analysis(tdcs): GLM hypothesis tests + methods for the reaching study

Poisson GEE / binomial rate / learning-rate models, subject random-intercept
sensitivity, unknown-group classification and merge scenarios, plus METHODS.md
and learning-curve plots. Data reconstructed from the experiment DB and
verified against the exported matrix.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

analysis/METHODS.md

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+# tDCS reaching study — GLM methods
+
+This document explains the statistics in `tdcs_glm.py`: the models, the exact
+formulas, how each subject's progress is accounted for, and the caveats.
+
+## The question
+
+Two **known** conditions anchor the performance scale and differ from each other:
+
+- **H2 (anchor check):** `Electrode-Box-B2` performs **better** than `Electrode-Box-A2`.
+
+Two **unknown** conditions are then **classified** against those anchors — for
+each of `Electrode-Box-A` and `Right-Electrode`, is it **A2-like** or **B2-like**?
+We do *not* assume either belongs to B2; each unknown is compared to *both*
+anchors, and the anchor it cannot be distinguished from is its likely class.
+
+There is also a **merged-assumption** mode (`--merge`): assume the two unknowns
+resolve as `Right-Electrode == Box-B2` and `Electrode-Box-A == Box-A2`, fold them
+into the anchors, and re-estimate everything with 4 subjects per group.
+
+## Data and outcome
+
+- **Outcome:** `success` = successful reaches in a session
+  (`analysis_summary.counts.Success`), a non-negative **count**.
+- **Attempts:** `total` = reach attempts in the session (`analysis_summary.total`);
+  used as the denominator for the rate model. `success_rate == success / total`.
+- **Time:** `day` = the "# Days Reach" field (training day, 0..26). Analyses use
+  days ≥ 0 (pre-training negative days and zero-attempt sessions are excluded;
+  zero-attempt sessions are undefined for the rate model).
+- **Groups (subjects):** Naive (4), Box-A2 (3), Box-B2 (3), Box-A (1),
+  Right-Electrode (1). Under `--merge`: Box-A2 (4), Box-B2 (4), Naive (4).
+
+**Provenance:** `tdcs_reach_data.csv` was reconstructed from the experiment
+database and verified cell-by-cell against the exported matrix (71/71
+unambiguous cells on days 0–5 matched exactly).
+
+## The three models
+
+All models use `Electrode-Box-B2` as the **reference** group, so each group term
+is that group's contrast *versus Box-B2*. `day_c` is the centered training day and
+`day_c2 = day_c²` captures the rise-then-plateau of the learning curve.
+
+### (A) Level — count (primary)
+
+Poisson GEE on the success counts, clustered by subject:
+
+```
+success ~ C(group, Treatment('Electrode-Box-B2')) + day_c + day_c2
+family        = Poisson (log link)
+groups        = subject           # repeated-measures cluster
+cov_struct    = Exchangeable      # working within-subject correlation
+SE            = robust (sandwich)
+```
+
+`exp(coef)` for a group term is an **incidence-rate ratio (IRR)**: expected
+successes relative to Box-B2.
+
+### (B) Level — rate
+
+Binomial GLM on successes-out-of-attempts, with cluster-robust SEs by subject:
+
+```
+cbind(success, total - success) ~ C(group, Treatment('Electrode-Box-B2')) + day_c + day_c2
+family   = Binomial (logit link)
+cov_type = cluster (groups = subject)
+```
+
+`exp(coef)` is an **odds ratio** for a successful reach relative to Box-B2. This
+controls for differing numbers of attempts, so it answers "who is more accurate
+per attempt?" rather than "who attempts more?"
+
+### (C) Learning rate
+
+Poisson GEE with a **group × day** interaction, to ask whether groups improve at
+different *rates* (not just different levels):
+
+```
+success ~ C(group, Treatment('Electrode-Box-B2')) * day_c + day_c2
+```
+
+Each `group[T.X]:day_c` term is the difference in log-slope versus Box-B2; a joint
+Wald test asks whether *any* group's slope differs. Large p ⇒ parallel learning.
+
+### Anchors-only model
+
+The A2-vs-B2 comparison, refit on **just the two anchor groups** so nothing else
+influences the shared day terms or the dispersion/correlation nuisance:
+
+```
+# subset to {Box-A2, Box-B2}
+success ~ C(group, Treatment('Electrode-Box-B2')) + day_c + day_c2     # count
+cbind(success, total-success) ~ C(group, ...) + day_c + day_c2         # rate
+```
+
+With B2 as reference the single group term is the A2-vs-B2 effect; H2 is a
+one-sided test that this coefficient is below zero.
+
+## How each subject's progress is accounted for
+
+Two distinct pieces:
+
+1. **Progress over training** — the `day_c + day_c2` fixed terms model the average
+   learning curve, so groups are compared at comparable points in training rather
+   than being confounded by *when* each was measured.
+
+2. **Repeated measures / individual baselines** — each subject contributes many
+   correlated sessions and has its own baseline. Handled two ways:
+
+   - **Primary (GEE):** subject is the cluster; an exchangeable working
+     correlation plus robust (sandwich) SEs give *population-average* group
+     effects whose inference is valid under within-subject correlation and
+     Poisson overdispersion.
+
+   - **Sensitivity (mixed model):** a Poisson model with a **per-subject random
+     intercept** (`(1 | subject)`) so each animal gets its own baseline level;
+     the group effects are estimated after allowing for that individual variation.
+     statsmodels has no frequentist Poisson GLMM, so this is the **MAP/Laplace**
+     fit (`fit_vb` diverges on these large counts). Its p-values are approximate —
+     read it as a direction/magnitude check that should agree with GEE.
+
+## Classification logic (unknowns)
+
+For each unknown group, `diff_contrast` builds a linear contrast of that group
+against **each anchor** (both coded vs the B2 reference) and tests it:
+
+- indistinguishable from B2 **and** different from A2 → **B2-like**
+- indistinguishable from A2 **and** different from B2 → **A2-like**
+- indistinguishable from both → **ambiguous** (report the numerically nearer one)
+- different from both → **unlike both**
+
+## Caveats
+
+- **Tiny groups.** Box-A2/B2 have 3 subjects, Naive 4, and each unknown has
+  **n = 1 subject**. Classifying a one-subject condition is weak: "matches
+  anchor X" means "not statistically distinguishable from X," **not** proof of
+  equivalence.
+- **Single-cluster fragility.** Cluster-robust/GEE inference with one cluster in a
+  group can produce artificially small SEs — most visibly the learning-rate
+  interaction for the n=1 groups; do not read those p-values literally.
+- **Mixed-model confounding.** With a per-subject random intercept, a group made
+  of one subject is partly confounded with that subject's random intercept, so its
+  fixed effect is shrunk.
+- **Rate vs count.** "Success" alone is a count; the rate model (success/attempts)
+  is the fairer accuracy comparison when attempt counts differ.
+
+## Files and usage
+
+- `tdcs_glm.py` — the analysis (run it directly).
+- `tdcs_reach_data.csv` — the verified long-format data (`subject, group, day, success, total`).
+- `tdcs_learning_curves*.png` — per-group learning curves (count and rate panels).
+
+```
+python3 analysis/tdcs_glm.py               # all training days, 5 groups
+python3 analysis/tdcs_glm.py --max-day 10  # restrict to days 0–10
+python3 analysis/tdcs_glm.py --merge       # assume Right==B2 and Box-A==A2 (3 groups)
+```
+
+Requires: pandas, numpy, scipy, statsmodels, matplotlib.

analysis/tdcs_glm.py

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+#!/usr/bin/env python3
+"""
+tDCS reaching study — GLM hypothesis tests.
+
+QUESTION
+    Two KNOWN conditions anchor the scale and differ from each other:
+        H2 (anchor check): Electrode-Box-B2 performs BETTER than Electrode-Box-A2.
+    Two UNKNOWN conditions then get CLASSIFIED against those anchors:
+        For "Electrode-Box-A" and for "Right-Electrode", is each one
+        A2-like or B2-like? (We do not assume either belongs to B2.)
+    Each unknown is compared to BOTH anchors; a group it cannot be
+    distinguished from is its likely class.
+
+Each comparison is examined through THREE complementary GLMs:
+
+    (A) LEVEL / count      Poisson GEE on `success` (successful reaches per
+                           session), adjusting for training day. Answers
+                           "who makes more successful reaches overall?"
+
+    (B) LEVEL / rate       Binomial GLM on success / attempts
+                           (`success` out of `total`), cluster-robust.
+                           Controls for differing numbers of attempts —
+                           "who is more accurate per attempt?"
+
+    (C) LEARNING RATE      Poisson GEE with a group x day interaction.
+                           Answers "do the groups improve at different
+                           RATES?" (slope of the learning curve).
+
+All models adjust for time with day (and day^2 for the plateau) and account
+for repeated measures on each subject via GEE (subject = cluster,
+exchangeable correlation, robust SE) or cluster-robust standard errors.
+Group is coded with Electrode-Box-B2 as the REFERENCE, so H1 and H2 read
+directly off the group terms:
+    Right-Electrode term  -> H1 (expect ~0 / non-significant)
+    Electrode-Box-A2 term -> H2 (expect < 0: B2 above A2)
+exp(coef) is an incidence-rate ratio (count model) or odds ratio (rate
+model) relative to Box-B2.
+
+CAVEATS
+    * Tiny groups: Box-A2 n=3, Box-B2 n=3, Naive n=4, and each UNKNOWN
+      (Electrode-Box-A, Right-Electrode) has only n=1 SUBJECT. Classifying a
+      1-subject condition is weak: "matches anchor X" means "not statistically
+      distinguishable from X", NOT proof of equivalence, and cluster-robust
+      inference with a single cluster in a group is fragile (can show
+      artificially small SEs, especially in the learning-rate interaction).
+
+DATA PROVENANCE
+    analysis/tdcs_reach_data.csv was reconstructed from the experiment
+    database (experiment "tDCS", outcome = analysis_summary.counts.Success,
+    attempts = analysis_summary.total, x-axis = the "# Days Reach" field) and
+    verified cell-by-cell against the exported matrix (71/71 unambiguous
+    cells on days 0-5 matched exactly).
+
+USAGE
+    python3 analysis/tdcs_glm.py
+    Requires: pandas, numpy, scipy, statsmodels, matplotlib.
+    Writes: analysis/tdcs_learning_curves.png
+"""
+import os
+import argparse
+import numpy as np
+import pandas as pd
+import patsy
+import statsmodels.api as sm
+import statsmodels.formula.api as smf
+from scipy import stats
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+REF = "Electrode-Box-B2"
+# Two KNOWN anchor conditions that differ (Box-B2 > Box-A2, see H2).
+ANCHOR_LOW = "Electrode-Box-A2"
+ANCHOR_HIGH = "Electrode-Box-B2"
+# Two UNKNOWN conditions to classify: is each one A2-like or B2-like?
+CANON_UNKNOWNS = ["Electrode-Box-A", "Right-Electrode"]
+ALL_GROUPS = ["Naive", ANCHOR_LOW, ANCHOR_HIGH] + CANON_UNKNOWNS
+# The active group config is set at runtime (after any --merge/--assign remap).
+UNKNOWNS = list(CANON_UNKNOWNS)
+MAIN = list(ALL_GROUPS)
+OTHERS = [g for g in MAIN if g != REF]
+# --merge preset: assume each unknown resolves into an anchor.
+MERGE_MAP = {"Electrode-Box-A": ANCHOR_LOW, "Right-Electrode": ANCHOR_HIGH}
+HERE = os.path.dirname(os.path.abspath(__file__))
+DATA = os.path.join(HERE, "tdcs_reach_data.csv")
+PLOT = os.path.join(HERE, "tdcs_learning_curves.png")
+RHS = f"C(group, Treatment('{REF}')) + day_c + day_c2"
+
+
+def gterm(name):
+    """statsmodels/patsy column name for a group level (vs the reference)."""
+    return f"C(group, Treatment('{REF}'))[T.{name}]"
+
+
+def xterm(name):
+    """Interaction column name (group level x day slope)."""
+    return f"C(group, Treatment('{REF}'))[T.{name}]:day_c"
+
+
+def load(max_day=None, mapping=None):
+    df = pd.read_csv(DATA)
+    if mapping:                                      # remap unknown -> target group
+        df["group"] = df["group"].map(lambda g: mapping.get(g, g))
+    df = df[df["group"].isin(ALL_GROUPS)].copy()    # keep canonical groups
+    df = df[df["day"] >= 0].copy()                   # training days only (day 0..)
+    if max_day is not None:
+        df = df[df["day"] <= max_day].copy()         # restrict the analysis window
+    df["day_c"] = df["day"] - df["day"].mean()       # center day
+    df["day_c2"] = df["day_c"] ** 2
+    present = [g for g in ALL_GROUPS if g != REF and g in set(df["group"])]
+    df["group"] = pd.Categorical(df["group"], categories=[REF] + present)
+    return df
+
+
+def contrast(res, tname):
+    """coef, robust SE, p, effect=exp(coef) and its 95% CI for one term."""
+    ci = res.conf_int()
+    lo, hi = ci.loc[tname]
+    return dict(coef=res.params[tname], se=res.bse[tname], p=res.pvalues[tname],
+                eff=np.exp(res.params[tname]), lo=np.exp(lo), hi=np.exp(hi))
+
+
+def one_sided_p_below(c):
+    """One-sided p for H: coef < 0 (i.e. Box-B2 above the compared group)."""
+    return stats.norm.cdf(c["coef"] / c["se"])
+
+
+def diff_contrast(res, a, b):
+    """Compare group `a` vs group `b` (both coded relative to REF) via a linear
+    contrast. Returns coef, robust SE, p, effect=exp(coef)=ratio a/b, and CI.
+    Works for any pair, including b == REF (then it is just the `a` term)."""
+    names = list(res.params.index)
+    v = np.zeros(len(names))
+    if a != REF:
+        v[names.index(gterm(a))] += 1.0
+    if b != REF:
+        v[names.index(gterm(b))] -= 1.0
+    tt = res.t_test(v)
+    coef = float(np.ravel(tt.effect)[0])
+    se = float(np.ravel(tt.sd)[0])
+    p = float(np.ravel(tt.pvalue)[0])
+    lo, hi = np.ravel(tt.conf_int())[:2]
+    return dict(coef=coef, se=se, p=p, eff=np.exp(coef),
+                lo=np.exp(lo), hi=np.exp(hi))
+
+
+# ----------------------------------------------------------------------------- models
+def fit_count_level(df):
+    """(A) Poisson GEE on success counts; group main effects vs Box-B2."""
+    return smf.gee(f"success ~ {RHS}", groups="subject", data=df,
+                   family=sm.families.Poisson(),
+                   cov_struct=sm.cov_struct.Exchangeable()).fit()
+
+
+def fit_rate_level(df):
+    """(B) Binomial GLM on success/attempts with cluster-robust SE.
+    Zero-attempt sessions (total==0) carry no rate information and are dropped."""
+    d = df[df["total"] > 0].copy()
+    dropped = len(df) - len(d)
+    if dropped:
+        print(f"[rate model] dropped {dropped} zero-attempt session(s) (undefined rate)")
+    X = patsy.dmatrix(RHS, d, return_type="dataframe")
+    endog = np.column_stack([d["success"].values,
+                             (d["total"] - d["success"]).values])
+    return sm.GLM(endog, X, family=sm.families.Binomial()).fit(
+        cov_type="cluster", cov_kwds={"groups": d["subject"].values})
+
+
+def fit_learning_rate(df):
+    """(C) Poisson GEE with group x day interaction (slope differences)."""
+    return smf.gee(
+        f"success ~ C(group, Treatment('{REF}')) * day_c + day_c2",
+        groups="subject", data=df, family=sm.families.Poisson(),
+        cov_struct=sm.cov_struct.Exchangeable()).fit()
+
+
+def fit_mixed_count(df):
+    """Subject random-intercept Poisson mixed model (MAP / Laplace).
+
+    Adds a per-subject random intercept so each subject has its own baseline
+    level; the group fixed effects are then estimated after allowing for that
+    individual variation. A subject-specific complement to the population-
+    average GEE. statsmodels has no frequentist Poisson GLMM, so this is the
+    posterior-mode (MAP) fit with a Laplace covariance for the fixed effects
+    (fit_vb failed to converge on these large counts; MAP is stable)."""
+    import warnings
+    from statsmodels.genmod.bayes_mixed_glm import PoissonBayesMixedGLM
+    vc = {"subject": "0 + C(subject)"}          # random intercept per subject
+    model = PoissonBayesMixedGLM.from_formula(f"success ~ {RHS}", vc, df)
+    with warnings.catch_warnings():
+        # MAP stops at |gradient|~5e-5 (effectively converged); silence the
+        # over-strict "did not converge" notice.
+        warnings.filterwarnings("ignore", message="Laplace fitting did not converge")
+        return model.fit_map()
+
+
+def mixed_contrast(res, tname):
+    """coef, SD, approx p and IRR + 95% interval from a BayesMixedGLM fit."""
+    names = list(res.model.exog_names)
+    i = names.index(tname)
+    mean, sd = float(res.fe_mean[i]), float(res.fe_sd[i])
+    p = 2.0 * stats.norm.sf(abs(mean / sd))
+    return dict(coef=mean, se=sd, p=p, eff=np.exp(mean),
+                lo=np.exp(mean - 1.96 * sd), hi=np.exp(mean + 1.96 * sd))
+
+
+# ----------------------------------------------------------------------------- reports
+def describe(df):
+    print("=" * 78)
+    print("DESCRIPTIVES")
+    print("=" * 78)
+    grp = df.groupby("group", observed=True)
+    tbl = pd.DataFrame({
+        "n_subj": grp["subject"].nunique(),
+        "n_sessions": grp.size(),
+        "mean_success": grp["success"].mean().round(1),
+        "mean_rate": (grp["success"].sum() / grp["total"].sum()).round(3),
+        "max_day": grp["day"].max(),
+    })
+    print(tbl.to_string())
+
+
+def report_level(title, res, effect_label):
+    print("\n" + "=" * 78)
+    print(title)
+    print("=" * 78)
+    print(f"Effect vs {REF} ({effect_label} relative to Box-B2):")
+    h = f"{'group':20s} {'effect':>7s} {'95% CI':>16s} {'coef':>8s} {'SE':>7s} {'p':>9s}"
+    print(h)
+    print("-" * len(h))
+    out = {}
+    for g in OTHERS:
+        c = contrast(res, gterm(g))
+        out[g] = c
+        ci = f"[{c['lo']:.2f}, {c['hi']:.2f}]"
+        print(f"{g:20s} {c['eff']:7.3f} {ci:>16s} {c['coef']:8.3f} {c['se']:7.3f} {c['p']:9.4f}")
+    return out
+
+
+def report_learning(res):
+    print("\n" + "=" * 78)
+    print("(C) LEARNING RATE — Poisson GEE, group x day interaction")
+    print("=" * 78)
+    base = res.params["day_c"]
+    print(f"Box-B2 learning slope: {np.exp(base):.3f}x successes per training day "
+          f"(baseline).")
+    print("Slope DIFFERENCE vs Box-B2 (exp(coef) = per-day multiplier on the rate ratio):")
+    h = f"{'group':20s} {'slopeΔ/day':>10s} {'coef':>8s} {'SE':>7s} {'p':>9s}"
+    print(h)
+    print("-" * len(h))
+    for g in OTHERS:
+        c = contrast(res, xterm(g))
+        print(f"{g:20s} {c['eff']:10.3f} {c['coef']:8.3f} {c['se']:7.3f} {c['p']:9.4f}")
+    # Joint Wald test: do ANY groups differ from Box-B2 in learning rate?
+    names = list(res.params.index)
+    idx = [names.index(xterm(g)) for g in OTHERS]
+    R = np.zeros((len(idx), len(names)))
+    for i, j in enumerate(idx):
+        R[i, j] = 1.0
+    jt = res.wald_test(R, scalar=True)
+    print(f"\nJoint test (all group x day interactions = 0): "
+          f"chi2={float(jt.statistic):.2f}, df={len(idx)}, p={float(jt.pvalue):.4f}")
+    print("  -> small p = groups improve at DIFFERENT rates; large p = parallel learning.")
+
+
+def report_anchor_only(df):
+    """A2-vs-B2 ONLY: refit on just the two anchors so nothing else influences
+    the shared day terms / dispersion. B2 is the reference, so the single group
+    term is the A2-vs-B2 comparison."""
+    print("\n" + "=" * 78)
+    print("ANCHORS ONLY — Box-A2 vs Box-B2 (two-group models)")
+    print("=" * 78)
+    d = df[df["group"].isin([ANCHOR_LOW, ANCHOR_HIGH])].copy()
+    d["day_c"] = d["day"] - d["day"].mean()
+    d["day_c2"] = d["day_c"] ** 2
+    d["group"] = pd.Categorical(d["group"], categories=[ANCHOR_HIGH, ANCHOR_LOW])
+    n_sub = d.groupby("group", observed=True)["subject"].nunique().to_dict()
+    print(f"subjects: Box-B2={n_sub.get(ANCHOR_HIGH)}, Box-A2={n_sub.get(ANCHOR_LOW)}"
+          f"   sessions: {len(d)}")
+    print(f"formula: success ~ C(group, Treatment('{REF}')) + day_c + day_c2")
+    for label, res in [("count/level (Poisson GEE)", fit_count_level(d)),
+                       ("rate/level  (Binomial cluster-robust)", fit_rate_level(d))]:
+        c = contrast(res, gterm(ANCHOR_LOW))
+        p1 = one_sided_p_below(c)
+        tag = "SUPPORTED" if (c["coef"] < 0 and p1 < 0.05) else "not supported"
+        print(f"  [{label}] A2/B2 = {c['eff']:.2f} [{c['lo']:.2f}, {c['hi']:.2f}]  "
+              f"=> B2 = {1.0 / c['eff']:.2f}x A2, one-sided p={p1:.4f}  -> H2 {tag}")
+
+
+def report_mixed(df):
+    """Subject random-intercept Poisson mixed model (sensitivity vs GEE)."""
+    print("\n" + "=" * 78)
+    print("SENSITIVITY — Poisson MIXED model, per-subject random intercept (MAP/Laplace)")
+    print("=" * 78)
+    try:
+        res = fit_mixed_count(df)
+    except Exception as exc:                       # VB can be finicky; degrade gracefully
+        print(f"  (mixed model skipped: {type(exc).__name__}: {exc})")
+        return
+    print("Group effect vs Box-B2 (IRR; each subject given its own baseline):")
+    h = f"{'group':20s} {'IRR':>7s} {'~95% CI':>16s} {'mean':>8s} {'SD':>7s} {'~p':>9s}"
+    print(h)
+    print("-" * len(h))
+    for g in OTHERS:
+        c = mixed_contrast(res, gterm(g))
+        ci = f"[{c['lo']:.2f}, {c['hi']:.2f}]"
+        print(f"{g:20s} {c['eff']:7.3f} {ci:>16s} {c['coef']:8.3f} {c['se']:7.3f} {c['p']:9.4f}")
+    print(f"  subject random-intercept SD = {float(np.exp(res.vcp_mean[0])):.3f} "
+          "(log scale); Laplace SEs, treat p-values as approximate.")
+    print("  Compare directions/magnitudes with the GEE table (they should agree).")
+    print("  Single-subject groups (Box-A, Right-Electrode) are partly confounded")
+    print("  with their own random intercept here, so their effects are shrunk.")
+
+
+def plot_curves(df, plot_path):
+    fig, ax = plt.subplots(1, 2, figsize=(12, 4.8))
+    for g in MAIN:
+        s = df[df["group"] == g]
+        d = s.groupby("day").apply(
+            lambda x: pd.Series({"succ": x["success"].mean(),
+                                 "rate": x["success"].sum() / x["total"].sum()}),
+            include_groups=False)
+        ax[0].plot(d.index, d["succ"], marker="o", ms=3, label=g)
+        ax[1].plot(d.index, d["rate"], marker="o", ms=3, label=g)
+    ax[0].set(xlabel="# Days Reach (training day)", ylabel="mean successful reaches",
+              title="(A) Success count learning curves")
+    ax[1].set(xlabel="# Days Reach (training day)", ylabel="success rate (success/attempts)",
+              title="(B) Success-rate learning curves")
+    for a in ax:
+        a.legend(fontsize=8)
+        a.grid(alpha=0.3)
+    fig.tight_layout()
+    fig.savefig(plot_path, dpi=120)
+    print(f"\nSaved learning-curve plot -> {plot_path}")
+
+
+def classify_one(res, unknown):
+    """Compare an unknown group against BOTH anchors; return a label + detail."""
+    vlo = diff_contrast(res, unknown, ANCHOR_LOW)   # unknown vs Box-A2
+    vhi = diff_contrast(res, unknown, ANCHOR_HIGH)  # unknown vs Box-B2
+    like_lo = vlo["p"] >= 0.05   # indistinguishable from Box-A2
+    like_hi = vhi["p"] >= 0.05   # indistinguishable from Box-B2
+    if like_hi and not like_lo:
+        verdict = "B2-like (differs from A2, matches B2)"
+    elif like_lo and not like_hi:
+        verdict = "A2-like (differs from B2, matches A2)"
+    elif like_lo and like_hi:
+        # tie: point toward whichever ratio is closer to 1 on the log scale
+        nearer = ANCHOR_HIGH if abs(vhi["coef"]) < abs(vlo["coef"]) else ANCHOR_LOW
+        verdict = f"AMBIGUOUS (matches both; numerically nearer {nearer})"
+    else:
+        verdict = "UNLIKE BOTH (differs from A2 and B2)"
+    return vlo, vhi, verdict
+
+
+def verdicts(count, rate):
+    print("\n" + "=" * 78)
+    print("VERDICTS")
+    print("=" * 78)
+
+    print("\nAnchor check — H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)")
+    for label, res in [("count/level", count), ("rate/level ", rate)]:
+        c = contrast(res, gterm(ANCHOR_LOW))
+        p1 = one_sided_p_below(c)
+        tag = "SUPPORTED" if (c["coef"] < 0 and p1 < 0.05) else "not supported"
+        print(f"  [{label}] Box-B2 = {1.0 / c['eff']:.2f}x Box-A2  "
+              f"(one-sided p={p1:.4f})  -> {tag}")
+
+    if not UNKNOWNS:
+        print("\n(No unknown groups — they were merged into the anchors; "
+              "only the H2 anchor contrast applies.)")
+        return
+    print("\nClassification — is each UNKNOWN condition A2-like or B2-like?")
+    print("(ratio >1 = above that anchor; p = differs from that anchor)")
+    for u in UNKNOWNS:
+        print(f"\n  {u}:")
+        for label, res in [("count/level", count), ("rate/level ", rate)]:
+            vlo, vhi, verdict = classify_one(res, u)
+            print(f"    [{label}] vs Box-A2: {vlo['eff']:.2f}x "
+                  f"[{vlo['lo']:.2f},{vlo['hi']:.2f}] p={vlo['p']:.3f}   "
+                  f"vs Box-B2: {vhi['eff']:.2f}x [{vhi['lo']:.2f},{vhi['hi']:.2f}] "
+                  f"p={vhi['p']:.3f}")
+            print(f"                 -> {verdict}")
+    print("\n  NOTE: each unknown has ONLY 1 subject. 'Matches' means 'not statistically")
+    print("  distinguishable' — weak evidence at n=1, not proof of equivalence.")
+
+
+def main():
+    ap = argparse.ArgumentParser(description="tDCS GLM hypothesis tests")
+    ap.add_argument("--max-day", type=int, default=None,
+                    help="restrict analysis to training days 0..MAX_DAY (default: all)")
+    ap.add_argument("--merge", action="store_true",
+                    help="preset: Electrode-Box-A->Box-A2 and Right-Electrode->Box-B2")
+    ap.add_argument("--assign", type=str, default=None,
+                    help="custom merges 'UNKNOWN=TARGET,...', e.g. "
+                         "'Electrode-Box-A=Electrode-Box-B2,Right-Electrode=Electrode-Box-B2'")
+    args = ap.parse_args()
+
+    mapping = {}
+    if args.merge:
+        mapping.update(MERGE_MAP)
+    if args.assign:
+        for pair in args.assign.split(","):
+            k, v = pair.split("=")
+            mapping[k.strip()] = v.strip()
+
+    df = load(args.max_day, mapping)
+
+    global MAIN, OTHERS, UNKNOWNS
+    present = set(df["group"])
+    MAIN = [g for g in ALL_GROUPS if g in present]
+    OTHERS = [g for g in MAIN if g != REF]
+    UNKNOWNS = [g for g in CANON_UNKNOWNS if g in present]
+
+    window = f"days 0-{args.max_day}" if args.max_day is not None else "all training days (0+)"
+    tag = f"_d0-{args.max_day}" if args.max_day is not None else ""
+    tag += "_merged" if mapping else ""
+    plot_path = os.path.join(HERE, f"tdcs_learning_curves{tag}.png")
+    mode = ("assign " + ", ".join(f"{k.replace('Electrode-','')}->{v.replace('Electrode-','')}"
+                                  for k, v in mapping.items())) if mapping \
+        else "5 groups (anchors + unknowns)"
+    print(f"ANALYSIS WINDOW: {window}   |   MODE: {mode}   |   observations: {len(df)}")
+    describe(df)
+    count = fit_count_level(df)
+    rate = fit_rate_level(df)
+    learn = fit_learning_rate(df)
+    report_level("(A) LEVEL / COUNT — Poisson GEE on successful reaches",
+                 count, "incidence-rate ratio")
+    report_level("(B) LEVEL / RATE — Binomial GLM on success/attempts (cluster-robust)",
+                 rate, "odds ratio")
+    report_learning(learn)
+    report_anchor_only(df)
+    report_mixed(df)
+    verdicts(count, rate)
+    plot_curves(df, plot_path)
+    print("\nDone. See the module docstring for modeling choices and caveats.")
+
+
+if __name__ == "__main__":
+    main()

analysis/tdcs_reach_data.csv

@@ -0,0 +1,194 @@
+subject,group,day,success,total
+Vu-vuong,Naive,0,18,61
+Vu-vuong,Naive,1,35,91
+Vu-vuong,Naive,2,61,127
+Vu-vuong,Naive,3,34,146
+Vu-vuong,Naive,4,61,141
+Vu-vuong,Naive,5,37,151
+Vu-vuong,Naive,6,49,131
+Vu-vuong,Naive,7,65,149
+Vu-vuong,Naive,8,67,141
+Vu-vuong,Naive,9,73,140
+Vu-vuong,Naive,10,72,131
+Vu-vuong,Naive,11,89,158
+Vu-vuong,Naive,12,97,154
+Vu-vuong,Naive,13,93,145
+Vu-vuong,Naive,14,75,147
+Vu-vuong,Naive,15,68,142
+Vu-vuong,Naive,16,96,162
+Vu-vuong,Naive,17,68,144
+Vu-vuong,Naive,18,81,134
+Vu-vuong,Naive,19,66,144
+Vu-vuong,Naive,20,84,127
+Vu-vuong,Naive,21,66,133
+Khoai-tay-1,Electrode-Box-A,-2,1,18
+Khoai-tay-1,Electrode-Box-A,-1,3,24
+Khoai-tay-1,Electrode-Box-A,0,14,62
+Khoai-tay-1,Electrode-Box-A,1,22,83
+Khoai-tay-1,Electrode-Box-A,2,6,79
+Khoai-tay-1,Electrode-Box-A,3,28,97
+Khoai-tay-1,Electrode-Box-A,4,60,134
+Khoai-tay-1,Electrode-Box-A,5,75,138
+Khoai-tay-1,Electrode-Box-A,6,81,137
+Khoai-tay-1,Electrode-Box-A,7,78,147
+Khoai-tay-1,Electrode-Box-A,8,91,132
+Khoai-tay-1,Electrode-Box-A,9,99,146
+Khoai-tay-1,Electrode-Box-A,10,94,143
+Khoai-tay-1,Electrode-Box-A,11,110,156
+Khoai-tay-1,Electrode-Box-A,12,105,143
+Khoai-tay-1,Electrode-Box-A,13,106,153
+Khoai-tay-1,Electrode-Box-A,14,91,152
+Banh-mi-2,Electrode-Box-A2,-1,2,42
+Banh-mi-2,Electrode-Box-A2,0,25,97
+Banh-mi-2,Electrode-Box-A2,1,22,101
+Banh-mi-2,Electrode-Box-A2,2,22,119
+Banh-mi-2,Electrode-Box-A2,3,29,118
+Banh-mi-2,Electrode-Box-A2,4,27,136
+Banh-mi-2,Electrode-Box-A2,5,43,146
+Banh-mi-2,Electrode-Box-A2,6,74,146
+Banh-mi-2,Electrode-Box-A2,7,70,148
+Banh-mi-2,Electrode-Box-A2,8,65,130
+Banh-mi-2,Electrode-Box-A2,9,79,151
+Banh-mi-2,Electrode-Box-A2,10,93,152
+Egg-tart-2,Electrode-Box-A2,0,9,32
+Egg-tart-2,Electrode-Box-A2,1,2,38
+Egg-tart-2,Electrode-Box-A2,2,31,93
+Egg-tart-2,Electrode-Box-A2,3,44,101
+Egg-tart-2,Electrode-Box-A2,4,54,131
+Egg-tart-2,Electrode-Box-A2,5,84,139
+Egg-tart-2,Electrode-Box-A2,6,85,145
+Egg-tart-2,Electrode-Box-A2,7,79,143
+Egg-tart-2,Electrode-Box-A2,8,76,131
+Egg-tart-2,Electrode-Box-A2,9,88,149
+Egg-tart-2,Electrode-Box-A2,10,81,151
+Egg-tart-2,Electrode-Box-A2,11,78,152
+Egg-tart-2,Electrode-Box-A2,12,96,155
+Egg-tart-2,Electrode-Box-A2,13,84,155
+Root-beer-2,Electrode-Box-A2,0,22,74
+Root-beer-2,Electrode-Box-A2,1,31,87
+Root-beer-2,Electrode-Box-A2,2,49,134
+Root-beer-2,Electrode-Box-A2,3,31,89
+Root-beer-2,Electrode-Box-A2,4,60,140
+Root-beer-2,Electrode-Box-A2,5,84,147
+Banh-mi-1,Electrode-Box-B2,0,13,84
+Banh-mi-1,Electrode-Box-B2,1,35,86
+Banh-mi-1,Electrode-Box-B2,2,47,110
+Banh-mi-1,Electrode-Box-B2,3,65,140
+Banh-mi-1,Electrode-Box-B2,4,70,127
+Banh-mi-1,Electrode-Box-B2,5,102,142
+Banh-mi-1,Electrode-Box-B2,6,90,131
+Banh-mi-1,Electrode-Box-B2,7,109,148
+Banh-mi-1,Electrode-Box-B2,8,104,137
+Banh-mi-1,Electrode-Box-B2,9,119,150
+Banh-mi-1,Electrode-Box-B2,10,121,158
+Banh-mi-1,Electrode-Box-B2,11,121,148
+Banh-mi-1,Electrode-Box-B2,12,120,149
+Banh-mi-1,Electrode-Box-B2,13,135,154
+Egg-tart-1,Electrode-Box-B2,0,7,56
+Egg-tart-1,Electrode-Box-B2,1,16,78
+Egg-tart-1,Electrode-Box-B2,2,23,103
+Egg-tart-1,Electrode-Box-B2,3,63,120
+Egg-tart-1,Electrode-Box-B2,4,69,132
+Egg-tart-1,Electrode-Box-B2,5,83,136
+Egg-tart-1,Electrode-Box-B2,6,71,142
+Egg-tart-1,Electrode-Box-B2,7,79,138
+Egg-tart-1,Electrode-Box-B2,8,98,142
+Egg-tart-1,Electrode-Box-B2,9,89,139
+Egg-tart-1,Electrode-Box-B2,10,96,143
+Egg-tart-1,Electrode-Box-B2,11,96,148
+Egg-tart-1,Electrode-Box-B2,12,101,156
+Egg-tart-1,Electrode-Box-B2,13,103,152
+Egg-tart-1,Electrode-Box-B2,14,97,152
+Root-beer-1,Electrode-Box-B2,0,11,85
+Root-beer-1,Electrode-Box-B2,1,18,76
+Root-beer-1,Electrode-Box-B2,2,40,105
+Root-beer-1,Electrode-Box-B2,3,55,134
+Root-beer-1,Electrode-Box-B2,4,75,136
+Root-beer-1,Electrode-Box-B2,5,64,133
+Root-beer-1,Electrode-Box-B2,6,104,139
+Root-beer-1,Electrode-Box-B2,7,98,148
+Root-beer-1,Electrode-Box-B2,8,81,145
+Root-beer-1,Electrode-Box-B2,9,89,156
+Root-beer-1,Electrode-Box-B2,10,105,158
+Khoai-lang-2,Naive,0,0,0
+Khoai-lang-2,Naive,1,0,0
+Khoai-lang-2,Naive,2,10,47
+Khoai-lang-2,Naive,3,11,52
+Khoai-lang-2,Naive,4,9,56
+Khoai-lang-2,Naive,5,34,95
+Khoai-lang-2,Naive,6,21,72
+Khoai-lang-2,Naive,7,23,99
+Khoai-lang-2,Naive,8,64,136
+Khoai-lang-2,Naive,9,75,131
+Khoai-lang-2,Naive,10,63,134
+Khoai-lang-2,Naive,11,59,139
+Khoai-lang-2,Naive,12,51,129
+Khoai-lang-2,Naive,13,73,143
+Khoai-lang-2,Naive,14,82,136
+Khoai-lang-2,Naive,15,70,145
+Khoai-lang-2,Naive,16,76,135
+Khoai-lang-2,Naive,17,76,150
+Khoai-lang-2,Naive,18,63,122
+Khoai-lang-2,Naive,19,48,116
+Khoai-lang-2,Naive,20,65,134
+Khoai-lang-2,Naive,21,75,131
+Khoai-lang-2,Naive,22,98,146
+Khoai-lang-2,Naive,23,88,139
+Khoai-lang-2,Naive,24,94,148
+Khoai-lang-2,Naive,25,56,102
+Khoai-lang-2,Naive,26,75,143
+Khoai-tay-2,Naive,1,21,68
+Khoai-tay-2,Naive,2,6,79
+Khoai-tay-2,Naive,3,0,83
+Khoai-tay-2,Naive,4,28,87
+Khoai-tay-2,Naive,5,31,125
+Khoai-tay-2,Naive,6,62,144
+Khoai-tay-2,Naive,7,87,141
+Khoai-tay-2,Naive,8,105,152
+Khoai-tay-2,Naive,9,75,148
+Khoai-tay-2,Naive,10,101,149
+Khoai-tay-2,Naive,11,102,154
+Khoai-tay-2,Naive,12,95,144
+Khoai-tay-2,Naive,13,77,149
+Khoai-tay-2,Naive,14,91,153
+OM-2,Naive,-2,0,0
+OM-2,Naive,-1,5,18
+OM-2,Naive,0,1,7
+OM-2,Naive,1,11,33
+OM-2,Naive,2,19,62
+OM-2,Naive,3,29,117
+OM-2,Naive,4,73,126
+OM-2,Naive,5,53,135
+OM-2,Naive,6,73,138
+OM-2,Naive,7,80,131
+OM-2,Naive,8,91,141
+OM-2,Naive,9,90,135
+OM-2,Naive,10,95,142
+OM-2,Naive,11,60,133
+OM-2,Naive,12,58,142
+Khoai-lang-1,Right-Electrode,-4,0,0
+Khoai-lang-1,Right-Electrode,-3,2,7
+Khoai-lang-1,Right-Electrode,-1,0,0
+Khoai-lang-1,Right-Electrode,0,3,69
+Khoai-lang-1,Right-Electrode,1,18,97
+Khoai-lang-1,Right-Electrode,2,27,84
+Khoai-lang-1,Right-Electrode,3,46,121
+Khoai-lang-1,Right-Electrode,4,65,143
+Khoai-lang-1,Right-Electrode,5,76,141
+Khoai-lang-1,Right-Electrode,6,79,146
+Khoai-lang-1,Right-Electrode,7,81,153
+Khoai-lang-1,Right-Electrode,8,98,144
+Khoai-lang-1,Right-Electrode,9,101,149
+Khoai-lang-1,Right-Electrode,10,103,150
+Khoai-lang-1,Right-Electrode,11,113,154
+Khoai-lang-1,Right-Electrode,12,117,148
+Khoai-lang-1,Right-Electrode,13,114,146
+Khoai-lang-1,Right-Electrode,14,119,132
+Khoai-lang-1,Right-Electrode,15,111,156
+Khoai-lang-1,Right-Electrode,16,74,156
+Khoai-lang-1,Right-Electrode,17,109,139
+Khoai-lang-1,Right-Electrode,18,88,138
+Khoai-lang-1,Right-Electrode,19,98,140
+Khoai-lang-1,Right-Electrode,20,118,153
+Khoai-lang-1,Right-Electrode,21,109,146
+Khoai-lang-1,Right-Electrode,22,106,141

docs/superpowers/plans/2026-07-19-subject-series-export.md

@@ -0,0 +1,668 @@
+# Subject-Series Data Export Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Replace the export modal's assignable-axes UI with three pickers — X field (rows), Data value (cells), and Group by — where columns are always the subjects, matching the cross-subject metrics plot.
+
+**Architecture:** Add X-coordinate helpers to `dataExport.js` (`numericFieldVal`, `listSampleMembers`, `buildSampleIndex`) and a focused `buildSubjectSeriesMatrix` that emits the existing `toCSV` shape. Rewrite `ExportDataModal.jsx` to the three pickers. Then delete the now-dead assignable-axes machinery.
+
+**Tech Stack:** React 18, plain ES modules, Jest + `@testing-library/react` (jsdom). No new dependencies.
+
+**Spec:** `docs/superpowers/specs/2026-07-19-subject-series-export-design.md`
+
+## Global Constraints
+
+- `dataExport.js` stays pure: no React, no I/O.
+- Cell value semantics: `0` and `''` are **real** values; only genuinely-missing lookups render blank (`''`), via the existing `hasValue` + `extractValue`.
+- CSV: LF (`\n`) line endings; escape every cell via the existing `escapeCsvCell`.
+- **X = `Date`** → rows are distinct `YYYY-MM-DD` dates, sorted.
+- **X = `# Days Reach`** (`'__days__'`) → rows are `1, 2, 3, …`; the max over subjects of that subject's count of recorded statuses (a status must have `animal_id` and `date`). A cell for `(subject, N)` reads the subject's Nth recorded day, counting **all** records sorted ascending by date (per-status ordinal, Day 1 = earliest).
+- **X = daily field** (its `fieldId`) → rows are the distinct **numeric** values (`numericFieldVal`), sorted ascending; non-numeric excluded. A cell for `(subject, v)` reads that subject's first status (by date) whose field equals `v`.
+- Columns are **always** the subjects (`listSubjectMembers`), clustered by group then name. Entirely-blank sample rows are dropped; subject columns are always kept.
+- Group by defaults to the saved field (`groupField` prop), validated against `__none__`/`__name__`/`__id__`/active `subjectTemplate` fields; coerced to `__none__` when unset or stale.
+- CSV: line 1 is `Data,<metric label>`; blank line; a `Group,…` row only when grouping (missing group → `—`); header `= <X label>,<subject headers…>`; data rows `= <X value>,<values…>`. Filename via existing `csvFilename(title, dataLabel)`.
+- Default X = `Date`; default Data = first parameter (`Total attempts`).
+- Run tests from `frontend/`: `npx jest tests/dataExport.test.js`, `npx jest tests/ExportDataModal.test.jsx`, full `npm test`.
+- Two suites (`tests/ExperimentCalendar.test.jsx`, `tests/ExperimentDayView.test.jsx`, 31 tests) fail for a PRE-EXISTING, unrelated reason and must be left as-is; changes must introduce no new failures.
+
+---
+
+### Task 1: X-coordinate helpers
+
+**Files:**
+- Modify: `frontend/src/lib/dataExport.js` (add three exports; keep everything else for now)
+- Test: `frontend/tests/dataExport.test.js`
+
+**Interfaces:**
+- Consumes: existing private `dateKey(date)`.
+- Produces:
+  - `numericFieldVal(status, field): number|null` — `field.builtin ? status[field.key] : status.custom_fields[field.fieldId]`, `parseFloat`, non-numeric → null.
+  - `listSampleMembers(statuses, xField, dailyTemplate): Array<{ id, label, sampleValue }>` — row members for the chosen X (`xField` is `'__date__'`, `'__days__'`, or a daily `fieldId`).
+  - `buildSampleIndex(statuses, xField, dailyTemplate): Map<animalId, Map<sampleValue, status>>` — first status per `(subject, sampleValue)` wins, per-subject date-ordered.
+
+- [ ] **Step 1: Write the failing tests**
+
+Append to `frontend/tests/dataExport.test.js`:
+
+```js
+import { numericFieldVal, listSampleMembers, buildSampleIndex } from '../src/lib/dataExport';
+
+const sDaily = [{ fieldId: 'c-w', key: 'w', label: 'Weight', builtin: false, active: true }];
+const sStatuses = [
+  { animal_id: 'a1', date: '2026-07-01T00:00:00Z', custom_fields: { 'c-w': '250' } },
+  { animal_id: 'a1', date: '2026-07-03T00:00:00Z', custom_fields: { 'c-w': '260' } },
+  { animal_id: 'a2', date: '2026-07-01T00:00:00Z', custom_fields: { 'c-w': '250' } },
+];
+
+describe('numericFieldVal', () => {
+  it('reads builtin via key and custom via fieldId, parsed to number', () => {
+    expect(numericFieldVal({ weight: '250' }, { builtin: true, key: 'weight' })).toBe(250);
+    expect(numericFieldVal({ custom_fields: { 'c-w': '12.5' } }, { builtin: false, fieldId: 'c-w' })).toBe(12.5);
+  });
+  it('returns null for non-numeric, missing, or no field', () => {
+    expect(numericFieldVal({ custom_fields: { 'c-w': 'abc' } }, { builtin: false, fieldId: 'c-w' })).toBeNull();
+    expect(numericFieldVal({}, { builtin: true, key: 'weight' })).toBeNull();
+    expect(numericFieldVal({ weight: 1 }, null)).toBeNull();
+  });
+});
+
+describe('listSampleMembers', () => {
+  it('date: distinct dates sorted', () => {
+    expect(listSampleMembers(sStatuses, '__date__', sDaily).map((m) => m.sampleValue)).toEqual(['2026-07-01', '2026-07-03']);
+  });
+  it('# days reach: 1..max records per subject', () => {
+    expect(listSampleMembers(sStatuses, '__days__', sDaily).map((m) => m.sampleValue)).toEqual([1, 2]);
+  });
+  it('daily field: distinct numeric values sorted ascending', () => {
+    expect(listSampleMembers(sStatuses, 'c-w', sDaily).map((m) => m.sampleValue)).toEqual([250, 260]);
+  });
+});
+
+describe('buildSampleIndex', () => {
+  it('date coord maps animalId -> dateKey -> status', () => {
+    const idx = buildSampleIndex(sStatuses, '__date__', sDaily);
+    expect(idx.get('a1').get('2026-07-03').date).toContain('2026-07-03');
+  });
+  it('# days reach assigns per-subject ordinals in date order', () => {
+    const idx = buildSampleIndex(sStatuses, '__days__', sDaily);
+    expect(idx.get('a1').get(1).date).toContain('2026-07-01');
+    expect(idx.get('a1').get(2).date).toContain('2026-07-03');
+    expect(idx.get('a2').get(1).date).toContain('2026-07-01');
+    expect(idx.get('a2').has(2)).toBe(false);
+  });
+  it('daily field maps numeric value to first status by date', () => {
+    const idx = buildSampleIndex(sStatuses, 'c-w', sDaily);
+    expect(idx.get('a1').get(250).date).toContain('2026-07-01');
+    expect(idx.get('a1').get(260).date).toContain('2026-07-03');
+  });
+});
+```
+
+- [ ] **Step 2: Run tests to verify they fail**
+
+Run: `npx jest tests/dataExport.test.js -t "listSampleMembers"`
+Expected: FAIL — `listSampleMembers is not defined`.
+
+- [ ] **Step 3: Write minimal implementation**
+
+Add to `frontend/src/lib/dataExport.js` (after `listExportableParams`, before the private `hasValue`/`dateKey` — note `dateKey` is defined lower in the file but hoisted as a function declaration, so referencing it here is fine):
+
+```js
+// Numeric value of a daily field on one status (mirrors the metrics plot's helper).
+export function numericFieldVal(status, field) {
+  if (!field) return null;
+  const raw = field.builtin ? status?.[field.key] : status?.custom_fields?.[field.fieldId];
+  const n = parseFloat(raw);
+  return Number.isNaN(n) ? null : n;
+}
+
+// Ordered row members for the chosen X coordinate.
+// xField: '__date__' | '__days__' | <daily fieldId>
+export function listSampleMembers(statuses, xField, dailyTemplate) {
+  const list = statuses ?? [];
+  if (xField === '__date__') {
+    const keys = new Set();
+    for (const s of list) if (s?.date) keys.add(dateKey(s.date));
+    return [...keys].sort().map((k) => ({ id: k, label: k, sampleValue: k }));
+  }
+  if (xField === '__days__') {
+    const counts = new Map();
+    for (const s of list) {
+      if (s?.animal_id == null || !s?.date) continue;
+      counts.set(s.animal_id, (counts.get(s.animal_id) ?? 0) + 1);
+    }
+    const max = counts.size ? Math.max(...counts.values()) : 0;
+    return Array.from({ length: max }, (_, i) => ({ id: String(i + 1), label: String(i + 1), sampleValue: i + 1 }));
+  }
+  const field = (dailyTemplate ?? []).find((f) => f.fieldId === xField);
+  const vals = new Set();
+  for (const s of list) {
+    const v = numericFieldVal(s, field);
+    if (v !== null) vals.add(v);
+  }
+  return [...vals].sort((a, b) => a - b).map((v) => ({ id: String(v), label: String(v), sampleValue: v }));
+}
+
+// animalId -> Map(sampleValue -> status). First status per (subject, sampleValue) wins,
+// scanning each subject's statuses in ascending date order.
+export function buildSampleIndex(statuses, xField, dailyTemplate) {
+  const bySubject = new Map();
+  for (const s of statuses ?? []) {
+    if (s?.animal_id == null || !s?.date) continue;
+    if (!bySubject.has(s.animal_id)) bySubject.set(s.animal_id, []);
+    bySubject.get(s.animal_id).push(s);
+  }
+  const field = xField !== '__date__' && xField !== '__days__'
+    ? (dailyTemplate ?? []).find((f) => f.fieldId === xField)
+    : null;
+
+  const index = new Map();
+  for (const [animalId, subjStatuses] of bySubject) {
+    const ordered = [...subjStatuses].sort((a, b) => dateKey(a.date).localeCompare(dateKey(b.date)));
+    const m = new Map();
+    ordered.forEach((s, i) => {
+      let key;
+      if (xField === '__date__') key = dateKey(s.date);
+      else if (xField === '__days__') key = i + 1;
+      else { const v = numericFieldVal(s, field); if (v === null) return; key = v; }
+      if (!m.has(key)) m.set(key, s);
+    });
+    index.set(animalId, m);
+  }
+  return index;
+}
+```
+
+- [ ] **Step 4: Run tests to verify they pass**
+
+Run: `npx jest tests/dataExport.test.js`
+Expected: PASS (new suites green; all pre-existing suites still green).
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add frontend/src/lib/dataExport.js frontend/tests/dataExport.test.js
+git commit -m "feat(export): X-coordinate helpers (date, # days reach, daily field)"
+```
+
+---
+
+### Task 2: buildSubjectSeriesMatrix
+
+**Files:**
+- Modify: `frontend/src/lib/dataExport.js` (add `buildSubjectSeriesMatrix` + private `xFieldLabel`)
+- Test: `frontend/tests/dataExport.test.js`
+
+**Interfaces:**
+- Consumes: `listSubjectMembers`, `listSampleMembers`, `buildSampleIndex`, `extractValue`, private `hasValue`.
+- Produces: `buildSubjectSeriesMatrix({ xField, dataParam, groupField }, { statuses, animals, dailyTemplate })` → `{ corner, context: { label: 'Data', value }, groupAxis: 'col'|null, columns: [{ id, label, animalId, group }], rows: [{ member: { id, label, sampleValue }, values: [value|''] }] }` — the same shape the existing `toCSV` consumes (`groupAxis: 'col'` triggers its group-row branch).
+
+- [ ] **Step 1: Write the failing tests**
+
+Append to `frontend/tests/dataExport.test.js`:
+
+```js
+import { buildSubjectSeriesMatrix } from '../src/lib/dataExport';
+
+const ssAnimals = [
+  { id: 'a1', animal_name: 'Alpha', animal_id_string: 'R-001', subject_info: { grp: 'Control' } },
+  { id: 'a2', animal_name: 'Beta', animal_id_string: 'R-002', subject_info: { grp: 'Drug' } },
+];
+const ssStatuses = [
+  { animal_id: 'a1', date: '2026-07-01T00:00:00Z', analysis_summary: { total: 5 } },
+  { animal_id: 'a1', date: '2026-07-02T00:00:00Z', analysis_summary: { total: 7 } },
+  { animal_id: 'a2', date: '2026-07-01T00:00:00Z', analysis_summary: { total: 9 } },
+];
+const totalParam = { id: '__total__', label: 'Total attempts', kind: 'total', group: 'metrics' };
+
+describe('buildSubjectSeriesMatrix', () => {
+  it('date X: subjects as columns, date rows, blank where missing, context names the metric', () => {
+    const m = buildSubjectSeriesMatrix(
+      { xField: '__date__', dataParam: totalParam, groupField: '__none__' },
+      { statuses: ssStatuses, animals: ssAnimals, dailyTemplate: [] },
+    );
+    expect(m.corner).toBe('Date');
+    expect(m.context).toEqual({ label: 'Data', value: 'Total attempts' });
+    expect(m.groupAxis).toBeNull();
+    expect(m.columns.map((c) => c.label)).toEqual(['Alpha', 'Beta']);
+    expect(m.rows.map((r) => r.member.label)).toEqual(['2026-07-01', '2026-07-02']);
+    expect(m.rows[0].values).toEqual([5, 9]);
+    expect(m.rows[1].values).toEqual([7, '']);
+  });
+  it('# days reach X: rows align each subject by day ordinal', () => {
+    const m = buildSubjectSeriesMatrix(
+      { xField: '__days__', dataParam: totalParam, groupField: '__none__' },
+      { statuses: ssStatuses, animals: ssAnimals, dailyTemplate: [] },
+    );
+    expect(m.corner).toBe('# Days Reach');
+    expect(m.rows.map((r) => r.member.label)).toEqual(['1', '2']);
+    expect(m.rows[0].values).toEqual([5, 9]);
+    expect(m.rows[1].values).toEqual([7, '']);
+  });
+  it('grouping sets groupAxis col and clusters subjects', () => {
+    const m = buildSubjectSeriesMatrix(
+      { xField: '__date__', dataParam: totalParam, groupField: 'grp' },
+      { statuses: ssStatuses, animals: ssAnimals, dailyTemplate: [] },
+    );
+    expect(m.groupAxis).toBe('col');
+    expect(m.columns.map((c) => [c.group, c.label])).toEqual([['Control', 'Alpha'], ['Drug', 'Beta']]);
+  });
+  it('drops entirely-blank rows', () => {
+    const statuses = [
+      { animal_id: 'a1', date: '2026-07-01T00:00:00Z', analysis_summary: { total: 5 } },
+      { animal_id: 'a1', date: '2026-07-05T00:00:00Z', analysis_summary: null },
+    ];
+    const m = buildSubjectSeriesMatrix(
+      { xField: '__date__', dataParam: totalParam, groupField: '__none__' },
+      { statuses, animals: ssAnimals, dailyTemplate: [] },
+    );
+    expect(m.rows.map((r) => r.member.label)).toEqual(['2026-07-01']);
+  });
+});
+```
+
+- [ ] **Step 2: Run tests to verify they fail**
+
+Run: `npx jest tests/dataExport.test.js -t "buildSubjectSeriesMatrix"`
+Expected: FAIL — `buildSubjectSeriesMatrix is not defined`.
+
+- [ ] **Step 3: Write minimal implementation**
+
+Add to `frontend/src/lib/dataExport.js` (after `buildSampleIndex`):
+
+```js
+function xFieldLabel(xField, dailyTemplate) {
+  if (xField === '__date__') return 'Date';
+  if (xField === '__days__') return '# Days Reach';
+  return (dailyTemplate ?? []).find((f) => f.fieldId === xField)?.label ?? '';
+}
+
+// Subjects as columns, X-field values as rows, one Data value per cell.
+// Returns the { context, corner, groupAxis, columns, rows } shape toCSV consumes.
+export function buildSubjectSeriesMatrix({ xField, dataParam, groupField }, { statuses, animals, dailyTemplate }) {
+  const columns = listSubjectMembers(animals, groupField);
+  const rowMembers = listSampleMembers(statuses, xField, dailyTemplate);
+  const index = buildSampleIndex(statuses, xField, dailyTemplate);
+
+  const cellValue = (sampleValue, animalId) => {
+    const status = index.get(animalId)?.get(sampleValue);
+    if (!status || !dataParam) return '';
+    const v = extractValue(status, dataParam);
+    return hasValue(v) ? v : '';
+  };
+
+  let rows = rowMembers.map((rm) => ({
+    member: rm,
+    values: columns.map((c) => cellValue(rm.sampleValue, c.animalId)),
+  }));
+  rows = rows.filter((r) => r.values.some((v) => v !== '')); // drop entirely-blank sample rows
+
+  const grouped = !!groupField && groupField !== '__none__';
+  return {
+    corner: xFieldLabel(xField, dailyTemplate),
+    context: { label: 'Data', value: dataParam?.label ?? '' },
+    groupAxis: grouped ? 'col' : null,
+    columns,
+    rows,
+  };
+}
+```
+
+- [ ] **Step 4: Run tests to verify they pass**
+
+Run: `npx jest tests/dataExport.test.js`
+Expected: PASS (all suites green).
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add frontend/src/lib/dataExport.js frontend/tests/dataExport.test.js
+git commit -m "feat(export): buildSubjectSeriesMatrix — subjects as columns over an X field"
+```
+
+---
+
+### Task 3: Rewrite ExportDataModal to the three pickers
+
+**Files:**
+- Modify: `frontend/src/components/ExportDataModal.jsx` (full body rewrite; keep `triggerDownload`)
+- Test: `frontend/tests/ExportDataModal.test.jsx` (rewrite)
+
+**Interfaces:**
+- Consumes: `listExportableParams`, `buildSubjectSeriesMatrix`, `toCSV`, `csvFilename` from `../lib/dataExport`; `experimentsApi.getDailyStatuses`.
+- Props unchanged: `experimentTitle`, `experimentId`, `dailyTemplate`, `animals`, `subjectTemplate` (default `[]`), `groupField` (default `'__none__'`), `onClose`.
+- Produces: modal with three selects — X (rows), Data (cells), Group by.
+
+- [ ] **Step 1: Rewrite the component test**
+
+Replace the entire contents of `frontend/tests/ExportDataModal.test.jsx` with:
+
+```jsx
+import React from 'react';
+import { render, screen, waitFor, fireEvent } from '@testing-library/react';
+import ExportDataModal from '../src/components/ExportDataModal';
+import * as client from '../src/api/client';
+
+jest.mock('../src/api/client', () => ({
+  experimentsApi: { getDailyStatuses: jest.fn() },
+}));
+
+const animals = [
+  { id: 'a1', animal_name: 'Alpha', animal_id_string: 'R-001', subject_info: { grp: 'Control' } },
+  { id: 'a2', animal_name: 'Beta', animal_id_string: 'R-002', subject_info: { grp: 'Drug' } },
+];
+const statuses = [
+  { animal_id: 'a1', date: '2026-07-01T00:00:00Z', analysis_summary: { total: 5, success_rate: 0.5 } },
+  { animal_id: 'a1', date: '2026-07-02T00:00:00Z', analysis_summary: { total: 7, success_rate: 0.7 } },
+  { animal_id: 'a2', date: '2026-07-01T00:00:00Z', analysis_summary: { total: 9, success_rate: 0.9 } },
+];
+const subjectTemplate = [{ fieldId: 'grp', label: 'Group', active: true }];
+
+beforeEach(() => {
+  jest.clearAllMocks();
+  client.experimentsApi.getDailyStatuses.mockResolvedValue(statuses);
+});
+
+// Intercept the Blob handed to URL.createObjectURL so tests can read the real CSV.
+// Also stub anchor.click() — jsdom treats a click on <a href="blob:..."> as an
+// unimplemented navigation that throws async and can flake a later test.
+function mockDownload() {
+  let blob = null;
+  const origCreate = global.URL.createObjectURL;
+  const origRevoke = global.URL.revokeObjectURL;
+  const origClick = window.HTMLAnchorElement.prototype.click;
+  global.URL.createObjectURL = jest.fn((b) => { blob = b; return 'blob:mock'; });
+  global.URL.revokeObjectURL = jest.fn();
+  window.HTMLAnchorElement.prototype.click = jest.fn();
+  return {
+    getBlob: () => blob,
+    restore: async () => {
+      await new Promise((r) => setTimeout(r, 0)); // flush handleExport's setTimeout revoke
+      global.URL.createObjectURL = origCreate;
+      global.URL.revokeObjectURL = origRevoke;
+      window.HTMLAnchorElement.prototype.click = origClick;
+    },
+  };
+}
+
+function readBlobText(blob) {
+  return new Promise((resolve, reject) => {
+    const fr = new FileReader();
+    fr.onload = () => resolve(fr.result);
+    fr.onerror = reject;
+    fr.readAsText(blob);
+  });
+}
+
+function renderModal(props = {}) {
+  return render(
+    <ExportDataModal
+      experimentId="exp-1"
+      experimentTitle="My Study"
+      dailyTemplate={[]}
+      animals={animals}
+      subjectTemplate={subjectTemplate}
+      groupField="__none__"
+      onClose={jest.fn()}
+      {...props}
+    />,
+  );
+}
+
+describe('ExportDataModal', () => {
+  it('renders X / Data / Group by pickers; X defaults to Date', async () => {
+    renderModal();
+    expect(await screen.findByLabelText('X axis (rows)')).toHaveValue('__date__');
+    expect(screen.getByLabelText('Data (cells)')).toBeInTheDocument();
+    expect(screen.getByLabelText('Group by')).toHaveValue('__none__');
+    await waitFor(() => expect(screen.getByText(/2 rows × 2 subjects/i)).toBeInTheDocument());
+  });
+
+  it('default export (X=Date, Data=Total attempts) has subject columns, date rows, blank where missing', async () => {
+    const dl = mockDownload();
+    renderModal();
+    await screen.findByLabelText('X axis (rows)');
+    await waitFor(() => expect(screen.getByText(/2 rows/i)).toBeInTheDocument());
+    fireEvent.click(screen.getByText('Export CSV'));
+    const text = await readBlobText(dl.getBlob());
+    expect(text).toBe('Data,Total attempts\n\nDate,Alpha,Beta\n2026-07-01,5,9\n2026-07-02,7,');
+    await dl.restore();
+  });
+
+  it('switching X to # Days Reach makes rows the day ordinals', async () => {
+    const dl = mockDownload();
+    renderModal();
+    fireEvent.change(await screen.findByLabelText('X axis (rows)'), { target: { value: '__days__' } });
+    await waitFor(() => expect(screen.getByText(/2 rows/i)).toBeInTheDocument());
+    fireEvent.click(screen.getByText('Export CSV'));
+    const text = await readBlobText(dl.getBlob());
+    expect(text).toBe('Data,Total attempts\n\n# Days Reach,Alpha,Beta\n1,5,9\n2,7,');
+    await dl.restore();
+  });
+
+  it('a valid group field adds a Group row and clusters subjects', async () => {
+    const dl = mockDownload();
+    renderModal({ groupField: 'grp' });
+    await screen.findByLabelText('X axis (rows)');
+    await waitFor(() => expect(screen.getByText(/2 rows/i)).toBeInTheDocument());
+    fireEvent.click(screen.getByText('Export CSV'));
+    const text = await readBlobText(dl.getBlob());
+    expect(text).toMatch(/^Group,Control,Drug$/m);
+    await dl.restore();
+  });
+});
+```
+
+- [ ] **Step 2: Run the test to verify it fails**
+
+Run: `npx jest tests/ExportDataModal.test.jsx`
+Expected: FAIL — old modal has no `X axis (rows)` / `Data (cells)` labels.
+
+- [ ] **Step 3: Rewrite the component**
+
+Replace the entire contents of `frontend/src/components/ExportDataModal.jsx` with:
+
+```jsx
+import React, { useEffect, useMemo, useState } from 'react';
+import { experimentsApi } from '../api/client';
+import Button from './ui/Button';
+import Alert from './ui/Alert';
+import { listExportableParams, buildSubjectSeriesMatrix, toCSV, csvFilename } from '../lib/dataExport';
+
+const PARAM_GROUP_LABELS = { metrics: 'Session metrics', daily: 'Daily record fields' };
+
+function triggerDownload(filename, text) {
+  const blob = new Blob([text], { type: 'text/csv;charset=utf-8;' });
+  const url = URL.createObjectURL(blob);
+  const a = document.createElement('a');
+  a.href = url;
+  a.download = filename;
+  document.body.appendChild(a);
+  a.click();
+  document.body.removeChild(a);
+  setTimeout(() => URL.revokeObjectURL(url), 0);
+}
+
+export default function ExportDataModal({
+  experimentTitle, experimentId, dailyTemplate, animals, subjectTemplate = [], groupField = '__none__', onClose,
+}) {
+  const [loading, setLoading] = useState(true);
+  const [error, setError] = useState(null);
+  const [statuses, setStatuses] = useState([]);
+
+  const [xField, setXField] = useState('__date__');
+  const [dataId, setDataId] = useState(null);
+  const [groupBy, setGroupBy] = useState(() => {
+    const valid = groupField === '__none__' || groupField === '__name__' || groupField === '__id__'
+      || subjectTemplate.some((f) => f.active && f.fieldId === groupField);
+    return valid ? groupField : '__none__';
+  });
+
+  useEffect(() => {
+    let alive = true;
+    setLoading(true);
+    setError(null);
+    experimentsApi.getDailyStatuses(experimentId, {})
+      .then((data) => { if (alive) setStatuses(data); })
+      .catch((err) => { if (alive) setError(err.message); })
+      .finally(() => { if (alive) setLoading(false); });
+    return () => { alive = false; };
+  }, [experimentId]);
+
+  const params = useMemo(() => listExportableParams(dailyTemplate, statuses), [dailyTemplate, statuses]);
+  const effDataId = dataId ?? params[0]?.id ?? null;
+  const dataParam = params.find((p) => p.id === effDataId) ?? params[0] ?? null;
+
+  const activeDaily = useMemo(() => (dailyTemplate ?? []).filter((f) => f.active), [dailyTemplate]);
+  const xOptions = useMemo(() => [
+    { id: '__date__', label: 'Date' },
+    { id: '__days__', label: '# Days Reach' },
+    ...activeDaily.map((f) => ({ id: f.fieldId, label: f.label })),
+  ], [activeDaily]);
+
+  const matrix = useMemo(
+    () => (dataParam
+      ? buildSubjectSeriesMatrix({ xField, dataParam, groupField: groupBy }, { statuses, animals, dailyTemplate })
+      : { columns: [], rows: [], context: { label: 'Data', value: '' }, corner: '', groupAxis: null }),
+    [xField, dataParam, groupBy, statuses, animals, dailyTemplate],
+  );
+  const hasData = matrix.rows.length > 0;
+
+  const groupedParams = useMemo(() => {
+    const out = [];
+    for (const p of params) {
+      let g = out.find((x) => x.group === p.group);
+      if (!g) { g = { group: p.group, items: [] }; out.push(g); }
+      g.items.push(p);
+    }
+    return out;
+  }, [params]);
+
+  function handleExport() {
+    if (!hasData || !dataParam) return;
+    triggerDownload(csvFilename(experimentTitle, dataParam.label), toCSV(matrix));
+    onClose();
+  }
+
+  if (loading) return <p className="text-sm text-gray-400" aria-live="polite">Loading data…</p>;
+  if (error) return (
+    <div className="space-y-4">
+      <Alert type="error" message={error} />
+      <div className="flex justify-end"><Button variant="secondary" onClick={onClose}>Close</Button></div>
+    </div>
+  );
+
+  const selectCls = 'w-full border border-gray-200 rounded px-2 py-1.5 text-sm bg-white focus:outline-none focus:ring-1 focus:ring-indigo-400';
+
+  return (
+    <div className="space-y-4">
+      <p className="text-sm text-gray-500">
+        One value per subject over an X axis — each column is a subject, each row an X value. Blank where a subject has no value.
+      </p>
+
+      <div>
+        <label htmlFor="export-x" className="block text-xs font-medium text-gray-500 mb-1">X axis (rows)</label>
+        <select id="export-x" value={xField} onChange={(e) => setXField(e.target.value)} className={selectCls}>
+          {xOptions.map((o) => <option key={o.id} value={o.id}>{o.label}</option>)}
+        </select>
+      </div>
+
+      <div>
+        <label htmlFor="export-data" className="block text-xs font-medium text-gray-500 mb-1">Data (cells)</label>
+        <select id="export-data" value={effDataId ?? ''} onChange={(e) => setDataId(e.target.value)} className={selectCls}>
+          {groupedParams.map((g) => (
+            <optgroup key={g.group} label={PARAM_GROUP_LABELS[g.group] ?? g.group}>
+              {g.items.map((p) => <option key={p.id} value={p.id}>{p.label}</option>)}
+            </optgroup>
+          ))}
+        </select>
+      </div>
+
+      <div>
+        <label htmlFor="export-group" className="block text-xs font-medium text-gray-500 mb-1">Group by</label>
+        <select id="export-group" value={groupBy} onChange={(e) => setGroupBy(e.target.value)} className={selectCls}>
+          <option value="__none__">— None —</option>
+          <option value="__name__">Name</option>
+          <option value="__id__">Subject ID</option>
+          {subjectTemplate.filter((f) => f.active).map((f) => (
+            <option key={f.fieldId} value={f.fieldId}>{f.label}</option>
+          ))}
+        </select>
+      </div>
+
+      <p className="text-xs text-gray-400">
+        {hasData
+          ? `${matrix.rows.length} row${matrix.rows.length !== 1 ? 's' : ''} × ${matrix.columns.length} subject${matrix.columns.length !== 1 ? 's' : ''}`
+          : 'No data for this selection yet.'}
+      </p>
+
+      <div className="flex justify-end gap-2 pt-2">
+        <Button variant="secondary" onClick={onClose}>Cancel</Button>
+        <Button onClick={handleExport} disabled={!hasData}>Export CSV</Button>
+      </div>
+    </div>
+  );
+}
+```
+
+- [ ] **Step 4: Run the test to verify it passes**
+
+Run: `npx jest tests/ExportDataModal.test.jsx`
+Expected: PASS (all four cases).
+
+- [ ] **Step 5: Run the full suite**
+
+Run: `npm test`
+Expected: `dataExport` + `ExportDataModal` green; only the two known pre-existing suites fail; no new failures.
+
+- [ ] **Step 6: Commit**
+
+```bash
+git add frontend/src/components/ExportDataModal.jsx frontend/tests/ExportDataModal.test.jsx
+git commit -m "feat(export): modal becomes X / Data / Group by pickers, subjects as columns"
+```
+
+---
+
+### Task 4: Remove the dead assignable-axes code
+
+**Files:**
+- Modify: `frontend/src/lib/dataExport.js` (delete unused exports)
+- Test: `frontend/tests/dataExport.test.js` (delete their describe blocks + imports)
+
+**Interfaces:**
+- Removes (now unused after Task 3): `EXPORT_DIMENSIONS`, `dimLabel`, `otherDimension`, `listDateMembers`, `buildStatusIndex`, `listParameterMembers`, `listDimensionMembers`, and `buildMatrix`.
+- Keeps: `extractValue`, `listExportableParams`, `subjectGroupValue`, `listSubjectMembers`, `numericFieldVal`, `listSampleMembers`, `buildSampleIndex`, `buildSubjectSeriesMatrix`, `toCSV`, `csvFilename`, and the private `hasValue`, `dateKey`, `escapeCsvCell`, `xFieldLabel`.
+
+- [ ] **Step 1: Delete the dead source exports**
+
+In `frontend/src/lib/dataExport.js`, delete these eight exported definitions entirely (they are only used by each other and the old modal, now replaced): `EXPORT_DIMENSIONS`, `dimLabel`, `otherDimension`, `listDateMembers`, `buildStatusIndex`, `listParameterMembers`, `listDimensionMembers`, `buildMatrix`. Also update the top-of-file comment to describe the subject-series export (drop the "dimension-agnostic buildMatrix" wording). Keep `dateKey`, `hasValue`, `escapeCsvCell`, and everything listed under "Keeps" above.
+
+- [ ] **Step 2: Delete their tests**
+
+In `frontend/tests/dataExport.test.js`, delete the describe blocks and their now-unused import identifiers for the removed functions: the `describe('dimension primitives', …)`, `describe('subjectGroupValue', …)` KEEP (subjectGroupValue is kept — do NOT delete it), `describe('listDateMembers', …)`, `describe('buildStatusIndex', …)`, `describe('listParameterMembers / listDimensionMembers', …)`, and `describe('buildMatrix', …)` blocks. Remove `EXPORT_DIMENSIONS`, `dimLabel`, `otherDimension`, `listDateMembers`, `buildStatusIndex`, `listDimensionMembers`, `listParameterMembers`, and `buildMatrix` from the file's `import` statements, leaving the imports for kept functions (`extractValue`, `listExportableParams`, `subjectGroupValue`, `listSubjectMembers`, `numericFieldVal`, `listSampleMembers`, `buildSampleIndex`, `buildSubjectSeriesMatrix`, `toCSV`, `csvFilename`).
+
+- [ ] **Step 3: Verify nothing references the removed names**
+
+Run: `grep -rnE "buildMatrix|EXPORT_DIMENSIONS|listDimensionMembers|buildStatusIndex|listDateMembers|listParameterMembers|otherDimension|dimLabel" frontend/src frontend/tests`
+Expected: no output (all references gone).
+
+- [ ] **Step 4: Run the suites**
+
+Run: `npx jest tests/dataExport.test.js tests/ExportDataModal.test.jsx`
+Expected: PASS. Then `npm test` — only the two known pre-existing suites fail, no new failures.
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add frontend/src/lib/dataExport.js frontend/tests/dataExport.test.js
+git commit -m "refactor(export): remove dead assignable-axes helpers and their tests"
+```
+
+---
+
+## Self-Review Notes
+
+- **Spec coverage:** three pickers (Task 3) · X = Date/# Days Reach/daily field with the exact semantics (Tasks 1–2) · subjects always columns, clustered by group, blank rows dropped (Task 2) · Group by validated/defaulted (Task 3) · CSV `Data,<metric>` + group row + X-corner (Tasks 2–3, reusing `toCSV`) · assignable-axes UI/machinery removed (Tasks 3–4). All covered.
+- **Type consistency:** sample member `{ id, label, sampleValue }`, subject column `{ id, label, animalId, group }`, and matrix `{ context, corner, groupAxis, columns, rows }` are used identically across Tasks 1–3 and match the existing `toCSV`.
+- **Out of scope (per spec):** assignable columns/pinned, subject subsetting, analyzed-only day counting, footer/repeat line.

docs/superpowers/specs/2026-07-19-subject-series-export-design.md

@@ -0,0 +1,86 @@
+# Subject-Series Data Export — Design
+
+**Date:** 2026-07-19
+**Status:** Approved (design)
+**Area:** `frontend/src/lib/dataExport.js`, `frontend/src/components/ExportDataModal.jsx`, `frontend/tests/`
+**Supersedes:** the assignable-axes UI from `2026-07-19-configurable-data-export-design.md` (that branch's `buildMatrix`/`toCSV`/subject-group work is reused; its rows/columns/pinned modal is replaced).
+
+## Problem
+
+The export modal currently offers assignable row/column/pinned axes. That is more than needed. The user wants the export to mirror the **cross-subject metrics plot**: pick an **X field** and a **data value**, with each **subject as a column series**, empty where a subject has no data point, plus each subject's **group** included. The one capability missing today is choosing the X (row) field — it is fixed to calendar Date, whereas the plot's X-axis offers **# Days Reach** and daily fields.
+
+## Layout (fixed)
+
+- **Columns = subjects**, one series per subject, ordered clustered-by-group then by name (existing `listSubjectMembers`).
+- **Rows = the chosen X field's values.**
+- **Cells = the chosen Data value** for that (subject, X); empty (`''`) when absent. `0` and `''` remain real values (existing `hasValue`/`extractValue`).
+- **Group row** gives each subject's group value (existing group-row support).
+
+There is no pinned dimension and no assignable columns.
+
+## The three pickers
+
+1. **X (rows).** Options: `Date`, `# Days Reach`, and each **active** daily-template field. Default `Date` (keeps the same date-rows × subject-columns grid as today; only the line-1 label changes to `Data`, see CSV output).
+2. **Data (cells).** Options: `listExportableParams(dailyTemplate, statuses)` — total, success rate, each count category, each daily field. Default: first parameter (`Total attempts`).
+3. **Group by.** Options: `— None —`, `Name`, `Subject ID`, and each active `subject_info` field. Defaults to the experiment's saved group field (`localStorage['exp-subject-group-<id>']`), validated against the available options and coerced to `__none__` when unset or stale. Always visible (subjects are always the columns).
+
+Preview line: `N rows × M subjects`. Export disabled when there are no data rows.
+
+## X-field semantics
+
+Each X coordinate maps a `(subject, X-value)` pair to a single daily status, from which the Data value is read:
+
+- **`Date`** → rows are the distinct calendar dates present (`YYYY-MM-DD`), sorted. Cell reads the subject's status on that date. (Today's behavior.)
+- **`# Days Reach`** → rows are `1, 2, 3, …` up to the maximum, per subject, of the count of that subject's recorded daily statuses. A cell for `(subject, N)` reads the subject's **Nth recorded day**, counting **all** recorded statuses sorted ascending by date (Day 1 = earliest record; days without an analysis summary are still counted — this intentionally differs from the plot, which counts analyzed sessions only).
+- **daily field** (e.g. `Weight (g)`) → rows are the distinct **numeric** values that field takes across the data (`numericFieldVal`: builtin via `status[field.key]`, custom via `status.custom_fields[field.fieldId]`, `parseFloat`, non-numeric → excluded), sorted ascending. A cell for `(subject, v)` reads that subject's status where the field equals `v`; on ties, first status by date wins.
+
+Row members that are entirely blank across all subjects are dropped (matching Date's existing behavior); subject columns are always kept even when empty.
+
+## CSV output
+
+Structure (X = # Days Reach, Data = Success rate, Group by = Treatment):
+
+```
+Data,Success rate
+Group,Control,Control,Drug
+# Days Reach,Mouse-A,Mouse-B,Mouse-C
+1,0.5,0.6,0.7
+2,0.55,,0.72
+3,0.6,0.65,
+```
+
+- **Line 1** names the Data metric so the file is self-describing: two cells, `Data` and the metric label.
+- **Blank line.**
+- **Group row** (`Group` + each subject's group), only when Group by ≠ None; missing group value → `—`.
+- **Header row:** the X-field label in the corner, then subject headers.
+- **Data rows:** the X value, then one cell per subject.
+
+LF line endings; every cell escaped via existing `escapeCsvCell`. Filename: existing `csvFilename(title, dataLabel)`.
+
+## Implementation notes
+
+Reuse from the current branch: `extractValue`, `listExportableParams`, `subjectGroupValue`, `listSubjectMembers`, `escapeCsvCell`, `toCSV` (its `{ context, corner, groupAxis, columns, rows }` shape is kept), and `csvFilename`.
+
+Replace: the general assignable-axes `buildMatrix(config, ctx)` and its members/dispatch helpers (`otherDimension`, `dimLabel`, `EXPORT_DIMENSIONS`, `listDateMembers`, `listDimensionMembers`, `listParameterMembers`, `buildStatusIndex` in its current form) — remove what the fixed layout no longer uses rather than leaving dead code.
+
+Add:
+- `numericFieldVal(status, field)` — mirror of the plot's helper (or import/share it).
+- `listSampleMembers(statuses, xField, dailyTemplate)` → the ordered row members for the chosen X coordinate (`{ id, label, ... }`).
+- `buildSampleIndex(statuses, xField, dailyTemplate)` → `Map(animalId → Map(sampleValue → status))`, first-status-per-cell wins, encoding the date/#days/field resolution above.
+- `buildSubjectSeriesMatrix({ xField, dataParam, groupField }, { statuses, animals, dailyTemplate })` → `{ context: { label: 'Data', value: dataParam.label }, corner: <X label>, groupAxis: grouping ? 'col' : null, columns: <subjects>, rows: [{ member, values }] }`, consumable by the existing `toCSV`.
+
+Simplify `ExportDataModal.jsx` to the three pickers over these helpers; drop the Rows/Columns/Fixed controls.
+
+## Testing
+
+- `listSampleMembers` / `buildSampleIndex` for each X coordinate: Date (unchanged), # Days Reach (all-records ordinal, per-subject alignment), daily field (distinct numeric values sorted, non-numeric dropped, tie first-by-date).
+- `buildSubjectSeriesMatrix`: subjects as columns clustered by group; empty cells where a subject lacks a point; blank row dropped; `groupAxis` null when Group by None.
+- `toCSV` reused; add/keep a snapshot for the new context line + group row.
+- `ExportDataModal`: renders three pickers; default X = Date produces date rows × subject columns with the chosen metric in cells; switching X to # Days Reach changes rows to ordinals; group row present when a valid group field is chosen; stale/unset group field coerces to None.
+
+## Out of scope (YAGNI)
+
+- Assignable columns / pinned dimension (removed).
+- Choosing a subset of subjects (all subjects always included).
+- Matching the plot's analyzed-only day counting (we count all recorded days).
+- A footer or repeated context line.

frontend/src/components/ExportDataModal.jsx

@@ -2,9 +2,9 @@ import React, { useEffect, useMemo, useState } from 'react';
 import { experimentsApi } from '../api/client';
 import Button from './ui/Button';
 import Alert from './ui/Alert';
-import { listExportableParams, buildMatrix, toCSV, csvFilename } from '../lib/dataExport';
+import { listExportableParams, buildSubjectSeriesMatrix, toCSV, csvFilename } from '../lib/dataExport';
 
-const GROUP_LABELS = { metrics: 'Session metrics', daily: 'Daily record fields' };
+const PARAM_GROUP_LABELS = { metrics: 'Session metrics', daily: 'Daily record fields' };
 
 function triggerDownload(filename, text) {
   const blob = new Blob([text], { type: 'text/csv;charset=utf-8;' });
@@ -18,11 +18,20 @@ function triggerDownload(filename, text) {
   setTimeout(() => URL.revokeObjectURL(url), 0);
 }
 
-export default function ExportDataModal({ experimentTitle, experimentId, dailyTemplate, animals, onClose }) {
+export default function ExportDataModal({
+  experimentTitle, experimentId, dailyTemplate, animals, subjectTemplate = [], groupField = '__none__', onClose,
+}) {
   const [loading, setLoading] = useState(true);
   const [error, setError] = useState(null);
   const [statuses, setStatuses] = useState([]);
-  const [selectedId, setSelectedId] = useState(null);
+
+  const [xField, setXField] = useState('__date__');
+  const [dataId, setDataId] = useState(null);
+  const [groupBy, setGroupBy] = useState(() => {
+    const valid = groupField === '__none__' || groupField === '__name__' || groupField === '__id__'
+      || subjectTemplate.some((f) => f.active && f.fieldId === groupField);
+    return valid ? groupField : '__none__';
+  });
 
   useEffect(() => {
     let alive = true;
@@ -35,21 +44,26 @@ export default function ExportDataModal({ experimentTitle, experimentId, dailyTe
     return () => { alive = false; };
   }, [experimentId]);
 
-  const params = useMemo(() => listExportableParams(dailyTemplate, statuses), [dailyTemplate, statuses]);
+  const params = useMemo(() => listExportableParams(dailyTemplate ?? [], statuses), [dailyTemplate, statuses]);
+  const effDataId = dataId ?? params[0]?.id ?? null;
+  const dataParam = params.find((p) => p.id === effDataId) ?? params[0] ?? null;
 
-  // Derive the effective selection: the user's pick, else the first parameter.
-  const effectiveId = selectedId ?? params[0]?.id ?? null;
-  const selectedParam = params.find((p) => p.id === effectiveId) ?? null;
+  const activeDaily = useMemo(() => (dailyTemplate ?? []).filter((f) => f.active), [dailyTemplate]);
+  const xOptions = useMemo(() => [
+    { id: '__date__', label: 'Date' },
+    { id: '__days__', label: '# Days Reach (ordinal)' },
+    ...activeDaily.map((f) => ({ id: f.fieldId, label: f.label })),
+  ], [activeDaily]);
 
   const matrix = useMemo(
-    () => (selectedParam ? buildMatrix(statuses, animals, selectedParam) : { columns: [], rows: [] }),
-    [statuses, animals, selectedParam],
+    () => (dataParam
+      ? buildSubjectSeriesMatrix({ xField, dataParam, groupField: groupBy }, { statuses, animals, dailyTemplate })
+      : { columns: [], rows: [], context: { label: 'Data', value: '' }, corner: '', groupAxis: null }),
+    [xField, dataParam, groupBy, statuses, animals, dailyTemplate],
   );
-
   const hasData = matrix.rows.length > 0;
 
-  // Group params for <optgroup> rendering, preserving order.
-  const grouped = useMemo(() => {
+  const groupedParams = useMemo(() => {
     const out = [];
     for (const p of params) {
       let g = out.find((x) => x.group === p.group);
@@ -60,8 +74,8 @@ export default function ExportDataModal({ experimentTitle, experimentId, dailyTe
   }, [params]);
 
   function handleExport() {
-    if (!hasData || !selectedParam) return;
-    triggerDownload(csvFilename(experimentTitle, selectedParam.label), toCSV(matrix));
+    if (!hasData || !dataParam) return;
+    triggerDownload(csvFilename(experimentTitle, dataParam.label), toCSV(matrix));
     onClose();
   }
 
@@ -69,38 +83,52 @@ export default function ExportDataModal({ experimentTitle, experimentId, dailyTe
   if (error) return (
     <div className="space-y-4">
       <Alert type="error" message={error} />
-      <div className="flex justify-end">
-        <Button variant="secondary" onClick={onClose}>Close</Button>
-      </div>
+      <div className="flex justify-end"><Button variant="secondary" onClick={onClose}>Close</Button></div>
     </div>
   );
 
+  const selectCls = 'w-full border border-gray-200 rounded px-2 py-1.5 text-sm bg-white focus:outline-none focus:ring-1 focus:ring-indigo-400';
+
   return (
     <div className="space-y-4">
       <p className="text-sm text-gray-500">
-        Export one parameter as a CSV file — each row is a date, each column is a subject.
+        One value per subject over an X axis — each column is a subject, each row an X value. Blank where a subject has no value.
       </p>
 
       <div>
-        <label htmlFor="export-param" className="block text-xs font-medium text-gray-500 mb-1">Parameter</label>
-        <select
-          id="export-param"
-          value={effectiveId ?? ''}
-          onChange={(e) => setSelectedId(e.target.value)}
-          className="w-full border border-gray-200 rounded px-2 py-1.5 text-sm bg-white focus:outline-none focus:ring-1 focus:ring-indigo-400"
-        >
-          {grouped.map((g) => (
-            <optgroup key={g.group} label={GROUP_LABELS[g.group] ?? g.group}>
+        <label htmlFor="export-x" className="block text-xs font-medium text-gray-500 mb-1">X axis (rows)</label>
+        <select id="export-x" value={xField} onChange={(e) => setXField(e.target.value)} className={selectCls}>
+          {xOptions.map((o) => <option key={o.id} value={o.id}>{o.label}</option>)}
+        </select>
+      </div>
+
+      <div>
+        <label htmlFor="export-data" className="block text-xs font-medium text-gray-500 mb-1">Data (cells)</label>
+        <select id="export-data" value={effDataId ?? ''} onChange={(e) => setDataId(e.target.value)} className={selectCls}>
+          {groupedParams.map((g) => (
+            <optgroup key={g.group} label={PARAM_GROUP_LABELS[g.group] ?? g.group}>
               {g.items.map((p) => <option key={p.id} value={p.id}>{p.label}</option>)}
             </optgroup>
           ))}
         </select>
       </div>
 
+      <div>
+        <label htmlFor="export-group" className="block text-xs font-medium text-gray-500 mb-1">Group by</label>
+        <select id="export-group" value={groupBy} onChange={(e) => setGroupBy(e.target.value)} className={selectCls}>
+          <option value="__none__">— None —</option>
+          <option value="__name__">Name</option>
+          <option value="__id__">Subject ID</option>
+          {subjectTemplate.filter((f) => f.active).map((f) => (
+            <option key={f.fieldId} value={f.fieldId}>{f.label}</option>
+          ))}
+        </select>
+      </div>
+
       <p className="text-xs text-gray-400">
         {hasData
-          ? `${matrix.rows.length} date${matrix.rows.length !== 1 ? 's' : ''} × ${matrix.columns.length} subject${matrix.columns.length !== 1 ? 's' : ''}`
-          : 'No data for this parameter yet.'}
+          ? `${matrix.rows.length} row${matrix.rows.length !== 1 ? 's' : ''} × ${matrix.columns.length} subject${matrix.columns.length !== 1 ? 's' : ''}`
+          : 'No data for this selection yet.'}
       </p>
 
       <div className="flex justify-end gap-2 pt-2">

frontend/src/lib/dataExport.js

@@ -1,4 +1,7 @@
-// Pure helpers for exporting daily-parameter data as a date×subject CSV matrix.
+// Pure helpers for exporting daily-parameter data as a CSV matrix. The export is a
+// subject-series matrix: subjects are always columns, the chosen X coordinate
+// (Date / # Days Reach / a daily field) provides the rows, and one Data
+// parameter supplies the cell values.
 // No React, no I/O — mirrors the crossSubjectChart.js pure-helper pattern.
 
 // Read a single parameter's raw value from one daily status.
@@ -54,6 +57,140 @@ export function listExportableParams(dailyTemplate, statuses) {
   return params;
 }
 
+// Numeric value of a daily field on one status (mirrors the metrics plot's helper).
+export function numericFieldVal(status, field) {
+  if (!field) return null;
+  const raw = field.builtin ? status?.[field.key] : status?.custom_fields?.[field.fieldId];
+  const n = parseFloat(raw);
+  return Number.isNaN(n) ? null : n;
+}
+
+// Ordered row members for the chosen X coordinate.
+// xField: '__date__' | '__days__' | <daily fieldId>
+export function listSampleMembers(statuses, xField, dailyTemplate) {
+  const list = statuses ?? [];
+  if (xField === '__date__') {
+    const keys = new Set();
+    for (const s of list) if (s?.date) keys.add(dateKey(s.date));
+    return [...keys].sort().map((k) => ({ id: k, label: k, sampleValue: k }));
+  }
+  if (xField === '__days__') {
+    const counts = new Map();
+    for (const s of list) {
+      if (s?.animal_id == null || !s?.date) continue;
+      counts.set(s.animal_id, (counts.get(s.animal_id) ?? 0) + 1);
+    }
+    const max = counts.size ? Math.max(...counts.values()) : 0;
+    return Array.from({ length: max }, (_, i) => ({ id: String(i + 1), label: String(i + 1), sampleValue: i + 1 }));
+  }
+  const field = (dailyTemplate ?? []).find((f) => f.fieldId === xField);
+  const vals = new Set();
+  for (const s of list) {
+    const v = numericFieldVal(s, field);
+    if (v !== null) vals.add(v);
+  }
+  return [...vals].sort((a, b) => a - b).map((v) => ({ id: String(v), label: String(v), sampleValue: v }));
+}
+
+// animalId -> Map(sampleValue -> status). First status per (subject, sampleValue) wins,
+// scanning each subject's statuses in ascending date order.
+export function buildSampleIndex(statuses, xField, dailyTemplate) {
+  const bySubject = new Map();
+  for (const s of statuses ?? []) {
+    if (s?.animal_id == null || !s?.date) continue;
+    if (!bySubject.has(s.animal_id)) bySubject.set(s.animal_id, []);
+    bySubject.get(s.animal_id).push(s);
+  }
+  const field = xField !== '__date__' && xField !== '__days__'
+    ? (dailyTemplate ?? []).find((f) => f.fieldId === xField)
+    : null;
+
+  const index = new Map();
+  for (const [animalId, subjStatuses] of bySubject) {
+    const ordered = [...subjStatuses].sort((a, b) => dateKey(a.date).localeCompare(dateKey(b.date)));
+    const m = new Map();
+    ordered.forEach((s, i) => {
+      let key;
+      if (xField === '__date__') key = dateKey(s.date);
+      else if (xField === '__days__') key = i + 1;
+      else { const v = numericFieldVal(s, field); if (v === null) return; key = v; }
+      if (!m.has(key)) m.set(key, s);
+    });
+    index.set(animalId, m);
+  }
+  return index;
+}
+
+function xFieldLabel(xField, dailyTemplate) {
+  if (xField === '__date__') return 'Date';
+  if (xField === '__days__') return '# Days Reach (ordinal)';
+  return (dailyTemplate ?? []).find((f) => f.fieldId === xField)?.label ?? '';
+}
+
+// Subjects as columns, X-field values as rows, one Data value per cell.
+// Returns the { context, corner, groupAxis, columns, rows } shape toCSV consumes.
+export function buildSubjectSeriesMatrix({ xField, dataParam, groupField }, { statuses, animals, dailyTemplate }) {
+  const columns = listSubjectMembers(animals, groupField);
+  const rowMembers = listSampleMembers(statuses, xField, dailyTemplate);
+  const index = buildSampleIndex(statuses, xField, dailyTemplate);
+
+  const cellValue = (sampleValue, animalId) => {
+    const status = index.get(animalId)?.get(sampleValue);
+    if (!status || !dataParam) return '';
+    const v = extractValue(status, dataParam);
+    return hasValue(v) ? v : '';
+  };
+
+  let rows = rowMembers.map((rm) => ({
+    member: rm,
+    values: columns.map((c) => cellValue(rm.sampleValue, c.animalId)),
+  }));
+  rows = rows.filter((r) => r.values.some((v) => v !== '')); // drop entirely-blank sample rows
+
+  const grouped = !!groupField && groupField !== '__none__';
+  return {
+    corner: xFieldLabel(xField, dailyTemplate),
+    context: { label: 'Data', value: dataParam?.label ?? '' },
+    groupAxis: grouped ? 'col' : null,
+    columns,
+    rows,
+  };
+}
+
+// Resolve a subject's group value for a given group field id.
+// null  -> no grouping (field is falsy or '__none__')
+// '—'   -> grouping is on but this subject has no value
+export function subjectGroupValue(animal, groupField) {
+  if (!groupField || groupField === '__none__') return null;
+  if (groupField === '__name__') return animal?.animal_name ?? '—';
+  if (groupField === '__id__') return animal?.animal_id_string ?? '—';
+  const v = animal?.subject_info?.[groupField];
+  return v === null || v === undefined || v === '' ? '—' : v;
+}
+
+export function listSubjectMembers(animals, groupField) {
+  const list = [...(animals ?? [])];
+  const nameCounts = new Map();
+  for (const a of list) {
+    const n = a?.animal_name ?? '';
+    nameCounts.set(n, (nameCounts.get(n) ?? 0) + 1);
+  }
+  const members = list.map((a) => {
+    const name = a?.animal_name ?? '';
+    const label = nameCounts.get(name) > 1 ? `${name} (${a?.animal_id_string ?? ''})` : name;
+    return { id: a?.id, label, animalId: a?.id, group: subjectGroupValue(a, groupField) };
+  });
+  const grouped = !!groupField && groupField !== '__none__';
+  members.sort((x, y) => {
+    if (grouped) {
+      const g = String(x.group ?? '').localeCompare(String(y.group ?? ''));
+      if (g !== 0) return g;
+    }
+    return String(x.label).localeCompare(String(y.label));
+  });
+  return members;
+}
+
 // A cell has a value unless it is null, undefined, or the empty string.
 // (0 and false are real values.)
 function hasValue(v) {
@@ -64,66 +201,42 @@ function dateKey(date) {
   return String(date).slice(0, 10); // ISO datetime or date → YYYY-MM-DD
 }
 
-// Pivot statuses into { columns, rows }.
-//   columns: [{ id, header }] — all animals, ordered by name, headers
-//            disambiguated with animal_id_string on duplicate names.
-//   rows:    [{ date, values }] — one per date that has ≥1 value; values are
-//            aligned to columns, blank cells are '' (empty string).
-export function buildMatrix(statuses, animals, param) {
-  const sortedAnimals = [...(animals ?? [])].sort((a, b) =>
-    String(a.animal_name ?? '').localeCompare(String(b.animal_name ?? '')),
-  );
-
-  const nameCounts = new Map();
-  for (const a of sortedAnimals) {
-    const n = a.animal_name ?? '';
-    nameCounts.set(n, (nameCounts.get(n) ?? 0) + 1);
-  }
-  const columns = sortedAnimals.map((a) => {
-    const name = a.animal_name ?? '';
-    const header = nameCounts.get(name) > 1 ? `${name} (${a.animal_id_string ?? ''})` : name;
-    return { id: a.id, header };
-  });
-  const colIndex = new Map(columns.map((c, i) => [c.id, i]));
-
-  // cells: dateKey -> Map(animalId -> value); first status per (date, animal) wins.
-  const cells = new Map();
-  const datesWithData = new Set();
-  const ordered = [...(statuses ?? [])].sort((a, b) => dateKey(a.date).localeCompare(dateKey(b.date)));
-  for (const s of ordered) {
-    if (!colIndex.has(s.animal_id)) continue;
-    const dk = dateKey(s.date);
-    if (!cells.has(dk)) cells.set(dk, new Map());
-    const byAnimal = cells.get(dk);
-    if (byAnimal.has(s.animal_id)) continue; // first wins
-    const v = extractValue(s, param);
-    byAnimal.set(s.animal_id, v);
-    if (hasValue(v)) datesWithData.add(dk);
-  }
-
-  const rows = [...datesWithData].sort().map((dk) => {
-    const byAnimal = cells.get(dk);
-    const values = columns.map((c) => {
-      const v = byAnimal.has(c.id) ? byAnimal.get(c.id) : null;
-      return hasValue(v) ? v : '';
-    });
-    return { date: dk, values };
-  });
-
-  return { columns, rows };
-}
-
 function escapeCsvCell(value) {
   const s = value === null || value === undefined ? '' : String(value);
   return /[",\n]/.test(s) ? `"${s.replace(/"/g, '""')}"` : s;
 }
 
-// Serialize a matrix to an RFC-4180-ish CSV string (LF line endings).
-// First column is the date; remaining columns follow matrix.columns order.
+// Serialize a matrix to an RFC-4180-ish CSV string (LF line endings):
+//   Data,<metric>
+//   (blank line)
+//   [Group,<group per subject column>]    -- only when groupAxis === 'col'
+//   <corner>[,Group?],<column headers...>
+//   <row label>[,group?],<values...>
 export function toCSV(matrix) {
-  const header = ['Date', ...matrix.columns.map((c) => c.header)].map(escapeCsvCell).join(',');
-  const lines = matrix.rows.map((r) => [r.date, ...r.values].map(escapeCsvCell).join(','));
-  return [header, ...lines].join('\n');
+  const lines = [];
+  lines.push([matrix.context.label, matrix.context.value].map(escapeCsvCell).join(','));
+  lines.push('');
+
+  if (matrix.groupAxis === 'col') {
+    const groupRow = ['Group', ...matrix.columns.map((c) => c.group ?? '—')];
+    lines.push(groupRow.map(escapeCsvCell).join(','));
+  }
+
+  // toCSV is a general serializer; the subject-series builder never emits groupAxis
+  // === 'row' (only 'col' or null), but the branch is retained for that generality.
+  const header = matrix.groupAxis === 'row'
+    ? ['Group', matrix.corner, ...matrix.columns.map((c) => c.label)]
+    : [matrix.corner, ...matrix.columns.map((c) => c.label)];
+  lines.push(header.map(escapeCsvCell).join(','));
+
+  for (const r of matrix.rows) {
+    const lead = matrix.groupAxis === 'row'
+      ? [r.member.group ?? '—', r.member.label]
+      : [r.member.label];
+    lines.push([...lead, ...r.values].map(escapeCsvCell).join(','));
+  }
+
+  return lines.join('\n');
 }
 
 // Build a download filename from the experiment title and parameter label.

frontend/src/pages/ExperimentDetail.jsx

@@ -416,6 +416,8 @@ export default function ExperimentDetail() {
           experimentTitle={experiment.title}
           dailyTemplate={dailyTemplate}
           animals={animals}
+          subjectTemplate={subjectTemplate}
+          groupField={localStorage.getItem(`exp-subject-group-${id}`) ?? '__none__'}
           onClose={() => setShowExport(false)}
         />
       </Modal>

frontend/tests/ExportDataModal.test.jsx

@@ -0,0 +1,124 @@
+import React from 'react';
+import { render, screen, waitFor, fireEvent } from '@testing-library/react';
+import ExportDataModal from '../src/components/ExportDataModal';
+import * as client from '../src/api/client';
+
+jest.mock('../src/api/client', () => ({
+  experimentsApi: { getDailyStatuses: jest.fn() },
+}));
+
+const animals = [
+  { id: 'a1', animal_name: 'Alpha', animal_id_string: 'R-001', subject_info: { grp: 'Control' } },
+  { id: 'a2', animal_name: 'Beta', animal_id_string: 'R-002', subject_info: { grp: 'Drug' } },
+];
+const statuses = [
+  { animal_id: 'a1', date: '2026-07-01T00:00:00Z', analysis_summary: { total: 5, success_rate: 0.5 } },
+  { animal_id: 'a1', date: '2026-07-02T00:00:00Z', analysis_summary: { total: 7, success_rate: 0.7 } },
+  { animal_id: 'a2', date: '2026-07-01T00:00:00Z', analysis_summary: { total: 9, success_rate: 0.9 } },
+];
+const subjectTemplate = [{ fieldId: 'grp', label: 'Group', active: true }];
+
+beforeEach(() => {
+  jest.clearAllMocks();
+  client.experimentsApi.getDailyStatuses.mockResolvedValue(statuses);
+});
+
+// Intercept the Blob handed to URL.createObjectURL so tests can read the real CSV.
+// Also stub anchor.click() — jsdom treats a click on <a href="blob:..."> as an
+// unimplemented navigation that throws async and can flake a later test.
+function mockDownload() {
+  let blob = null;
+  const origCreate = global.URL.createObjectURL;
+  const origRevoke = global.URL.revokeObjectURL;
+  const origClick = window.HTMLAnchorElement.prototype.click;
+  global.URL.createObjectURL = jest.fn((b) => { blob = b; return 'blob:mock'; });
+  global.URL.revokeObjectURL = jest.fn();
+  window.HTMLAnchorElement.prototype.click = jest.fn();
+  return {
+    getBlob: () => blob,
+    restore: async () => {
+      await new Promise((r) => setTimeout(r, 0)); // flush handleExport's setTimeout revoke
+      global.URL.createObjectURL = origCreate;
+      global.URL.revokeObjectURL = origRevoke;
+      window.HTMLAnchorElement.prototype.click = origClick;
+    },
+  };
+}
+
+function readBlobText(blob) {
+  return new Promise((resolve, reject) => {
+    const fr = new FileReader();
+    fr.onload = () => resolve(fr.result);
+    fr.onerror = reject;
+    fr.readAsText(blob);
+  });
+}
+
+function renderModal(props = {}) {
+  return render(
+    <ExportDataModal
+      experimentId="exp-1"
+      experimentTitle="My Study"
+      dailyTemplate={[]}
+      animals={animals}
+      subjectTemplate={subjectTemplate}
+      groupField="__none__"
+      onClose={jest.fn()}
+      {...props}
+    />,
+  );
+}
+
+describe('ExportDataModal', () => {
+  it('renders X / Data / Group by pickers; X defaults to Date', async () => {
+    renderModal();
+    expect(await screen.findByLabelText('X axis (rows)')).toHaveValue('__date__');
+    expect(screen.getByLabelText('Data (cells)')).toBeInTheDocument();
+    expect(screen.getByLabelText('Group by')).toHaveValue('__none__');
+    await waitFor(() => expect(screen.getByText(/2 rows × 2 subjects/i)).toBeInTheDocument());
+  });
+
+  it('default export (X=Date, Data=Total attempts) has subject columns, date rows, blank where missing', async () => {
+    const dl = mockDownload();
+    renderModal();
+    await screen.findByLabelText('X axis (rows)');
+    await waitFor(() => expect(screen.getByText(/2 rows/i)).toBeInTheDocument());
+    fireEvent.click(screen.getByText('Export CSV'));
+    const text = await readBlobText(dl.getBlob());
+    expect(text).toBe('Data,Total attempts\n\nDate,Alpha,Beta\n2026-07-01,5,9\n2026-07-02,7,');
+    await dl.restore();
+  });
+
+  it('switching X to # Days Reach makes rows the day ordinals', async () => {
+    const dl = mockDownload();
+    renderModal();
+    fireEvent.change(await screen.findByLabelText('X axis (rows)'), { target: { value: '__days__' } });
+    await waitFor(() => expect(screen.getByText(/2 rows/i)).toBeInTheDocument());
+    fireEvent.click(screen.getByText('Export CSV'));
+    const text = await readBlobText(dl.getBlob());
+    expect(text).toBe('Data,Total attempts\n\n# Days Reach (ordinal),Alpha,Beta\n1,5,9\n2,7,');
+    await dl.restore();
+  });
+
+  it('a valid group field adds a Group row and clusters subjects', async () => {
+    const dl = mockDownload();
+    renderModal({ groupField: 'grp' });
+    await screen.findByLabelText('X axis (rows)');
+    await waitFor(() => expect(screen.getByText(/2 rows/i)).toBeInTheDocument());
+    fireEvent.click(screen.getByText('Export CSV'));
+    const text = await readBlobText(dl.getBlob());
+    expect(text).toMatch(/^Group,Control,Drug$/m);
+    await dl.restore();
+  });
+
+  it('a stale/unset group field coerces to None (no Group row in the export)', async () => {
+    const dl = mockDownload();
+    renderModal({ groupField: 'ghost-field' });
+    await screen.findByLabelText('X axis (rows)');
+    await waitFor(() => expect(screen.getByText(/2 rows/i)).toBeInTheDocument());
+    fireEvent.click(screen.getByText('Export CSV'));
+    const text = await readBlobText(dl.getBlob());
+    expect(text).not.toMatch(/^Group,/m);
+    await dl.restore();
+  });
+});

frontend/tests/dataExport.test.js

@@ -71,78 +71,61 @@ describe('listExportableParams', () => {
   });
 });
 
-import { buildMatrix } from '../src/lib/dataExport';
-
-const animals = [
-  { id: 'a2', animal_name: 'Beta', animal_id_string: 'R-002' },
-  { id: 'a1', animal_name: 'Alpha', animal_id_string: 'R-001' },
-];
-const totalParam = { kind: 'total' };
-
-describe('buildMatrix', () => {
-  it('orders columns by animal_name and rows by date ascending', () => {
-    const statuses = [
-      { animal_id: 'a1', date: '2026-07-02T00:00:00.000Z', analysis_summary: { total: 5 } },
-      { animal_id: 'a2', date: '2026-07-01T00:00:00.000Z', analysis_summary: { total: 9 } },
-    ];
-    const m = buildMatrix(statuses, animals, totalParam);
-    expect(m.columns.map((c) => c.header)).toEqual(['Alpha', 'Beta']);
-    expect(m.rows.map((r) => r.date)).toEqual(['2026-07-01', '2026-07-02']);
-    // Row for 07-01: Alpha blank, Beta 9
-    expect(m.rows[0].values).toEqual(['', 9]);
-    // Row for 07-02: Alpha 5, Beta blank
-    expect(m.rows[1].values).toEqual([5, '']);
-  });
-  it('includes only dates that have at least one value (0 counts as a value)', () => {
-    const statuses = [
-      { animal_id: 'a1', date: '2026-07-01T00:00:00.000Z', analysis_summary: { total: 0 } },
-      { animal_id: 'a1', date: '2026-07-03T00:00:00.000Z', analysis_summary: null }, // no value
-    ];
-    const m = buildMatrix(statuses, animals, totalParam);
-    expect(m.rows.map((r) => r.date)).toEqual(['2026-07-01']);
-    expect(m.rows[0].values).toEqual([0, '']);
-  });
-  it('disambiguates duplicate animal names with the id string', () => {
-    const dupAnimals = [
-      { id: 'a1', animal_name: 'Rat', animal_id_string: 'R-001' },
-      { id: 'a2', animal_name: 'Rat', animal_id_string: 'R-002' },
-    ];
-    const statuses = [{ animal_id: 'a1', date: '2026-07-01T00:00:00.000Z', analysis_summary: { total: 1 } }];
-    const m = buildMatrix(statuses, dupAnimals, totalParam);
-    expect(m.columns.map((c) => c.header)).toEqual(['Rat (R-001)', 'Rat (R-002)']);
-  });
-  it('keeps the first status when a subject has duplicates on one date', () => {
-    const statuses = [
-      { animal_id: 'a1', date: '2026-07-01T00:00:00.000Z', analysis_summary: { total: 7 } },
-      { animal_id: 'a1', date: '2026-07-01T00:00:00.000Z', analysis_summary: { total: 99 } },
-    ];
-    const m = buildMatrix(statuses, animals, totalParam);
-    expect(m.rows[0].values).toEqual([7, '']);
-  });
-  it('returns empty rows when no status has a value', () => {
-    const m = buildMatrix([{ animal_id: 'a1', date: '2026-07-01T00:00:00.000Z', analysis_summary: null }], animals, totalParam);
-    expect(m.rows).toEqual([]);
-    expect(m.columns.length).toBe(2);
-  });
-});
-
 import { toCSV, csvFilename } from '../src/lib/dataExport';
 
 describe('toCSV', () => {
-  it('writes a header row of Date + column headers, then data rows', () => {
+  it('emits context line, blank line, header, data (no group)', () => {
     const matrix = {
-      columns: [{ id: 'a1', header: 'Alpha' }, { id: 'a2', header: 'Beta' }],
-      rows: [{ date: '2026-07-01', values: [5, ''] }, { date: '2026-07-02', values: ['', 9] }],
-    };
-    expect(toCSV(matrix)).toBe('Date,Alpha,Beta\n2026-07-01,5,\n2026-07-02,,9');
-  });
-  it('escapes commas, quotes, and newlines per RFC 4180', () => {
-    const matrix = {
-      columns: [{ id: 'a1', header: 'Note, field' }],
-      rows: [{ date: '2026-07-01', values: ['he said "hi"'] }, { date: '2026-07-02', values: ['line1\nline2'] }],
+      corner: 'Date',
+      context: { label: 'Parameter', value: 'Total attempts' },
+      groupAxis: null,
+      columns: [{ id: 'a1', label: 'Alpha', group: null }, { id: 'a2', label: 'Beta', group: null }],
+      rows: [{ member: { label: '2026-07-01' }, values: [5, ''] }, { member: { label: '2026-07-02' }, values: ['', 9] }],
     };
     expect(toCSV(matrix)).toBe(
-      'Date,"Note, field"\n2026-07-01,"he said ""hi"""\n2026-07-02,"line1\nline2"',
+      'Parameter,Total attempts\n\nDate,Alpha,Beta\n2026-07-01,5,\n2026-07-02,,9',
+    );
+  });
+
+  it('adds a Group header row when subjects are columns', () => {
+    const matrix = {
+      corner: 'Date',
+      context: { label: 'Parameter', value: 'Total attempts' },
+      groupAxis: 'col',
+      columns: [{ id: 'a1', label: 'Alpha', group: 'Control' }, { id: 'a2', label: 'Beta', group: 'Drug' }],
+      rows: [{ member: { label: '2026-07-01' }, values: [5, 9] }],
+    };
+    expect(toCSV(matrix)).toBe(
+      'Parameter,Total attempts\n\nGroup,Control,Drug\nDate,Alpha,Beta\n2026-07-01,5,9',
+    );
+  });
+
+  it('adds a leading Group column when subjects are rows', () => {
+    const matrix = {
+      corner: 'Subject',
+      context: { label: 'Date', value: '2026-07-01' },
+      groupAxis: 'row',
+      columns: [{ id: 'p1', label: 'Total attempts', group: 'metrics' }],
+      rows: [
+        { member: { label: 'Alpha', group: 'Control' }, values: [5] },
+        { member: { label: 'Beta', group: 'Drug' }, values: [9] },
+      ],
+    };
+    expect(toCSV(matrix)).toBe(
+      'Date,2026-07-01\n\nGroup,Subject,Total attempts\nControl,Alpha,5\nDrug,Beta,9',
+    );
+  });
+
+  it('escapes commas, quotes, and newlines per RFC 4180', () => {
+    const matrix = {
+      corner: 'Date',
+      context: { label: 'Parameter', value: 'Note, field' },
+      groupAxis: null,
+      columns: [{ id: 'a1', label: 'he said "hi"', group: null }],
+      rows: [{ member: { label: '2026-07-01' }, values: ['line1\nline2'] }],
+    };
+    expect(toCSV(matrix)).toBe(
+      'Parameter,"Note, field"\n\nDate,"he said ""hi"""\n2026-07-01,"line1\nline2"',
     );
   });
 });
@@ -155,3 +138,171 @@ describe('csvFilename', () => {
     expect(csvFilename('', '')).toBe('export.csv');
   });
 });
+
+import { subjectGroupValue } from '../src/lib/dataExport';
+
+describe('subjectGroupValue', () => {
+  const animal = { animal_name: 'Alpha', animal_id_string: 'R-001', subject_info: { sex: 'M', cohort: '' } };
+  it('returns null when no group field', () => {
+    expect(subjectGroupValue(animal, '__none__')).toBeNull();
+    expect(subjectGroupValue(animal, '')).toBeNull();
+  });
+  it('resolves name / id / subject_info fields', () => {
+    expect(subjectGroupValue(animal, '__name__')).toBe('Alpha');
+    expect(subjectGroupValue(animal, '__id__')).toBe('R-001');
+    expect(subjectGroupValue(animal, 'sex')).toBe('M');
+  });
+  it('renders missing or empty values as an em dash', () => {
+    expect(subjectGroupValue(animal, 'cohort')).toBe('—');
+    expect(subjectGroupValue({}, 'sex')).toBe('—');
+  });
+});
+
+import { listSubjectMembers } from '../src/lib/dataExport';
+
+describe('listSubjectMembers', () => {
+  const animals = [
+    { id: 'a2', animal_name: 'Beta', animal_id_string: 'R-002', subject_info: { grp: 'Drug' } },
+    { id: 'a1', animal_name: 'Alpha', animal_id_string: 'R-001', subject_info: { grp: 'Control' } },
+    { id: 'a3', animal_name: 'Gamma', animal_id_string: 'R-003', subject_info: { grp: 'Control' } },
+  ];
+  it('orders by name and carries group=null when no group field', () => {
+    const m = listSubjectMembers(animals, '__none__');
+    expect(m.map((s) => s.label)).toEqual(['Alpha', 'Beta', 'Gamma']);
+    expect(m.every((s) => s.group === null)).toBe(true);
+    expect(m[0]).toMatchObject({ id: 'a1', animalId: 'a1' });
+  });
+  it('clusters by group value then name when grouping is on', () => {
+    const m = listSubjectMembers(animals, 'grp');
+    expect(m.map((s) => [s.group, s.label])).toEqual([
+      ['Control', 'Alpha'], ['Control', 'Gamma'], ['Drug', 'Beta'],
+    ]);
+  });
+  it('disambiguates duplicate names with the id string', () => {
+    const dup = [
+      { id: 'a1', animal_name: 'Rat', animal_id_string: 'R-001' },
+      { id: 'a2', animal_name: 'Rat', animal_id_string: 'R-002' },
+    ];
+    expect(listSubjectMembers(dup, '__none__').map((s) => s.label)).toEqual(['Rat (R-001)', 'Rat (R-002)']);
+  });
+});
+
+import { numericFieldVal, listSampleMembers, buildSampleIndex } from '../src/lib/dataExport';
+
+const sDaily = [{ fieldId: 'c-w', key: 'w', label: 'Weight', builtin: false, active: true }];
+const sStatuses = [
+  { animal_id: 'a1', date: '2026-07-01T00:00:00Z', custom_fields: { 'c-w': '250' } },
+  { animal_id: 'a1', date: '2026-07-03T00:00:00Z', custom_fields: { 'c-w': '260' } },
+  { animal_id: 'a2', date: '2026-07-01T00:00:00Z', custom_fields: { 'c-w': '250' } },
+];
+
+describe('numericFieldVal', () => {
+  it('reads builtin via key and custom via fieldId, parsed to number', () => {
+    expect(numericFieldVal({ weight: '250' }, { builtin: true, key: 'weight' })).toBe(250);
+    expect(numericFieldVal({ custom_fields: { 'c-w': '12.5' } }, { builtin: false, fieldId: 'c-w' })).toBe(12.5);
+  });
+  it('returns null for non-numeric, missing, or no field', () => {
+    expect(numericFieldVal({ custom_fields: { 'c-w': 'abc' } }, { builtin: false, fieldId: 'c-w' })).toBeNull();
+    expect(numericFieldVal({}, { builtin: true, key: 'weight' })).toBeNull();
+    expect(numericFieldVal({ weight: 1 }, null)).toBeNull();
+  });
+});
+
+describe('listSampleMembers', () => {
+  it('date: distinct dates sorted', () => {
+    expect(listSampleMembers(sStatuses, '__date__', sDaily).map((m) => m.sampleValue)).toEqual(['2026-07-01', '2026-07-03']);
+  });
+  it('# days reach: 1..max records per subject', () => {
+    expect(listSampleMembers(sStatuses, '__days__', sDaily).map((m) => m.sampleValue)).toEqual([1, 2]);
+  });
+  it('daily field: distinct numeric values sorted ascending', () => {
+    expect(listSampleMembers(sStatuses, 'c-w', sDaily).map((m) => m.sampleValue)).toEqual([250, 260]);
+  });
+});
+
+describe('buildSampleIndex', () => {
+  it('date coord maps animalId -> dateKey -> status', () => {
+    const idx = buildSampleIndex(sStatuses, '__date__', sDaily);
+    expect(idx.get('a1').get('2026-07-03').date).toContain('2026-07-03');
+  });
+  it('# days reach assigns per-subject ordinals in date order', () => {
+    const idx = buildSampleIndex(sStatuses, '__days__', sDaily);
+    expect(idx.get('a1').get(1).date).toContain('2026-07-01');
+    expect(idx.get('a1').get(2).date).toContain('2026-07-03');
+    expect(idx.get('a2').get(1).date).toContain('2026-07-01');
+    expect(idx.get('a2').has(2)).toBe(false);
+  });
+  it('daily field maps numeric value to first status by date', () => {
+    const idx = buildSampleIndex(sStatuses, 'c-w', sDaily);
+    expect(idx.get('a1').get(250).date).toContain('2026-07-01');
+    expect(idx.get('a1').get(260).date).toContain('2026-07-03');
+  });
+  it('# days reach counts per-status, not per-distinct-date (same-day statuses get separate ordinals)', () => {
+    const sameDay = [
+      { animal_id: 'z1', date: '2026-07-01T08:00:00Z', analysis_summary: { total: 1 } },
+      { animal_id: 'z1', date: '2026-07-01T20:00:00Z', analysis_summary: { total: 2 } },
+    ];
+    const idx = buildSampleIndex(sameDay, '__days__', []);
+    expect(idx.get('z1').has(1)).toBe(true);
+    expect(idx.get('z1').has(2)).toBe(true);
+    expect(idx.get('z1').has(3)).toBe(false);
+  });
+});
+
+import { buildSubjectSeriesMatrix } from '../src/lib/dataExport';
+
+const ssAnimals = [
+  { id: 'a1', animal_name: 'Alpha', animal_id_string: 'R-001', subject_info: { grp: 'Control' } },
+  { id: 'a2', animal_name: 'Beta', animal_id_string: 'R-002', subject_info: { grp: 'Drug' } },
+];
+const ssStatuses = [
+  { animal_id: 'a1', date: '2026-07-01T00:00:00Z', analysis_summary: { total: 5 } },
+  { animal_id: 'a1', date: '2026-07-02T00:00:00Z', analysis_summary: { total: 7 } },
+  { animal_id: 'a2', date: '2026-07-01T00:00:00Z', analysis_summary: { total: 9 } },
+];
+const totalParam = { id: '__total__', label: 'Total attempts', kind: 'total', group: 'metrics' };
+
+describe('buildSubjectSeriesMatrix', () => {
+  it('date X: subjects as columns, date rows, blank where missing, context names the metric', () => {
+    const m = buildSubjectSeriesMatrix(
+      { xField: '__date__', dataParam: totalParam, groupField: '__none__' },
+      { statuses: ssStatuses, animals: ssAnimals, dailyTemplate: [] },
+    );
+    expect(m.corner).toBe('Date');
+    expect(m.context).toEqual({ label: 'Data', value: 'Total attempts' });
+    expect(m.groupAxis).toBeNull();
+    expect(m.columns.map((c) => c.label)).toEqual(['Alpha', 'Beta']);
+    expect(m.rows.map((r) => r.member.label)).toEqual(['2026-07-01', '2026-07-02']);
+    expect(m.rows[0].values).toEqual([5, 9]);
+    expect(m.rows[1].values).toEqual([7, '']);
+  });
+  it('# days reach X: rows align each subject by day ordinal', () => {
+    const m = buildSubjectSeriesMatrix(
+      { xField: '__days__', dataParam: totalParam, groupField: '__none__' },
+      { statuses: ssStatuses, animals: ssAnimals, dailyTemplate: [] },
+    );
+    expect(m.corner).toBe('# Days Reach (ordinal)');
+    expect(m.rows.map((r) => r.member.label)).toEqual(['1', '2']);
+    expect(m.rows[0].values).toEqual([5, 9]);
+    expect(m.rows[1].values).toEqual([7, '']);
+  });
+  it('grouping sets groupAxis col and clusters subjects', () => {
+    const m = buildSubjectSeriesMatrix(
+      { xField: '__date__', dataParam: totalParam, groupField: 'grp' },
+      { statuses: ssStatuses, animals: ssAnimals, dailyTemplate: [] },
+    );
+    expect(m.groupAxis).toBe('col');
+    expect(m.columns.map((c) => [c.group, c.label])).toEqual([['Control', 'Alpha'], ['Drug', 'Beta']]);
+  });
+  it('drops entirely-blank rows', () => {
+    const statuses = [
+      { animal_id: 'a1', date: '2026-07-01T00:00:00Z', analysis_summary: { total: 5 } },
+      { animal_id: 'a1', date: '2026-07-05T00:00:00Z', analysis_summary: null },
+    ];
+    const m = buildSubjectSeriesMatrix(
+      { xField: '__date__', dataParam: totalParam, groupField: '__none__' },
+      { statuses, animals: ssAnimals, dailyTemplate: [] },
+    );
+    expect(m.rows.map((r) => r.member.label)).toEqual(['2026-07-01']);
+  });
+});