cfx 0.1.0.post1

Lightweight self-documenting configuration classes via Python descriptors.

cfg

Declare configuration fields next to the classes that use them. Each field carries its own default value, type checking, and documentation.

from cfg import Config, Float, Int, Options, Bool

class RunConfig(Config):
    """Configuration for a data-processing run."""
    confid = "run"
    iterations = Int(100, "Number of processing iterations", minval=1)
    threshold = Float(0.5, "Acceptance threshold", minval=0.0, maxval=1.0)
    mode = Options(("fast", "balanced", "thorough"), "Processing mode")
    verbose = Bool(False, "Enable verbose logging")

cfg = RunConfig()
print(cfg)

RunConfig:
Configuration for a data-processing run.
Key        | Value | Description
-----------+-------+----------------------------------
iterations | 100   | Number of processing iterations
threshold  | 0.5   | Acceptance threshold
mode       | fast  | Processing mode
verbose    | False | Enable verbose logging

What you get

• Validated fields — typos and bad values raise immediately at the point of assignment, not silently hours later.
• Self-documenting — print(cfg) and Jupyter repr show a table of every field with its current value and description.
• Composable — assemble configs from multiple subsystem configs, either flat (all fields in one namespace) or nested (sub-objects by name).
• Serializable — round-trip to/from dict, YAML, and TOML with one method call.
• Extensible — subclass ConfigField to add your own field types with custom validation and normalization.
• Zero hard dependencies — YAML and TOML support are optional soft dependencies.

Installation

pip install cfg

With optional serialization backends:

pip install "cfg[yaml]"   # adds PyYAML
pip install "cfg[toml]"   # adds tomli-w
pip install "cfg[all]"    # both

Quick start

from cfg import Config, Int, Float, String, Options, Bool, Path

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)}

Composition

Combine configs into nested sub-objects or a flat merged namespace:

from cfg import Config, Int, String

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

class PipelineConfig(Config, components=[ProcessingConfig, FormatConfig]):
    pass

cfg = PipelineConfig()
cfg.processing.iterations = 500
cfg.format.precision = 3

License

GPL-3.0-only — see LICENSE.