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:

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:

{
  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:

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:

// 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:

// 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.

​Data Pipeline

​Pipeline flowchart

​Marker key convention

​Entry storage format

​Values arrays — aligned with dates

​singlePoint categories

​Custom markers

​Calculated markers

​The data parameter pattern