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name: matrix-profile-basics description: "Compute and validate 1-D STUMPY matrix profiles and distance profiles, including normalized and non-normalized routes, AB-joins, top-k output, pandas/polars inputs, and profile interpretation." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

STUMPY matrix-profile basics

Use this sub-skill for 1-D profile computation, inspection, and smoke validation with stumpy.stump, stumpy.aamp, and stumpy.mass.

Do not use it for:

• multidimensional profiles or subspace selection -> multidimensional-profiles
• motifs, matches, discords, snippets, or segmentation -> motifs-anomalies-segmentation
• approximate, streaming, or pan profiles -> approximate-streaming-pan
• Dask, Ray, or CUDA setup -> distributed-gpu-acceleration

Fast routing

• Z-normalized matrix profile: stumpy.stump(T_A, m, ...)
• Raw matrix profile: stumpy.aamp(T_A, m, ...)
• Distance profile: stumpy.mass(Q, T, ...)
• AB-join: pass T_B and set ignore_trivial=False
• Top-k results: set k > 1 and read the mparray attributes

Input rules

• Use floating-point inputs, usually np.float64, pandas Series, or polars Series.
• Keep 1-D inputs 1-D. DataFrames are transposed before conversion and are better suited to multidimensional workflows.
• Window size must be at least 3 and no larger than the shortest input series.
• Self-joins use ignore_trivial=True; AB-joins use ignore_trivial=False.
• If you already know constant subsequences, you can supply T_A_subseq_isconstant / T_B_subseq_isconstant / Q_subseq_isconstant to the exact API.
• If the task turns into motifs, matches, discords, snippets, or segmentation, stop after the profile and route out.

Runtime handoff

• Verify the install/import path with scripts/profile_smoke.py.
• Default smoke command: python scripts/profile_smoke.py --mode exact
• Use --mode ab-join, --mode aamp, or --mode mass for the other basic routes.
• The smoke script uses tiny synthetic data only and does not touch the network or any files.

Read the profile

• stump and aamp return an object-dtype mparray.
• For k = 1, each row stores [P, I, left_I, right_I].
• Use mp.P_, mp.I_, mp.left_I_, and mp.right_I_ instead of slicing when possible.
• Negative indices mean no eligible neighbor was found.
• mass returns a float64 distance profile, not a matrix-profile object.
• If you need to interpret motifs or follow-on pattern matches, route to the downstream analysis sub-skill.

Normalize or not

• stump(..., normalize=False) routes to the raw-distance implementation.
• mass(..., normalize=False) routes to the raw-distance implementation.
• p is only meaningful for non-normalized distance calculations.