objectstate

Generic lazy dataclass configuration framework with dual-axis inheritance

Features

• Lazy Dataclass Factory: Dynamically create dataclasses with lazy field resolution
• Dual-Axis Inheritance:
  • X-Axis: Context hierarchy (Step → Pipeline → Global)
  • Y-Axis: Sibling inheritance within same context
• Contextvars-Based: Uses Python's contextvars for clean context management
• UI Integration: Placeholder text generation for configuration forms
• Thread-Safe: Thread-local global configuration storage
• 100% Generic: No application-specific dependencies
• Pure Stdlib: No external dependencies

Quick Start

Simple Usage (Manual Factory)

from dataclasses import dataclass
from objectstate import LazyDataclassFactory, config_context, set_base_config_type

# Define your base configuration
@dataclass
class MyConfig:
    output_dir: str = "/tmp"
    num_workers: int = 4
    debug: bool = False

# Initialize the framework with your base config type
set_base_config_type(MyConfig)

# Create lazy version manually
LazyMyConfig = LazyDataclassFactory.make_lazy_simple(MyConfig)

# Use with context
concrete_config = MyConfig(output_dir="/data", num_workers=8)

with config_context(concrete_config):
    lazy_cfg = LazyMyConfig()
    print(lazy_cfg.output_dir)  # "/data" (resolved from context)
    print(lazy_cfg.num_workers)  # 8 (resolved from context)
    print(lazy_cfg.debug)        # False (inherited from defaults)

Setting Up Global Config Context (For Advanced Usage)

When using the decorator pattern with auto_create_decorator, you need to establish the global configuration context for lazy resolution:

from objectstate import ensure_global_config_context

# After creating your global config instance
global_config = GlobalPipelineConfig(
    num_workers=8,
    # ... other fields
)

# REQUIRED: Establish global config context for lazy resolution
ensure_global_config_context(GlobalPipelineConfig, global_config)

# Now lazy configs can resolve from the global context

Key differences:

• set_base_config_type(MyConfig): Sets the type (class) for the framework
• ensure_global_config_context(GlobalConfig, instance): Sets the instance (concrete values) for resolution
• Call ensure_global_config_context() at application startup (GUI) or before pipeline execution

Installation

pip install objectstate

ObjectState Registry

ObjectState separates mutable working state from saved baseline, enabling dirty tracking and undo/redo:

from objectstate import ObjectState, ObjectStateRegistry

# Register an object (e.g., when added to pipeline)
state = ObjectState(my_step_config, scope_id="/pipeline::step_0")
ObjectStateRegistry.register(state)

# Query the registry
state = ObjectStateRegistry.get_by_scope("/pipeline::step_0")
all_states = ObjectStateRegistry.get_all()

# Update a parameter (marks field as dirty)
state.update_parameter("output_dir", "/new/path")

# Check dirty state
if state.dirty_fields:
    print(f"Unsaved changes: {state.dirty_fields}")

# Save changes (updates baseline)
state.save()

# Or restore to saved baseline
state.restore_saved()

# Unregister when removed
ObjectStateRegistry.unregister(state)

Undo/Redo and Time Travel

Git-like DAG history with branching timelines:

from objectstate import ObjectStateRegistry

# Undo/redo (automatically recorded on parameter changes)
ObjectStateRegistry.undo()
ObjectStateRegistry.redo()

# Batch multiple changes into one undo step
with ObjectStateRegistry.atomic("add item"):
    ObjectStateRegistry.register(item_state)
    parent_state.update_parameter("items", new_items)

# Time travel to specific point
history = ObjectStateRegistry.get_branch_history()
ObjectStateRegistry.time_travel_to_snapshot(history[5].id)
ObjectStateRegistry.time_travel_to_head()  # Return to latest

# Branching timelines
ObjectStateRegistry.create_branch("experiment", description="Testing new approach")
ObjectStateRegistry.switch_branch("main")
branches = ObjectStateRegistry.list_branches()

# Persist history
history_dict = ObjectStateRegistry.export_history_to_dict()
ObjectStateRegistry.import_history_from_dict(history_dict)

# Or save to file
ObjectStateRegistry.save_history_to_file("history.json")
ObjectStateRegistry.load_history_from_file("history.json")

Automatic Lazy Config Generation with Decorators

For more complex applications with multiple config types, use the auto_create_decorator pattern to automatically generate lazy versions and field injection decorators:

from dataclasses import dataclass
from objectstate import auto_create_decorator, config_context

# Step 1: Create a global config class with "Global" prefix and apply auto_create_decorator
@auto_create_decorator
@dataclass
class GlobalPipelineConfig:
    base_output_dir: str = "/tmp"
    verbose: bool = False

# This automatically creates:
# - A decorator named `global_pipeline_config` (snake_case of class name)
#   that you can use to decorate other config classes
# - A lazy class `PipelineConfig` (removes "Global" prefix) for lazy resolution

# Step 2: Use the generated decorator on other config classes
@global_pipeline_config  # Automatically creates LazyStepConfig
@dataclass
class StepConfig:
    step_name: str = "default_step"
    iterations: int = 100

@global_pipeline_config  # Automatically creates LazyDatabaseConfig  
@dataclass
class DatabaseConfig:
    host: str = "localhost"
    port: int = 5432

# The decorator automatically:
# - Creates lazy versions: LazyStepConfig, LazyDatabaseConfig
# - Registers them for potential field injection into GlobalPipelineConfig
# - Makes them available in your module namespace

Key Benefits:

• Auto-generated lazy classes: Each decorated config automatically gets a lazy version
• Simplified imports: Lazy classes are automatically added to your module
• Decorator factory: auto_create_decorator generates a decorator specific to your global config
• Type-safe: All generated classes are proper dataclasses with full IDE support

Field Injection Behavior

When you use the generated decorator (e.g., @global_pipeline_config), the decorated class is automatically injected as a field into the global config class:

from dataclasses import dataclass
from objectstate import auto_create_decorator

# Create global config with auto_create_decorator
@auto_create_decorator
@dataclass
class GlobalPipelineConfig:
    num_workers: int = 1

# This creates:
# - A decorator named `global_pipeline_config`
# - A lazy class named `PipelineConfig`

# Use the decorator on a new config class
@global_pipeline_config
@dataclass
class WellFilterConfig:
    well_filter: str = None
    mode: str = "include"

# After module loading, GlobalPipelineConfig automatically has:
# - well_filter_config: WellFilterConfig = WellFilterConfig()
# And LazyWellFilterConfig is auto-created

How it works:

• Decorated classes are injected as fields into GlobalPipelineConfig
• Field name is snake_case of class name (e.g., WellFilterConfig → well_filter_config)
• Lazy version is automatically created (e.g., LazyWellFilterConfig)
• Injection happens at end of module loading via _inject_all_pending_fields()

This enables a clean, modular configuration structure where each component's config is automatically part of the global configuration.

Decorator Parameters

The generated decorator (e.g., @global_pipeline_config) supports optional parameters:

inherit_as_none (Default: True)

Sets all inherited fields from parent classes to None by default, enabling proper lazy resolution:

@dataclass
class BaseConfig:
    timeout: int = 30
    retries: int = 3

@global_pipeline_config(inherit_as_none=True)  # Default behavior
@dataclass
class ServiceConfig(BaseConfig):
    service_name: str = "my-service"
    # timeout and retries automatically set to None for lazy inheritance

# This allows ServiceConfig to inherit timeout/retries from context
# rather than using the base class defaults

Why this matters:

• Enables polymorphic access without type-specific attribute names
• Critical for dual-axis inheritance with multiple inheritance
• Uses InheritAsNoneMeta metaclass internally

ui_hidden (Default: False)

Hides configs from UI while still applying decorator behavior and keeping them in the resolution context:

@global_pipeline_config(ui_hidden=True)
@dataclass
class InternalConfig:
    internal_setting: str = "hidden"
    # This config won't appear in UI but is still available for inheritance

Use cases:

• Intermediate configs that are only inherited by other configs
• Internal implementation details not meant for user configuration
• Base classes that should never be directly instantiated in UI

Nested Dataclass Lazification

When creating a lazy dataclass, nested dataclass fields are automatically converted to their lazy versions:

from dataclasses import dataclass
from objectstate import LazyDataclassFactory

@dataclass
class DatabaseConfig:
    host: str = "localhost"
    port: int = 5432

@dataclass
class AppConfig:
    db_config: DatabaseConfig = DatabaseConfig()
    app_name: str = "MyApp"

# Create lazy version - nested configs are automatically lazified
LazyAppConfig = LazyDataclassFactory.make_lazy_simple(AppConfig)

# The db_config field is automatically converted to LazyDatabaseConfig
# You don't need to manually create LazyDatabaseConfig first!

Benefits:

• No need to manually create lazy versions of nested configs
• Preserves field metadata (e.g., ui_hidden flag)
• Creates default factories for Optional dataclass fields
• Uses register_lazy_type_mapping() internally

Why objectstate?

Before (Manual parameter passing):

def process_step(data, output_dir, num_workers, debug, ...):
    # Pass 20+ parameters through every function
    result = sub_process(data, output_dir, num_workers, debug, ...)
    return result

def sub_process(data, output_dir, num_workers, debug, ...):
    # Repeat parameter declarations everywhere
    ...

After (objectstate):

@dataclass
class StepConfig:
    output_dir: str = None
    num_workers: int = None
    debug: bool = None

def process_step(data, config: LazyStepConfig):
    # Config fields resolve automatically from context
    print(config.output_dir)  # Resolved from context hierarchy
    result = sub_process(data, config)
    return result

Advanced Features

Dual-Axis Inheritance

# X-Axis: Context hierarchy
with config_context(global_config):
    with config_context(pipeline_config):
        with config_context(step_config):
            # Resolves: step → pipeline → global → defaults
            value = objectstate.some_field

# Y-Axis: Sibling inheritance (MRO-based)
@dataclass
class BaseConfig:
    field_a: str = "base"

@dataclass
class SpecializedConfig(BaseConfig):
    field_b: str = "specialized"

# SpecializedConfig inherits field_a from BaseConfig

Accessing Resolved Values

from objectstate import ObjectState, ObjectStateRegistry

# ObjectState stores both saved baseline and live (edited) values
state = ObjectStateRegistry.get_by_scope("/pipeline::step_0")

# Access resolved value (from _live_resolved cache)
output_dir = state.get_resolved_value("output_dir")

# Check if field is dirty (live != saved)
is_dirty = "output_dir" in state.dirty_fields

# Get provenance (where did this value come from?)
source_scope, source_type = state.get_resolved_provenance("output_dir")
# Returns: ("/pipeline", GlobalPipelineConfig) if inherited from pipeline

Architecture

Dual-Axis Resolution

The framework uses pure MRO-based dual-axis resolution:

X-Axis (Context Hierarchy):

Step Context → Pipeline Context → Global Context → Static Defaults

Y-Axis (MRO Traversal):

Most specific class → Least specific class (following Python's MRO)

How it works:

1. Context hierarchy is flattened into a single available_configs dict
2. For each field resolution, traverse the requesting object's MRO from most to least specific
3. For each MRO class, check if there's a config instance in available_configs with a concrete (non-None) value
4. Return the first concrete value found

Parametric Axes Prototype (PEP Draft)

The parametric_axes module demonstrates extending Python's type system with arbitrary semantic axes beyond (B, S):

from objectstate.parametric_axes import AxesBase

class Step(AxesBase):
    pass

class MyStep(Step, axes={"scope": "/pipeline/step_0", "registry": "handlers"}):
    pass

MyStep.__axes__  # {'scope': '/pipeline/step_0', 'registry': 'handlers'}
MyStep.__scope__  # '/pipeline/step_0' (convenience attribute)

This works TODAY via __init_subclass__ (PEP 487) - no grammar changes required.

Three usage patterns:

• AxesBase inheritance: class Foo(Base, axes={...}) (preferred)
• Factory function: axes_type("Foo", (Base,), {}, scope="...", registry="...")
• Decorator: @with_axes(scope="...", registry="...") (when base can't be modified)

Try It Out

Run the tests to see the prototype in action:

# Run all parametric axes tests
python -m pytest tests/test_parametric_axes.py -v

# Interactive exploration
python -c "
from objectstate.parametric_axes import AxesBase, axes_type, with_axes, get_axes

# Pattern 1: Class statement syntax (preferred)
class Step(AxesBase):
    pass

class ProcessingStep(Step, axes={'scope': '/pipeline/step_0', 'registry': 'processing'}):
    def process(self):
        return 'processed'

print(f'ProcessingStep.__axes__ = {dict(ProcessingStep.__axes__)}')
print(f'ProcessingStep.__scope__ = {ProcessingStep.__scope__}')

# Pattern 2: Factory function (mimics extended type())
Handler = axes_type('Handler', (), {}, format='imagexpress', version=2)
print(f'Handler.__axes__ = {Handler.__axes__}')

# Pattern 3: Decorator (when base can't be modified)
@with_axes(scope='/decorated', priority=10)
class DecoratedStep:
    pass
print(f'DecoratedStep.__axes__ = {DecoratedStep.__axes__}')

# MRO-based axis resolution with multiple inheritance
class A(AxesBase, axes={'x': 1, 'from_a': True}):
    pass
class B(AxesBase, axes={'x': 2, 'from_b': True}):
    pass
class C(A, B):  # x=1 from A (leftmost in MRO)
    pass
print(f'C.__axes__ = {dict(C.__axes__)}')  # x=1, from_a=True, from_b=True
"

See src/objectstate/parametric_axes.py for full implementation and docstrings.

Reified Generics Prototype

The reified_generics module provides runtime-accessible type parameters for generics:

from objectstate.reified_generics import List, Dict

# Type parameters are preserved at runtime
IntList = List[int]
StrDict = Dict[str, float]

# Introspection works
IntList.__args__      # (int,)
IntList.__origin__    # list
StrDict.__args__      # (str, float)

# isinstance checks work with reified types
my_list = IntList([1, 2, 3])
isinstance(my_list, IntList)  # True
isinstance(my_list, List[str])  # False - different type parameter!

# Type caching ensures identity
List[int] is List[int]  # True - same object

Try It Out

# Run reified generics tests
python -m pytest tests/test_reified_generics.py -v

# Interactive exploration
python -c "
from objectstate.reified_generics import List, Dict, Set, Optional

# Create reified types
IntList = List[int]
StrIntDict = Dict[str, int]

print(f'IntList.__args__ = {IntList.__args__}')
print(f'IntList.__origin__ = {IntList.__origin__}')
print(f'StrIntDict.__args__ = {StrIntDict.__args__}')

# Type identity (caching)
print(f'List[int] is List[int]: {List[int] is List[int]}')
print(f'List[int] is List[str]: {List[int] is List[str]}')

# Create instances
my_list = IntList([1, 2, 3])
print(f'isinstance(my_list, IntList): {isinstance(my_list, IntList)}')
print(f'isinstance(my_list, List[str]): {isinstance(my_list, List[str])}')
"

See src/objectstate/reified_generics.py for full implementation.

Documentation

Full documentation available at objectstate.readthedocs.io

Requirements

• Python 3.11+
• No external dependencies (pure stdlib)

License

MIT License - see LICENSE file for details

Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

Credits

Developed by Tristan Simas as part of the OpenHCS project.