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136 lines
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3.5 KiB
Markdown
136 lines
No EOL
3.5 KiB
Markdown
# Syntax
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General form:
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```python
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field: NDArray[Shape["{shape_expression}"], dtype]
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```
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## Dtype
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Dtype checking is for the most part as simple as an `isinstance` check -
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the `dtype` attribute of the array is checked against the `dtype` provided in the
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`NDArray` annotation. Both numpy and builtin python types can be used.
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A tuple of types can also be passed:
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```python
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field: NDArray[Shape["2, 3"], (np.int8, np.uint8)]
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```
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Like `nptyping`, the {mod}`~numpydantic.dtype` module provides convenient access
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and aliases to the common dtypes, but also provides "generic" dtypes like
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{class}`~numpydantic.dtype.Float` that is a tuple of all subclasses of
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{class}`numpy.floating`. Numpy interprets `float` as being equivalent to
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{class}`numpy.float64`, and {class}`numpy.floating` is an abstract parent class,
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so "generic" tuple dtypes fill that narrow gap.
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```{todo}
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Future versions will support interfaces providing type maps for declaring
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equality between dtypes that may be specific to that library but should be
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considered equivalent to numpy or other library's dtypes.
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```
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```{todo}
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Future versions will also support declaring minimum or maximum precisions,
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so one might say "at least a 16-bit float" and also accept a 32-bit float.
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```
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## Shape
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Full documentation of nptyping's shape syntax is available in the [nptyping docs](https://github.com/ramonhagenaars/nptyping/blob/master/USERDOCS.md#Shape-expressions),
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but for the sake of self-contained docs, the high points are:
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### Numerical Shape
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A comma-separated list of integers.
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For a 2-dimensional, 3 x 4-shaped array:
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```python
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Shape["3, 4"]
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```
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### Wildcards
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Wildcards indicate a dimension can be any size
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For a 2-dimensional, 3 x any-shaped array:
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```python
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Shape["3, *"]
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```
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(shape-ranges)=
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### Ranges
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Dimension sizes can also be specified as ranges[^ranges].
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Ranges must have no whitespace, and may use integers or wildcards.
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Range specifications are **inclusive** on both ends.
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For an array whose...
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- First dimension can be of length 2, 3, or 4
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- Second dimension is 2 or greater
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- Third dimension is 4 or less
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```python
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Shape["2-4, 2-*, *-4"]
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```
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[^ranges]: This is an extension to nptyping's syntax, and so using `nptyping.Shape` is unsupported - use {class}`numpydantic.Shape`
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### Labels
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Dimensions can be given labels, and in future versions these labels will be
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propagated to the generated JSON Schema
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```python
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Shape["3 x, 4 y, 5 z"]
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```
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### Arbitrary dimensions
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After some specified dimensions, one can express that there can be any number
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of additional dimensions with an `...` like
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```python
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Shape["3, 4, ..."]
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```
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### Any-Shaped
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If `dtype` is also `Any`, one can just use
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```python
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field: NDArray
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```
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If a `dtype` is being passed, use the `'*'` wildcard along with the `'...'`
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```python
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field: NDArray[Shape['*, ...'], int]
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```
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## Caveats
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```{todo}
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numpydantic currently does not support structured dtypes or {class}`numpy.recarray`
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specifications like nptyping does. It will in future versions.
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```
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````{todo}
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numpydantic also does not support the variable shape definition form like
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```python
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Shape['Dim, Dim']
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```
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where there are two dimensions of any shape as long as they are equal
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because at the moment it appears impossible to express dynamic constraints
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(ie. `minItems`/`maxItems` that depend on the shape of another array)
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in JSON Schema. A future minor version will allow them by generating a JSON
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schema with a warning that the equal shape constraint will not be represented.
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See: https://github.com/orgs/json-schema-org/discussions/730
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```` |