restructuring docs, getting started on design but need 2 go home

docs/api/dtype.md

@@ -1 +1,7 @@
-# DType
+# dtype
+
+```{eval-rst}
+.. automodule:: numpydantic.dtype
+    :members:
+    :undoc-members:
+```

docs/api/schema.md

@@ -0,0 +1,7 @@
+# schema
+
+```{eval-rst}
+.. automodule:: numpydantic.schema
+    :members:
+    :undoc-members:
+```

docs/api/types.md

@@ -1 +1,7 @@
-# Types
+# types
+
+```{eval-rst}
+.. automodule:: numpydantic.types
+    :members:
+    :undoc-members:
+```

docs/design.md

@@ -1,9 +1,18 @@
-# Overview
+# Design
+
+## Why do this?
+
+We want to bring the tidyness of modeling data with pydantic to the universe of
+software that uses arrays - particularly formats and packages that need to be very 
+particular about what *kind* of arrays they are able to handle or match a specific schema.
+
+## Challenges
 
 The Python type annotation system is weird and not like the rest of Python! 
 (at least until [PEP 0649](https://peps.python.org/pep-0649/) gets mainlined).
-Similarly, Pydantic 2's core_schema system is wonderful but still relatively poorly
-documented for custom types! This package does the work of plugging them in
+Similarly, Pydantic 2's core_schema system is wonderful but still has a few mysteries
+lurking under the documented surface.
+This package does the work of plugging them in
 together to make some kind of type validation frankenstein.
 
 The first problem is that type annotations are evaluated statically by python, mypy,

docs/index.md

@@ -416,9 +416,8 @@ dumped = instance.model_dump_json(context={'zarr_dump_array': True})
 :caption: Contents
 :hidden: true
 
-overview
-ndarray
-hooks
+design
+interfaces
 todo
 ```
 
@@ -427,12 +426,13 @@ todo
 :caption: API
 :hidden: true
 
-api/interface/index
 api/index
+api/interface/index
 api/dtype
 api/ndarray
 api/maps
 api/monkeypatch
+api/schema
 api/types
 
 ```

docs/ndarray.md

@@ -1,135 +0,0 @@
-# Constrained Arrays
-
-## Implementation details
-
-```{todo}
-**Docs:**
-
-Describe implementation details!
-```
-
-## Examples
-
-### Declaration
-
-Type with a single {class}`~numpydantic.NDArray` class, or use a {class}`~typing.Union`
-to express more complex array constraints.
-
-This package is effectively a Pydantic interface to [nptyping](https://github.com/ramonhagenaars/nptyping),
-so any array syntax is valid there. (see [TODO](todo) for caveats)
-
-```python
-from typing import Union
-from pydantic import BaseModel
-from src.numpydantic import NDArray, Shape, UInt8, Float, Int
-
-
-class Image(BaseModel):
-    """
-    Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension should always represent time. This can also be used to store binary data (e.g., image frames). This can also be a link to data stored in an external file.
-    """
-    array: Union[
-        NDArray[Shape["* x, * y"], UInt8],
-        NDArray[Shape["* x, * y, 3 rgb"], UInt8],
-        NDArray[Shape["* x, * y, 4 rgba"], UInt8],
-        NDArray[Shape["* t, * x, * y, 3 rgb"], UInt8],
-        NDArray[Shape["* t, * x, * y, 4 rgba"], Float]
-    ]
-```
-
-### Validation:
-
-```python
-import numpy as np
-# works
-frame_gray = Image(array=np.ones((1280, 720), dtype=np.uint8))
-frame_rgb  = Image(array=np.ones((1280, 720, 3), dtype=np.uint8))
-frame_rgba = Image(array=np.ones((1280, 720, 4), dtype=np.uint8))
-video_rgb  = Image(array=np.ones((100, 1280, 720, 3), dtype=np.uint8))
-
-# fails
-wrong_n_dimensions = Image(array=np.ones((1280,), dtype=np.uint8))
-wrong_shape = Image(array=np.ones((1280,720,10), dtype=np.uint8))
-wrong_type = Image(array=np.ones((1280,720,3), dtype=np.float64))
-
-# shapes and types are checked together
-float_video = Image(array=np.ones((100, 1280, 720, 4),dtype=float))
-wrong_shape_float_video = Image(array=np.ones((100, 1280, 720, 3),dtype=float))
-```
-
-### JSON schema generation:
-
-```python
-class MyArray(BaseModel):
-  array: NDArray[Shape["2 x, * y, 4 z"], Float]
-```
-
-```python
->>> print(json.dumps(MyArray.model_json_schema(), indent=2))
-```
-
-```json
-{
-  "properties": {
-    "array": {
-      "items": {
-        "items": {
-          "items": {
-            "type": "number"
-          },
-          "maxItems": 4,
-          "minItems": 4,
-          "type": "array"
-        },
-        "type": "array"
-      },
-      "maxItems": 2,
-      "minItems": 2,
-      "title": "Array",
-      "type": "array"
-    }
-  },
-  "required": [
-    "array"
-  ],
-  "title": "MyArray",
-  "type": "object"
-}
-```
-
-### Serialization
-
-```python
-class SmolArray(BaseModel):
-    array: NDArray[Shape["2 x, 2 y"], Int]
-
-class BigArray(BaseModel):
-    array: NDArray[Shape["1000 x, 1000 y"], Int]
-```
-
-Serialize small arrays as lists of lists, and big arrays as a b64-encoded blosc compressed string
-
-```python
->>> smol = SmolArray(array=np.array([[1,2],[3,4]], dtype=int))
->>> big = BigArray(array=np.random.randint(0,255,(1000,1000),int))
-
->>> print(smol.model_dump_json())
-{"array":[[1,2],[3,4]]}
->>> print(big.model_dump_json())
-{
-  "array": "( long b64 encoded string )",
-  "shape": [1000, 1000],
-  "dtype": "int64",
-  "unpack_fns": ["base64.b64decode", "blosc2.unpack_array2"],
-}
-```
-
-## TODO
-
-```{todo}
-Implement structured arrays
-```
-
-```{todo}
-Implement pandas dataframe validation?
-```