# Texture Baking Analytics — Base-100 Series + Two-Measure Correlation

> Texture Baking Analytics — Base-100 Series + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-22).

Computes a base-100 indexed time series and a two-measure correlation report for supplied texture baking data (bake_seconds and texture_pixels) across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-creative-10-165?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-22
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/texture-baking-analytics-base-100-series-two-measure-correlation-ef2262da
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_aZaoq0LL0wymEe2clGXwC

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability texture-baking-analytics-base-100-series-two-measure-correlation-ef2262da -d '<json body>'
```

Example prompt: Can you run a base-100 index series and a two-measure correlation on these texture baking records — I have 4 rows with bake_seconds and texture_pixels for groups A and B from October 2026, and I want to use row index 0 as the base, indexing on bake_seconds?

## When to prefer this

Choose this endpoint when you have texture baking pipeline data (bake_seconds and texture_pixels per row) and need both a normalized base-100 time series and a two-measure correlation in a single low-cost call. Ideal for 3D asset pipeline analytics where you want to track baking performance over time relative to a reference point and understand the relationship between baking duration and output texture size, without needing external data sources or predictions.

## Known failure modes

- More than 200 rows supplied — request rejected or truncated
- Missing required fields (row_id, bake_seconds, texture_pixels) — validation error
- base_index out of range for supplied row count — error returned
- Non-numeric values in bake_seconds or texture_pixels — computation failure
- Invalid date format (not YYYY-MM-DD) — schema validation error

## How this service works

Texture baking — Base-100 series + Two-measure correlation: Base-100 series + Two-measure correlation 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.

## Output

Returns two analytical reports: (1) a base-100 indexed time series for the chosen field (bake_seconds or texture_pixels) normalized to the specified base row, and (2) a Pearson or equivalent correlation between bake_seconds and texture_pixels across all supplied rows. Both reports are computed locally from the supplied data with no external lookups.

## Request schema (JSON Schema)

```json
{
 "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"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/texture-baking-analytics-base-100-series-two-measure-correlation-ef2262da/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)