pathable 0.5.0

Object-oriented paths

pathable

About

Pathable provides a small set of "path" objects for traversing hierarchical data (mappings, lists, and other subscriptable trees) using a familiar path-like syntax.

It’s especially handy when you want to:

• express deep lookups as a single object (and pass it around)
• build paths incrementally (p / "a" / 0 / "b")
• safely probe (exists(), get(...)) or strictly require segments (//)

Key features

• Intuitive path-based navigation for nested data (e.g., dicts/lists)
• Pluggable accessor layer for custom backends
• Pythonic, chainable API for concise and readable code
• Per-instance (bounded LRU) cached lookup accessor for repeated reads of the same tree

Quickstart

from pathable import LookupPath

data = {
    "parts": {
        "part1": {"name": "Part One"},
        "part2": {"name": "Part Two"},
    }
}

root = LookupPath.from_lookup(data)

name = (root / "parts" / "part2" / "name").read_value()
assert name == "Part Two"

Usage

from pathable import LookupPath

data = {
    "parts": {
        "part1": {"name": "Part One"},
        "part2": {"name": "Part Two"},
    }
}

p = LookupPath.from_lookup(data)

# Concatenate path segments with /
parts = p / "parts"

# Check membership (mapping keys or list indexes)
assert "part2" in parts

# Read a value
assert (parts / "part2" / "name").read_value() == "Part Two"

# Iterate children as paths
for child in parts:
    print(child, child.read_value())

# Work with keys/items
print(list(parts.keys()))
print({k: v.read_value() for k, v in parts.items()})

# Safe access
print(parts.get("missing", default=None))

# Strict access (raises KeyError if missing)
must_exist = parts // "part2"

# "Open" yields the current value as a context manager
with parts.open() as parts_value:
    assert isinstance(parts_value, dict)

# Optional metadata
print(parts.stat())

Filesystem example

Pathable can also traverse the filesystem via an accessor.

from pathlib import Path

from pathable import FilesystemPath

root_dir = Path(".")
p = FilesystemPath.from_path(root_dir)

readme = p / "README.md"
if readme.exists():
    content = readme.read_value()  # bytes
    print(content[:100])

Core concepts

• BasePath is a pure path (segments + separator) with / joining.
• AccessorPath is a BasePath bound to a NodeAccessor, enabling read_value(), exists(), keys(), iteration, etc.
• FilesystemPath is an AccessorPath specialized for filesystem objects.
• LookupPath is an AccessorPath specialized for mapping/list lookups.

Notes on parsing:

• A segment like "a/b" is split into parts using the separator.
• None segments are ignored.
• "." segments are ignored (relative no-op).
• Operations like relative_to() and is_relative_to() also respect the instance separator.

Equality and ordering:

• BasePath equality, hashing, and ordering are all based on both separator and parts.
• Ordering is separator-sensitive and deterministic, even when parts mix types (e.g. ints and strings).
• Path parts are type-sensitive (0 is not equal to "0").

Lookup caching:

• LookupPath uses a per-instance LRU cache (default maxsize: 128) on its accessor.
• You can control it via path.accessor.clear_cache(), path.accessor.disable_cache(), and path.accessor.enable_cache(maxsize=...).
• path.accessor.node is immutable; to point at a different tree, create a new LookupPath/accessor.

Installation

Recommended way (via pip):

pip install pathable

Alternatively you can download the code and install from the repository:

pip install -e git+https://github.com/p1c2u/pathable.git#egg=pathable

Benchmarks

Benchmarks live in tests/benchmarks/ and produce JSON reports.

Local run (recommended as modules):

poetry run python -m tests.benchmarks.bench_parse --output reports/bench-parse.json
poetry run python -m tests.benchmarks.bench_lookup --output reports/bench-lookup.json

Quick sanity run:

poetry run python -m tests.benchmarks.bench_parse --quick --output reports/bench-parse.quick.json
poetry run python -m tests.benchmarks.bench_lookup --quick --output reports/bench-lookup.quick.json

Compare two results (fails if candidate is >20% slower in any scenario):

poetry run python -m tests.benchmarks.compare_results \
    --baseline reports/bench-before.json \
    --candidate reports/bench-after.json \
    --tolerance 0.20

CI (on-demand):

• GitHub Actions workflow Benchmarks runs via workflow_dispatch and uploads the JSON artifacts.