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Affine Six Coefficient Layout Converter

Affine six-coefficient layout [a,b,c,d,e,f] maps x,y to a*x+c*y+e,b*x+d*y+f; convert SVG-style compact coefficients to a 2x3 row-major six-element layout, not an arbitrary matrix transpose.

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Description

Convert SVG-style compact coefficients to a 2x3 row-major six-element layout, not an arbitrary matrix transpose.

A planar affine transform combines a linear map and translation. This family stores [a,b,c,d,e,f], mapping [x,y] to [a×x+c×y+e,b×x+d×y+f]. Positions include translation; displacements use only the linear block, and normal covectors use its inverse transpose. Composition order changes the result.

Convert SVG-style compact coefficients to a 2x3 row-major six-element layout, not an arbitrary matrix transpose.

When to use this tool

Use this tool to convert SVG-style compact coefficients to a 2x3 row-major six-element layout, not an arbitrary matrix transpose.

How to use the tool and how it works

  1. Enter coefficients using the input definitions below.
  2. Run the operation. Resolve malformed-input errors before interpreting the result.
  3. Read result.rowMajor. Interpret signs, units, ordering and null values using the rules below.

Reorder [a,b,c,d,e,f] into [a,c,e,b,d,f], representing two row-major rows [a,c,e] and [b,d,f].1

Input fieldMeaning and unitsAccepted input
coefficientsFinite coefficient list to audit or convert; the operation specifies whether six values are required or checked.Dense list of at most 128 finite numbers, subject to the stated integer/shape rules.
Output fieldInterpretation
resultObject containing the fields below.
result.rowMajorSix-element 2×3 row-major layout [a,c,e,b,d,f].
json
{
  "input": {
    "coefficients": [
      2,
      0,
      0,
      3,
      4,
      5
    ]
  },
  "output": {
    "result": {
      "rowMajor": [
        2,
        0,
        4,
        0,
        3,
        5
      ]
    }
  }
}

Limitations and assumptions

  • This is a compact-layout conversion rather than a general matrix transpose or its inverse conversion.
  • Matrices have six finite coefficients. Point/vector inputs have two components within ±1,000,000,000; rectangle bounds are likewise bounded and ordered. Coordinate units and axis directions are caller-defined. No conditioning threshold is inferred for inversion.
  • Every declared top-level input is required and unknown fields are rejected. Ordinary strings are limited to 8,192 UTF-16 code units and lists to 128 entries, with tighter operation-specific bounds where stated. JSON text has bounded nesting and node counts.
  • Numeric outputs must be finite and within the absolute safe-integer magnitude; exact larger integers use decimal text. Source positions are zero-based UTF-16 offsets and span ends are exclusive unless a different convention is explicitly stated.

Alternative or Complementary methods

Check coefficient layout and application order when exchanging matrices. Use point, displacement or normal operations according to the quantity being transformed, and a separate conditioning analysis for numerically sensitive inverses.

References

  1. Tool behavior and example: Affine Six Coefficient Layout Converter — tool.io

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