| .. | ||
| __init__.py | ||
| color.py | ||
| content.py | ||
| cryptography.py | ||
| lifecycle.py | ||
| meta.py | ||
| README.md | ||
| representation.py | ||
| three_dimensional.py | ||
| time.py | ||
| trait.py | ||
| two_dimensional.py | ||
| utils.py | ||
Representations and traits
Introduction
The Representation is the lowest level entity, describing the concrete data chunk that pipeline can act on. It can be specific file or just a set of metadata. Idea is that one product version can have multiple representations - Image product can be jpeg or tiff, both formats are representation of the same source.
Brief look into the past (and current state)
So far, representation was defined as dict-like structure:
{
"name": "foo",
"ext": "exr",
"files": ["foo_001.exr", "foo_002.exr"],
"stagingDir": "/bar/dir"
}
This is minimal form, but it can have additional keys like frameStart, fps, resolutionWidth, and more. Thare is also tags key that can hold review, thumbnail, delete, toScanline and other tag that are controlling the processing.
This will be "translated" to similar structure in database:
{
"name": "foo",
"version_id": "...",
"files": [
{
"id": ...,
"hash": ...,
"name": "foo_001.exr",
"path": "{root[work]}/bar/dir/foo_001.exr",
"size": 1234,
"hash_type": "...",
},
...
],
"attrib": {
"path": "root/bar/dir/foo_001.exr",
"template": "{root[work]}/{project[name]}...",
},
"data": {
"context": {
"ext": "exr",
"root": {...},
...
},
"active": True
...
}
There are also some assumptions and limitations - like that if files in the
representation are list they need to be sequence of files (it can't be a bunch of
unrelated files).
This system is very flexible in one way, but it lack few very important things:
- it is not clearly defined - you can add easily keys, values, tags but without unforeseeable consequences
- it cannot handle "bundles" - multiple files that needs to be versioned together and belong together
- it cannot describe important information that you can't get from the file itself or it is very pricy (like axis orientation and units from alembic files)
New Representation model
The idea about new representation model is obviously around solving points mentioned above and also adding some benefits, like consistent IDE hints, typing, built-in validators and much more.
Design
The new representation is "just" a dictionary of traits. Trait can be anything provided
it is based on TraitBase. It shouldn't really duplicate information that is
available in a moment of loading (or any usage) by other means. It should contain
information that couldn't be determined by the file, or the AYON context. Some of
those traits are aligned with OpenAssetIO Media Creation with hopes of maintained compatibility (it
should be easy enough to convert between OpenAssetIO Traits and AOYN Traits).
Details: Representation
Representation has methods to deal with adding, removing, getting
traits. It has all the usual stuff like get_trait(), add_trait(),
remove_trait(), etc. But it also has plural forms so you can get/set
several traits at the same time with get_traits() and so on.
Representation also behaves like dictionary. so you can access/set
traits in the same way as you would do with dict:
# import Image trait
from ayon_core.pipeline.traits import Image, Tagged, Representation
# create new representation with name "foo" and add Image trait to it
rep = Representation(name="foo", traits=[Image()])
# you can add another trait like so
rep.add_trait(Tagged(tags=["tag"]))
# or you can
rep[Tagged.id] = Tagged(tags=["tag"])
# and getting them in analogous
image = rep.get_trait(Image)
# or
image = rep[Image.id]
Note
Trait and their ids - every Trait has its id as s string with version appended - so Image has
ayon.2d.Image.v1. This is is used on several places (you see its use above for indexing traits). When querying, you can also omit the version at the end and it will try its best to find the latest possible version. More on that in Traits
You can construct the Representation from dictionary (for example
serialized as JSON) using Representation.from_dict(), or you can
serialize Representation to dict to store with Representation.traits_as_dict().
Every time representation is created, new id is generated. You can pass existing id when creating new representation instance.
Equality
Two Representations are equal if:
- their names are the same
- their IDs are the same
- they have the same traits
- the traits have the same values
Validation
Representation has validate() method that will run validate() on
all it's traits.
Details: Traits
As mentioned there are several traits defined directly in ayon-core. They are namespaced to different packages based on their use:
| namespace | trait | description |
|---|---|---|
| color | ColorManaged | hold color management information |
| content | MimeType | use MIME type (RFC 2046) to describe content (like image/jpeg) |
| LocatableContent | describe some location (file or URI) | |
| FileLocation | path to file, with size and checksum | |
| FileLocations | list of FileLocation |
|
| RootlessLocation | Path where root is replaced with AYON root token | |
| Compressed | describes compression (of file or other) | |
| Bundle | list of list of Traits - compound of inseparable "sub-representations" | |
| Fragment | compound type marking the representation as a part of larger group of representations | |
| cryptography | DigitallySigned | Type traits marking data to be digitally signed |
| PGPSigned | Representation is signed by PGP | |
| lifecycle | Transient | Marks the representation to be temporary - not to be stored. |
| Persistent | Representation should be integrated (stored). Opposite of Transient. | |
| meta | Tagged | holds list of tag strings. |
| TemplatePath | Template consisted of tokens/keys and data to be used to resolve the template into string | |
| Variant | Used to differentiate between data variants of the same output (mp4 as h.264 and h.265 for example) | |
| KeepOriginalLocation | Marks the representation to keep the original location of the file | |
| KeepOriginalName | Marks the representation to keep the original name of the file | |
| three dimensional | Spatial | Spatial information like up-axis, units and handedness. |
| Geometry | Type trait to mark the representation as a geometry. | |
| Shader | Type trait to mark the representation as a Shader. | |
| Lighting | Type trait to mark the representation as Lighting. | |
| IESProfile | States that the representation is IES Profile | |
| time | FrameRanged | Contains start and end frame information with in and out. |
| Handless | define additional frames at the end or beginning and if those frames are inclusive of the range or not. | |
| Sequence | Describes sequence of frames and how the frames are defined in that sequence. | |
| SMPTETimecode | Adds timecode information in SMPTE format. | |
| Static | Marks the content as not time-variant. | |
| two dimensional | Image | Type traits of image. |
| PixelBased | Defines resolution and pixel aspect for the image data. | |
| Planar | Whether pixel data is in planar configuration or packed. | |
| Deep | Image encodes deep pixel data. | |
| Overscan | holds overscan/underscan information (added pixels to bottom/sides) | |
| UDIM | Representation is UDIM tile set |
Traits are Pydantic models with optional
validation and helper methods. If they implement TraitBase.validate(Representation) method, they can validate against all other traits
in the representation if needed. They can also implement pydantic form of
data validators.
Note
Every trait has id, name and some human readable description. Every trait also has
persistentproperty that is by default set to True. This Controls whether this trait should be stored with the persistent representation or not. Useful for traits to be used just to control the publishing process.
Examples
Create simple image representation to be integrated by AYON:
from pathlib import Path
from ayon_core.pipeline.traits import (
FileLocation,
Image,
PixelBased,
Persistent,
Representation,
Static,
)
rep = Representation(name="reference image", traits=[
FileLocation(
file_path=Path("/foo/bar/baz.exr"),
file_size=1234,
file_hash="sha256:...",
),
Image(),
PixelBased(
display_window_width=1920,
display_window_height=1080,
pixel_aspect_ratio=1.0,
),
Persistent(),
Static()
])
# validate the representation
try:
rep.validate()
except TraitValidationError as e:
print(f"Representation {rep.name} is invalid: {e}")
To work with the resolution of such representation:
try:
width = rep.get_trait(PixelBased).display_window_width
# or like this:
height = rep[PixelBased.id].display_window_height
except MissingTraitError:
print(f"resolution isn't set on {rep.name}")
Accessing non-existent traits will result in exception. To test if
representation has some specific trait, you can use .contains_trait() method.
You can also prepare the whole representation data as a dict and create it from it:
rep_dict = {
"ayon.content.FileLocation.v1": {
"file_path": Path("/path/to/file"),
"file_size": 1024,
"file_hash": None,
},
"ayon.two_dimensional.Image": {},
"ayon.two_dimensional.PixelBased": {
"display_window_width": 1920,
"display_window_height": 1080,
"pixel_aspect_ratio": 1.0,
},
"ayon.two_dimensional.Planar": {
"planar_configuration": "RGB",
}
}
rep = Representation.from_dict(name="image", rep_dict)
Addon specific traits
Addon can define its own traits. To do so, it needs to implement ITraits interface:
from ayon_core.pipeline.traits import TraitBase
from ayon_core.addon import (
AYONAddon,
ITraits,
)
class MyTraitFoo(TraitBase):
id = "myaddon.mytrait.foo.v1"
name = "My Trait Foo"
description = "This is my trait foo"
persistent = True
class MyTraitBar(TraitBase):
id = "myaddon.mytrait.bar.v1"
name = "My Trait Bar"
description = "This is my trait bar"
persistent = True
class MyAddon(AYONAddon, ITraits):
def __init__(self):
super().__init__()
def get_addon_traits(self):
return [
MyTraitFoo,
MyTraitBar,
]