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117 changes: 117 additions & 0 deletions codecs/reshape/README.md
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# reshape codec

Defines an `array -> array` codec that performs a reshaping operation.

Note that `reshape` always preserves the (logical) lexicographical order (i.e. C
order traversal) of elements within an array, but may be combined with the
`transpose` codec to both reorder and reshape an array.

## Codec name

The value of the `name` member in the codec object MUST be `reshape`.

## Configuration parameters

### `shape`
An array specifying the size `B_shape[i]` of each dimension `i` of
the *output* array `B` as a function of the shape `A_shape` of the input array
`A`. Each element `shape[i]` must be one of:

- a positive integer `size`, specifying that `B_shape[i] := size`.

- an array of integers `input_dims`, specifying that:

```
B_shape[i] := prod(A_shape[input_dims]]
```.
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Specifying the corresponding `input_dims` rather than an explicit `size` is
paticularly useful when using variable-size chunking.
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- the special value `-1`, which must occur at most once, specifying that
`B_shape[i]` is determined automatically in order to satisfy the
invariant that `prod(B_shape) == prod(A_shape)`.

Implementations MUST return an error if the invariant

```
prod(B_shape) == prod(A_shape)
```

cannot be satisfied.

Additionally, when `shape[i]` is specified as an array of integers `input_dims`,
implementations MUST return an error if the following constraints are not
satisfied:

- the flattened list of input dimensions, over all elements of `shape`, must be
in monotonically increasing order, i.e. `"shape": [[0, 1], 10, [3, 4]]` is
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allowed but the following are NOT allowed:

- `"shape": [[1], [0]]`
- `"shape": [[1, 0], 10, [3, 4]]`
- `"shape": [[3, 4], 10, [0, 1]]` are not allowed.

This constraint serves to avoid confusing `shape` configurations that may
(incorrectly) suggest a transpose, when in fact the `reshape` codec never
performs a transpose.

- If `input_dims` specifies `k > 0` input dimensions:

`prod(B_shape[:i]) == prod(A_shape[input_dims[0]])` and
`prod(B_shape[i+1:]) == prod(A_shape[input_dims[k-1]+1:])`.

This two constraints ensure that if the size of output dimension `i` is
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specified by `input_dims`, the coordinates in the input array along `input_dims`
actually correspond to the raveled index along output dimension `i`.

## Example

For example, the array metadata below specifies that the compressor is the Zstd
codec configured with a compression level of 1 and with the checksum stored:
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```json
{
"chunk_grid": {
"name": "regular",
"configuration": {
"chunk_shape": [100, 50, 64, 3]
}
},
"codecs": [
{
"name": "reshape",
"configuration": {
"shape": [[0, 1], [2], 3]
}
},
{
"name": "bytes"

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It'd be good to demo high-dimensional zfp since it is a motivating use case for this codec

"configuration": {"endian": "little"}
}
]
}
```

## Format and algorithm

This is an `array -> array` codec.

The dimensionality of the output array `B` is equal to the length of the `shape`
configuration parameter, and the output shape `B_shape` is determined as
specified above.

As this codec does NOT alter the lexicographical order of elements, the contents
of the output array `B` is related to the contents of the input array `A` by:
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`ravel(B) == ravel(A)`.

Implementations should, when possible, construct a virtual view rather than copy
the array.

## Change log

No changes yet.

## Current maintainers

* Jeremy Maitin-Shepard ([@jbms](https://github.com/jbms)), Google
27 changes: 27 additions & 0 deletions codecs/reshape/schema.json
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{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"name": {
"const": "reshape"
},
"configuration": {
"type": "object",
"properties": {
"shape": {
"type": "array",
"items": {
"oneOf": [
{"type": "integer", "minimum": -1},
{"type": "array", "items": {"type": "integer", "minimum": 0}}
]
}
}
},
"required": ["shape"],
"additionalProperties": false
}
},
"required": ["name", "configuration"],
"additionalProperties": false
}