strictyamlx 0.1.1

An extension of StrictYAML that adds more expressive schema tools for validation.

strictyamlx

An extension of StrictYAML that adds more expressive schema tools for validation.

Installation

pip install strictyamlx

Features

DMap (Dynamic Map)

DMap allows you to validate YAML where the schema of a map depends on the value of one or more of its keys. It evaluates a "control" schema first, and uses its values to determine which Case schema applies. The final parsed object safely merges the validated control and case values.

from strictyamlx import Map, Str, Int, load, Control, Case, DMap

# Control schema evaluates the "action" key to route the document
ctrl = Control(Map({"action": Str()}))

# Blocks conditionally apply a schema based on the evaluated control value
blocks = [
    Case(
        when=lambda raw, ctrl: ctrl["action"] == "message", 
        schema=Map({"text": Str()})
    ),
    Case(
        when=lambda raw, ctrl: ctrl["action"] == "transfer", 
        schema=Map({"amount": Int(), "to": Str()})
    ),
]

# Create the schema 
schema = DMap(ctrl, blocks)

# Validation merges the control ("action") and case schema values ("text")
yaml_str = """
action: message
text: Hello!
"""
doc = load(yaml_str, schema)
assert doc.data == {"action": "message", "text": "Hello!"}

Using source in Control

Sometimes the values needed to determine the schema aren't placed at the root of the document, but nested inside another key (like "metadata"). You can use source to define where the Control values should be drawn from:

from strictyamlx import Map, Str, Int, load, Control, Case, DMap

# Evaluates the control values from the `meta` key
ctrl = Control(Map({"type": Str()}), source="meta")

blocks = [
    Case(
        when=lambda raw, ctrl: ctrl["type"] == "number", 
        schema=Map({"value": Int()})
    )
]

schema = DMap(ctrl, blocks)

yaml_str = """
meta:
  type: number
value: 42
"""
doc = load(yaml_str, schema)
assert doc.data == {"meta": {"type": "number"}, "value": 42}

Constraints

You can append constraints to Case blocks or globally on the DMap. Constraints are callables that validate the incoming data.

Case(
    when=lambda raw, ctrl: ctrl["action"] == "transfer",
    schema=Map({"amount": Int(), "from": Str(), "to": Str()}),
    constraints=[
        lambda raw, ctrl, validated: validated["amount"] > 0,
        lambda raw, ctrl, validated: validated["from"] != validated["to"]
    ]
)

Nesting DMaps

DMaps can nested to create complex state graphs. A Case block can even have another DMap as its schema!

from strictyamlx import Map, Str, Int, Control, Case, DMap

inner_schema = DMap(
    Control(Map({"subkind": Str()})),
    [
        Case(when=lambda raw, ctrl: ctrl["subkind"] == "V1", schema=Map({"v1": Int()})),
        Case(when=lambda raw, ctrl: ctrl["subkind"] == "V2", schema=Map({"v2": Str()})),
    ]
)

schema = DMap(
    Control(Map({"kind": Str()})),
    [
        Case(when=lambda raw, ctrl: ctrl["kind"] == "complex", schema=inner_schema),
        Case(when=lambda raw, ctrl: ctrl["kind"] == "simple", schema=Map({"value": Str()})),
    ]
)

ForwardRef

ForwardRef defines recursive or mutually dependent schemas, letting you use a schema component before it is fully defined.

from strictyamlx import Map, Str, Optional, Seq, load, ForwardRef

# 1. Create the reference
tree = ForwardRef()

# 2. Define the schema recursively and assign it using .set()
tree.set(Map({"name": Str(), Optional("children"): Seq(tree)}))

# 3. Validation handles recursive resolution automatically
yaml_str = """
name: root
children:
  - name: child
    children:
      - name: grandchild
"""
doc = load(yaml_str, tree)

DMaps with ForwardRef

You can use a ForwardRef inside DMap case schemas to easily build deep recursive dynamic behavior:

from strictyamlx import Map, Str, Int, load, Control, Case, DMap, ForwardRef

ref = ForwardRef()

schema = DMap(
    Control(Map({"type": Str()})),
    [
        Case(
            when=lambda raw, ctrl: ctrl["type"] == "node", 
            schema=Map({"value": Int(), "child": ref})
        ),
        Case(
            when=lambda raw, ctrl: ctrl["type"] == "leaf", 
            schema=Map({"value": Int()})
        ),
    ]
)

# Reference points to the DMap itself, allowing infinite nesting!
ref.set(schema)

yaml_str = """
type: node
value: 1
child:
  type: leaf
  value: 2
"""
doc = load(yaml_str, schema)