Two-Sided Design Difference Recorder

Record differences of independently aligned two-sided design attributes after registration; no arbitrary image/text equivalence or orientation inference.

Description

Two flag faces can be compared only after their attributes have been aligned under the same registration convention.

Two flag faces can be compared only after their attributes have been aligned under the same registration convention.^[1]

Exact attribute differences then provide a reproducible record of which positions changed.

When to use Two-Sided Design Difference Recorder

Use this tool when you need to reproduce the stated calculation from measured records or independently selected model inputs. Establish the flag dimensions, orientation, specification, palette, or visual annotations needed for this particular calculation.

How to use Two-Sided Design Difference Recorder and understand its results

Establish the flag dimensions, orientation, specification, palette, or visual annotations needed for this particular calculation.
Enter independentlyAlignedObverseAttributeIds, independentlyAlignedReverseAttributeIds, following the field table and rules below, then run the tool.
Read result. Use the output meanings below to interpret units, signs, flags, and any nested result fields.
Correct reported input issues and run again. For a successful result, check the stated evidence coverage and assumptions before using it in your review.

Input field	Meaning	Value type	Requirement
independentlyAlignedObverseAttributeIds	Independently aligned obverse attribute ids	list of string	Required
independentlyAlignedReverseAttributeIds	Independently aligned reverse attribute ids	list of string	Required

Enter nonempty equally sized obverse and reverse attribute lists. Compare strings at the same index with exact equality; return separate zero-based lists of different and unchanged positions. The tool does not infer image orientation.

differentIndices={i: obverse_i ≠ reverse_i}

Symbols follow the input and output meanings and the calculation rules above.

Output field	Meaning	Value type
result	Calculated observation or reference	object
result.differentAttributeIndices	Different Attribute Indices	list
result.unchangedAttributeIndices	Unchanged Attribute Indices	list

json

{
  "input": {
    "independentlyAlignedObverseAttributeIds": [
      "seal-a",
      "red"
    ],
    "independentlyAlignedReverseAttributeIds": [
      "seal-b",
      "red"
    ]
  },
  "output": {
    "result": {
      "differentAttributeIndices": [
        0
      ],
      "unchangedAttributeIndices": [
        1
      ]
    }
  }
}

Limitations and assumptions

The calculation uses supplied geometry, observations, or annotations. It does not establish an official construction specification, national identity, or universal design quality.

Missing required fields, unknown fields, wrong types, and violated calculation rules are input issues. Evidence arrays must be dense, with no missing cells. String lists allow at most 128 strings of at most 128 characters. Extremely large composite outputs are rejected.

Alternative or Complementary methods

For a related review, use Mirror Reversibility Obverse-Reverse Checker: Mirror reversibility compares registered obverse and reverse designs under horizontal reflection. Exact palette grids make that comparison reproducible when both views share the same top and hoist registration. For a complementary step, use Flag Difference Hamming Pixel Comparator: Hamming distance counts exact mismatches between categorical cells. For registered flag grids it measures label differences without introducing resampling or perceptual color judgments.

References

Two-Sided Design Difference Recorder — tool.io

Similar or alternative tools

Mirror Reversibility Obverse-Reverse Checker
Mirror reversibility compares registered obverse and reverse designs under horizontal reflection. Exact palette grids make that comparison reproducible when both views share the same top and hoist registration.
Flag Difference Hamming Pixel Comparator
Hamming distance counts exact mismatches between categorical cells. For registered flag grids it measures label differences without introducing resampling or perceptual color judgments.