n\",\n",
-    "    \"ax[0].set_ylabel(\\\"crossline\\\")\\n\",\n",
-    "    \"ax[1].set_xlabel(\\\"inline\\\")\\n\",\n",
-    "    \"ax[1].set_ylabel(\\\"sample\\\")\\n\",\n",
-    "    \"ax[0].set_title(\\\"Live Mask\\\")\\n\",\n",
-    "    \"ax[1].set_title(\\\"Middle Crossline\\\")\\n\",\n",
-    "    \"fig.tight_layout();\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"markdown\",\n",
-    "   \"metadata\": {},\n",
-    "   \"source\": [\n",
-    "    \"## Write to SEG-Y\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"Because we populated all the fields necessary for SEG-Y output, we can call the\\n\",\n",
-    "    \"MDIO to SEGY converter and get a file out.\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 7,\n",
-    "   \"metadata\": {\n",
-    "    \"ExecuteTime\": {\n",
-    "     \"end_time\": \"2025-04-16T14:50:15.788812Z\",\n",
-    "     \"start_time\": \"2025-04-16T14:50:15.009889Z\"\n",
-    "    }\n",
-    "   },\n",
-    "   \"outputs\": [\n",
-    "    {\n",
-    "     \"name\": \"stderr\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"Unwrapping MDIO Blocks: 100%|██████████| 7/7 [00:00<00:00, 10.55it/s]\\n\",\n",
-    "      \"Merging lines: 100%|██████████| 1/1 [00:00<00:00, 17.26it/s]\"\n",
-    "     ]\n",
-    "    },\n",
-    "    {\n",
-    "     \"name\": \"stdout\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"[(0, 0, 0, 0, 0, 4, 4000, 750, 750, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0)]\\n\",\n",
-    "      \"[[1. 1. 1. ... 1. 1. 1.]\\n\",\n",
-    "      \" [1. 1. 1. ... 1. 1. 1.]\\n\",\n",
-    "      \" [1. 1. 1. ... 1. 1. 1.]\\n\",\n",
-    "      \" ...\\n\",\n",
-    "      \" [1. 1. 1. ... 1. 1. 1.]\\n\",\n",
-    "      \" [1. 1. 1. ... 1. 1. 1.]\\n\",\n",
-    "      \" [1. 1. 1. ... 1. 1. 1.]]\\n\"\n",
-    "     ]\n",
-    "    },\n",
-    "    {\n",
-    "     \"name\": \"stderr\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"\\n\"\n",
-    "     ]\n",
-    "    }\n",
-    "   ],\n",
-    "   \"source\": [\n",
-    "    \"from segy import SegyFile\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"from mdio import mdio_to_segy\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"mdio_to_segy(\\\"demo.mdio\\\", \\\"demo.segy\\\")\\n\",\n",
-    "    \"segy = SegyFile(\\\"demo.segy\\\")\\n\",\n",
-    "    \"print(segy.binary_header)\\n\",\n",
-    "    \"print(segy.sample[:100])\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"markdown\",\n",
-    "   \"metadata\": {},\n",
-    "   \"source\": [\n",
-    "    \"## Copying an Existing MDIO without Data and Headers\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"We also provide a `create_empty_like` function which allows us to create an MDIO file\\n\",\n",
-    "    \"with the same structure and basic metadata as an existing one.\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"This will carry over:\\n\",\n",
-    "    \"* All access patterns in source MDIO\\n\",\n",
-    "    \"* Grid (shape, coordinates, dimensions, etc)\\n\",\n",
-    "    \"* Text Header\\n\",\n",
-    "    \"* Binary Header\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"It will **NOT** carry over:\\n\",\n",
-    "    \"* Trace data\\n\",\n",
-    "    \"* Header data\\n\",\n",
-    "    \"* Statistics\\n\",\n",
-    "    \"* Live Mask\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 8,\n",
-    "   \"metadata\": {\n",
-    "    \"ExecuteTime\": {\n",
-    "     \"end_time\": \"2025-04-16T14:50:17.246513Z\",\n",
-    "     \"start_time\": \"2025-04-16T14:50:17.111779Z\"\n",
-    "    }\n",
-    "   },\n",
-    "   \"outputs\": [],\n",
-    "   \"source\": [\n",
-    "    \"from mdio import create_empty_like\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"copy_path = \\\"copy_of_existing.mdio\\\"\\n\",\n",
-    "    \"create_empty_like(create_conf.path, copy_path)\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"copy_reader = MDIOReader(copy_path)\\n\",\n",
-    "    \"assert copy_reader.shape == reader.shape\\n\",\n",
-    "    \"assert copy_reader.binary_header == reader.binary_header\\n\",\n",
-    "    \"assert copy_reader.stats != reader.stats\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": null,\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [],\n",
-    "   \"source\": []\n",
     "  }\n",
     " ],\n",
     " \"metadata\": {\n",

From 793f3f149cf0ca0140b3f0051f37d4434905ab68 Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Tue, 29 Apr 2025 15:38:15 -0500
Subject: [PATCH 09/19] fix creation notebook

---
 docs/tutorials/creation.ipynb | 1225 ++++++++++++++++++++++-----------
 1 file changed, 838 insertions(+), 387 deletions(-)

diff --git a/docs/tutorials/creation.ipynb b/docs/tutorials/creation.ipynb
index 25a67c4f..73ff5b67 100644
--- a/docs/tutorials/creation.ipynb
+++ b/docs/tutorials/creation.ipynb
@@ -13,396 +13,847 @@
     "    \"{\\n\",\n",
     "    \" \\\"cells\\\": [\\n\",\n",
     "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"markdown\\\",\\n\",\n",
-    "    \"   \\\"metadata\\\": {\\n\",\n",
-    "    \"    \\\"pycharm\\\": {\\n\",\n",
-    "    \"     \\\"name\\\": \\\"#%% md\\\\n\\\"\\n\",\n",
-    "    \"    }\\n\",\n",
-    "    \"   },\\n\",\n",
-    "    \"   \\\"source\\\": [\\n\",\n",
-    "    \"    \\\"# Create an Empty MDIO\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"```{article-info}\\\\n\\\",\\n\",\n",
-    "    \"    \\\":author: Altay Sansal\\\\n\\\",\\n\",\n",
-    "    \"    \\\":date: \\\\\\\"{sub-ref}`today`\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\":read-time: \\\\\\\"{sub-ref}`wordcount-minutes` min read\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\":class-container: sd-p-0 sd-outline-muted sd-rounded-3 sd-font-weight-light\\\\n\\\",\\n\",\n",
-    "    \"    \\\"```\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"Here we will create an empty MDIO file and populate its fields.\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"```{warning}\\\\n\\\",\\n\",\n",
-    "    \"    \\\"For plotting, the notebook requires [Matplotlib](https://matplotlib.org/) as a dependency. Please install it before executing using\\\\n\\\",\\n\",\n",
-    "    \"    \\\"`pip install matplotlib` or `conda install matplotlib`.\\\\n\\\",\\n\",\n",
-    "    \"    \\\"```\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"We first need to import the configuration classes and create function to be able to create the empty MDIO.\\\"\\n\",\n",
-    "    \"   ]\\n\",\n",
-    "    \"  },\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
-    "    \"   \\\"execution_count\\\": 2,\\n\",\n",
-    "    \"   \\\"metadata\\\": {\\n\",\n",
-    "    \"    \\\"ExecuteTime\\\": {\\n\",\n",
-    "    \"     \\\"end_time\\\": \\\"2025-04-16T14:50:04.371422Z\\\",\\n\",\n",
-    "    \"     \\\"start_time\\\": \\\"2025-04-16T14:50:04.368107Z\\\"\\n\",\n",
-    "    \"    },\\n\",\n",
-    "    \"    \\\"pycharm\\\": {\\n\",\n",
-    "    \"     \\\"name\\\": \\\"#%%\\\\n\\\"\\n\",\n",
-    "    \"    }\\n\",\n",
-    "    \"   },\\n\",\n",
-    "    \"   \\\"outputs\\\": [],\\n\",\n",
-    "    \"   \\\"source\\\": [\\n\",\n",
-    "    \"    \\\"import matplotlib.pyplot as plt\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"from mdio import Dimension\\\\n\\\",\\n\",\n",
-    "    \"    \\\"from mdio import Grid\\\\n\\\",\\n\",\n",
-    "    \"    \\\"from mdio import MDIOCreateConfig\\\\n\\\",\\n\",\n",
-    "    \"    \\\"from mdio import MDIOReader\\\\n\\\",\\n\",\n",
-    "    \"    \\\"from mdio import MDIOVariableConfig\\\\n\\\",\\n\",\n",
-    "    \"    \\\"from mdio import MDIOWriter\\\\n\\\",\\n\",\n",
-    "    \"    \\\"from mdio import create_empty\\\\n\\\",\\n\",\n",
-    "    \"    \\\"from mdio.converters.segy import get_compressor\\\\n\\\",\\n\",\n",
-    "    \"    \\\"from mdio.segy.compat import mdio_segy_spec\\\"\\n\",\n",
-    "    \"   ]\\n\",\n",
-    "    \"  },\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"markdown\\\",\\n\",\n",
-    "    \"   \\\"metadata\\\": {\\n\",\n",
-    "    \"    \\\"pycharm\\\": {\\n\",\n",
-    "    \"     \\\"name\\\": \\\"#%% md\\\\n\\\"\\n\",\n",
-    "    \"    }\\n\",\n",
-    "    \"   },\\n\",\n",
-    "    \"   \\\"source\\\": [\\n\",\n",
-    "    \"    \\\"## Setting Up Grid Dimensions and Variable(s)\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"Let's try to create an MDIO file in 3D with known `inline`, `crossline`, and `sample`\\\\n\\\",\\n\",\n",
-    "    \"    \\\"dimensions. This is exactly like a 3D Post-Stack dataset.\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"We will use the standard `chunked_012` access pattern. All variable names must start\\\\n\\\",\\n\",\n",
-    "    \"    \\\"with `chunked_*` for `MDIOReader` to be able to open them with any access pattern.\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"We will also use an MDIO private utility function to get the compressor easily. The\\\\n\\\",\\n\",\n",
-    "    \"    \\\"compressor is fully customizable, but this function gives us ones available in SEG-Y\\\\n\\\",\\n\",\n",
-    "    \"    \\\"ingestion.\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"If we want headers, we can also provide `header_dtype` which is a Numpy `dtype`. Again,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"we will be using the standard one MDIO uses for ingesting SEG-Ys. (Similar to SEG-Y Rev1).\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"At the end we create empty MDIO on local disk or cloud, based on the path string.\\\"\\n\",\n",
-    "    \"   ]\\n\",\n",
-    "    \"  },\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
-    "    \"   \\\"execution_count\\\": 3,\\n\",\n",
-    "    \"   \\\"metadata\\\": {\\n\",\n",
-    "    \"    \\\"ExecuteTime\\\": {\\n\",\n",
-    "    \"     \\\"end_time\\\": \\\"2025-04-16T14:50:06.121450Z\\\",\\n\",\n",
-    "    \"     \\\"start_time\\\": \\\"2025-04-16T14:50:06.002634Z\\\"\\n\",\n",
-    "    \"    },\\n\",\n",
-    "    \"    \\\"pycharm\\\": {\\n\",\n",
-    "    \"     \\\"name\\\": \\\"#%%\\\\n\\\"\\n\",\n",
-    "    \"    }\\n\",\n",
-    "    \"   },\\n\",\n",
-    "    \"   \\\"outputs\\\": [],\\n\",\n",
-    "    \"   \\\"source\\\": [\\n\",\n",
-    "    \"    \\\"grid = Grid(\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    dims=[\\\\n\\\",\\n\",\n",
-    "    \"    \\\"        Dimension(name=\\\\\\\"inline\\\\\\\", coords=range(100, 300, 1)),  # 100-300 with step 1\\\\n\\\",\\n\",\n",
-    "    \"    \\\"        Dimension(name=\\\\\\\"crossline\\\\\\\", coords=range(1000, 1600, 2)),  # 1000-1600 with step 2\\\\n\\\",\\n\",\n",
-    "    \"    \\\"        Dimension(name=\\\\\\\"sample\\\\\\\", coords=range(0, 3000, 4)),  # 0-3 seconds 4ms sample rate\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\")\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"compressor = get_compressor(lossless=True)\\\\n\\\",\\n\",\n",
-    "    \"    \\\"header_dtype = mdio_segy_spec().trace.header.dtype\\\\n\\\",\\n\",\n",
-    "    \"    \\\"variable = MDIOVariableConfig(\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    name=\\\\\\\"chunked_012\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    dtype=\\\\\\\"float32\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    chunks=(128, 128, 128),\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    compressors=compressor,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    header_dtype=header_dtype,\\\\n\\\",\\n\",\n",
-    "    \"    \\\")\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"create_conf = MDIOCreateConfig(path=\\\\\\\"demo.mdio\\\\\\\", grid=grid, variables=[variable])\\\\n\\\",\\n\",\n",
-    "    \"    \\\"create_empty(config=create_conf);\\\"\\n\",\n",
-    "    \"   ]\\n\",\n",
-    "    \"  },\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"markdown\\\",\\n\",\n",
     "    \"   \\\"metadata\\\": {},\\n\",\n",
+    "    \"   \\\"cell_type\\\": \\\"raw\\\",\\n\",\n",
     "    \"   \\\"source\\\": [\\n\",\n",
-    "    \"    \\\"## Populating the MDIO\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"Once empty MDIO is created, you can populate it with sample data, header data,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"metadata, and statistics. The `live_mask` is automatically flipped to 1 after\\\\n\\\",\\n\",\n",
-    "    \"    \\\"every write. However, the consistency of the `live_mask` is up to the user. If\\\\n\\\",\\n\",\n",
-    "    \"    \\\"not populated correctly, at read time, traces may be skipped.\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"We will read the usual stats and as you can see they're all empty. Text header\\\\n\\\",\\n\",\n",
-    "    \"    \\\"and binary header are only required if we are going to export this back to SEG-Y,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"and can be modified. We can edit these if necessary using the writer.\\\"\\n\",\n",
-    "    \"   ]\\n\",\n",
-    "    \"  },\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
-    "    \"   \\\"execution_count\\\": 4,\\n\",\n",
-    "    \"   \\\"metadata\\\": {\\n\",\n",
-    "    \"    \\\"ExecuteTime\\\": {\\n\",\n",
-    "    \"     \\\"end_time\\\": \\\"2025-04-16T14:50:09.974442Z\\\",\\n\",\n",
-    "    \"     \\\"start_time\\\": \\\"2025-04-16T14:50:09.951257Z\\\"\\n\",\n",
-    "    \"    },\\n\",\n",
-    "    \"    \\\"pycharm\\\": {\\n\",\n",
-    "    \"     \\\"name\\\": \\\"#%%\\\\n\\\"\\n\",\n",
-    "    \"    }\\n\",\n",
-    "    \"   },\\n\",\n",
-    "    \"   \\\"outputs\\\": [\\n\",\n",
-    "    \"    {\\n\",\n",
-    "    \"     \\\"data\\\": {\\n\",\n",
-    "    \"      \\\"text/plain\\\": [\\n\",\n",
-    "    \"       \\\"(['C00                                                                             ',\\\\n\\\",\\n\",\n",
-    "    \"       \\\"  'C01                                                                             ',\\\\n\\\",\\n\",\n",
-    "    \"       \\\"  'C02                                                                             ',\\\\n\\\",\\n\",\n",
-    "    \"       \\\"  'C03                                                                             ',\\\\n\\\",\\n\",\n",
-    "    \"       \\\"  'C04                                                                             '],\\\\n\\\",\\n\",\n",
-    "    \"       \\\" {},\\\\n\\\",\\n\",\n",
-    "    \"       \\\" {'rms': 0, 'min': 0, 'mean': 0, 'max': 0, 'std': 0})\\\"\\n\",\n",
-    "    \"      ]\\n\",\n",
-    "    \"     },\\n\",\n",
-    "    \"     \\\"execution_count\\\": 4,\\n\",\n",
-    "    \"     \\\"metadata\\\": {},\\n\",\n",
-    "    \"     \\\"output_type\\\": \\\"execute_result\\\"\\n\",\n",
-    "    \"    }\\n\",\n",
-    "    \"   ],\\n\",\n",
-    "    \"   \\\"source\\\": [\\n\",\n",
-    "    \"    \\\"writer = MDIOWriter(create_conf.path)\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"# Truncate text header for display\\\\n\\\",\\n\",\n",
-    "    \"    \\\"writer.text_header[:5], writer.binary_header, writer.stats\\\"\\n\",\n",
+    "    \"    \\\"{\\\\n\\\",\\n\",\n",
+    "    \"    \\\" \\\\\\\"cells\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"markdown\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"pycharm\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"#%% md\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"# Create an Empty MDIO\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"```{article-info}\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\":author: Altay Sansal\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\":date: \\\\\\\\\\\\\\\"{sub-ref}`today`\\\\\\\\\\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\":read-time: \\\\\\\\\\\\\\\"{sub-ref}`wordcount-minutes` min read\\\\\\\\\\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\":class-container: sd-p-0 sd-outline-muted sd-rounded-3 sd-font-weight-light\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"```\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"Here we will create an empty MDIO file and populate its fields.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"```{warning}\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"For plotting, the notebook requires [Matplotlib](https://matplotlib.org/) as a dependency. Please install it before executing using\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"`pip install matplotlib` or `conda install matplotlib`.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"```\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"We first need to import the configuration classes and create function to be able to create the empty MDIO.\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": 2,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ExecuteTime\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"end_time\\\\\\\": \\\\\\\"2025-04-16T14:50:04.371422Z\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"start_time\\\\\\\": \\\\\\\"2025-04-16T14:50:04.368107Z\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"pycharm\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"#%%\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [],\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"import matplotlib.pyplot as plt\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"from mdio import Dimension\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"from mdio import Grid\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"from mdio import MDIOCreateConfig\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"from mdio import MDIOReader\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"from mdio import MDIOVariableConfig\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"from mdio import MDIOWriter\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"from mdio import create_empty\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"from mdio.converters.segy import get_compressor\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"from mdio.segy.compat import mdio_segy_spec\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"markdown\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"pycharm\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"#%% md\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"## Setting Up Grid Dimensions and Variable(s)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"Let's try to create an MDIO file in 3D with known `inline`, `crossline`, and `sample`\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"dimensions. This is exactly like a 3D Post-Stack dataset.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"We will use the standard `chunked_012` access pattern. All variable names must start\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"with `chunked_*` for `MDIOReader` to be able to open them with any access pattern.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"We will also use an MDIO private utility function to get the compressor easily. The\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"compressor is fully customizable, but this function gives us ones available in SEG-Y\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ingestion.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"If we want headers, we can also provide `header_dtype` which is a Numpy `dtype`. Again,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"we will be using the standard one MDIO uses for ingesting SEG-Ys. (Similar to SEG-Y Rev1).\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"At the end we create empty MDIO on local disk or cloud, based on the path string.\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": 3,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ExecuteTime\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"end_time\\\\\\\": \\\\\\\"2025-04-16T14:50:06.121450Z\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"start_time\\\\\\\": \\\\\\\"2025-04-16T14:50:06.002634Z\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"pycharm\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"#%%\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [],\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"grid = Grid(\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"    dims=[\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"        Dimension(name=\\\\\\\\\\\\\\\"inline\\\\\\\\\\\\\\\", coords=range(100, 300, 1)),  # 100-300 with step 1\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"        Dimension(name=\\\\\\\\\\\\\\\"crossline\\\\\\\\\\\\\\\", coords=range(1000, 1600, 2)),  # 1000-1600 with step 2\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"        Dimension(name=\\\\\\\\\\\\\\\"sample\\\\\\\\\\\\\\\", coords=range(0, 3000, 4)),  # 0-3 seconds 4ms sample rate\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"    ]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"compressor = get_compressor(lossless=True)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"header_dtype = mdio_segy_spec().trace.header.dtype\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"variable = MDIOVariableConfig(\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"    name=\\\\\\\\\\\\\\\"chunked_012\\\\\\\\\\\\\\\",\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"    dtype=\\\\\\\\\\\\\\\"float32\\\\\\\\\\\\\\\",\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"    chunks=(128, 128, 128),\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"    compressors=compressor,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"    header_dtype=header_dtype,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"create_conf = MDIOCreateConfig(path=\\\\\\\\\\\\\\\"demo.mdio\\\\\\\\\\\\\\\", grid=grid, variables=[variable])\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"create_empty(config=create_conf);\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"markdown\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"## Populating the MDIO\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"Once empty MDIO is created, you can populate it with sample data, header data,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"metadata, and statistics. The `live_mask` is automatically flipped to 1 after\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"every write. However, the consistency of the `live_mask` is up to the user. If\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"not populated correctly, at read time, traces may be skipped.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"We will read the usual stats and as you can see they're all empty. Text header\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"and binary header are only required if we are going to export this back to SEG-Y,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"and can be modified. We can edit these if necessary using the writer.\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": 4,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ExecuteTime\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"end_time\\\\\\\": \\\\\\\"2025-04-16T14:50:09.974442Z\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"start_time\\\\\\\": \\\\\\\"2025-04-16T14:50:09.951257Z\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"pycharm\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"#%%\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"data\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"text/plain\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"(['C00                                                                             ',\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"  'C01                                                                             ',\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"  'C02                                                                             ',\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"  'C03                                                                             ',\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"  'C04                                                                             '],\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\" {},\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\" {'rms': 0, 'min': 0, 'mean': 0, 'max': 0, 'std': 0})\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"execution_count\\\\\\\": 4,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"output_type\\\\\\\": \\\\\\\"execute_result\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ],\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"writer = MDIOWriter(create_conf.path)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"# Truncate text header for display\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"writer.text_header[:5], writer.binary_header, writer.stats\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"markdown\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"## Updating File and Checking with `MDIOReader`.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"We write traces here, the headers are still empty. If you need the headers\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"populated, this is where they would be written to file as well.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"All the extra metadata we are writing is so that we can export back to SEG-Y\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"in the future. If that's not a requirement most of the metadata can be\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"omitted.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"If you already have an MDIO prior to this, all the metadata can be copied\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"over from the previous version.\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": 5,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ExecuteTime\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"end_time\\\\\\\": \\\\\\\"2025-04-16T14:50:12.498287Z\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"start_time\\\\\\\": \\\\\\\"2025-04-16T14:50:11.670230Z\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"pycharm\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"#%%\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"data\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"text/plain\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"({'data_sample_format': 1,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"  'sample_interval': 4,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"  'samples_per_trace': 750,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"  'segy_revision_major': 1,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"  'segy_revision_minor': 0},\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\" {'rms': 3, 'min': -4, 'mean': 12, 'max': 4, 'std': 5})\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"execution_count\\\\\\\": 5,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"output_type\\\\\\\": \\\\\\\"execute_result\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ],\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"sample_dim = writer.grid.select_dim(\\\\\\\\\\\\\\\"sample\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"sample_interval = int(sample_dim.coords[1] - sample_dim.coords[0])\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"num_samples = sample_dim.size\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"writer.binary_header = {\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"    \\\\\\\\\\\\\\\"data_sample_format\\\\\\\\\\\\\\\": 1,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"    \\\\\\\\\\\\\\\"sample_interval\\\\\\\\\\\\\\\": sample_interval,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"    \\\\\\\\\\\\\\\"samples_per_trace\\\\\\\\\\\\\\\": num_samples,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"    \\\\\\\\\\\\\\\"segy_revision_major\\\\\\\\\\\\\\\": 1,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"    \\\\\\\\\\\\\\\"segy_revision_minor\\\\\\\\\\\\\\\": 0,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"}\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"# Write some values to the file\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"writer[:5] = 1\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"writer[5:10] = 2\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"writer[50:100] = 3\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"writer[150:175] = -1\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"# Write some made up stats\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"writer.stats = {\\\\\\\\\\\\\\\"mean\\\\\\\\\\\\\\\": 12, \\\\\\\\\\\\\\\"std\\\\\\\\\\\\\\\": 5, \\\\\\\\\\\\\\\"rms\\\\\\\\\\\\\\\": 3, \\\\\\\\\\\\\\\"min\\\\\\\\\\\\\\\": -4, \\\\\\\\\\\\\\\"max\\\\\\\\\\\\\\\": 4}\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"reader = MDIOReader(create_conf.path)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"reader.binary_header, reader.stats\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"markdown\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": \\\\\\\"Plot some of the data we populated:\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": 6,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ExecuteTime\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"end_time\\\\\\\": \\\\\\\"2025-04-16T14:50:14.256953Z\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"start_time\\\\\\\": \\\\\\\"2025-04-16T14:50:14.036094Z\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"data\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"image/png\\\\\\\": 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\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"text/plain\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"<Figure size 640x480 with 2 Axes>\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"output_type\\\\\\\": \\\\\\\"display_data\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ],\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"fig, ax = plt.subplots(1, 2, sharex=\\\\\\\\\\\\\\\"all\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ax[0].imshow(writer.live_mask[:].T, aspect=\\\\\\\\\\\\\\\"auto\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ax[1].imshow(reader[:, 150].T, aspect=\\\\\\\\\\\\\\\"auto\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ax[0].set_xlabel(\\\\\\\\\\\\\\\"inline\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ax[0].set_ylabel(\\\\\\\\\\\\\\\"crossline\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ax[1].set_xlabel(\\\\\\\\\\\\\\\"inline\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ax[1].set_ylabel(\\\\\\\\\\\\\\\"sample\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ax[0].set_title(\\\\\\\\\\\\\\\"Live Mask\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ax[1].set_title(\\\\\\\\\\\\\\\"Middle Crossline\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"fig.tight_layout();\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"markdown\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"## Write to SEG-Y\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"Because we populated all the fields necessary for SEG-Y output, we can call the\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"MDIO to SEGY converter and get a file out.\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": 7,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ExecuteTime\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"end_time\\\\\\\": \\\\\\\"2025-04-16T14:50:15.788812Z\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"start_time\\\\\\\": \\\\\\\"2025-04-16T14:50:15.009889Z\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"stderr\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"output_type\\\\\\\": \\\\\\\"stream\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"text\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"Unwrapping MDIO Blocks: 100%|██████████| 7/7 [00:00<00:00, 10.55it/s]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"Merging lines: 100%|██████████| 1/1 [00:00<00:00, 17.26it/s]\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"stdout\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"output_type\\\\\\\": \\\\\\\"stream\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"text\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"[(0, 0, 0, 0, 0, 4, 4000, 750, 750, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0)]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"[[1. 1. 1. ... 1. 1. 1.]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\" [1. 1. 1. ... 1. 1. 1.]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\" [1. 1. 1. ... 1. 1. 1.]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\" ...\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\" [1. 1. 1. ... 1. 1. 1.]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\" [1. 1. 1. ... 1. 1. 1.]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\" [1. 1. 1. ... 1. 1. 1.]]\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"stderr\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"output_type\\\\\\\": \\\\\\\"stream\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"text\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ],\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"from segy import SegyFile\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"from mdio import mdio_to_segy\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"mdio_to_segy(\\\\\\\\\\\\\\\"demo.mdio\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"demo.segy\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"segy = SegyFile(\\\\\\\\\\\\\\\"demo.segy\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"print(segy.binary_header)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"print(segy.sample[:100])\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"markdown\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"## Copying an Existing MDIO without Data and Headers\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"We also provide a `create_empty_like` function which allows us to create an MDIO file\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"with the same structure and basic metadata as an existing one.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"This will carry over:\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"* All access patterns in source MDIO\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"* Grid (shape, coordinates, dimensions, etc)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"* Text Header\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"* Binary Header\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"It will **NOT** carry over:\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"* Trace data\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"* Header data\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"* Statistics\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"* Live Mask\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": 8,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"ExecuteTime\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"end_time\\\\\\\": \\\\\\\"2025-04-16T14:50:17.246513Z\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"start_time\\\\\\\": \\\\\\\"2025-04-16T14:50:17.111779Z\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [],\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"from mdio import create_empty_like\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"copy_path = \\\\\\\\\\\\\\\"copy_of_existing.mdio\\\\\\\\\\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"create_empty_like(create_conf.path, copy_path)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"copy_reader = MDIOReader(copy_path)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"assert copy_reader.shape == reader.shape\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"assert copy_reader.binary_header == reader.binary_header\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"assert copy_reader.stats != reader.stats\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": null,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [],\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"source\\\\\\\": []\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  }\\\\n\\\",\\n\",\n",
+    "    \"    \\\" ],\\\\n\\\",\\n\",\n",
+    "    \"    \\\" \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  \\\\\\\"kernelspec\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"display_name\\\\\\\": \\\\\\\"Python 3 (ipykernel)\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"language\\\\\\\": \\\\\\\"python\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"name\\\\\\\": \\\\\\\"python3\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  \\\\\\\"language_info\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"codemirror_mode\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"name\\\\\\\": \\\\\\\"ipython\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"version\\\\\\\": 3\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"file_extension\\\\\\\": \\\\\\\".py\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"mimetype\\\\\\\": \\\\\\\"text/x-python\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"name\\\\\\\": \\\\\\\"python\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"nbconvert_exporter\\\\\\\": \\\\\\\"python\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"pygments_lexer\\\\\\\": \\\\\\\"ipython3\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"version\\\\\\\": \\\\\\\"3.12.3\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  \\\\\\\"widgets\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   \\\\\\\"application/vnd.jupyter.widget-state+json\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"104c0d4e90244ebc959e51a58794dcf4\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/base\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"LayoutModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"display\\\\\\\": \\\\\\\"inline-flex\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"flex_flow\\\\\\\": \\\\\\\"row wrap\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"width\\\\\\\": \\\\\\\"100%\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"1c0f5b458ee1401eaa8de63c65802073\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLStyleModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"description_width\\\\\\\": \\\\\\\"\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"font_size\\\\\\\": null,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"text_color\\\\\\\": null\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"21b25dd6080d42368afc06770d5e1755\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"layout\\\\\\\": \\\\\\\"IPY_MODEL_35fb49026dac479eba87217c2f6bcb9a\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"style\\\\\\\": \\\\\\\"IPY_MODEL_3ab3e27c83aa423d9ecdac94e576f0fe\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"value\\\\\\\": \\\\\\\"Ingesting SEG-Y in 6 chunks: 100%\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"2693aae410f04e7587f0400b5eef1079\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"ProgressStyleModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"description_width\\\\\\\": \\\\\\\"\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"318aea09479a479981dcfa29bc856c67\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"ProgressStyleModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"description_width\\\\\\\": \\\\\\\"\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"35fb49026dac479eba87217c2f6bcb9a\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/base\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"LayoutModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {}\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"3ab3e27c83aa423d9ecdac94e576f0fe\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLStyleModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"description_width\\\\\\\": \\\\\\\"\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"font_size\\\\\\\": null,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"text_color\\\\\\\": null\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"420b4a9492c54eb48d7779f38a09fd20\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/base\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"LayoutModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"flex\\\\\\\": \\\\\\\"2\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"42afe4fae9dd4bc4a341a231cf7a9c32\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/base\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"LayoutModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {}\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"4df685a95de542ffab6f265df89b8949\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLStyleModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"description_width\\\\\\\": \\\\\\\"\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"font_size\\\\\\\": null,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"text_color\\\\\\\": null\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"58eaddb54c8840da83d1f6503d6ba64f\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/base\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"LayoutModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {}\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"5b3e72b71f304230bc36bb91f9c173be\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLStyleModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"description_width\\\\\\\": \\\\\\\"\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"font_size\\\\\\\": null,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"text_color\\\\\\\": null\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"5c1fab0ccc7e42c69c2b663b67ca9e3c\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/base\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"LayoutModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {}\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"63a54817ae8b4c8d85b30d8c97e7ed0d\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HBoxModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"children\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"        \\\\\\\"IPY_MODEL_e170d60a55a14c489d32b101293a9d33\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"        \\\\\\\"IPY_MODEL_662ad93cf7b3470f8e197350441f09ca\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"        \\\\\\\"IPY_MODEL_ac4abab2367e461e956e4e0edb27be44\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       ],\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"layout\\\\\\\": \\\\\\\"IPY_MODEL_c59d6fe969a04f4da332ae2a6fdb9c77\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"63f87a2410ba47d38ad9f55a81357a29\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
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+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HBoxModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"children\\\\\\\": [\\\\n\\\",\\n\",\n",
+    "    \"    \\\"        \\\\\\\"IPY_MODEL_db696e15587c4a54b82886b3eb5a0c96\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"        \\\\\\\"IPY_MODEL_a6aabcd2e57348a49bb59477f6a9fd90\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"        \\\\\\\"IPY_MODEL_b9810ee38bbe43f4bba100e53379be65\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       ],\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"layout\\\\\\\": \\\\\\\"IPY_MODEL_eab4b060672b4dd5a73ca7bb8f782cd2\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"db696e15587c4a54b82886b3eb5a0c96\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"layout\\\\\\\": \\\\\\\"IPY_MODEL_64eb2b9c8a094a1ea5f1991e5f09f5d9\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"style\\\\\\\": \\\\\\\"IPY_MODEL_5b3e72b71f304230bc36bb91f9c173be\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"value\\\\\\\": \\\\\\\"Step 1 / 2 Writing Blocks: 100%\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"e170d60a55a14c489d32b101293a9d33\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"layout\\\\\\\": \\\\\\\"IPY_MODEL_9fe5348964cd4f7880bcd33204bbc7a2\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"style\\\\\\\": \\\\\\\"IPY_MODEL_4df685a95de542ffab6f265df89b8949\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"value\\\\\\\": \\\\\\\"Step 2 / 2 Concat Blocks: \\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"e66368cda8124001b16459a0634029e2\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLStyleModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"description_width\\\\\\\": \\\\\\\"\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"font_size\\\\\\\": null,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"text_color\\\\\\\": null\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"eab4b060672b4dd5a73ca7bb8f782cd2\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/base\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"LayoutModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"display\\\\\\\": \\\\\\\"inline-flex\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"flex_flow\\\\\\\": \\\\\\\"row wrap\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"width\\\\\\\": \\\\\\\"100%\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"f65266f861cf40ef84e9ec74a8e0b44c\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"layout\\\\\\\": \\\\\\\"IPY_MODEL_fff4199774534d9f982f03738fbb08e7\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"style\\\\\\\": \\\\\\\"IPY_MODEL_e66368cda8124001b16459a0634029e2\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"value\\\\\\\": \\\\\\\"Scanning SEG-Y for geometry attributes: 100%\\\\\\\"\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"fdcea609ee7349629604217b625371d3\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLStyleModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"description_width\\\\\\\": \\\\\\\"\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"font_size\\\\\\\": null,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"       \\\\\\\"text_color\\\\\\\": null\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     \\\\\\\"fff4199774534d9f982f03738fbb08e7\\\\\\\": {\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/base\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"LayoutModel\\\\\\\",\\\\n\\\",\\n\",\n",
+    "    \"    \\\"      \\\\\\\"state\\\\\\\": {}\\\\n\\\",\\n\",\n",
+    "    \"    \\\"     }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    },\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"version_major\\\\\\\": 2,\\\\n\\\",\\n\",\n",
+    "    \"    \\\"    \\\\\\\"version_minor\\\\\\\": 0\\\\n\\\",\\n\",\n",
+    "    \"    \\\"   }\\\\n\\\",\\n\",\n",
+    "    \"    \\\"  }\\\\n\\\",\\n\",\n",
+    "    \"    \\\" },\\\\n\\\",\\n\",\n",
+    "    \"    \\\" \\\\\\\"nbformat\\\\\\\": 4,\\\\n\\\",\\n\",\n",
+    "    \"    \\\" \\\\\\\"nbformat_minor\\\\\\\": 4\\\\n\\\",\\n\",\n",
+    "    \"    \\\"}\\\\n\\\"\\n\",\n",
     "    \"   ]\\n\",\n",
-    "    \"  },\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"markdown\\\",\\n\",\n",
-    "    \"   \\\"metadata\\\": {},\\n\",\n",
-    "    \"   \\\"source\\\": [\\n\",\n",
-    "    \"    \\\"## Updating File and Checking with `MDIOReader`.\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"We write traces here, the headers are still empty. If you need the headers\\\\n\\\",\\n\",\n",
-    "    \"    \\\"populated, this is where they would be written to file as well.\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"All the extra metadata we are writing is so that we can export back to SEG-Y\\\\n\\\",\\n\",\n",
-    "    \"    \\\"in the future. If that's not a requirement most of the metadata can be\\\\n\\\",\\n\",\n",
-    "    \"    \\\"omitted.\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"If you already have an MDIO prior to this, all the metadata can be copied\\\\n\\\",\\n\",\n",
-    "    \"    \\\"over from the previous version.\\\"\\n\",\n",
-    "    \"   ]\\n\",\n",
-    "    \"  },\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
-    "    \"   \\\"execution_count\\\": 5,\\n\",\n",
-    "    \"   \\\"metadata\\\": {\\n\",\n",
-    "    \"    \\\"ExecuteTime\\\": {\\n\",\n",
-    "    \"     \\\"end_time\\\": \\\"2025-04-16T14:50:12.498287Z\\\",\\n\",\n",
-    "    \"     \\\"start_time\\\": \\\"2025-04-16T14:50:11.670230Z\\\"\\n\",\n",
-    "    \"    },\\n\",\n",
-    "    \"    \\\"pycharm\\\": {\\n\",\n",
-    "    \"     \\\"name\\\": \\\"#%%\\\\n\\\"\\n\",\n",
-    "    \"    }\\n\",\n",
-    "    \"   },\\n\",\n",
-    "    \"   \\\"outputs\\\": [\\n\",\n",
-    "    \"    {\\n\",\n",
-    "    \"     \\\"data\\\": {\\n\",\n",
-    "    \"      \\\"text/plain\\\": [\\n\",\n",
-    "    \"       \\\"({'data_sample_format': 1,\\\\n\\\",\\n\",\n",
-    "    \"       \\\"  'sample_interval': 4,\\\\n\\\",\\n\",\n",
-    "    \"       \\\"  'samples_per_trace': 750,\\\\n\\\",\\n\",\n",
-    "    \"       \\\"  'segy_revision_major': 1,\\\\n\\\",\\n\",\n",
-    "    \"       \\\"  'segy_revision_minor': 0},\\\\n\\\",\\n\",\n",
-    "    \"       \\\" {'rms': 3, 'min': -4, 'mean': 12, 'max': 4, 'std': 5})\\\"\\n\",\n",
-    "    \"      ]\\n\",\n",
-    "    \"     },\\n\",\n",
-    "    \"     \\\"execution_count\\\": 5,\\n\",\n",
-    "    \"     \\\"metadata\\\": {},\\n\",\n",
-    "    \"     \\\"output_type\\\": \\\"execute_result\\\"\\n\",\n",
-    "    \"    }\\n\",\n",
-    "    \"   ],\\n\",\n",
-    "    \"   \\\"source\\\": [\\n\",\n",
-    "    \"    \\\"sample_dim = writer.grid.select_dim(\\\\\\\"sample\\\\\\\")\\\\n\\\",\\n\",\n",
-    "    \"    \\\"sample_interval = int(sample_dim.coords[1] - sample_dim.coords[0])\\\\n\\\",\\n\",\n",
-    "    \"    \\\"num_samples = sample_dim.size\\\\n\\\",\\n\",\n",
-    "    \"    \\\"writer.binary_header = {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"data_sample_format\\\\\\\": 1,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"sample_interval\\\\\\\": sample_interval,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"samples_per_trace\\\\\\\": num_samples,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"segy_revision_major\\\\\\\": 1,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"segy_revision_minor\\\\\\\": 0,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"}\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"# Write some values to the file\\\\n\\\",\\n\",\n",
-    "    \"    \\\"writer[:5] = 1\\\\n\\\",\\n\",\n",
-    "    \"    \\\"writer[5:10] = 2\\\\n\\\",\\n\",\n",
-    "    \"    \\\"writer[50:100] = 3\\\\n\\\",\\n\",\n",
-    "    \"    \\\"writer[150:175] = -1\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"# Write some made up stats\\\\n\\\",\\n\",\n",
-    "    \"    \\\"writer.stats = {\\\\\\\"mean\\\\\\\": 12, \\\\\\\"std\\\\\\\": 5, \\\\\\\"rms\\\\\\\": 3, \\\\\\\"min\\\\\\\": -4, \\\\\\\"max\\\\\\\": 4}\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"reader = MDIOReader(create_conf.path)\\\\n\\\",\\n\",\n",
-    "    \"    \\\"reader.binary_header, reader.stats\\\"\\n\",\n",
-    "    \"   ]\\n\",\n",
-    "    \"  },\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"markdown\\\",\\n\",\n",
-    "    \"   \\\"metadata\\\": {},\\n\",\n",
-    "    \"   \\\"source\\\": \\\"Plot some of the data we populated:\\\"\\n\",\n",
-    "    \"  },\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
-    "    \"   \\\"execution_count\\\": 6,\\n\",\n",
-    "    \"   \\\"metadata\\\": {\\n\",\n",
-    "    \"    \\\"ExecuteTime\\\": {\\n\",\n",
-    "    \"     \\\"end_time\\\": \\\"2025-04-16T14:50:14.256953Z\\\",\\n\",\n",
-    "    \"     \\\"start_time\\\": \\\"2025-04-16T14:50:14.036094Z\\\"\\n\",\n",
-    "    \"    }\\n\",\n",
-    "    \"   },\\n\",\n",
-    "    \"   \\\"outputs\\\": [\\n\",\n",
-    "    \"    {\\n\",\n",
-    "    \"     \\\"data\\\": {\\n\",\n",
-    "    \"      \\\"image/png\\\": 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\\\",\\n\",\n",
-    "    \"      \\\"text/plain\\\": [\\n\",\n",
-    "    \"       \\\"<Figure size 640x480 with 2 Axes>\\\"\\n\",\n",
-    "    \"      ]\\n\",\n",
-    "    \"     },\\n\",\n",
-    "    \"     \\\"metadata\\\": {},\\n\",\n",
-    "    \"     \\\"output_type\\\": \\\"display_data\\\"\\n\",\n",
-    "    \"    }\\n\",\n",
-    "    \"   ],\\n\",\n",
-    "    \"   \\\"source\\\": [\\n\",\n",
-    "    \"    \\\"fig, ax = plt.subplots(1, 2, sharex=\\\\\\\"all\\\\\\\")\\\\n\\\",\\n\",\n",
-    "    \"    \\\"ax[0].imshow(writer.live_mask[:].T, aspect=\\\\\\\"auto\\\\\\\")\\\\n\\\",\\n\",\n",
-    "    \"    \\\"ax[1].imshow(reader[:, 150].T, aspect=\\\\\\\"auto\\\\\\\")\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"ax[0].set_xlabel(\\\\\\\"inline\\\\\\\")\\\\n\\\",\\n\",\n",
-    "    \"    \\\"ax[0].set_ylabel(\\\\\\\"crossline\\\\\\\")\\\\n\\\",\\n\",\n",
-    "    \"    \\\"ax[1].set_xlabel(\\\\\\\"inline\\\\\\\")\\\\n\\\",\\n\",\n",
-    "    \"    \\\"ax[1].set_ylabel(\\\\\\\"sample\\\\\\\")\\\\n\\\",\\n\",\n",
-    "    \"    \\\"ax[0].set_title(\\\\\\\"Live Mask\\\\\\\")\\\\n\\\",\\n\",\n",
-    "    \"    \\\"ax[1].set_title(\\\\\\\"Middle Crossline\\\\\\\")\\\\n\\\",\\n\",\n",
-    "    \"    \\\"fig.tight_layout();\\\"\\n\",\n",
-    "    \"   ]\\n\",\n",
-    "    \"  },\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"markdown\\\",\\n\",\n",
-    "    \"   \\\"metadata\\\": {},\\n\",\n",
-    "    \"   \\\"source\\\": [\\n\",\n",
-    "    \"    \\\"## Write to SEG-Y\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"Because we populated all the fields necessary for SEG-Y output, we can call the\\\\n\\\",\\n\",\n",
-    "    \"    \\\"MDIO to SEGY converter and get a file out.\\\"\\n\",\n",
-    "    \"   ]\\n\",\n",
-    "    \"  },\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
-    "    \"   \\\"execution_count\\\": 7,\\n\",\n",
-    "    \"   \\\"metadata\\\": {\\n\",\n",
-    "    \"    \\\"ExecuteTime\\\": {\\n\",\n",
-    "    \"     \\\"end_time\\\": \\\"2025-04-16T14:50:15.788812Z\\\",\\n\",\n",
-    "    \"     \\\"start_time\\\": \\\"2025-04-16T14:50:15.009889Z\\\"\\n\",\n",
-    "    \"    }\\n\",\n",
-    "    \"   },\\n\",\n",
-    "    \"   \\\"outputs\\\": [\\n\",\n",
-    "    \"    {\\n\",\n",
-    "    \"     \\\"name\\\": \\\"stderr\\\",\\n\",\n",
-    "    \"     \\\"output_type\\\": \\\"stream\\\",\\n\",\n",
-    "    \"     \\\"text\\\": [\\n\",\n",
-    "    \"      \\\"Unwrapping MDIO Blocks: 100%|██████████| 7/7 [00:00<00:00, 10.55it/s]\\\\n\\\",\\n\",\n",
-    "    \"      \\\"Merging lines: 100%|██████████| 1/1 [00:00<00:00, 17.26it/s]\\\"\\n\",\n",
-    "    \"     ]\\n\",\n",
-    "    \"    },\\n\",\n",
-    "    \"    {\\n\",\n",
-    "    \"     \\\"name\\\": \\\"stdout\\\",\\n\",\n",
-    "    \"     \\\"output_type\\\": \\\"stream\\\",\\n\",\n",
-    "    \"     \\\"text\\\": [\\n\",\n",
-    "    \"      \\\"[(0, 0, 0, 0, 0, 4, 4000, 750, 750, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0)]\\\\n\\\",\\n\",\n",
-    "    \"      \\\"[[1. 1. 1. ... 1. 1. 1.]\\\\n\\\",\\n\",\n",
-    "    \"      \\\" [1. 1. 1. ... 1. 1. 1.]\\\\n\\\",\\n\",\n",
-    "    \"      \\\" [1. 1. 1. ... 1. 1. 1.]\\\\n\\\",\\n\",\n",
-    "    \"      \\\" ...\\\\n\\\",\\n\",\n",
-    "    \"      \\\" [1. 1. 1. ... 1. 1. 1.]\\\\n\\\",\\n\",\n",
-    "    \"      \\\" [1. 1. 1. ... 1. 1. 1.]\\\\n\\\",\\n\",\n",
-    "    \"      \\\" [1. 1. 1. ... 1. 1. 1.]]\\\\n\\\"\\n\",\n",
-    "    \"     ]\\n\",\n",
-    "    \"    },\\n\",\n",
-    "    \"    {\\n\",\n",
-    "    \"     \\\"name\\\": \\\"stderr\\\",\\n\",\n",
-    "    \"     \\\"output_type\\\": \\\"stream\\\",\\n\",\n",
-    "    \"     \\\"text\\\": [\\n\",\n",
-    "    \"      \\\"\\\\n\\\"\\n\",\n",
-    "    \"     ]\\n\",\n",
-    "    \"    }\\n\",\n",
-    "    \"   ],\\n\",\n",
-    "    \"   \\\"source\\\": [\\n\",\n",
-    "    \"    \\\"from segy import SegyFile\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"from mdio import mdio_to_segy\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"mdio_to_segy(\\\\\\\"demo.mdio\\\\\\\", \\\\\\\"demo.segy\\\\\\\")\\\\n\\\",\\n\",\n",
-    "    \"    \\\"segy = SegyFile(\\\\\\\"demo.segy\\\\\\\")\\\\n\\\",\\n\",\n",
-    "    \"    \\\"print(segy.binary_header)\\\\n\\\",\\n\",\n",
-    "    \"    \\\"print(segy.sample[:100])\\\"\\n\",\n",
-    "    \"   ]\\n\",\n",
-    "    \"  },\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"markdown\\\",\\n\",\n",
-    "    \"   \\\"metadata\\\": {},\\n\",\n",
-    "    \"   \\\"source\\\": [\\n\",\n",
-    "    \"    \\\"## Copying an Existing MDIO without Data and Headers\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"We also provide a `create_empty_like` function which allows us to create an MDIO file\\\\n\\\",\\n\",\n",
-    "    \"    \\\"with the same structure and basic metadata as an existing one.\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"This will carry over:\\\\n\\\",\\n\",\n",
-    "    \"    \\\"* All access patterns in source MDIO\\\\n\\\",\\n\",\n",
-    "    \"    \\\"* Grid (shape, coordinates, dimensions, etc)\\\\n\\\",\\n\",\n",
-    "    \"    \\\"* Text Header\\\\n\\\",\\n\",\n",
-    "    \"    \\\"* Binary Header\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"It will **NOT** carry over:\\\\n\\\",\\n\",\n",
-    "    \"    \\\"* Trace data\\\\n\\\",\\n\",\n",
-    "    \"    \\\"* Header data\\\\n\\\",\\n\",\n",
-    "    \"    \\\"* Statistics\\\\n\\\",\\n\",\n",
-    "    \"    \\\"* Live Mask\\\"\\n\",\n",
-    "    \"   ]\\n\",\n",
-    "    \"  },\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
-    "    \"   \\\"execution_count\\\": 8,\\n\",\n",
-    "    \"   \\\"metadata\\\": {\\n\",\n",
-    "    \"    \\\"ExecuteTime\\\": {\\n\",\n",
-    "    \"     \\\"end_time\\\": \\\"2025-04-16T14:50:17.246513Z\\\",\\n\",\n",
-    "    \"     \\\"start_time\\\": \\\"2025-04-16T14:50:17.111779Z\\\"\\n\",\n",
-    "    \"    }\\n\",\n",
-    "    \"   },\\n\",\n",
-    "    \"   \\\"outputs\\\": [],\\n\",\n",
-    "    \"   \\\"source\\\": [\\n\",\n",
-    "    \"    \\\"from mdio import create_empty_like\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"copy_path = \\\\\\\"copy_of_existing.mdio\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"create_empty_like(create_conf.path, copy_path)\\\\n\\\",\\n\",\n",
-    "    \"    \\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"copy_reader = MDIOReader(copy_path)\\\\n\\\",\\n\",\n",
-    "    \"    \\\"assert copy_reader.shape == reader.shape\\\\n\\\",\\n\",\n",
-    "    \"    \\\"assert copy_reader.binary_header == reader.binary_header\\\\n\\\",\\n\",\n",
-    "    \"    \\\"assert copy_reader.stats != reader.stats\\\"\\n\",\n",
-    "    \"   ]\\n\",\n",
-    "    \"  },\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"code\\\",\\n\",\n",
-    "    \"   \\\"execution_count\\\": null,\\n\",\n",
-    "    \"   \\\"metadata\\\": {},\\n\",\n",
-    "    \"   \\\"outputs\\\": [],\\n\",\n",
-    "    \"   \\\"source\\\": []\\n\",\n",
     "    \"  }\\n\",\n",
     "    \" ],\\n\",\n",
     "    \" \\\"metadata\\\": {\\n\",\n",

From 0e64e09d4e9aabb303aa3270c5caa174e82d916d Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Tue, 29 Apr 2025 16:10:07 -0500
Subject: [PATCH 10/19] Add basic unit test for v1 dataset schema validation

---
 tests/unit/test_schema.py | 54 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 54 insertions(+)
 create mode 100644 tests/unit/test_schema.py

diff --git a/tests/unit/test_schema.py b/tests/unit/test_schema.py
new file mode 100644
index 00000000..1bccde5a
--- /dev/null
+++ b/tests/unit/test_schema.py
@@ -0,0 +1,54 @@
+"""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():
+    """Test that the dataset schema validates correctly."""
+    V1Dataset.model_validate(TEST_SCHEMA)

From a1a312e41f7e79a98c04b1b5591a193fa5efa321 Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Wed, 30 Apr 2025 09:56:44 -0500
Subject: [PATCH 11/19] update lockfile

---
 uv.lock | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/uv.lock b/uv.lock
index 92ce6cdc..68edf47f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1890,7 +1890,7 @@ requires-dist = [
     { name = "segy", specifier = ">=0.4.0,<0.5.0" },
     { name = "tqdm", specifier = ">=4.67.0,<5.0.0" },
     { name = "xarray", specifier = ">=2025.3.1" },
-    { name = "zarr", specifier = ">=3.0.6,<4.0.0" },
+    { name = "zarr", specifier = ">=3.0.3,<3.0.7" },
     { name = "zfpy", marker = "extra == 'lossy'", specifier = ">=1.0.1,<2.0.0" },
 ]
 provides-extras = ["cloud", "distributed", "lossy"]

From dfe775198e0f0289eddf7e0c9fd94142f67762bf Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Wed, 7 May 2025 09:32:28 -0500
Subject: [PATCH 12/19] fix broken creation nb

---
 docs/tutorials/creation.ipynb | 2127 ++++++---------------------------
 1 file changed, 387 insertions(+), 1740 deletions(-)

diff --git a/docs/tutorials/creation.ipynb b/docs/tutorials/creation.ipynb
index 73ff5b67..af4d3b65 100644
--- a/docs/tutorials/creation.ipynb
+++ b/docs/tutorials/creation.ipynb
@@ -1,1749 +1,396 @@
 {
  "cells": [
   {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Create an Empty MDIO\n",
+    "\n",
+    "```{article-info}\n",
+    ":author: Altay Sansal\n",
+    ":date: \"{sub-ref}`today`\"\n",
+    ":read-time: \"{sub-ref}`wordcount-minutes` min read\"\n",
+    ":class-container: sd-p-0 sd-outline-muted sd-rounded-3 sd-font-weight-light\n",
+    "```\n",
+    "\n",
+    "Here we will create an empty MDIO file and populate its fields.\n",
+    "\n",
+    "```{warning}\n",
+    "For plotting, the notebook requires [Matplotlib](https://matplotlib.org/) as a dependency. Please install it before executing using\n",
+    "`pip install matplotlib` or `conda install matplotlib`.\n",
+    "```\n",
+    "\n",
+    "We first need to import the configuration classes and create function to be able to create the empty MDIO."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-16T14:50:04.371422Z",
+     "start_time": "2025-04-16T14:50:04.368107Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from mdio import Dimension\n",
+    "from mdio import Grid\n",
+    "from mdio import MDIOCreateConfig\n",
+    "from mdio import MDIOReader\n",
+    "from mdio import MDIOVariableConfig\n",
+    "from mdio import MDIOWriter\n",
+    "from mdio import create_empty\n",
+    "from mdio.converters.segy import get_compressor\n",
+    "from mdio.segy.compat import mdio_segy_spec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Setting Up Grid Dimensions and Variable(s)\n",
+    "\n",
+    "Let's try to create an MDIO file in 3D with known `inline`, `crossline`, and `sample`\n",
+    "dimensions. This is exactly like a 3D Post-Stack dataset.\n",
+    "\n",
+    "We will use the standard `chunked_012` access pattern. All variable names must start\n",
+    "with `chunked_*` for `MDIOReader` to be able to open them with any access pattern.\n",
+    "\n",
+    "We will also use an MDIO private utility function to get the compressor easily. The\n",
+    "compressor is fully customizable, but this function gives us ones available in SEG-Y\n",
+    "ingestion.\n",
+    "\n",
+    "If we want headers, we can also provide `header_dtype` which is a Numpy `dtype`. Again,\n",
+    "we will be using the standard one MDIO uses for ingesting SEG-Ys. (Similar to SEG-Y Rev1).\n",
+    "\n",
+    "At the end we create empty MDIO on local disk or cloud, based on the path string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-16T14:50:06.121450Z",
+     "start_time": "2025-04-16T14:50:06.002634Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "grid = Grid(\n",
+    "    dims=[\n",
+    "        Dimension(name=\"inline\", coords=range(100, 300, 1)),  # 100-300 with step 1\n",
+    "        Dimension(name=\"crossline\", coords=range(1000, 1600, 2)),  # 1000-1600 with step 2\n",
+    "        Dimension(name=\"sample\", coords=range(0, 3000, 4)),  # 0-3 seconds 4ms sample rate\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "compressor = get_compressor(lossless=True)\n",
+    "header_dtype = mdio_segy_spec().trace.header.dtype\n",
+    "variable = MDIOVariableConfig(\n",
+    "    name=\"chunked_012\",\n",
+    "    dtype=\"float32\",\n",
+    "    chunks=(128, 128, 128),\n",
+    "    compressors=compressor,\n",
+    "    header_dtype=header_dtype,\n",
+    ")\n",
+    "\n",
+    "create_conf = MDIOCreateConfig(path=\"demo.mdio\", grid=grid, variables=[variable])\n",
+    "create_empty(config=create_conf);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Populating the MDIO\n",
+    "\n",
+    "Once empty MDIO is created, you can populate it with sample data, header data,\n",
+    "metadata, and statistics. The `live_mask` is automatically flipped to 1 after\n",
+    "every write. However, the consistency of the `live_mask` is up to the user. If\n",
+    "not populated correctly, at read time, traces may be skipped.\n",
+    "\n",
+    "We will read the usual stats and as you can see they're all empty. Text header\n",
+    "and binary header are only required if we are going to export this back to SEG-Y,\n",
+    "and can be modified. We can edit these if necessary using the writer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-16T14:50:09.974442Z",
+     "start_time": "2025-04-16T14:50:09.951257Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(['C00                                                                             ',\n",
+       "  'C01                                                                             ',\n",
+       "  'C02                                                                             ',\n",
+       "  'C03                                                                             ',\n",
+       "  'C04                                                                             '],\n",
+       " {},\n",
+       " {'rms': 0, 'min': 0, 'mean': 0, 'max': 0, 'std': 0})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "writer = MDIOWriter(create_conf.path)\n",
+    "\n",
+    "# Truncate text header for display\n",
+    "writer.text_header[:5], writer.binary_header, writer.stats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Updating File and Checking with `MDIOReader`.\n",
+    "\n",
+    "We write traces here, the headers are still empty. If you need the headers\n",
+    "populated, this is where they would be written to file as well.\n",
+    "\n",
+    "All the extra metadata we are writing is so that we can export back to SEG-Y\n",
+    "in the future. If that's not a requirement most of the metadata can be\n",
+    "omitted.\n",
+    "\n",
+    "If you already have an MDIO prior to this, all the metadata can be copied\n",
+    "over from the previous version."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-16T14:50:12.498287Z",
+     "start_time": "2025-04-16T14:50:11.670230Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "({'data_sample_format': 1,\n",
+       "  'sample_interval': 4,\n",
+       "  'samples_per_trace': 750,\n",
+       "  'segy_revision_major': 1,\n",
+       "  'segy_revision_minor': 0},\n",
+       " {'rms': 3, 'min': -4, 'mean': 12, 'max': 4, 'std': 5})"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample_dim = writer.grid.select_dim(\"sample\")\n",
+    "sample_interval = int(sample_dim.coords[1] - sample_dim.coords[0])\n",
+    "num_samples = sample_dim.size\n",
+    "writer.binary_header = {\n",
+    "    \"data_sample_format\": 1,\n",
+    "    \"sample_interval\": sample_interval,\n",
+    "    \"samples_per_trace\": num_samples,\n",
+    "    \"segy_revision_major\": 1,\n",
+    "    \"segy_revision_minor\": 0,\n",
+    "}\n",
+    "\n",
+    "# Write some values to the file\n",
+    "writer[:5] = 1\n",
+    "writer[5:10] = 2\n",
+    "writer[50:100] = 3\n",
+    "writer[150:175] = -1\n",
+    "\n",
+    "# Write some made up stats\n",
+    "writer.stats = {\"mean\": 12, \"std\": 5, \"rms\": 3, \"min\": -4, \"max\": 4}\n",
+    "\n",
+    "reader = MDIOReader(create_conf.path)\n",
+    "reader.binary_header, reader.stats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "Plot some of the data we populated:"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-16T14:50:14.256953Z",
+     "start_time": "2025-04-16T14:50:14.036094Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, sharex=\"all\")\n",
+    "ax[0].imshow(writer.live_mask[:].T, aspect=\"auto\")\n",
+    "ax[1].imshow(reader[:, 150].T, aspect=\"auto\")\n",
+    "\n",
+    "ax[0].set_xlabel(\"inline\")\n",
+    "ax[0].set_ylabel(\"crossline\")\n",
+    "ax[1].set_xlabel(\"inline\")\n",
+    "ax[1].set_ylabel(\"sample\")\n",
+    "ax[0].set_title(\"Live Mask\")\n",
+    "ax[1].set_title(\"Middle Crossline\")\n",
+    "fig.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Write to SEG-Y\n",
+    "\n",
+    "Because we populated all the fields necessary for SEG-Y output, we can call the\n",
+    "MDIO to SEGY converter and get a file out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-16T14:50:15.788812Z",
+     "start_time": "2025-04-16T14:50:15.009889Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Unwrapping MDIO Blocks: 100%|██████████| 7/7 [00:00<00:00, 10.55it/s]\n",
+      "Merging lines: 100%|██████████| 1/1 [00:00<00:00, 17.26it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(0, 0, 0, 0, 0, 4, 4000, 750, 750, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0)]\n",
+      "[[1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " ...\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from segy import SegyFile\n",
+    "\n",
+    "from mdio import mdio_to_segy\n",
+    "\n",
+    "mdio_to_segy(\"demo.mdio\", \"demo.segy\")\n",
+    "segy = SegyFile(\"demo.segy\")\n",
+    "print(segy.binary_header)\n",
+    "print(segy.sample[:100])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "cell_type": "raw",
    "source": [
-    "{\n",
-    " \"cells\": [\n",
-    "  {\n",
-    "   \"metadata\": {},\n",
-    "   \"cell_type\": \"raw\",\n",
-    "   \"source\": [\n",
-    "    \"{\\n\",\n",
-    "    \" \\\"cells\\\": [\\n\",\n",
-    "    \"  {\\n\",\n",
-    "    \"   \\\"metadata\\\": {},\\n\",\n",
-    "    \"   \\\"cell_type\\\": \\\"raw\\\",\\n\",\n",
-    "    \"   \\\"source\\\": [\\n\",\n",
-    "    \"    \\\"{\\\\n\\\",\\n\",\n",
-    "    \"    \\\" \\\\\\\"cells\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"markdown\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"pycharm\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"#%% md\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"# Create an Empty MDIO\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"```{article-info}\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\":author: Altay Sansal\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\":date: \\\\\\\\\\\\\\\"{sub-ref}`today`\\\\\\\\\\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\":read-time: \\\\\\\\\\\\\\\"{sub-ref}`wordcount-minutes` min read\\\\\\\\\\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\":class-container: sd-p-0 sd-outline-muted sd-rounded-3 sd-font-weight-light\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"```\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"Here we will create an empty MDIO file and populate its fields.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"```{warning}\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"For plotting, the notebook requires [Matplotlib](https://matplotlib.org/) as a dependency. Please install it before executing using\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"`pip install matplotlib` or `conda install matplotlib`.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"```\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"We first need to import the configuration classes and create function to be able to create the empty MDIO.\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": 2,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ExecuteTime\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"end_time\\\\\\\": \\\\\\\"2025-04-16T14:50:04.371422Z\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"start_time\\\\\\\": \\\\\\\"2025-04-16T14:50:04.368107Z\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"pycharm\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"#%%\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [],\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"import matplotlib.pyplot as plt\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"from mdio import Dimension\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"from mdio import Grid\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"from mdio import MDIOCreateConfig\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"from mdio import MDIOReader\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"from mdio import MDIOVariableConfig\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"from mdio import MDIOWriter\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"from mdio import create_empty\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"from mdio.converters.segy import get_compressor\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"from mdio.segy.compat import mdio_segy_spec\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"markdown\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"pycharm\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"#%% md\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"## Setting Up Grid Dimensions and Variable(s)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"Let's try to create an MDIO file in 3D with known `inline`, `crossline`, and `sample`\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"dimensions. This is exactly like a 3D Post-Stack dataset.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"We will use the standard `chunked_012` access pattern. All variable names must start\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"with `chunked_*` for `MDIOReader` to be able to open them with any access pattern.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"We will also use an MDIO private utility function to get the compressor easily. The\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"compressor is fully customizable, but this function gives us ones available in SEG-Y\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ingestion.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"If we want headers, we can also provide `header_dtype` which is a Numpy `dtype`. Again,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"we will be using the standard one MDIO uses for ingesting SEG-Ys. (Similar to SEG-Y Rev1).\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"At the end we create empty MDIO on local disk or cloud, based on the path string.\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": 3,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ExecuteTime\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"end_time\\\\\\\": \\\\\\\"2025-04-16T14:50:06.121450Z\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"start_time\\\\\\\": \\\\\\\"2025-04-16T14:50:06.002634Z\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"pycharm\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"#%%\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [],\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"grid = Grid(\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"    dims=[\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"        Dimension(name=\\\\\\\\\\\\\\\"inline\\\\\\\\\\\\\\\", coords=range(100, 300, 1)),  # 100-300 with step 1\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"        Dimension(name=\\\\\\\\\\\\\\\"crossline\\\\\\\\\\\\\\\", coords=range(1000, 1600, 2)),  # 1000-1600 with step 2\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"        Dimension(name=\\\\\\\\\\\\\\\"sample\\\\\\\\\\\\\\\", coords=range(0, 3000, 4)),  # 0-3 seconds 4ms sample rate\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"    ]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"compressor = get_compressor(lossless=True)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"header_dtype = mdio_segy_spec().trace.header.dtype\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"variable = MDIOVariableConfig(\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"    name=\\\\\\\\\\\\\\\"chunked_012\\\\\\\\\\\\\\\",\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"    dtype=\\\\\\\\\\\\\\\"float32\\\\\\\\\\\\\\\",\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"    chunks=(128, 128, 128),\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"    compressors=compressor,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"    header_dtype=header_dtype,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"create_conf = MDIOCreateConfig(path=\\\\\\\\\\\\\\\"demo.mdio\\\\\\\\\\\\\\\", grid=grid, variables=[variable])\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"create_empty(config=create_conf);\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"markdown\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"## Populating the MDIO\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"Once empty MDIO is created, you can populate it with sample data, header data,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"metadata, and statistics. The `live_mask` is automatically flipped to 1 after\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"every write. However, the consistency of the `live_mask` is up to the user. If\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"not populated correctly, at read time, traces may be skipped.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"We will read the usual stats and as you can see they're all empty. Text header\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"and binary header are only required if we are going to export this back to SEG-Y,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"and can be modified. We can edit these if necessary using the writer.\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": 4,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ExecuteTime\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"end_time\\\\\\\": \\\\\\\"2025-04-16T14:50:09.974442Z\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"start_time\\\\\\\": \\\\\\\"2025-04-16T14:50:09.951257Z\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"pycharm\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"#%%\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"data\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"text/plain\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"(['C00                                                                             ',\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"  'C01                                                                             ',\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"  'C02                                                                             ',\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"  'C03                                                                             ',\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"  'C04                                                                             '],\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\" {},\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\" {'rms': 0, 'min': 0, 'mean': 0, 'max': 0, 'std': 0})\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"execution_count\\\\\\\": 4,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"output_type\\\\\\\": \\\\\\\"execute_result\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ],\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"writer = MDIOWriter(create_conf.path)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"# Truncate text header for display\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"writer.text_header[:5], writer.binary_header, writer.stats\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"markdown\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"## Updating File and Checking with `MDIOReader`.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"We write traces here, the headers are still empty. If you need the headers\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"populated, this is where they would be written to file as well.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"All the extra metadata we are writing is so that we can export back to SEG-Y\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"in the future. If that's not a requirement most of the metadata can be\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"omitted.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"If you already have an MDIO prior to this, all the metadata can be copied\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"over from the previous version.\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": 5,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ExecuteTime\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"end_time\\\\\\\": \\\\\\\"2025-04-16T14:50:12.498287Z\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"start_time\\\\\\\": \\\\\\\"2025-04-16T14:50:11.670230Z\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"pycharm\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"#%%\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"data\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"text/plain\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"({'data_sample_format': 1,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"  'sample_interval': 4,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"  'samples_per_trace': 750,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"  'segy_revision_major': 1,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"  'segy_revision_minor': 0},\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\" {'rms': 3, 'min': -4, 'mean': 12, 'max': 4, 'std': 5})\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"execution_count\\\\\\\": 5,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"output_type\\\\\\\": \\\\\\\"execute_result\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ],\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"sample_dim = writer.grid.select_dim(\\\\\\\\\\\\\\\"sample\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"sample_interval = int(sample_dim.coords[1] - sample_dim.coords[0])\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"num_samples = sample_dim.size\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"writer.binary_header = {\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"    \\\\\\\\\\\\\\\"data_sample_format\\\\\\\\\\\\\\\": 1,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"    \\\\\\\\\\\\\\\"sample_interval\\\\\\\\\\\\\\\": sample_interval,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"    \\\\\\\\\\\\\\\"samples_per_trace\\\\\\\\\\\\\\\": num_samples,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"    \\\\\\\\\\\\\\\"segy_revision_major\\\\\\\\\\\\\\\": 1,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"    \\\\\\\\\\\\\\\"segy_revision_minor\\\\\\\\\\\\\\\": 0,\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"}\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"# Write some values to the file\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"writer[:5] = 1\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"writer[5:10] = 2\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"writer[50:100] = 3\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"writer[150:175] = -1\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"# Write some made up stats\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"writer.stats = {\\\\\\\\\\\\\\\"mean\\\\\\\\\\\\\\\": 12, \\\\\\\\\\\\\\\"std\\\\\\\\\\\\\\\": 5, \\\\\\\\\\\\\\\"rms\\\\\\\\\\\\\\\": 3, \\\\\\\\\\\\\\\"min\\\\\\\\\\\\\\\": -4, \\\\\\\\\\\\\\\"max\\\\\\\\\\\\\\\": 4}\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"reader = MDIOReader(create_conf.path)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"reader.binary_header, reader.stats\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"markdown\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": \\\\\\\"Plot some of the data we populated:\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": 6,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ExecuteTime\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"end_time\\\\\\\": \\\\\\\"2025-04-16T14:50:14.256953Z\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"start_time\\\\\\\": \\\\\\\"2025-04-16T14:50:14.036094Z\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"data\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"image/png\\\\\\\": 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\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"text/plain\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"<Figure size 640x480 with 2 Axes>\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"output_type\\\\\\\": \\\\\\\"display_data\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ],\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"fig, ax = plt.subplots(1, 2, sharex=\\\\\\\\\\\\\\\"all\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ax[0].imshow(writer.live_mask[:].T, aspect=\\\\\\\\\\\\\\\"auto\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ax[1].imshow(reader[:, 150].T, aspect=\\\\\\\\\\\\\\\"auto\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ax[0].set_xlabel(\\\\\\\\\\\\\\\"inline\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ax[0].set_ylabel(\\\\\\\\\\\\\\\"crossline\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ax[1].set_xlabel(\\\\\\\\\\\\\\\"inline\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ax[1].set_ylabel(\\\\\\\\\\\\\\\"sample\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ax[0].set_title(\\\\\\\\\\\\\\\"Live Mask\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ax[1].set_title(\\\\\\\\\\\\\\\"Middle Crossline\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"fig.tight_layout();\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"markdown\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"## Write to SEG-Y\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"Because we populated all the fields necessary for SEG-Y output, we can call the\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"MDIO to SEGY converter and get a file out.\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": 7,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ExecuteTime\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"end_time\\\\\\\": \\\\\\\"2025-04-16T14:50:15.788812Z\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"start_time\\\\\\\": \\\\\\\"2025-04-16T14:50:15.009889Z\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"stderr\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"output_type\\\\\\\": \\\\\\\"stream\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"text\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"Unwrapping MDIO Blocks: 100%|██████████| 7/7 [00:00<00:00, 10.55it/s]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"Merging lines: 100%|██████████| 1/1 [00:00<00:00, 17.26it/s]\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"stdout\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"output_type\\\\\\\": \\\\\\\"stream\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"text\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"[(0, 0, 0, 0, 0, 4, 4000, 750, 750, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0)]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"[[1. 1. 1. ... 1. 1. 1.]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\" [1. 1. 1. ... 1. 1. 1.]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\" [1. 1. 1. ... 1. 1. 1.]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\" ...\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\" [1. 1. 1. ... 1. 1. 1.]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\" [1. 1. 1. ... 1. 1. 1.]\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\" [1. 1. 1. ... 1. 1. 1.]]\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"name\\\\\\\": \\\\\\\"stderr\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"output_type\\\\\\\": \\\\\\\"stream\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"text\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"\\\\\\\\n\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ],\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"from segy import SegyFile\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"from mdio import mdio_to_segy\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"mdio_to_segy(\\\\\\\\\\\\\\\"demo.mdio\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"demo.segy\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"segy = SegyFile(\\\\\\\\\\\\\\\"demo.segy\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"print(segy.binary_header)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"print(segy.sample[:100])\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"markdown\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"## Copying an Existing MDIO without Data and Headers\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"We also provide a `create_empty_like` function which allows us to create an MDIO file\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"with the same structure and basic metadata as an existing one.\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"This will carry over:\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"* All access patterns in source MDIO\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"* Grid (shape, coordinates, dimensions, etc)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"* Text Header\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"* Binary Header\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"It will **NOT** carry over:\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"* Trace data\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"* Header data\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"* Statistics\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"* Live Mask\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": 8,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"ExecuteTime\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"end_time\\\\\\\": \\\\\\\"2025-04-16T14:50:17.246513Z\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"start_time\\\\\\\": \\\\\\\"2025-04-16T14:50:17.111779Z\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [],\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"from mdio import create_empty_like\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"copy_path = \\\\\\\\\\\\\\\"copy_of_existing.mdio\\\\\\\\\\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"create_empty_like(create_conf.path, copy_path)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"copy_reader = MDIOReader(copy_path)\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"assert copy_reader.shape == reader.shape\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"assert copy_reader.binary_header == reader.binary_header\\\\\\\\n\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"assert copy_reader.stats != reader.stats\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   ]\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"cell_type\\\\\\\": \\\\\\\"code\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"execution_count\\\\\\\": null,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"metadata\\\\\\\": {},\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"outputs\\\\\\\": [],\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"source\\\\\\\": []\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  }\\\\n\\\",\\n\",\n",
-    "    \"    \\\" ],\\\\n\\\",\\n\",\n",
-    "    \"    \\\" \\\\\\\"metadata\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  \\\\\\\"kernelspec\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"display_name\\\\\\\": \\\\\\\"Python 3 (ipykernel)\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"language\\\\\\\": \\\\\\\"python\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"name\\\\\\\": \\\\\\\"python3\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  \\\\\\\"language_info\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"codemirror_mode\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"name\\\\\\\": \\\\\\\"ipython\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"version\\\\\\\": 3\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"file_extension\\\\\\\": \\\\\\\".py\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"mimetype\\\\\\\": \\\\\\\"text/x-python\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"name\\\\\\\": \\\\\\\"python\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"nbconvert_exporter\\\\\\\": \\\\\\\"python\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"pygments_lexer\\\\\\\": \\\\\\\"ipython3\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"version\\\\\\\": \\\\\\\"3.12.3\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"  \\\\\\\"widgets\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"   \\\\\\\"application/vnd.jupyter.widget-state+json\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"    \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"104c0d4e90244ebc959e51a58794dcf4\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/base\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"LayoutModel\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"display\\\\\\\": \\\\\\\"inline-flex\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"flex_flow\\\\\\\": \\\\\\\"row wrap\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"width\\\\\\\": \\\\\\\"100%\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"1c0f5b458ee1401eaa8de63c65802073\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLStyleModel\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"description_width\\\\\\\": \\\\\\\"\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"font_size\\\\\\\": null,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"text_color\\\\\\\": null\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"21b25dd6080d42368afc06770d5e1755\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLModel\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"layout\\\\\\\": \\\\\\\"IPY_MODEL_35fb49026dac479eba87217c2f6bcb9a\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"style\\\\\\\": \\\\\\\"IPY_MODEL_3ab3e27c83aa423d9ecdac94e576f0fe\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"value\\\\\\\": \\\\\\\"Ingesting SEG-Y in 6 chunks: 100%\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"2693aae410f04e7587f0400b5eef1079\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"ProgressStyleModel\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"description_width\\\\\\\": \\\\\\\"\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"318aea09479a479981dcfa29bc856c67\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"ProgressStyleModel\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"description_width\\\\\\\": \\\\\\\"\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"35fb49026dac479eba87217c2f6bcb9a\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/base\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"LayoutModel\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"state\\\\\\\": {}\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"3ab3e27c83aa423d9ecdac94e576f0fe\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLStyleModel\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"description_width\\\\\\\": \\\\\\\"\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"font_size\\\\\\\": null,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"text_color\\\\\\\": null\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"420b4a9492c54eb48d7779f38a09fd20\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/base\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"LayoutModel\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"flex\\\\\\\": \\\\\\\"2\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"42afe4fae9dd4bc4a341a231cf7a9c32\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/base\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"LayoutModel\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"state\\\\\\\": {}\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"4df685a95de542ffab6f265df89b8949\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLStyleModel\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"description_width\\\\\\\": \\\\\\\"\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"font_size\\\\\\\": null,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"text_color\\\\\\\": null\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"58eaddb54c8840da83d1f6503d6ba64f\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/base\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"LayoutModel\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"state\\\\\\\": {}\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"5b3e72b71f304230bc36bb91f9c173be\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HTMLStyleModel\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"description_width\\\\\\\": \\\\\\\"\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"font_size\\\\\\\": null,\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"text_color\\\\\\\": null\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"5c1fab0ccc7e42c69c2b663b67ca9e3c\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/base\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"LayoutModel\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"state\\\\\\\": {}\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"63a54817ae8b4c8d85b30d8c97e7ed0d\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_name\\\\\\\": \\\\\\\"HBoxModel\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"state\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"children\\\\\\\": [\\\\n\\\",\\n\",\n",
-    "    \"    \\\"        \\\\\\\"IPY_MODEL_e170d60a55a14c489d32b101293a9d33\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"        \\\\\\\"IPY_MODEL_662ad93cf7b3470f8e197350441f09ca\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"        \\\\\\\"IPY_MODEL_ac4abab2367e461e956e4e0edb27be44\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       ],\\\\n\\\",\\n\",\n",
-    "    \"    \\\"       \\\\\\\"layout\\\\\\\": \\\\\\\"IPY_MODEL_c59d6fe969a04f4da332ae2a6fdb9c77\\\\\\\"\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      }\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     },\\\\n\\\",\\n\",\n",
-    "    \"    \\\"     \\\\\\\"63f87a2410ba47d38ad9f55a81357a29\\\\\\\": {\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module\\\\\\\": \\\\\\\"@jupyter-widgets/controls\\\\\\\",\\\\n\\\",\\n\",\n",
-    "    \"    \\\"      \\\\\\\"model_module_version\\\\\\\": \\\\\\\"2.0.0\\\\\\\",\\\\n\\\",\\n\",\n",
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-    "       \"value\": \" 26/26 [00:01&lt;00:00, 18.92block/s]\"\n",
-    "      }\n",
-    "     },\n",
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-    "      \"model_name\": \"FloatProgressModel\",\n",
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-    "       \"bar_style\": \"success\",\n",
-    "       \"layout\": \"IPY_MODEL_d23d88490a1b4941b8ca228b7d4a39cb\",\n",
-    "       \"max\": 6,\n",
-    "       \"style\": \"IPY_MODEL_2693aae410f04e7587f0400b5eef1079\",\n",
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-    "     \"d565a20d37e84ed0a1195e9a90154960\": {\n",
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-    "      \"model_name\": \"HBoxModel\",\n",
-    "      \"state\": {\n",
-    "       \"children\": [\n",
-    "        \"IPY_MODEL_db696e15587c4a54b82886b3eb5a0c96\",\n",
-    "        \"IPY_MODEL_a6aabcd2e57348a49bb59477f6a9fd90\",\n",
-    "        \"IPY_MODEL_b9810ee38bbe43f4bba100e53379be65\"\n",
-    "       ],\n",
-    "       \"layout\": \"IPY_MODEL_eab4b060672b4dd5a73ca7bb8f782cd2\"\n",
-    "      }\n",
-    "     },\n",
-    "     \"db696e15587c4a54b82886b3eb5a0c96\": {\n",
-    "      \"model_module\": \"@jupyter-widgets/controls\",\n",
-    "      \"model_module_version\": \"2.0.0\",\n",
-    "      \"model_name\": \"HTMLModel\",\n",
-    "      \"state\": {\n",
-    "       \"layout\": \"IPY_MODEL_64eb2b9c8a094a1ea5f1991e5f09f5d9\",\n",
-    "       \"style\": \"IPY_MODEL_5b3e72b71f304230bc36bb91f9c173be\",\n",
-    "       \"value\": \"Step 1 / 2 Writing Blocks: 100%\"\n",
-    "      }\n",
-    "     },\n",
-    "     \"e170d60a55a14c489d32b101293a9d33\": {\n",
-    "      \"model_module\": \"@jupyter-widgets/controls\",\n",
-    "      \"model_module_version\": \"2.0.0\",\n",
-    "      \"model_name\": \"HTMLModel\",\n",
-    "      \"state\": {\n",
-    "       \"layout\": \"IPY_MODEL_9fe5348964cd4f7880bcd33204bbc7a2\",\n",
-    "       \"style\": \"IPY_MODEL_4df685a95de542ffab6f265df89b8949\",\n",
-    "       \"value\": \"Step 2 / 2 Concat Blocks: \"\n",
-    "      }\n",
-    "     },\n",
-    "     \"e66368cda8124001b16459a0634029e2\": {\n",
-    "      \"model_module\": \"@jupyter-widgets/controls\",\n",
-    "      \"model_module_version\": \"2.0.0\",\n",
-    "      \"model_name\": \"HTMLStyleModel\",\n",
-    "      \"state\": {\n",
-    "       \"description_width\": \"\",\n",
-    "       \"font_size\": null,\n",
-    "       \"text_color\": null\n",
-    "      }\n",
-    "     },\n",
-    "     \"eab4b060672b4dd5a73ca7bb8f782cd2\": {\n",
-    "      \"model_module\": \"@jupyter-widgets/base\",\n",
-    "      \"model_module_version\": \"2.0.0\",\n",
-    "      \"model_name\": \"LayoutModel\",\n",
-    "      \"state\": {\n",
-    "       \"display\": \"inline-flex\",\n",
-    "       \"flex_flow\": \"row wrap\",\n",
-    "       \"width\": \"100%\"\n",
-    "      }\n",
-    "     },\n",
-    "     \"f65266f861cf40ef84e9ec74a8e0b44c\": {\n",
-    "      \"model_module\": \"@jupyter-widgets/controls\",\n",
-    "      \"model_module_version\": \"2.0.0\",\n",
-    "      \"model_name\": \"HTMLModel\",\n",
-    "      \"state\": {\n",
-    "       \"layout\": \"IPY_MODEL_fff4199774534d9f982f03738fbb08e7\",\n",
-    "       \"style\": \"IPY_MODEL_e66368cda8124001b16459a0634029e2\",\n",
-    "       \"value\": \"Scanning SEG-Y for geometry attributes: 100%\"\n",
-    "      }\n",
-    "     },\n",
-    "     \"fdcea609ee7349629604217b625371d3\": {\n",
-    "      \"model_module\": \"@jupyter-widgets/controls\",\n",
-    "      \"model_module_version\": \"2.0.0\",\n",
-    "      \"model_name\": \"HTMLStyleModel\",\n",
-    "      \"state\": {\n",
-    "       \"description_width\": \"\",\n",
-    "       \"font_size\": null,\n",
-    "       \"text_color\": null\n",
-    "      }\n",
-    "     },\n",
-    "     \"fff4199774534d9f982f03738fbb08e7\": {\n",
-    "      \"model_module\": \"@jupyter-widgets/base\",\n",
-    "      \"model_module_version\": \"2.0.0\",\n",
-    "      \"model_name\": \"LayoutModel\",\n",
-    "      \"state\": {}\n",
-    "     }\n",
-    "    },\n",
-    "    \"version_major\": 2,\n",
-    "    \"version_minor\": 0\n",
-    "   }\n",
-    "  }\n",
-    " },\n",
-    " \"nbformat\": 4,\n",
-    " \"nbformat_minor\": 4\n",
-    "}\n"
+    "## Copying an Existing MDIO without Data and Headers\n",
+    "\n",
+    "We also provide a `create_empty_like` function which allows us to create an MDIO file\n",
+    "with the same structure and basic metadata as an existing one.\n",
+    "\n",
+    "This will carry over:\n",
+    "* All access patterns in source MDIO\n",
+    "* Grid (shape, coordinates, dimensions, etc)\n",
+    "* Text Header\n",
+    "* Binary Header\n",
+    "\n",
+    "It will **NOT** carry over:\n",
+    "* Trace data\n",
+    "* Header data\n",
+    "* Statistics\n",
+    "* Live Mask"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-16T14:50:17.246513Z",
+     "start_time": "2025-04-16T14:50:17.111779Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from mdio import create_empty_like\n",
+    "\n",
+    "copy_path = \"copy_of_existing.mdio\"\n",
+    "create_empty_like(create_conf.path, copy_path)\n",
+    "\n",
+    "copy_reader = MDIOReader(copy_path)\n",
+    "assert copy_reader.shape == reader.shape\n",
+    "assert copy_reader.binary_header == reader.binary_header\n",
+    "assert copy_reader.stats != reader.stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From f6b2c34487b912f8bbd21ba263a26efa2a652105 Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Wed, 7 May 2025 09:34:33 -0500
Subject: [PATCH 13/19] update lockfile

---
 uv.lock | 291 ++++++--------------------------------------------------
 1 file changed, 28 insertions(+), 263 deletions(-)

diff --git a/uv.lock b/uv.lock
index 68edf47f..f6894429 100644
--- a/uv.lock
+++ b/uv.lock
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@@ -1839,23 +1702,14 @@ lossy = [
 
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@@ -3571,18 +3357,6 @@ wheels = [
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From dbec58e9f5339ff5db71bc56351a7821c552a1bd Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Wed, 7 May 2025 09:37:45 -0500
Subject: [PATCH 14/19] lint v1 files

---
 src/mdio/schemas/__init__.py    |  1 -
 src/mdio/schemas/base.py        |  5 +----
 src/mdio/schemas/builder.py     |  2 --
 src/mdio/schemas/compressors.py |  4 +---
 src/mdio/schemas/dtype.py       |  4 +---
 src/mdio/schemas/v0/__init__.py |  1 -
 src/mdio/schemas/v0/dataset.py  | 12 +++---------
 src/mdio/schemas/v1/__init__.py |  1 -
 src/mdio/schemas/v1/stats.py    |  8 ++------
 src/mdio/schemas/v1/units.py    | 13 +++----------
 src/mdio/schemas/v1/variable.py |  4 +---
 tests/unit/test_schema.py       |  3 +--
 12 files changed, 13 insertions(+), 45 deletions(-)

diff --git a/src/mdio/schemas/__init__.py b/src/mdio/schemas/__init__.py
index 17ae4dd3..8d9d8b86 100644
--- a/src/mdio/schemas/__init__.py
+++ b/src/mdio/schemas/__init__.py
@@ -7,7 +7,6 @@
 from mdio.schemas.dtype import StructuredField
 from mdio.schemas.dtype import StructuredType
 
-
 __all__ = [
     "Blosc",
     "ZFP",
diff --git a/src/mdio/schemas/base.py b/src/mdio/schemas/base.py
index d4bdfd48..5684fa10 100644
--- a/src/mdio/schemas/base.py
+++ b/src/mdio/schemas/base.py
@@ -10,7 +10,6 @@
 from mdio.schemas.dimension import NamedDimension
 from mdio.schemas.dtype import DataTypeModel
 
-
 JSON_SCHEMA_DIALECT = GenerateJsonSchema.schema_dialect
 
 
@@ -31,9 +30,7 @@ class BaseArray(DataTypeModel, CamelCaseStrictModel):
     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."
-    )
+    compressor: Blosc | ZFP | None = Field(default=None, description="Compression settings.")
 
 
 class NamedArray(BaseArray):
diff --git a/src/mdio/schemas/builder.py b/src/mdio/schemas/builder.py
index 9a0e6cbb..40908ff0 100644
--- a/src/mdio/schemas/builder.py
+++ b/src/mdio/schemas/builder.py
@@ -15,7 +15,6 @@ class VariableBuilder:
     """Dataset builder."""
 
     def __init__(self) -> None:
-        """Initialize the builder."""
         self.name = None
         self.long_name = None
         self.dtype = None
@@ -126,7 +125,6 @@ class DatasetBuilder:
     """Dataset builder."""
 
     def __init__(self) -> None:
-        """Initialize the builder."""
         self.variables = []
         self.name = None
         self.metadata = None
diff --git a/src/mdio/schemas/compressors.py b/src/mdio/schemas/compressors.py
index b550b98d..e17948da 100644
--- a/src/mdio/schemas/compressors.py
+++ b/src/mdio/schemas/compressors.py
@@ -154,6 +154,4 @@ def make_instance(self):  # noqa: ANN201
 class CompressorModel(CamelCaseStrictModel):
     """Model representing compressor configuration."""
 
-    compressor: Blosc | ZFP | None = Field(
-        default=None, description="Compression settings."
-    )
+    compressor: Blosc | ZFP | None = Field(default=None, description="Compression settings.")
diff --git a/src/mdio/schemas/dtype.py b/src/mdio/schemas/dtype.py
index 5c2427b0..af7f6582 100644
--- a/src/mdio/schemas/dtype.py
+++ b/src/mdio/schemas/dtype.py
@@ -50,6 +50,4 @@ class StructuredType(CamelCaseStrictModel):
 class DataTypeModel(CamelCaseStrictModel):
     """Structured array type with fields and total item size."""
 
-    data_type: ScalarType | StructuredType = Field(
-        ..., description="Type of the array."
-    )
+    data_type: ScalarType | StructuredType = Field(..., description="Type of the array.")
diff --git a/src/mdio/schemas/v0/__init__.py b/src/mdio/schemas/v0/__init__.py
index e74f484f..9b953304 100644
--- a/src/mdio/schemas/v0/__init__.py
+++ b/src/mdio/schemas/v0/__init__.py
@@ -2,5 +2,4 @@
 
 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
index 58c3ae72..50103736 100644
--- a/src/mdio/schemas/v0/dataset.py
+++ b/src/mdio/schemas/v0/dataset.py
@@ -42,13 +42,9 @@ class DatasetMetadataModelV0(StrictModel):
     )
 
     # Statistical information
-    std: float | None = Field(
-        default=None, description="Standard deviation of the samples."
-    )
+    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."
-    )
+    rms: float | None = Field(default=None, description="Root mean squared value of the samples.")
 
     min: float | None = Field(
         default=None,
@@ -60,9 +56,7 @@ class DatasetMetadataModelV0(StrictModel):
         description="Maximum value of the samples.",
     )
 
-    trace_count: int | None = Field(
-        default=None, description="Number of traces in the SEG-Y file."
-    )
+    trace_count: int | None = Field(default=None, description="Number of traces in the SEG-Y file.")
 
 
 class VariableModelV0(BaseArray):
diff --git a/src/mdio/schemas/v1/__init__.py b/src/mdio/schemas/v1/__init__.py
index 4f9bcdf3..fb98d55e 100644
--- a/src/mdio/schemas/v1/__init__.py
+++ b/src/mdio/schemas/v1/__init__.py
@@ -2,5 +2,4 @@
 
 from mdio.schemas.v1.dataset import Dataset
 
-
 __all__ = ["Dataset"]
diff --git a/src/mdio/schemas/v1/stats.py b/src/mdio/schemas/v1/stats.py
index a8a319c0..29d50add 100644
--- a/src/mdio/schemas/v1/stats.py
+++ b/src/mdio/schemas/v1/stats.py
@@ -41,12 +41,8 @@ class CenteredBinHistogram(BaseHistogram):
 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."
-    )
+    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
diff --git a/src/mdio/schemas/v1/units.py b/src/mdio/schemas/v1/units.py
index dd46296c..1913ff2e 100644
--- a/src/mdio/schemas/v1/units.py
+++ b/src/mdio/schemas/v1/units.py
@@ -11,7 +11,6 @@
 from mdio.schemas.units import UnitEnum
 from mdio.schemas.units import create_unit_model
 
-
 ureg = UnitRegistry()
 ureg.default_format = "~C"  # compact, abbreviated (symbol).
 
@@ -30,9 +29,7 @@ class LengthUnitEnum(UnitEnum):
     MILE = ureg.mile
 
 
-LengthUnitModel = create_unit_model(
-    LengthUnitEnum, "LengthUnitModel", "length", __name__
-)
+LengthUnitModel = create_unit_model(LengthUnitEnum, "LengthUnitModel", "length", __name__)
 
 
 class TimeUnitEnum(UnitEnum):
@@ -58,9 +55,7 @@ class DensityUnitEnum(UnitEnum):
     POUNDS_PER_GAL = ureg.pounds / ureg.gallon
 
 
-DensityUnitModel = create_unit_model(
-    DensityUnitEnum, "DensityUnitModel", "density", __name__
-)
+DensityUnitModel = create_unit_model(DensityUnitEnum, "DensityUnitModel", "density", __name__)
 
 
 class SpeedUnitEnum(UnitEnum):
@@ -102,9 +97,7 @@ class VoltageUnitEnum(UnitEnum):
     VOLT = ureg.volt
 
 
-VoltageUnitModel = create_unit_model(
-    VoltageUnitEnum, "VoltageUnitModel", "voltage", __name__
-)
+VoltageUnitModel = create_unit_model(VoltageUnitEnum, "VoltageUnitModel", "voltage", __name__)
 
 
 # Composite model types
diff --git a/src/mdio/schemas/v1/variable.py b/src/mdio/schemas/v1/variable.py
index 22ad0bf3..519a89f4 100644
--- a/src/mdio/schemas/v1/variable.py
+++ b/src/mdio/schemas/v1/variable.py
@@ -50,6 +50,4 @@ class Variable(NamedArray):
         default=None,
         description="Coordinates of the MDIO variable dimensions.",
     )
-    metadata: VariableMetadata | None = Field(
-        default=None, description="Variable metadata."
-    )
+    metadata: VariableMetadata | None = Field(default=None, description="Variable metadata.")
diff --git a/tests/unit/test_schema.py b/tests/unit/test_schema.py
index 1bccde5a..0b2a9f54 100644
--- a/tests/unit/test_schema.py
+++ b/tests/unit/test_schema.py
@@ -2,7 +2,6 @@
 
 from mdio.schemas.v1 import Dataset as V1Dataset
 
-
 TEST_SCHEMA = {
     "metadata": {
         "name": "test_dataset",
@@ -49,6 +48,6 @@
 }
 
 
-def test_dataset_schema_validation():
+def test_dataset_schema_validation() -> None:
     """Test that the dataset schema validates correctly."""
     V1Dataset.model_validate(TEST_SCHEMA)

From ecb69d6d2a9ef4a8695243afda69c07c13de5d51 Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Tue, 27 May 2025 09:05:14 -0500
Subject: [PATCH 15/19] update lock file

---
 uv.lock | 1873 +++++++++++++++++++++++++++++--------------------------
 1 file changed, 1001 insertions(+), 872 deletions(-)

diff --git a/uv.lock b/uv.lock
index f6894429..de95c4e6 100644
--- a/uv.lock
+++ b/uv.lock
@@ -26,7 +26,7 @@ wheels = [
 
 [[package]]
 name = "aiobotocore"
-version = "2.21.1"
+version = "2.22.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -37,9 +37,9 @@ dependencies = [
     { name = "python-dateutil" },
     { name = "wrapt" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d2/dc/f5f872fb01ce37c09525cedc7ecfad7002ffe2a8a23f77d7d2c234399b51/aiobotocore-2.21.1.tar.gz", hash = "sha256:010357f43004413e92a9d066bb0db1f241aeb29ffed306e9197061ffc94e6577", size = 108900 }
+sdist = { url = "https://files.pythonhosted.org/packages/9c/4c/113c4f5611103bba8e5252805fbee7944f5d9541addba9a96b091c0c4308/aiobotocore-2.22.0.tar.gz", hash = "sha256:11091477266b75c2b5d28421c1f2bc9a87d175d0b8619cb830805e7a113a170b", size = 110322 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/95/67/026598918f92145156f2feb7957f57daefda20375cc2ac1a0692a9b8010b/aiobotocore-2.21.1-py3-none-any.whl", hash = "sha256:bd7c49a6d6f8a3d9444b0a94417c8da13813b5c7eec1c4f0ec2db7e8ce8f23e7", size = 78313 },
+    { url = "https://files.pythonhosted.org/packages/00/8e/ffa5840cb7de19ada85bda1fae1ae22671a18992e9373f2e2df9db5389b5/aiobotocore-2.22.0-py3-none-any.whl", hash = "sha256:b4e6306f79df9d81daff1f9d63189a2dbee4b77ce3ab937304834e35eaaeeccf", size = 78930 },
 ]
 
 [[package]]
@@ -53,7 +53,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.11.15"
+version = "3.12.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -64,56 +64,59 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
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-wheels = [
-    { url = "https://files.pythonhosted.org/packages/44/31/5c8552b13041decee3facbe4a8e6342e9227cc472d7835df7f148049550a/aiohttp-3.11.15-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:5bd37d615cd26d09321bd0168305f8508778712cf38aeffeed550274fb48a2ee", size = 708642 },
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+    { url = "https://files.pythonhosted.org/packages/4c/f6/0873a05563e5df29ccf35345a6ae0ac9e66588b41fdb7043a65848f03139/yarl-1.20.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:3b60a86551669c23dc5445010534d2c5d8a4e012163218fc9114e857c0586fdd", size = 423792 },
+    { url = "https://files.pythonhosted.org/packages/9e/35/43fbbd082708fa42e923f314c24f8277a28483d219e049552e5007a9aaca/yarl-1.20.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:3e429857e341d5e8e15806118e0294f8073ba9c4580637e59ab7b238afca836f", size = 422242 },
+    { url = "https://files.pythonhosted.org/packages/ed/f7/f0f2500cf0c469beb2050b522c7815c575811627e6d3eb9ec7550ddd0bfe/yarl-1.20.0-cp313-cp313t-win32.whl", hash = "sha256:65a4053580fe88a63e8e4056b427224cd01edfb5f951498bfefca4052f0ce0ac", size = 93816 },
+    { url = "https://files.pythonhosted.org/packages/3f/93/f73b61353b2a699d489e782c3f5998b59f974ec3156a2050a52dfd7e8946/yarl-1.20.0-cp313-cp313t-win_amd64.whl", hash = "sha256:53b2da3a6ca0a541c1ae799c349788d480e5144cac47dba0266c7cb6c76151fe", size = 101093 },
+    { url = "https://files.pythonhosted.org/packages/ea/1f/70c57b3d7278e94ed22d85e09685d3f0a38ebdd8c5c73b65ba4c0d0fe002/yarl-1.20.0-py3-none-any.whl", hash = "sha256:5d0fe6af927a47a230f31e6004621fd0959eaa915fc62acfafa67ff7229a3124", size = 46124 },
 ]
 
 [[package]]
 name = "zarr"
-version = "3.0.6"
+version = "3.0.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "donfig" },
@@ -3825,9 +3954,9 @@ dependencies = [
     { name = "packaging" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6f/bf/2820e8ad5f086ea5cd4c4d65eb5e0113f98145ddf5810cec1c75fb10fb98/zarr-3.0.6.tar.gz", hash = "sha256:6ef23c740e34917a2a1099471361537732942e49f0cabe95c9b7124cd0d6d84f", size = 240812 }
+sdist = { url = "https://files.pythonhosted.org/packages/52/60/9652fd0536fbaca8d08cbc1a5572c52e0ce01773297df75da8bb47e45907/zarr-3.0.8.tar.gz", hash = "sha256:88505d095af899a88ae8ac4db02f4650ef0801d2ff6f65b6d1f0a45dcf760a6d", size = 256825 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/fb/38/56a1c129577d20dc975a934ccc3f7f16276eab04ff0ffbe02ea348407a37/zarr-3.0.6-py3-none-any.whl", hash = "sha256:dba078726b6e4defb0ae6a852b7e7035ce163d89d485961681ece49191dcce82", size = 196357 },
+    { url = "https://files.pythonhosted.org/packages/00/3b/e20bdf84088c11f2c396d034506cbffadd53e024111c1aa4585c2aba1523/zarr-3.0.8-py3-none-any.whl", hash = "sha256:7f81e7aec086437d98882aa432209107114bd7f3a9f4958b2af9c6b5928a70a7", size = 205364 },
 ]
 
 [[package]]
@@ -3863,9 +3992,9 @@ wheels = [
 
 [[package]]
 name = "zipp"
-version = "3.21.0"
+version = "3.22.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/3f/50/bad581df71744867e9468ebd0bcd6505de3b275e06f202c2cb016e3ff56f/zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4", size = 24545 }
+sdist = { url = "https://files.pythonhosted.org/packages/12/b6/7b3d16792fdf94f146bed92be90b4eb4563569eca91513c8609aebf0c167/zipp-3.22.0.tar.gz", hash = "sha256:dd2f28c3ce4bc67507bfd3781d21b7bb2be31103b51a4553ad7d90b84e57ace5", size = 25257 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b7/1a/7e4798e9339adc931158c9d69ecc34f5e6791489d469f5e50ec15e35f458/zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931", size = 9630 },
+    { url = "https://files.pythonhosted.org/packages/ad/da/f64669af4cae46f17b90798a827519ce3737d31dbafad65d391e49643dc4/zipp-3.22.0-py3-none-any.whl", hash = "sha256:fe208f65f2aca48b81f9e6fd8cf7b8b32c26375266b009b413d45306b6148343", size = 9796 },
 ]

From 5816f8367cd5d6b07ee4b9074bacb131ed00aa35 Mon Sep 17 00:00:00 2001
From: Brian Michell <brianm314@comcast.net>
Date: Thu, 26 Jun 2025 08:54:16 -0500
Subject: [PATCH 16/19] V1 schema review (#553)

* Update from list to discrete values for coordinate metadata

* Add docs to help users understand difference

* Update docs and fix case sensitivity.

* Linting

* Add CoordianteMetadata to docs
---
 docs/data_models/version_1.md   |  3 +++
 src/mdio/schemas/v1/variable.py | 26 ++++++++++++++++++--------
 2 files changed, 21 insertions(+), 8 deletions(-)

diff --git a/docs/data_models/version_1.md b/docs/data_models/version_1.md
index caf0ed5c..2fd39753 100644
--- a/docs/data_models/version_1.md
+++ b/docs/data_models/version_1.md
@@ -45,6 +45,9 @@
 .. 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
 
diff --git a/src/mdio/schemas/v1/variable.py b/src/mdio/schemas/v1/variable.py
index 519a89f4..93b59117 100644
--- a/src/mdio/schemas/v1/variable.py
+++ b/src/mdio/schemas/v1/variable.py
@@ -23,14 +23,23 @@
 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):
-    """An MDIO coordinate array with metadata."""
+    """A simple MDIO Coordinate array with metadata.
 
-    data_type: ScalarType = Field(..., description="Data type of coordinate.")
-    metadata: list[AllUnits | UserAttributes] | None = Field(
-        default=None, description="Coordinate 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(
@@ -40,14 +49,15 @@ class Coordinate(NamedArray):
     **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."""
+    """An MDIO Variable that has coordinates and metadata."""
 
     coordinates: list[Coordinate] | list[str] | None = Field(
         default=None,
-        description="Coordinates of the MDIO variable dimensions.",
+        description="Coordinates of the MDIO Variable dimensions.",
     )
-    metadata: VariableMetadata | None = Field(default=None, description="Variable metadata.")
+    metadata: VariableMetadata | None = Field(default=None, description="Variable Metadata.")

From fe4af2bdab5219e2949d1cc28bec62091a226e24 Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Wed, 2 Jul 2025 10:16:01 -0500
Subject: [PATCH 17/19] [v1] Update dependencies to latest (#567)

* Update dependencies to latest versions

* Update linter type-checking code to 'TC' in pyproject.toml

https://astral.sh/blog/ruff-v0.8.0#new-error-codes-for-flake8-type-checking-rules

* Refactor: Move Zarr codec imports to top-level

* disable safety in CI (temporary)

* Refactor: Replace Zarr codec imports with numcodecs equivalents

* Refactor: Remove unused numcodecs imports and related methods

* pin zarr due to zarr 3.0.9 bug
---
 .github/workflows/tests.yml     |    2 +-
 pyproject.toml                  |   52 +-
 src/mdio/schemas/compressors.py |   22 -
 uv.lock                         | 1985 ++++++++++++++-----------------
 4 files changed, 928 insertions(+), 1133 deletions(-)

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/pyproject.toml b/pyproject.toml
index 7e01894e..a9e14605 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,30 +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)",
-    "pint>=0.24.3,<0.25",
-    "psutil (>=6.1.0,<7.0.0)",
-    "pydantic (>=2.8.2,<3.0.0)",
-    "pydantic-settings (>=2.4.0,<3.0.0)",
-    "rich (>=13.9.4,<14.0.0)",
-    "segy (>=0.4.0,<0.5.0)",
-    "tqdm (>=4.67.0,<5.0.0)",
-    "xarray>=2025.3.1",
-    "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 (>=3.0.8,<3.0.9)",
 ]
 
 [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)"]
 
@@ -60,17 +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 = [
@@ -116,7 +116,7 @@ select = [
     "RET", # return
     "SIM", # simplify
     "TID", # tidy-imports
-    "TCH", # type-checking
+    "TC", # type-checking
     "ARG", # unused-arguments
     "PTH", # use-pathlib
     "TD",  # todos
diff --git a/src/mdio/schemas/compressors.py b/src/mdio/schemas/compressors.py
index e17948da..2eb87f7a 100644
--- a/src/mdio/schemas/compressors.py
+++ b/src/mdio/schemas/compressors.py
@@ -53,17 +53,6 @@ class Blosc(CamelCaseStrictModel):
         description="The size of the block to be used for compression.",
     )
 
-    def make_instance(self):  # noqa: ANN201
-        """Translate parameters to compressor kwargs.."""
-        from zarr.codecs import Blosc as _Blosc
-
-        return _Blosc(
-            cname=self.algorithm,
-            clevel=self.level,
-            shuffle=self.shuffle,
-            blocksize=self.blocksize,
-        )
-
 
 zfp_mode_map = {
     "fixed_rate": 2,
@@ -139,17 +128,6 @@ def check_requirements(self) -> ZFP:
 
         return self
 
-    def make_instance(self):  # noqa: ANN201
-        """Translate parameters to compressor kwargs.."""
-        from zarr.codecs import ZFPY as _ZFPY
-
-        return _ZFPY(
-            mode=self.mode.int_code,
-            tolerance=self.tolerance,
-            rate=self.rate,
-            precision=self.precision,
-        )
-
 
 class CompressorModel(CamelCaseStrictModel):
     """Model representing compressor configuration."""
diff --git a/uv.lock b/uv.lock
index de95c4e6..964a1c2d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -26,7 +26,7 @@ wheels = [
 
 [[package]]
 name = "aiobotocore"
-version = "2.22.0"
+version = "2.23.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -37,9 +37,9 @@ dependencies = [
     { name = "python-dateutil" },
     { name = "wrapt" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9c/4c/113c4f5611103bba8e5252805fbee7944f5d9541addba9a96b091c0c4308/aiobotocore-2.22.0.tar.gz", hash = "sha256:11091477266b75c2b5d28421c1f2bc9a87d175d0b8619cb830805e7a113a170b", size = 110322 }
+sdist = { url = "https://files.pythonhosted.org/packages/9d/25/4b06ea1214ddf020a28df27dc7136ac9dfaf87929d51e6f6044dd350ed67/aiobotocore-2.23.0.tar.gz", hash = "sha256:0333931365a6c7053aee292fe6ef50c74690c4ae06bb019afdf706cb6f2f5e32", size = 115825 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/00/8e/ffa5840cb7de19ada85bda1fae1ae22671a18992e9373f2e2df9db5389b5/aiobotocore-2.22.0-py3-none-any.whl", hash = "sha256:b4e6306f79df9d81daff1f9d63189a2dbee4b77ce3ab937304834e35eaaeeccf", size = 78930 },
+    { url = "https://files.pythonhosted.org/packages/ea/43/ccf9b29669cdb09fd4bfc0a8effeb2973b22a0f3c3be4142d0b485975d11/aiobotocore-2.23.0-py3-none-any.whl", hash = "sha256:8202cebbf147804a083a02bc282fbfda873bfdd0065fd34b64784acb7757b66e", size = 84161 },
 ]
 
 [[package]]
@@ -53,7 +53,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.12.2"
+version = "3.12.13"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -64,59 +64,59 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/06/a2/a946c4f5c54233c97788c2278ea50beaadf45211f452f932b36ce322f660/aiohttp-3.12.2.tar.gz", hash = "sha256:0018956472ee535d2cad761a5bb88eb4ad80f94cd86472cee26a244799f7c79f", size = 7780423 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/08/39/9866f5996a7db870464e1f153b9f6a3412167ee79293f138bad09de783cd/aiohttp-3.12.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:536a37af26ed50bd4f3cf7d989955e5a987e9343f1a55f5393e7950a6ac93fce", size = 701750 },
-    { url = "https://files.pythonhosted.org/packages/81/eb/187fba5f1c210bed03c4e4fe50b6cc64d18c6776e6d17887b527ee2fb806/aiohttp-3.12.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6f8fbb48953238e7ba8ab9dee6757a4f6b72cd6242eb7fe1cb004b24f91effee", size = 474015 },
-    { url = "https://files.pythonhosted.org/packages/c5/e5/8f203120a8a932739face58614f8c93912ccd26c0b18da3f476b7372158b/aiohttp-3.12.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:74190229bd54bc3df7090f634b0b7fe53c45fb41aae5fbfae462093ced35c950", size = 462282 },
-    { url = "https://files.pythonhosted.org/packages/7d/0f/9c33853f4f1c6c75a0f1b3e7b6d955f5883bd14a189232115e2e0c8633f2/aiohttp-3.12.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7af4737ab145fb1ac6e2db24ee206ee9e9f3abb1f7c6b74bd75c9ce0d36fe286", size = 1732474 },
-    { url = "https://files.pythonhosted.org/packages/e6/72/2dee9dd29a6ce5abbfa5ee7b75db00ce9c213aaea588476464285a3aee57/aiohttp-3.12.2-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:2711392a2afe1dcf4a93b05a94ee25efa966971fa0bf3944f2ce101da182ce91", size = 1681143 },
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From 4062a770be50a16db4a5c2adc4185eea2454b854 Mon Sep 17 00:00:00 2001
From: Altay Sansal <tasansal@users.noreply.github.com>
Date: Mon, 7 Jul 2025 09:12:28 -0500
Subject: [PATCH 18/19] unpin zarr because breaking bug fixed (#569)

---
 pyproject.toml |   2 +-
 uv.lock        | 391 +++++++++++++++++++++++++------------------------
 2 files changed, 202 insertions(+), 191 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index a9e14605..e2f72190 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,7 +35,7 @@ dependencies = [
     "segy (>=0.4.1.post2,<0.5.0)",
     "tqdm (>=4.67.1,<5.0.0)",
     "xarray>=2025.6.1",
-    "zarr (>=3.0.8,<3.0.9)",
+    "zarr (>=3.0.10,<4.0.0)",
 ]
 
 [project.optional-dependencies]
diff --git a/uv.lock b/uv.lock
index 964a1c2d..6f04c363 100644
--- a/uv.lock
+++ b/uv.lock
@@ -130,14 +130,15 @@ wheels = [
 
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+version = "1.4.0"
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@@ -201,16 +202,16 @@ wheels = [
 
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From 90d31a1cd76724d2cb9ad64ede2a615533c80ca6 Mon Sep 17 00:00:00 2001
From: Dima From Texas <34629861+dmitriyrepin@users.noreply.github.com>
Date: Thu, 10 Jul 2025 10:55:47 -0500
Subject: [PATCH 19/19] Implement MDIO Dataset builder to create in-memory
 instance of schemas.v1.dataset.Dataset (#568)

* schema_v1-dataset_builder-add_dimension

* First take on add_dimension(), add_coordinate(), add_variable()

* Finished add_dimension, add_coordinate, add_variable

* Work on build

* Generalize _to_dictionary()

* build

* Dataset Build - pass one

* Revert .container changes

* PR review: remove DEVELOPER_NOTES.md

* PR Review: add_coordinate() should accept only data_type: ScalarType

* PR review: add_variable() data_type remove default

* RE review: do not add dimension variable

* PR Review: get api version from the package version

* PR Review: remove add_dimension_coordinate

* PR Review: add_coordinate() remove data_type default value

* PR Review: improve unit tests by extracting common functionality in validate* functions

* Remove the Dockerfile changes. They are not supposed to be a part of this PR

* PR Review: run ruff

* PR Review: fix pre-commit errors

* remove some noqa overrides

---------

Co-authored-by: Altay Sansal <tasansal@users.noreply.github.com>
---
 src/mdio/schemas/v1/dataset_builder.py        | 335 ++++++++++++++++++
 tests/unit/v1/__init__.py                     |   1 +
 tests/unit/v1/helpers.py                      | 254 +++++++++++++
 .../v1/test_dataset_builder_add_coordinate.py | 141 ++++++++
 .../v1/test_dataset_builder_add_dimension.py  |  43 +++
 .../v1/test_dataset_builder_add_variable.py   | 250 +++++++++++++
 tests/unit/v1/test_dataset_builder_build.py   | 155 ++++++++
 tests/unit/v1/test_dataset_builder_helpers.py |  98 +++++
 8 files changed, 1277 insertions(+)
 create mode 100644 src/mdio/schemas/v1/dataset_builder.py
 create mode 100644 tests/unit/v1/__init__.py
 create mode 100644 tests/unit/v1/helpers.py
 create mode 100644 tests/unit/v1/test_dataset_builder_add_coordinate.py
 create mode 100644 tests/unit/v1/test_dataset_builder_add_dimension.py
 create mode 100644 tests/unit/v1/test_dataset_builder_add_variable.py
 create mode 100644 tests/unit/v1/test_dataset_builder_build.py
 create mode 100644 tests/unit/v1/test_dataset_builder_helpers.py

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/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),
+    }