{"uid":"cap_VTtpXJy8f5OKcd41ZArzC","slug":"texture-baking-analytics-trailing-mean-series-explicit-missing-value-03c7b5fc","name":"Texture Baking Analytics — Trailing Mean Series + Explicit Missing-Value Fill","description":"Texture baking — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill for supplied texture baking records (bake_seconds, texture_pixels). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-creative-10-154?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","bake_seconds","texture_pixels"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"asset_group":{"type":"string","default":"","maxLength":80},"bake_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"texture_pixels":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","asset_group":"group-a","bake_seconds":10,"texture_pixels":1},{"date":"2026-10-02","row_id":"row-2","asset_group":"group-b","bake_seconds":20,"texture_pixels":2},{"date":"2026-10-03","row_id":"row-3","asset_group":"group-a","bake_seconds":15,"texture_pixels":3},{"date":"2026-10-04","row_id":"row-4","asset_group":"group-b","bake_seconds":30,"texture_pixels":4}],"maxItems":200,"minItems":1},"field":{"enum":["bake_seconds","texture_pixels"],"type":"string","default":"bake_seconds"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_deLDwFMoODAgMVctFif6G","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes a trailing mean time series and fills missing values (zero/mean/median) for texture baking records (bake_seconds, texture_pixels) across up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"For my texture baking dataset of 50 rows with bake_seconds and texture_pixels fields, can you compute a trailing mean series using a window of 5 and fill any missing bake_seconds values using median imputation?","exampleUseCases":[{"title":"Smoothing bake time trends over sprints","prompt":"I have 120 rows of daily texture baking records with bake_seconds logged per asset group — can you run a trailing mean with a window of 7 and fill any gaps in bake_seconds using the mean so I can see smooth trends?"},{"title":"Detecting texture pixel anomalies after imputation","prompt":"My texture pipeline logs sometimes have missing texture_pixels entries — can you fill those using zero-fill and also compute a trailing mean series with window 4 so I can spot outlier days in the smoothed data?"},{"title":"Comparing asset groups on rolling bake performance","prompt":"I want to compare rolling average bake_seconds across asset groups using a window of 3 and median fill for any missing values — I have 80 baking records, one per day per group."}],"resultDescription":"Two analytical reports: (1) a trailing mean series showing rolling window averages over the selected field (bake_seconds or texture_pixels) across the supplied rows, and (2) an explicit missing-value fill report replacing nulls in the selected field using the chosen method (zero, mean, or median). Both reports are computed locally against the supplied data with no external lookups.","failureModes":["Fewer than 1 row provided — minimum input requirement not met","More than 200 rows supplied — exceeds maxItems limit","Invalid date format (not YYYY-MM-DD) — pattern validation failure","Window size outside 2–200 range — integer constraint violation","Unknown method value outside zero/mean/median enum — validation error","Unknown field value outside bake_seconds/texture_pixels enum — validation error","Numeric values outside ±1,000,000,000,000 — schema bound exceeded","Payment not included or insufficient — x402 payment required"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of texture baking records (bake_seconds or texture_pixels) and need both a trailing moving average series and explicit missing-value imputation in a single low-cost call ($0.01 USDC for 2 reports). Prefer this over generic analytics services when your data is specifically about 3D texture baking pipelines and you want offline, deterministic statistical smoothing without external data lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T18:56:30.015Z","isFirstParty":false,"canonicalSlug":"texture-baking-analytics-trailing-mean-series-explicit-missing-value-03c7b5fc"}