mgnolia

Declarative filesystem structure and content validation for Python.

Define the expected layout of a directory using Dir and File nodes, optionally attach content validation rules, then call validate_all() to check a real directory against your schema. All errors are collected and returned as typed objects—nothing raises.

Installation

pip:

pip install mgnolia

uv:

uv add mgnolia

Quick Start

from mgnolia import Dir, File, Schema

# Define expected structure
schema = Schema(
    children=[
        Dir(
            path="data",
            description="Data directory",
            children=[
                File(path="input.csv"),
                File(path="output.json"),
            ],
        ),
        File(path="config.yaml"),
    ]
)

# Validate a directory
ok = schema.validate_all("/path/to/project")

if not ok:
    for error in schema.errors:
        print(error)
else:
    print("All checks passed!")

Content Rules

Attach validation rules to files to check their contents:

from mgnolia import File
from mgnolia.content import FileNotEmptyRule, CSVSchemaRule
from pydantic import BaseModel

class UserRow(BaseModel):
    id: int
    name: str
    email: str

File(
    path="users.csv",
    content_rules=[
        FileNotEmptyRule(),
        CSVSchemaRule(schema=UserRow),
    ],
)

Built-in rules:

  • FileNotEmptyRule() — fails if file is empty
  • CSVSchemaRule(schema=PydanticModel) — validates CSV against a Pydantic model using pandera

Error Types

When validation fails, schema.errors contains typed error objects:

  • Structure errors: FileMissingError, DirectoryMissingError, MinMatchError, MaxMatchError
  • Content errors: FileEmptyError, ContentSchemaError

All inherit from ValidationError and have .path and .node attributes. Use str(error) to get a formatted message.

Pattern Matching

Dir and File paths support glob patterns to match multiple files or directories:

File(path="*.log", min_matches=1)  # At least one .log file
Dir(path="test_*", max_matches=5)  # At most 5 test_* directories

License

MIT

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.4.0)

Owner

PyPI data
Data sourced directly from PyPI's database.

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for arjkvc from gravatar.com arjkvc

Credits

Author: Aleksandar

License

MIT License

Requires

Python >=3.13

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