All demonstrationsPOINT-CLOUD PROFILE / RUST CPU

Read the geometry before choosing a method.

Compare a square, a line and two separated clusters. Inspect the supplied positions, then ask Rust for the distance distribution, isolation and directional spread.

tour_profileTry the real call
Concept artwork · not calculation evidence
01 / UNDERSTAND THE METHOD

One cloud. Several ways to understand it.

INPUT PREVIEW · RUN TO CALCULATE
Choose a case. Then bring it to life.

Preset buttons change the inputs. Calculation starts only when you ask.

Input preview. No numerical result yet.
SUPPLIED POSITIONS5 points
Point 1: (0, 0)1Point 2: (1, 0)2Point 3: (1, 1)3Point 4: (0, 1)4Point 5: (0.5, 0.5)5x: 0 → 1 · y: 0 → 1equal unit scales

Point numbers identify the supplied input order. They are not a tour or a recommended visit sequence.

02 / ASK THE ACTUAL TOOL

Make it your experiment.

Complete tool arguments

Arrays, coordinates and nested contracts remain editable here. Units and limits are checked by the real tool.

Runs only when you press the button. Input edits discard the previous displayed result. Public compute limits apply.

KNOW WHAT THE RESULT MEANS

Useful evidence needs boundaries.

  • Distances use the supplied coordinate units in a Euclidean plane. Roads, travel time, terrain and uncertainty are absent.
  • The PCA ratio is zero for a line and approaches 0.5 for isotropic spread. A principal direction can be ambiguous; unavailable mirror measurements remain explicit.
  • The fingerprint is rounded and can collide. Equal fingerprints are not a proof that point clouds are equivalent.
  • The plots show supplied points and returned descriptors; they do not produce scientific measurements themselves. Direct MCP access does not require orchestration.
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