cfx 0.5.0

Lightweight self-documenting configuration classes via Python descriptors.

cfx

Declare configuration fields next to the classes that use them. Each field carries its own default, type checking, and documentation. Compose any set of configs into a larger one, nested or flat, and get serialization, CLI integration, and a self-documenting display for free.

from cfx import Config, Float, Int, String, Bool

class FormatConfig(Config):
    """Output formatting."""
    confid = "format"
    precision = Int(6, "Decimal places")
    encoding = String("utf-8", "Output encoding")

class WorkerConfig(Config, components=[FormatConfig]):
    """Worker settings."""
    confid = "worker"
    threads = Int(4, "Worker threads", minval=1)
    timeout = Float(30.0, "Request timeout in seconds", minval=0.0)

class AppConfig(Config, components=[WorkerConfig]):
    """Application configuration."""
    confid = "app"
    name = String("myapp", "Application name")
    debug = Bool(False, "Enable debug output")

cfg = AppConfig()
print(cfg)

AppConfig: Application configuration.
└─ WorkerConfig: Worker settings.
    └─ FormatConfig: Output formatting.
Config       | Key       | Value | Description
-------------+-----------+-------+---------------------------
AppConfig    | name      | myapp | Application name
AppConfig    | debug     | False | Enable debug output
WorkerConfig | threads   | 4     | Worker threads
WorkerConfig | timeout   | 30.0  | Request timeout in seconds
FormatConfig | precision | 6     | Decimal places
FormatConfig | encoding  | utf-8 | Output encoding

• Validated fields — typos and bad values raise immediately at the point of assignment, not silently hours later.
• Self-documenting — print(cfg) renders a tree of the config hierarchy followed by a unified table of all fields, nested included. In Jupyter the same layout renders as HTML automatically via _repr_html_.
• Composable — assemble configs from multiple subsystem configs, nested or flat, with serialization, display, and CLI support throughout.
• Views — project a config tree into a custom namespace. Expose a curated subset of fields under new names with ConfigView, auto-generate prefixed aliases with AliasedView, or enforce that two fields always stay in sync with Mirror.
• Serializable — round-trip to/from dict, YAML, and TOML with one method call.
• CLI-ready — every config exposes add_arguments / from_argparse for argparse and click_options / from_click for Click. Nested sub-configs use dot-notation flags (e.g. --worker.threads).
• Extensible — subclass ConfigField to add your own field types with custom validation and normalization.
• Zero hard dependencies — YAML, TOML, and Click support are optional.

Installation

pip install cfx

With optional serialization and CLI backends:

pip install "cfx[yaml]"   # adds PyYAML
pip install "cfx[toml]"   # adds tomli-w
pip install "cfx[click]"  # adds Click
pip install "cfx[all]"    # everything

Quick start

from cfx import Config, Int, Float, String, Options, Bool

class ProcessingConfig(Config):
    confid = "processing"
    iterations = Int(100, "Number of iterations", minval=1)
    threshold = Float(0.5, "Acceptance threshold", minval=0.0, maxval=1.0)
    label = String("run_01", "Human-readable run label")
    mode = Options(("fast", "balanced", "thorough"), "Processing mode")
    verbose = Bool(False, "Enable verbose logging")

cfg = ProcessingConfig()

# Dot access and dict-style access are interchangeable
cfg.iterations = 200
cfg["mode"] = "thorough"

# Bad values raise immediately
cfg.threshold = 1.5   # ValueError: Expected value <= 1.0, got 1.5

# Serialize to dict, YAML, or TOML
d = cfg.to_dict()
cfg2 = ProcessingConfig.from_dict(d)

# Copy with overrides, diff two instances
modified = cfg.copy(iterations=500)
cfg.diff(modified)   # {'iterations': (200, 500)}

Views

Project any config tree into a custom namespace — useful when the internal structure is more complex than what a consumer needs to see:

from cfx import Config, Int, Float, String, ConfigView, Alias, AliasedView

class ProcessingConfig(Config):
    confid = "processing"
    iterations = Int(100, "Number of iterations")
    threshold = Float(0.5, "Acceptance threshold")

class FormatConfig(Config):
    confid = "format"
    precision = Int(6, "Decimal places")
    encoding = String("utf-8", "Output encoding")

class PipelineConfig(Config, components=[ProcessingConfig, FormatConfig]):
    confid = "pipeline"

# Hand-written curated view
class RunSummary(ConfigView):
    n_iter   = Alias(PipelineConfig.processing.iterations)
    decimals = Alias(PipelineConfig.processing.threshold)

cfg = PipelineConfig()
v = RunSummary(cfg)
v.n_iter    # 100
v.n_iter = 500
cfg.processing.iterations  # 500 — write goes through

# Auto-generated prefixed view (owns its own component instances)
class JobView(AliasedView, components=[ProcessingConfig, FormatConfig]):
    pass

jv = JobView()
jv.processing_iterations = 300
jv.format_precision = 3

Environment variables

Back any field with an environment variable — the value is read lazily, so the same class works across environments without subclassing:

class ServiceConfig(Config):
    host = String("localhost", "Database host", env="DB_HOST")
    port = Int(5432, "Port", env="DB_PORT")

# DB_HOST=prod.example.com python run.py
cfg = ServiceConfig()
cfg.host   # 'prod.example.com' — from env, no code change needed

CLI

Every config exposes add_arguments / from_argparse for argparse and click_options / from_click for Click. Nested sub-configs use dot-notation flags automatically:

import argparse

parser = argparse.ArgumentParser()
ProcessingConfig.add_arguments(parser)
cfg = ProcessingConfig.from_argparse(parser.parse_args())
# python run.py --iterations 200 --mode thorough --no-verbose

License

GPL-3.0-only — see LICENSE.