> ## Documentation Index > Fetch the complete documentation index at: https://docs.getbased.health/llms.txt > Use this file to discover all available pages before exploring further. # Data pipeline > How getbased turns stored profile data into active chart, scoring, and AI data. # Data Pipeline `getActiveData()` in `js/data.js` is the central data pipeline. Every view — dashboard, category charts, compare, correlations, AI context — calls it to get processed data. It is a pure function with no side effects; it reads from `state` and returns a fresh `data` object each time. ## Pipeline flowchart ``` ┌──────────────────────┐ │ getActiveData() │ └──────────┬───────────┘ │ ▼ Deep-clone MARKER_SCHEMA into data.categories │ ▼ Merge custom markers from importedData.customMarkers │ ▼ Apply sex-specific ranges (replace refMin/refMax with _f if female) │ ▼ Merge OPTIMAL_RANGES into marker objects │ ▼ Apply refOverrides (ref + optimal range overrides per marker) │ ▼ Collect dates from entries (excluding singlePoint categories) │ ▼ Build sorted data.dates[] and data.dateLabels[] │ ▼ Populate marker.values[] aligned with dates, null = no data │ ▼ ◇ singlePoint? ◇ ╱ ╲ yes no │ │ ▼ │ Set cat.singleDate │ marker.singlePoint = true │ │ │ └───────┬──────────┘ │ ▼ Compute cycle phase labels (active cycle, no hormonal BC) │ ▼ Compute phase-specific ref ranges (estradiol, progesterone, LH, FSH) │ ▼ Calculate ratios TG/HDL, LDL/HDL, ApoB/ApoAI, NLR, PLR, De Ritis, Cu/Zn │ ▼ Calculate Free Water Deficit TBW × (Na / 140 − 1) │ ▼ Calculate PhenoAge Levine 2018, 9 biomarkers + DOB │ ▼ ◇ US units? ◇ ╱ ╲ yes no │ │ ▼ │ Apply unit conversions │ from UNIT_CONVERSIONS │ │ │ └────────┬────────┘ │ ▼ ┌──────────────────────┐ │ Return data │ └──────────────────────┘ ``` The caller is responsible for applying date range filtering after receiving `data`: ```js theme={null} const data = getActiveData(); filterDatesByRange(data); // mutates data.dates, data.dateLabels, marker.values in-place ``` ## Marker key convention Markers are always referenced as `"category.markerKey"`: ``` biochemistry.glucose hormones.testosterone lipids.triglycerides hematology.wbc ``` This format is used in: * `UNIT_CONVERSIONS` keys * `OPTIMAL_RANGES` keys * `PHASE_RANGES` keys * `importedData.entries[].markers` — each entry stores its values keyed this way * `importedData.customMarkers` — custom marker definitions * AI prompt references in `buildMarkerReference()` ## Entry storage format `importedData.entries` is an array of lab snapshots. Each entry has a date and a flat markers object: ```js theme={null} { date: "2025-03-15", markers: { "biochemistry.glucose": 5.2, "hormones.testosterone": 18.4, "lipids.triglycerides": 1.1, // ... } } ``` Multiple entries on the same date are merged (via `Object.assign`) into a single lookup slot. The pipeline builds `entryLookup: { "2025-03-15": { "biochemistry.glucose": 5.2, ... } }` before populating values arrays. ## Values arrays — aligned with dates Every marker in the output has a `values` array aligned with `data.dates`: ```js theme={null} data.dates = ["2024-09-01", "2024-12-01", "2025-03-15"] marker.values = [4.8, 5.1, null ] // ^ no result for this date ``` `null` means the marker was not measured on that date. Charts use `spanGaps: true` to draw lines across gaps. Status functions check for `null` before evaluating. ## singlePoint categories The `fattyAcids` category has `singlePoint: true` in the schema. These markers typically come from a single test, not a time series. The pipeline handles them differently: * Only the **latest** entry date is used, stored as `cat.singleDate` * Each marker gets `marker.singlePoint = true` and a single-element `marker.values` array * Views render grid cards instead of trend charts for these categories ## Custom markers Markers not in `MARKER_SCHEMA` are auto-imported from PDFs. The pipeline merges them into `data.categories` at runtime: ```js theme={null} // importedData.customMarkers: { "mylab.cortisol": { name: "Cortisol (AM)", unit: "nmol/L", refMin: 170, refMax: 720, categoryLabel: "My Lab" } } ``` If the category key does not exist in the schema, a new category is created with a bookmark icon. Custom markers have `marker.custom = true` and are treated identically to schema markers in all views. ## Calculated markers These are computed in-pipeline and stored in the `calculatedRatios` category: | Marker key | Formula | | -------------------- | ------------------------------------------------------------------------------------------------- | | `tgHdlRatio` | Triglycerides / HDL | | `ldlHdlRatio` | LDL / HDL | | `apoBapoAIRatio` | ApoB / ApoAI | | `nlr` | Neutrophils / Lymphocytes | | `plr` | Platelets / Lymphocytes | | `deRitisRatio` | AST / ALT | | `copperZincRatio` | Copper / Zinc | | `bunCreatinineRatio` | `(urea × 2.801) / (creatinine × 0.01131)` — computed in US units from SI-stored values, ref 10–20 | | `freeWaterDeficit` | `TBW × (Na / 140 − 1)` — assumes 70 kg body weight | | `phenoAge` | Levine 2018 — 9 biomarkers + chronological age from DOB | PhenoAge returns `null` if any of its 9 required inputs is missing or if DOB is not set. ## The `data` parameter pattern `getActiveData()` deep-clones the full schema on every call — it is not free. Views accept an optional `data` parameter so a single pipeline run can feed multiple renders: ```js theme={null} // Good: one pipeline call, passed to all sub-renders function refreshAll() { const data = getActiveData(); filterDatesByRange(data); showDashboard(data); } // Bad: each sub-render calls getActiveData() independently function refreshAll() { showDashboard(); // calls getActiveData() internally showCategory(cat); // calls getActiveData() again } ``` When writing a new rendering function, always accept `data` as an optional parameter and call `getActiveData()` only when it is not provided.