jycm 1.0.2

A highly flexible json diff framework for python.

JYCM

A flexible json diff framework for minimalist.

JYCM = Json You-Cha-Ma (「is there a difference」in Chinese)

Reference

• deepdiff

  • how results are reported in JYCM is learnt from this amazing framework.
  • how TreeLevel is designed in JYCM is learnt from this amazing framework.

• gojsondif

  • Fuzzy matching part of JYCM is learnt from this amazing framework.

Install

pip install jycm

Examples

Here's some examples showing you what you can do with JYCM.

Diff set-in-set

from jycm.helper import make_ignore_order_func
from jycm.jycm import YouchamaJsonDiffer

left = {
    "set_in_set": [
        {
            "id": 1,
            "label": "label:1",
            "set": [
                1,
                2,
                3
            ]
        },
        {
            "id": 2,
            "label": "label:2",
            "set": [
                4,
                5,
                6
            ]
        }
    ]
}

right = {
    "set_in_set": [
        {
            "id": 2,
            "label": "label:2",
            "set": [
                6,
                5,
                4
            ]
        },
        {
            "id": 1,
            "label": "label:1",
            "set": [
                3,
                2,
                1
            ]
        }
    ]
}

ycm = YouchamaJsonDiffer(left, right, ignore_order_func=make_ignore_order_func([
    f"^set_in_set$",
    f"^set_in_set->\\[\\d+\\]->set$"
]))

ycm.diff()


expected = {} # A.K.A No diff

assert ycm.to_dict(no_pairs=True) == expected

Custom operator

Define an operator

import math
from jycm.operator import  BaseOperator

class L2DistanceOperator(BaseOperator):
    __operator_name__ = "operator:l2distance"
    __event__ = "operator:l2distance"

    def __init__(self, path_regex, distance_threshold):
        super().__init__(path_regex=path_regex)
        self.distance_threshold = distance_threshold

    def diff(self, level: 'TreeLevel', instance, drill: bool) -> Tuple[bool, float]:
        print("damn")
        distance = math.sqrt(
            (level.left["x"] - level.right["x"]) ** 2 + (level.left["y"] - level.right["y"]) ** 2
        )
        info = {
            "distance": distance,
            "distance_threshold": self.distance_threshold,
            "pass": distance < self.distance_threshold
        }

        if not drill:
            instance.report(self.__event__, level, info)
        return True, 1 if info["pass"] else 0

And use it

from jycm.jycm import YouchamaJsonDiffer

left = {
    "distance_ok": {
        "x": 1,
        "y": 1
    },
    "distance_too_far": {
        "x": 5,
        "y": 5
    },
}

right = {
    "distance_ok": {
        "x": 2,
        "y": 2
    },
    "distance_too_far": {
        "x": 7,
        "y": 9
    },
}

ycm = YouchamaJsonDiffer(left, right, custom_operators=[
    L2DistanceOperator(f"distance.*", 3),
])

expected = {
    'operator:l2distance': [
        {'left': {'x': 1, 'y': 1}, 'right': {'x': 2, 'y': 2}, 'left_path': 'distance_ok',
         'right_path': 'distance_ok', 'distance': 1.4142135623730951, 'distance_threshold': 3, 'pass': True},
        {'left': {'x': 5, 'y': 5}, 'right': {'x': 7, 'y': 9}, 'left_path': 'distance_too_far',
         'right_path': 'distance_too_far', 'distance': 4.47213595499958, 'distance_threshold': 3, 'pass': False}
    ]
}
assert {**ycm.to_dict(), "pairs": []} == expected

Philosophy

Since determining two things are equal or not is heavily depend on the context, it is not possible to build a json diff tool to meet all requirements.

JYCM choose another way: making it easy to compare values.

JYCM allows users just need to focus on defining the differing logic or what a distance is between two values and JYCM will take care all the other dirty works such as array-item-matching, ignoring-array-order, recursively comparing and calculating similarity.

By the way, JYCM uses algorithms below to match items in array:

	exactly matching	fuzzy matching
with order	LCS	Edit distance
without order	Brute force	Kuhn–Munkres

Speaking of flexibility, to make a new operator, you just to need to extend a class called BaseOperator with such function signature:

diff: (level: 'TreeLevel', instance, drill: bool) => [float, boolean]

where the first return is a float number between zero and one for describing how similar level.left and level.right are and the second return is used to indicate whether comparing process should stop here.

Contribute

requirements

pip install -r requirements-dev.txt

Releases

git checkout master && git pull
bumpversion {patch|minor|major}
git push && git push --tags

run test with cov

make testcov 

make docs

make docs