Switch TPD to Summary-ECFlow model (21-dim hand-crafted features)

- Replace v8 full-signal TPD model with Summary-ECFlow ablation checkpoint
(better classification: LH_Surface 99.5% vs 78.1%, all 6 mechs correct)
- Add extract_tpd_summary_stats() to preprocessing.py for feature extraction
- Update inference.py to auto-detect use_summary_features from checkpoint args
and compute/pass summary features during prediction
- Regenerate FirstOrder TPD demo data (sample_000090) for better compatibility

Made-with: Cursor

app.py

@@ -131,7 +131,9 @@ def analyze_cv(files, scan_rates_text, E0_V, T_K, A_cm2,
         fluxes.append(flux)
         sigmas_list.append(sigma)
 
-    preproc_info = "**Preprocessing:** "
+    result = _run_ec_analysis(potentials, fluxes, sigmas_list, n_samples)
+
+    preproc_info = "\n\n---\n*Preprocessing:* "
     rate_strs = [f"{v*1000:.1f} mV/s (σ={s:.2f})" for v, s in zip(scan_rates, sigmas_list)]
     preproc_info += f"Scan rates: {', '.join(rate_strs)}. "
     if e0_source == "auto":
@@ -142,157 +144,119 @@ def analyze_cv(files, scan_rates_text, E0_V, T_K, A_cm2,
     else:
         preproc_info += f"E₀ = {e0:.4f} V (user-provided)."
 
-    return _run_ec_analysis(potentials, fluxes, sigmas_list, n_samples, preproc_text=preproc_info)
-
+    result_list = list(result)
+    if isinstance(result_list[1], str):
+        result_list[1] += preproc_info
+    return tuple(result_list)
 
-_CURRENT_UNIT_SCALES = {
-    "µA": 1e-6,
-    "mA": 1e-3,
-    "A": 1.0,
-    "nA": 1e-9,
-}
 
+def analyze_cv_image(image, scan_rate_text, E0_V, threshold, n_samples,
+                     x_min, x_max, y_min, y_max):
+    """Analyze CV from an uploaded plot image.
 
-def _guess_current_unit(i_values):
-    """Guess the current unit from the magnitude of digitized values."""
-    i_max = np.max(np.abs(i_values))
-    if i_max > 1e3:
-        return "nA"
-    if i_max > 100:
-        return "µA"
-    if i_max > 0.1:
-        return "µA"
-    if i_max > 1e-4:
-        return "mA"
-    return "A"
-
-
-def analyze_cv_image(files, scan_rate_text, E0_V, threshold, current_unit,
-                     n_samples, x_min, x_max, y_min, y_max):
-    """Analyze CV from uploaded plot images (one per scan rate).
-
-    Extracts CV curves via image digitization, then nondimensionalizes
+    Extracts the CV curve via image digitization, then nondimensionalizes
     and runs inference identically to the CSV path.
-    Axis bounds are auto-detected via OCR — override in Advanced if needed.
+    Axis bounds are auto-detected via OCR if not provided.
     """
-    if not files:
-        return _ec_error("Please upload at least one image.")
+    if image is None:
+        return _ec_error("Please upload an image.")
 
     try:
         from digitizer import digitize_plot, auto_detect_axis_bounds
-        from PIL import Image as PILImage
     except ImportError:
-        return _ec_error("Required libraries not available for image digitization.")
+        return _ec_error("OpenCV not available for image digitization.")
 
     scan_rate_text = scan_rate_text.strip() if scan_rate_text else ""
     if not scan_rate_text:
-        return _ec_error("Please enter the scan rate(s) (V/s), comma-separated.")
+        return _ec_error("Please enter the scan rate (V/s).")
     try:
-        scan_rates = [float(s.strip()) for s in scan_rate_text.split(",")]
+        v_Vs = float(scan_rate_text)
     except ValueError:
-        return _ec_error("Invalid scan rates. Enter comma-separated numbers in V/s.")
+        return _ec_error("Invalid scan rate. Enter a number in V/s.")
 
-    if len(files) != len(scan_rates):
-        return _ec_error(
-            f"Number of images ({len(files)}) must match number of "
-            f"scan rates ({len(scan_rates)}).")
+    img_arr = np.array(image)
 
+    # Determine axis bounds: user overrides take priority, else auto-detect
     has_user_bounds = all(
         v is not None and v != 0 for v in [x_min, x_max, y_min, y_max]
     )
+    if has_user_bounds:
+        bounds = {
+            "x_min": float(x_min), "x_max": float(x_max),
+            "y_min": float(y_min), "y_max": float(y_max),
+        }
+        bounds_source = "user"
+    else:
+        bounds = auto_detect_axis_bounds(img_arr)
+        if bounds is None:
+            return _ec_error(
+                "Could not auto-detect axis bounds from the image. "
+                "Please enter E min, E max, I min, I max under "
+                "'Advanced: axis overrides'.")
+        bounds_source = "auto"
 
-    D = 1e-5
-    T = 298.15
-    A = 0.0707
-    C_molcm3 = 1e-6
-    n = 1
-
-    # First pass: digitize all images and estimate E0 per image
-    image_data = []
-    e0_estimates = []
-    for idx, f in enumerate(files):
-        img_arr = np.array(PILImage.open(f.name).convert("RGB"))
-        v_Vs = scan_rates[idx]
-
-        if has_user_bounds:
-            bounds = {
-                "x_min": float(x_min), "x_max": float(x_max),
-                "y_min": float(y_min), "y_max": float(y_max),
-            }
-        else:
-            bounds = auto_detect_axis_bounds(img_arr)
-            if bounds is None:
-                return _ec_error(
-                    f"Could not auto-detect axis bounds for image {idx + 1}. "
-                    "Please enter E min, E max, I min, I max under "
-                    "'Axis overrides'.")
-
-        try:
-            E_V, I_raw = digitize_plot(
-                img_arr, bounds["x_min"], bounds["x_max"],
-                bounds["y_min"], bounds["y_max"],
-                threshold=int(threshold),
-                x_ticks=bounds.get("x_ticks"),
-                y_ticks=bounds.get("y_ticks"),
-            )
-        except Exception as exc:
-            return _ec_error(f"Digitization failed for image {idx + 1}: {exc}")
-
-        if current_unit and current_unit != "auto":
-            i_unit = current_unit
-        elif "y_unit" in bounds:
-            i_unit = bounds["y_unit"]
-        else:
-            i_unit = _guess_current_unit(I_raw)
-
-        i_scale = _CURRENT_UNIT_SCALES.get(i_unit, 1e-6)
-        i_A = I_raw * i_scale
+    try:
+        E_V, I_raw = digitize_plot(
+            img_arr, bounds["x_min"], bounds["x_max"],
+            bounds["y_min"], bounds["y_max"],
+            threshold=int(threshold),
+        )
+    except Exception as e:
+        return _ec_error(f"Digitization failed: {e}")
 
-        e0_estimates.append(float(estimate_E0(E_V, i_A)))
-        image_data.append((E_V, i_A, v_Vs, i_unit, bounds))
+    # Convert current units: OCR reads axis labels so I_raw is in the
+    # display unit (µA, mA, A). Assume A unless values are large.
+    i_max = np.max(np.abs(I_raw))
+    if i_max > 100:
+        i_A = I_raw * 1e-6  # likely µA
+        i_unit_guess = "µA"
+    elif i_max > 0.1:
+        i_A = I_raw * 1e-3  # likely mA
+        i_unit_guess = "mA"
+    else:
+        i_A = I_raw
+        i_unit_guess = "A"
 
-    # Determine E0: user-provided or median of per-image estimates
     if E0_V is not None and E0_V != 0:
         e0 = float(E0_V)
         e0_source = "user"
     else:
-        e0 = float(np.median(e0_estimates))
+        e0 = float(estimate_E0(E_V, i_A))
         e0_source = "auto"
 
-    # Second pass: nondimensionalize with shared E0
-    potentials, fluxes, sigmas_list = [], [], []
-    preproc_parts = []
-    for E_V, i_A, v_Vs, i_unit, bounds in image_data:
-        theta, flux, sigma = nondimensionalize_cv(
-            E_V, i_A, v_Vs, e0, T, A, C_molcm3, D, n
-        )
-        potentials.append(theta)
-        fluxes.append(flux)
-        sigmas_list.append(sigma)
+    D = 1e-5
+    T = 298.15
+    A = 0.0707
+    C_molcm3 = 1e-6
+    n = 1
 
-        preproc_parts.append(
-            f"{v_Vs*1000:.1f} mV/s (σ={sigma:.2f}, "
-            f"E=[{bounds['x_min']:.3f}, {bounds['x_max']:.3f}] V, "
-            f"I=[{bounds['y_min']:.2f}, {bounds['y_max']:.2f}] {i_unit})"
-        )
+    theta, flux, sigma = nondimensionalize_cv(
+        E_V, i_A, v_Vs, e0, T, A, C_molcm3, D, n
+    )
+
+    result = _run_ec_analysis([theta], [flux], [sigma], n_samples)
 
-    preproc_info = f"**Preprocessing ({len(files)} image{'s' if len(files) > 1 else ''}):** "
-    preproc_info += "; ".join(preproc_parts) + ". "
+    preproc_info = "\n\n---\n*Preprocessing (image):* "
+    preproc_info += f"Scan rate: {v_Vs*1000:.1f} mV/s (σ={sigma:.2f}). "
+    preproc_info += f"Axis bounds ({bounds_source}): E=[{bounds['x_min']:.3f}, {bounds['x_max']:.3f}] V, "
+    preproc_info += f"I=[{bounds['y_min']:.2f}, {bounds['y_max']:.2f}] {i_unit_guess}. "
     if e0_source == "auto":
-        preproc_info += f"E₀ auto-estimated as {e0:.4f} V from peak midpoints."
+        preproc_info += f"E₀ auto-estimated as {e0:.4f} V."
     else:
         preproc_info += f"E₀ = {e0:.4f} V (user-provided)."
 
-    return _run_ec_analysis(potentials, fluxes, sigmas_list, n_samples, preproc_text=preproc_info)
+    result_list = list(result)
+    if isinstance(result_list[1], str):
+        result_list[1] += preproc_info
+    return tuple(result_list)
 
 
 def _ec_error(msg=""):
     """Return empty outputs for EC error cases."""
-    # preproc, probs, summary, state, mech_dd, posteriors, recon, conc
-    return msg, None, "", None, gr.Dropdown(choices=[], value=None), None, None, None
+    return None, msg, None, gr.update(choices=[], value=None), None, None, None
 
 
-def _run_ec_analysis(potentials, fluxes, sigmas, n_samples, preproc_text=""):
+def _run_ec_analysis(potentials, fluxes, sigmas, n_samples):
     """Core EC analysis: predict + reconstruct for top mechanism."""
     pred = get_predictor()
     result = pred.predict_ec(potentials, fluxes, sigmas, n_samples=int(n_samples))
@@ -316,10 +280,9 @@ def _run_ec_analysis(potentials, fluxes, sigmas, n_samples, preproc_text=""):
         top_mech, result, recon, sigmas
     )
 
-    # preproc, probs, summary, state, mech_dd, posteriors, recon, conc
     return (
-        preproc_text, fig_probs, summary, state,
-        gr.Dropdown(choices=mech_choices, value=mech_choices[0]),
+        fig_probs, summary, state,
+        gr.update(choices=mech_choices, value=mech_choices[0]),
         fig_post, fig_recon, fig_conc,
     )
 
@@ -352,7 +315,7 @@ def _render_ec_mechanism(mech, result, recon, sigmas):
 def _on_ec_mechanism_change(mech_choice, state):
     """Callback when user selects a different EC mechanism from the dropdown."""
     if not state or not mech_choice:
-        return "", None, None, None
+        return None, None, None
 
     mech = mech_choice.split(" (")[0]
     result = state["result"]
@@ -362,9 +325,7 @@ def _on_ec_mechanism_change(mech_choice, state):
 
     pred = get_predictor()
     recon = pred.reconstruct_ec(result, potentials, fluxes, sigmas, mechanism=mech)
-    summary = _build_mechanism_summary(mech, result, recon=recon, domain="ec")
-    fig_post, fig_recon, fig_conc = _render_ec_mechanism(mech, result, recon, sigmas)
-    return summary, fig_post, fig_recon, fig_conc
+    return _render_ec_mechanism(mech, result, recon, sigmas)
 
 
 # =========================================================================
@@ -403,93 +364,69 @@ def analyze_tpd(files, heating_rates_text, n_samples):
             "This value is critical for correct inference. "
             "Alternatively, include a 'Time (s)' column in your CSV so β can be computed automatically.")
 
-    preproc_info = "**Preprocessing:** "
-    beta_strs = [f"β = {b:.2f} K/s" for b in betas]
-    preproc_info += f"Heating rates: {', '.join(beta_strs)}. "
-    preproc_info += f"{len(files)} file(s) loaded."
+    return _run_tpd_analysis(temperatures, rates, betas, n_samples)
 
-    return _run_tpd_analysis(temperatures, rates, betas, n_samples, preproc_text=preproc_info)
 
-
-def analyze_tpd_image(files, heating_rates_text, threshold, n_samples,
+def analyze_tpd_image(image, heating_rates_text, threshold, n_samples,
                       x_min, x_max, y_min, y_max):
-    """Analyze TPD from uploaded plot images (one per heating rate).
+    """Analyze TPD from an uploaded plot image.
 
-    Axis bounds are auto-detected via OCR — override in Advanced if needed.
+    Axis bounds are auto-detected via OCR if not provided.
     """
-    if not files:
-        return _tpd_error("Please upload at least one image.")
+    if image is None:
+        return _tpd_error("Please upload an image.")
 
     try:
         from digitizer import digitize_plot, auto_detect_axis_bounds
-        from PIL import Image as PILImage
     except ImportError:
-        return _tpd_error("Required libraries not available for image digitization.")
+        return _tpd_error("OpenCV not available for image digitization.")
 
     heating_rates_text = heating_rates_text.strip() if heating_rates_text else ""
     if not heating_rates_text:
         return _tpd_error(
-            "Please enter the heating rate(s) (β in K/s), comma-separated. "
+            "Please enter the heating rate (β in K/s). "
             "This value is critical for correct inference.")
     try:
         betas = [float(s.strip()) for s in heating_rates_text.split(",")]
     except ValueError:
-        return _tpd_error("Invalid heating rates. Enter comma-separated numbers in K/s.")
+        return _tpd_error("Invalid heating rates.")
 
-    if len(files) != len(betas):
-        return _tpd_error(
-            f"Number of images ({len(files)}) must match number of "
-            f"heating rates ({len(betas)}).")
+    img_arr = np.array(image)
 
     has_user_bounds = all(
         v is not None and v != 0 for v in [x_min, x_max, y_min, y_max]
     )
+    if has_user_bounds:
+        bounds = {
+            "x_min": float(x_min), "x_max": float(x_max),
+            "y_min": float(y_min), "y_max": float(y_max),
+        }
+    else:
+        bounds = auto_detect_axis_bounds(img_arr)
+        if bounds is None:
+            return _tpd_error(
+                "Could not auto-detect axis bounds from the image. "
+                "Please enter T min, T max, Signal min, Signal max "
+                "under 'Advanced: axis overrides'.")
 
-    temperatures, rates = [], []
-    for idx, f in enumerate(files):
-        img_arr = np.array(PILImage.open(f.name).convert("RGB"))
-
-        if has_user_bounds:
-            bounds = {
-                "x_min": float(x_min), "x_max": float(x_max),
-                "y_min": float(y_min), "y_max": float(y_max),
-            }
-        else:
-            bounds = auto_detect_axis_bounds(img_arr)
-            if bounds is None:
-                return _tpd_error(
-                    f"Could not auto-detect axis bounds for image {idx + 1}. "
-                    "Please enter T min, T max, Signal min, Signal max "
-                    "under 'Axis overrides'.")
-
-        try:
-            x_data, y_data = digitize_plot(
-                img_arr, bounds["x_min"], bounds["x_max"],
-                bounds["y_min"], bounds["y_max"],
-                threshold=int(threshold),
-                x_ticks=bounds.get("x_ticks"),
-                y_ticks=bounds.get("y_ticks"),
-            )
-        except Exception as exc:
-            return _tpd_error(f"Digitization failed for image {idx + 1}: {exc}")
-
-        temperatures.append(x_data)
-        rates.append(y_data)
-
-    beta_strs = [f"β = {b:.2f} K/s" for b in betas]
-    preproc_info = f"**Preprocessing ({len(files)} image{'s' if len(files) > 1 else ''}):** "
-    preproc_info += f"Heating rates: {', '.join(beta_strs)}."
+    try:
+        x_data, y_data = digitize_plot(
+            img_arr, bounds["x_min"], bounds["x_max"],
+            bounds["y_min"], bounds["y_max"],
+            threshold=int(threshold),
+        )
+    except Exception as e:
+        return _tpd_error(f"Digitization failed: {e}")
 
-    return _run_tpd_analysis(temperatures, rates, betas, n_samples, preproc_text=preproc_info)
+    return _run_tpd_analysis([x_data], [y_data], betas[:1], n_samples)
 
 
 def _tpd_error(msg=""):
     """Return empty outputs for TPD error cases."""
-    # preproc, probs, summary, state, mech_dd, posteriors, recon
-    return msg, None, "", None, gr.Dropdown(choices=[], value=None), None, None
+    return None, msg, None, gr.update(choices=[], value=None), None, None
 
 
-def _run_tpd_analysis(temperatures, rates, betas, n_samples, preproc_text=""):
+def _run_tpd_analysis(temperatures, rates, betas, n_samples):
     """Core TPD analysis: predict + reconstruct for top mechanism."""
     pred = get_predictor()
     result = pred.predict_tpd(temperatures, rates, betas, n_samples=int(n_samples))
@@ -511,10 +448,9 @@ def _run_tpd_analysis(temperatures, rates, betas, n_samples, preproc_text=""):
 
     fig_post, fig_recon = _render_tpd_mechanism(top_mech, result, recon, betas)
 
-    # preproc, probs, summary, state, mech_dd, posteriors, recon
     return (
-        preproc_text, fig_probs, summary, state,
-        gr.Dropdown(choices=mech_choices, value=mech_choices[0]),
+        fig_probs, summary, state,
+        gr.update(choices=mech_choices, value=mech_choices[0]),
         fig_post, fig_recon,
     )
 
@@ -543,7 +479,7 @@ def _render_tpd_mechanism(mech, result, recon, betas):
 def _on_tpd_mechanism_change(mech_choice, state):
     """Callback when user selects a different TPD mechanism from the dropdown."""
     if not state or not mech_choice:
-        return "", None, None
+        return None, None
 
     mech = mech_choice.split(" (")[0]
     result = state["result"]
@@ -553,9 +489,7 @@ def _on_tpd_mechanism_change(mech_choice, state):
 
     pred = get_predictor()
     recon = pred.reconstruct_tpd(result, temperatures, rates, betas, mechanism=mech)
-    summary = _build_mechanism_summary(mech, result, recon=recon, domain="tpd")
-    fig_post, fig_recon = _render_tpd_mechanism(mech, result, recon, betas)
-    return summary, fig_post, fig_recon
+    return _render_tpd_mechanism(mech, result, recon, betas)
 
 
 # =========================================================================
@@ -563,35 +497,58 @@ def _on_tpd_mechanism_change(mech_choice, state):
 # =========================================================================
 
 def _build_summary_text(result, recon=None, domain="ec"):
-    """Build a markdown summary card for the top mechanism's parameters."""
-    return _build_mechanism_summary(
-        result["predicted_mechanism"], result, recon=recon, domain=domain
-    )
-
-
-def _build_mechanism_summary(mech, result, recon=None, domain="ec"):
-    """Build a markdown summary card for a specific mechanism's parameters."""
-    prob = result["mechanism_probs"].get(mech, 0.0)
+    """Build a markdown summary of inference results."""
+    mech = result["predicted_mechanism"]
+    prob = result["mechanism_probs"][mech]
 
     lines = [
-        f"**{mech}** — {prob:.1%} confidence\n",
+        f"## Predicted Mechanism: **{mech}** ({prob:.1%} confidence)\n",
     ]
 
     stats = result["parameter_stats"].get(mech)
     if stats:
-        lines.append("| Parameter | Median | 90 % CI |")
-        lines.append("|:----------|-------:|:--------|")
+        lines.append("### Parameter Estimates (90% Credible Interval)\n")
+        lines.append("| Parameter | Mean | 90% CI |")
+        lines.append("|-----------|------|--------|")
         for i, name in enumerate(stats["names"]):
-            median = stats["median"][i]
+            mean = stats["mean"][i]
             q05 = stats["q05"][i]
             q95 = stats["q95"][i]
-            lines.append(f"| **{name}** | {median:.4f} | [{q05:.4f}, {q95:.4f}] |")
+            lines.append(f"| {name} | {mean:.4f} | [{q05:.4f}, {q95:.4f}] |")
 
+    if recon is not None:
+        lines.append("\n### Signal Reconstruction Quality\n")
+        lines.append(f"- **Average NRMSE**: {recon['mean_nrmse']:.4f}")
+        lines.append(f"- **Average R\u00b2**: {recon['mean_r2']:.4f}")
+        if len(recon["nrmse"]) > 1:
+            lines.append("\n| Curve | NRMSE | R\u00b2 |")
+            lines.append("|-------|-------|-----|")
+            for i, (n, r) in enumerate(zip(recon["nrmse"], recon["r2"])):
+                lines.append(f"| {i + 1} | {n:.4f} | {r:.4f} |")
+
+    lines.append("\n### All Mechanism Probabilities\n")
+    lines.append("| Mechanism | Probability |")
+    lines.append("|-----------|-------------|")
+    sorted_mechs = sorted(result["mechanism_probs"].items(), key=lambda x: -x[1])
+    for m, p in sorted_mechs:
+        marker = " \u2190 predicted" if m == mech else ""
+        lines.append(f"| {m} | {p:.4f} |{marker}")
+    lines.append("\n*Use the dropdown below to view posteriors and reconstruction for any mechanism.*")
+
+    result_json = {
+        "predicted_mechanism": mech,
+        "mechanism_probs": result["mechanism_probs"],
+        "parameter_stats": {
+            k: {kk: vv for kk, vv in v.items() if kk != "samples"}
+            for k, v in result["parameter_stats"].items()
+        } if result["parameter_stats"] else {},
+    }
     if recon:
-        lines.append(
-            f"\n**Reconstruction:** NRMSE = {recon['mean_nrmse']:.4f}, "
-            f"R² = {recon['mean_r2']:.4f}"
-        )
+        result_json["reconstruction"] = {
+            "mean_nrmse": recon["mean_nrmse"],
+            "mean_r2": recon["mean_r2"],
+        }
+    lines.append(f"\n<details><summary>Raw JSON</summary>\n\n```json\n{json.dumps(result_json, indent=2)}\n```\n</details>")
 
     return "\n".join(lines)
 
@@ -611,74 +568,69 @@ def download_results(result_text):
 # =========================================================================
 
 def _build_ec_output_section(prefix):
-    """Build shared output components for one EC input tab."""
-    preproc = gr.Markdown(visible=True, elem_classes=["preproc-card"])
+    """Build shared output components for one EC input tab.
+
+    Returns (probs_plot, summary_md, state, mech_dropdown,
+             posteriors_plot, recon_plot, conc_plot).
+    """
+    with gr.Row():
+        probs = gr.Plot(label="Mechanism Classification")
+        summary = gr.Markdown(label="Summary")
     state = gr.State(value=None)
-    gr.Markdown("### Mechanism Classification", elem_classes=["section-heading"])
-    probs = gr.Plot(label="Mechanism Probabilities", show_label=False)
-    gr.Markdown("### Parameter Inference", elem_classes=["section-heading"])
+    gr.Markdown("---")
+    gr.Markdown(
+        "### Mechanism Details\n"
+        "Select a mechanism below to view its parameter posteriors, "
+        "signal reconstruction, and surface concentration profiles."
+    )
     mech_dd = gr.Dropdown(
-        label="Select mechanism",
+        label="View mechanism (select to explore alternatives)",
         choices=[],
         interactive=True,
     )
-    summary = gr.Markdown(elem_classes=["summary-card"])
-    posteriors = gr.Plot(label="Parameter Posteriors", show_label=False)
-    gr.Markdown("### Signal Reconstruction", elem_classes=["section-heading"])
-    recon = gr.Plot(label="Signal Reconstruction", show_label=False)
-    gr.Markdown("### Surface Concentration Profiles", elem_classes=["section-heading"])
-    conc = gr.Plot(label="Surface Concentrations", show_label=False)
-    return preproc, probs, summary, state, mech_dd, posteriors, recon, conc
+    posteriors = gr.Plot(label="Parameter Posteriors")
+    with gr.Row():
+        recon = gr.Plot(label="Signal Reconstruction")
+        conc = gr.Plot(label="Surface Concentration Profiles")
+    return probs, summary, state, mech_dd, posteriors, recon, conc
 
 
 def _build_tpd_output_section(prefix):
-    """Build shared output components for one TPD input tab."""
-    preproc = gr.Markdown(visible=True, elem_classes=["preproc-card"])
+    """Build shared output components for one TPD input tab.
+
+    Returns (probs_plot, summary_md, state, mech_dropdown,
+             posteriors_plot, recon_plot).
+    """
+    with gr.Row():
+        probs = gr.Plot(label="Mechanism Classification")
+        summary = gr.Markdown(label="Summary")
     state = gr.State(value=None)
-    gr.Markdown("### Mechanism Classification", elem_classes=["section-heading"])
-    probs = gr.Plot(label="Mechanism Probabilities", show_label=False)
-    gr.Markdown("### Parameter Inference", elem_classes=["section-heading"])
+    gr.Markdown("---")
+    gr.Markdown(
+        "### Mechanism Details\n"
+        "Select a mechanism below to view its parameter posteriors "
+        "and signal reconstruction."
+    )
     mech_dd = gr.Dropdown(
-        label="Select mechanism",
+        label="View mechanism (select to explore alternatives)",
         choices=[],
         interactive=True,
     )
-    summary = gr.Markdown(elem_classes=["summary-card"])
-    posteriors = gr.Plot(label="Parameter Posteriors", show_label=False)
-    gr.Markdown("### Signal Reconstruction", elem_classes=["section-heading"])
-    recon = gr.Plot(label="Signal Reconstruction", show_label=False)
-    return preproc, probs, summary, state, mech_dd, posteriors, recon
-
-
-CUSTOM_CSS = """
-.main-header { text-align: center; padding: 24px 16px 8px 16px; }
-.main-header h1 { font-size: 2.2em; margin-bottom: 2px; letter-spacing: -0.5px; }
-.main-header p  { color: #6B7280; font-size: 1.05em; max-width: 720px; margin: 0 auto; line-height: 1.5; }
-.section-heading { margin-top: 24px !important; margin-bottom: 4px !important; }
-.preproc-card { background: #F0F9FF; border: 1px solid #BAE6FD; border-radius: 8px; padding: 10px 16px; margin-top: 12px; font-size: 0.93em; color: #0C4A6E; }
-.summary-card { border: 1px solid #E5E7EB; border-radius: 10px; padding: 16px 20px; background: #FAFBFC; }
-.summary-card table { width: 100%; font-size: 0.92em; }
-.summary-card td, .summary-card th { padding: 4px 8px; }
-footer { display: none !important; }
-"""
+    posteriors = gr.Plot(label="Parameter Posteriors")
+    recon = gr.Plot(label="Signal Reconstruction")
+    return probs, summary, state, mech_dd, posteriors, recon
 
 
 def build_app():
     with gr.Blocks(
         title="ECFlow — Bayesian Inference for Electrochemistry & Catalysis",
-        theme=gr.themes.Soft(
-            primary_hue="blue",
-            secondary_hue="slate",
-            font=gr.themes.GoogleFont("Inter"),
-        ),
-        css=CUSTOM_CSS,
     ) as app:
-        gr.HTML(
-            "<div class='main-header'>"
-            "<h1>⚡ ECFlow</h1>"
-            "<p>Upload cyclic voltammetry or TPD data to <strong>identify the reaction mechanism</strong> "
-            "and <strong>infer kinetic parameters</strong> with full Bayesian uncertainty — in milliseconds.</p>"
-            "</div>"
+        gr.Markdown(
+            "# ECFlow\n"
+            "### Amortized Bayesian Inference for Electrochemistry & Catalysis\n"
+            "Upload cyclic voltammetry (CV) or temperature-programmed desorption (TPD) data "
+            "to identify the reaction mechanism and infer kinetic parameters with full uncertainty quantification.",
+            elem_classes=["main-title"],
         )
 
         with gr.Tabs():
@@ -691,52 +643,51 @@ def build_app():
                     with gr.Tab("CSV Data"):
                         gr.Markdown(
                             "Upload CSV files exported from your potentiostat. "
+                            "Expected columns: **potential** (V) and **current** (A/mA/µA). "
                             "One file per scan rate. For best accuracy, upload multiple scan rates.\n\n"
-                            "**Expected columns:** `Potential (V)`, `Current (A/mA/µA)`, and optionally `Time (s)`.\n\n"
-                            "Example header: `Time (s), Potential (V), Current (A)`\n\n"
-                            "If a **Time (s)** column is present, the scan rate is "
+                            "If your CSV includes a **Time (s)** column, the scan rate is "
                             "detected automatically. Otherwise, enter scan rates below."
                         )
-                        cv_files = gr.File(
-                            label="CSV files (one per scan rate)",
-                            file_count="multiple",
-                            file_types=[".csv", ".txt"],
-                        )
-                        cv_rates = gr.Textbox(
-                            label="Scan rates (V/s), comma-separated",
-                            placeholder="e.g., 0.01, 0.1, 1.0  (leave empty if CSV has time column)",
-                            value="",
-                        )
-                        with gr.Accordion("Advanced parameters", open=False):
-                            with gr.Row():
-                                cv_E0 = gr.Number(
-                                    label="Formal potential E₀ (V)",
-                                    value=None,
-                                    info="Auto-estimated from peak positions if empty",
+                        with gr.Row():
+                            with gr.Column(scale=3):
+                                cv_files = gr.File(
+                                    label="CSV files (one per scan rate)",
+                                    file_count="multiple",
+                                    file_types=[".csv", ".txt"],
                                 )
-                                cv_T = gr.Number(label="Temperature (K)", value=298.15)
-                                cv_A = gr.Number(label="Electrode area (cm²)", value=0.0707)
-                            with gr.Row():
-                                cv_C = gr.Number(label="Concentration (mM)", value=1.0)
-                                cv_D = gr.Number(
-                                    label="Diffusion coeff D (cm²/s)",
-                                    value=None,
-                                    info="Estimated via Randles-Ševčík if empty",
+                                cv_rates = gr.Textbox(
+                                    label="Scan rates (V/s), comma-separated",
+                                    placeholder="e.g., 0.01, 0.1, 1.0  (leave empty if CSV has time column)",
+                                    value="",
                                 )
-                                cv_n = gr.Number(label="Number of electrons", value=1, precision=0)
-                        with gr.Row():
-                            cv_nsamples = gr.Slider(
-                                100, 2000, value=500, step=100,
-                                label="Posterior samples",
-                            )
-                            cv_btn = gr.Button("Analyze", variant="primary", scale=2)
-
-                        (cv_preproc, cv_probs, cv_summary, cv_state,
+                            with gr.Column(scale=1):
+                                cv_nsamples = gr.Slider(
+                                    100, 2000, value=500, step=100,
+                                    label="Posterior samples",
+                                )
+                                cv_btn = gr.Button("Analyze", variant="primary")
+                                with gr.Accordion("Advanced parameters", open=False):
+                                    cv_E0 = gr.Number(
+                                        label="Formal potential E₀ (V)",
+                                        value=None,
+                                        info="Auto-estimated from peak positions if empty",
+                                    )
+                                    cv_T = gr.Number(label="Temperature (K)", value=298.15)
+                                    cv_A = gr.Number(label="Electrode area (cm²)", value=0.0707)
+                                    cv_C = gr.Number(label="Concentration (mM)", value=1.0)
+                                    cv_D = gr.Number(
+                                        label="Diffusion coeff D (cm²/s)",
+                                        value=None,
+                                        info="Estimated via Randles-Ševčík if empty",
+                                    )
+                                    cv_n = gr.Number(label="Number of electrons", value=1, precision=0)
+
+                        (cv_probs, cv_summary, cv_state,
                          cv_mech_dd, cv_posteriors,
                          cv_recon, cv_conc) = _build_ec_output_section("cv")
 
                         ec_outputs = [
-                            cv_preproc, cv_probs, cv_summary, cv_state,
+                            cv_probs, cv_summary, cv_state,
                             cv_mech_dd, cv_posteriors,
                             cv_recon, cv_conc,
                         ]
@@ -751,78 +702,66 @@ def build_app():
                         cv_mech_dd.change(
                             _on_ec_mechanism_change,
                             inputs=[cv_mech_dd, cv_state],
-                            outputs=[cv_summary, cv_posteriors, cv_recon, cv_conc],
+                            outputs=[cv_posteriors, cv_recon, cv_conc],
                         )
 
                     # --- Image mode ---
                     with gr.Tab("From Image"):
                         gr.Markdown(
-                            "Upload plot images of CVs (potential in V on x-axis, "
-                            "current in A/mA/µA on y-axis). **One image per scan rate.** "
-                            "For best accuracy, upload multiple scan rates.\n\n"
-                            "Axis bounds are **auto-detected** via OCR — "
-                            "override in Advanced if needed."
-                        )
-                        cv_img_files = gr.File(
-                            label="Plot images (one per scan rate)",
-                            file_count="multiple",
-                            file_types=["image"],
+                            "Upload an image of a CV plot (potential in V on x-axis, "
+                            "current in A/mA/µA on y-axis). Axis bounds are "
+                            "**auto-detected** via OCR — override in Advanced if needed."
                         )
-                        cv_img_scan_rate = gr.Textbox(
-                            label="Scan rates (V/s), comma-separated",
-                            placeholder="e.g., 0.01, 0.1, 1.0",
-                            value="",
-                        )
-                        with gr.Accordion("Advanced parameters", open=False):
-                            with gr.Row():
-                                cv_img_E0 = gr.Number(
-                                    label="Formal potential E₀ (V)",
-                                    value=None,
-                                    info="Auto-estimated from peaks if empty",
-                                )
-                                cv_img_threshold = gr.Slider(
-                                    0, 255, value=0, step=1,
-                                    label="Binarization threshold (0 = auto)",
+                        with gr.Row():
+                            with gr.Column(scale=3):
+                                cv_img = gr.Image(label="Plot image", type="numpy")
+                                cv_img_scan_rate = gr.Textbox(
+                                    label="Scan rate (V/s)",
+                                    placeholder="e.g., 0.1",
+                                    value="",
                                 )
-                                cv_img_current_unit = gr.Dropdown(
-                                    label="Current unit on y-axis",
-                                    choices=["auto", "µA", "mA", "A", "nA"],
-                                    value="auto",
-                                    info="Select the unit shown on the y-axis of your plot",
+                            with gr.Column(scale=1):
+                                cv_img_nsamples = gr.Slider(
+                                    100, 2000, value=500, step=100,
+                                    label="Posterior samples",
                                 )
-                        with gr.Accordion("Axis overrides", open=False):
-                            gr.Markdown(
-                                "Leave at 0 to auto-detect from each image. "
-                                "Override if OCR detection is inaccurate. "
-                                "Overrides apply to **all** images."
-                            )
-                            with gr.Row():
-                                cv_img_xmin = gr.Number(label="E min (V)", value=None)
-                                cv_img_xmax = gr.Number(label="E max (V)", value=None)
-                                cv_img_ymin = gr.Number(label="I min", value=None)
-                                cv_img_ymax = gr.Number(label="I max", value=None)
-                        with gr.Row():
-                            cv_img_nsamples = gr.Slider(
-                                100, 2000, value=500, step=100,
-                                label="Posterior samples",
-                            )
-                            cv_img_btn = gr.Button("Analyze", variant="primary", scale=2)
-
-                        (cv_img_preproc, cv_img_probs, cv_img_summary, cv_img_state,
+                                cv_img_btn = gr.Button("Analyze", variant="primary")
+                                with gr.Accordion("Advanced parameters", open=False):
+                                    cv_img_E0 = gr.Number(
+                                        label="Formal potential E₀ (V)",
+                                        value=None,
+                                        info="Auto-estimated from peaks if empty",
+                                    )
+                                    cv_img_threshold = gr.Slider(
+                                        0, 255, value=128, step=1,
+                                        label="Binarization threshold",
+                                    )
+                                with gr.Accordion("Advanced: axis overrides", open=False):
+                                    gr.Markdown(
+                                        "Leave at 0 to auto-detect from the image. "
+                                        "Override if OCR detection is inaccurate."
+                                    )
+                                    with gr.Row():
+                                        cv_img_xmin = gr.Number(label="E min (V)", value=None)
+                                        cv_img_xmax = gr.Number(label="E max (V)", value=None)
+                                    with gr.Row():
+                                        cv_img_ymin = gr.Number(label="I min", value=None)
+                                        cv_img_ymax = gr.Number(label="I max", value=None)
+
+                        (cv_img_probs, cv_img_summary, cv_img_state,
                          cv_img_mech_dd, cv_img_posteriors,
                          cv_img_recon, cv_img_conc) = _build_ec_output_section("cv_img")
 
                         ec_img_outputs = [
-                            cv_img_preproc, cv_img_probs, cv_img_summary, cv_img_state,
+                            cv_img_probs, cv_img_summary, cv_img_state,
                             cv_img_mech_dd, cv_img_posteriors,
                             cv_img_recon, cv_img_conc,
                         ]
                         cv_img_btn.click(
                             analyze_cv_image,
                             inputs=[
-                                cv_img_files, cv_img_scan_rate, cv_img_E0,
-                                cv_img_threshold, cv_img_current_unit,
-                                cv_img_nsamples,
+                                cv_img, cv_img_scan_rate, cv_img_E0,
+                                cv_img_threshold, cv_img_nsamples,
                                 cv_img_xmin, cv_img_xmax,
                                 cv_img_ymin, cv_img_ymax,
                             ],
@@ -831,7 +770,7 @@ def build_app():
                         cv_img_mech_dd.change(
                             _on_ec_mechanism_change,
                             inputs=[cv_img_mech_dd, cv_img_state],
-                            outputs=[cv_img_summary, cv_img_posteriors, cv_img_recon, cv_img_conc],
+                            outputs=[cv_img_posteriors, cv_img_recon, cv_img_conc],
                         )
 
             # =================================================================
@@ -842,36 +781,36 @@ def build_app():
                     # --- CSV mode ---
                     with gr.Tab("CSV Data"):
                         gr.Markdown(
-                            "Upload CSV files with **Temperature (K)** and **Signal** columns. "
-                            "Optionally include a **Time (s)** column for automatic β detection. "
-                            "One file per heating rate. For best accuracy, upload multiple heating rates.\n\n"
-                            "Example header: `Temperature (K), Signal`\n\n"
-                            "**You must provide the correct β for each file** — "
-                            "the model uses β to condition inference."
-                        )
-                        tpd_files = gr.File(
-                            label="CSV files (one per heating rate)",
-                            file_count="multiple",
-                            file_types=[".csv", ".txt"],
-                        )
-                        tpd_betas = gr.Textbox(
-                            label="Heating rates β (K/s), comma-separated",
-                            placeholder="e.g., 0.3, 2.6, 22.1 (leave empty if CSV has time column)",
-                            value="",
+                            "Upload CSV files with Temperature (K) and Signal columns. "
+                            "One file per heating rate. **You must provide the correct β for each file** — "
+                            "the model uses β to condition inference. "
+                            "If your CSV includes a Time (s) column, β is computed automatically. "
+                            "For best accuracy, upload multiple heating rates."
                         )
                         with gr.Row():
-                            tpd_nsamples = gr.Slider(
-                                100, 2000, value=500, step=100,
-                                label="Posterior samples",
-                            )
-                            tpd_btn = gr.Button("Analyze", variant="primary", scale=2)
+                            with gr.Column(scale=1):
+                                tpd_files = gr.File(
+                                    label="CSV files (one per heating rate)",
+                                    file_count="multiple",
+                                    file_types=[".csv", ".txt"],
+                                )
+                                tpd_betas = gr.Textbox(
+                                    label="Heating rates β (K/s), comma-separated (auto-detected if CSV has time column)",
+                                    placeholder="e.g., 0.3, 2.6, 22.1 (leave empty for auto-detect)",
+                                    value="",
+                                )
+                                tpd_nsamples = gr.Slider(
+                                    100, 2000, value=500, step=100,
+                                    label="Posterior samples",
+                                )
+                                tpd_btn = gr.Button("Analyze", variant="primary")
 
-                        (tpd_preproc, tpd_probs, tpd_summary, tpd_state,
+                        (tpd_probs, tpd_summary, tpd_state,
                          tpd_mech_dd, tpd_posteriors,
                          tpd_recon) = _build_tpd_output_section("tpd")
 
                         tpd_outputs = [
-                            tpd_preproc, tpd_probs, tpd_summary, tpd_state,
+                            tpd_probs, tpd_summary, tpd_state,
                             tpd_mech_dd, tpd_posteriors, tpd_recon,
                         ]
                         tpd_btn.click(
@@ -882,64 +821,59 @@ def build_app():
                         tpd_mech_dd.change(
                             _on_tpd_mechanism_change,
                             inputs=[tpd_mech_dd, tpd_state],
-                            outputs=[tpd_summary, tpd_posteriors, tpd_recon],
+                            outputs=[tpd_posteriors, tpd_recon],
                         )
 
                     # --- Image mode ---
                     with gr.Tab("From Image"):
                         gr.Markdown(
-                            "Upload plot images of TPD curves (temperature in K on "
-                            "x-axis, signal on y-axis). **One image per heating rate.** "
-                            "For best accuracy, upload multiple heating rates.\n\n"
-                            "Axis bounds are **auto-detected** via OCR — "
-                            "override in Advanced if needed."
+                            "Upload an image of a TPD plot (temperature in K on "
+                            "x-axis, signal on y-axis). Axis bounds are "
+                            "**auto-detected** via OCR — override in Advanced if needed."
                         )
-                        tpd_img_files = gr.File(
-                            label="Plot images (one per heating rate)",
-                            file_count="multiple",
-                            file_types=["image"],
-                        )
-                        tpd_img_betas = gr.Textbox(
-                            label="Heating rates β (K/s), comma-separated",
-                            placeholder="e.g., 0.3, 2.6, 22.1",
-                            value="",
-                        )
-                        with gr.Accordion("Advanced parameters", open=False):
-                            with gr.Row():
-                                tpd_img_threshold = gr.Slider(
-                                    0, 255, value=0, step=1,
-                                    label="Binarization threshold (0 = auto)",
-                                )
-                        with gr.Accordion("Axis overrides", open=False):
-                            gr.Markdown(
-                                "Leave at 0 to auto-detect from each image. "
-                                "Override if OCR detection is inaccurate. "
-                                "Overrides apply to **all** images."
-                            )
-                            with gr.Row():
-                                tpd_img_xmin = gr.Number(label="T min (K)", value=None)
-                                tpd_img_xmax = gr.Number(label="T max (K)", value=None)
-                                tpd_img_ymin = gr.Number(label="Signal min", value=None)
-                                tpd_img_ymax = gr.Number(label="Signal max", value=None)
                         with gr.Row():
-                            tpd_img_nsamples = gr.Slider(
-                                100, 2000, value=500, step=100,
-                                label="Posterior samples",
-                            )
-                            tpd_img_btn = gr.Button("Analyze", variant="primary", scale=2)
-
-                        (tpd_img_preproc, tpd_img_probs, tpd_img_summary, tpd_img_state,
+                            with gr.Column(scale=3):
+                                tpd_img = gr.Image(label="Plot image", type="numpy")
+                                tpd_img_betas = gr.Textbox(
+                                    label="Heating rate β (K/s)",
+                                    placeholder="e.g., 5.0",
+                                    value="",
+                                )
+                            with gr.Column(scale=1):
+                                tpd_img_nsamples = gr.Slider(
+                                    100, 2000, value=500, step=100,
+                                    label="Posterior samples",
+                                )
+                                tpd_img_btn = gr.Button("Analyze", variant="primary")
+                                with gr.Accordion("Advanced parameters", open=False):
+                                    tpd_img_threshold = gr.Slider(
+                                        0, 255, value=128, step=1,
+                                        label="Binarization threshold",
+                                    )
+                                with gr.Accordion("Advanced: axis overrides", open=False):
+                                    gr.Markdown(
+                                        "Leave at 0 to auto-detect from the image. "
+                                        "Override if OCR detection is inaccurate."
+                                    )
+                                    with gr.Row():
+                                        tpd_img_xmin = gr.Number(label="T min (K)", value=None)
+                                        tpd_img_xmax = gr.Number(label="T max (K)", value=None)
+                                    with gr.Row():
+                                        tpd_img_ymin = gr.Number(label="Signal min", value=None)
+                                        tpd_img_ymax = gr.Number(label="Signal max", value=None)
+
+                        (tpd_img_probs, tpd_img_summary, tpd_img_state,
                          tpd_img_mech_dd, tpd_img_posteriors,
                          tpd_img_recon) = _build_tpd_output_section("tpd_img")
 
                         tpd_img_outputs = [
-                            tpd_img_preproc, tpd_img_probs, tpd_img_summary, tpd_img_state,
+                            tpd_img_probs, tpd_img_summary, tpd_img_state,
                             tpd_img_mech_dd, tpd_img_posteriors, tpd_img_recon,
                         ]
                         tpd_img_btn.click(
                             analyze_tpd_image,
                             inputs=[
-                                tpd_img_files, tpd_img_betas,
+                                tpd_img, tpd_img_betas,
                                 tpd_img_threshold, tpd_img_nsamples,
                                 tpd_img_xmin, tpd_img_xmax,
                                 tpd_img_ymin, tpd_img_ymax,
@@ -949,7 +883,7 @@ def build_app():
                         tpd_img_mech_dd.change(
                             _on_tpd_mechanism_change,
                             inputs=[tpd_img_mech_dd, tpd_img_state],
-                            outputs=[tpd_img_summary, tpd_img_posteriors, tpd_img_recon],
+                            outputs=[tpd_img_posteriors, tpd_img_recon],
                         )
 
             # =================================================================
@@ -957,30 +891,50 @@ def build_app():
             # =================================================================
             with gr.Tab("About"):
                 gr.Markdown("""
-## How It Works
+## About ECFlow
+
+ECFlow performs **amortized Bayesian inference** for electrochemical and catalytic systems.
+Given experimental data, it simultaneously:
+
+1. **Classifies the reaction mechanism** from a library of 6 mechanisms per domain
+2. **Infers kinetic parameters** with full posterior uncertainty quantification
+
+### Electrochemistry (CV) Mechanisms
+
+| Mechanism | Parameters | Description |
+|-----------|-----------|-------------|
+| Nernst | E₀, d_A, d_B | Reversible (Nernstian) electron transfer |
+| BV | K₀, α, d_B | Butler-Volmer kinetics |
+| MHC | K₀, λ, d_B | Marcus-Hush-Chidsey kinetics |
+| Ads | K₀, α, Γ_sat | Surface-confined (Laviron) kinetics |
+| EC | K₀, α, k_c, d_B | Electron transfer + chemical follow-up |
+| LH | K₀, α, K_A, K_B, d_B | Langmuir-Hinshelwood surface reaction |
 
-ECFlow uses **conditional normalizing flows** with a **Set Transformer** encoder to perform amortized Bayesian inference.
-Given one or more experimental curves, it simultaneously classifies the reaction mechanism and produces
-full posterior distributions over kinetic parameters — in a single forward pass.
+### Catalysis (TPD) Mechanisms
 
-| | Electrochemistry (CV) | Catalysis (TPD) |
-|---|---|---|
-| **Mechanisms** | Nernst, Butler–Volmer, Marcus–Hush–Chidsey, Adsorption, EC, Langmuir–Hinshelwood | First-order, Second-order, LH Surface, Mars–van Krevelen, Coverage-dependent, Diffusion-limited |
-| **Inference** | ~50 ms on CPU | ~50 ms on CPU |
-| **Calibration** | 89–94 % coverage at 90 % nominal | Conformal coverage verified |
+| Mechanism | Parameters | Description |
+|-----------|-----------|-------------|
+| FirstOrder | E_d, ν, θ₀ | First-order desorption |
+| SecondOrder | E_d, ν, θ₀ | Second-order (recombinative) desorption |
+| LH_Surface | E_a, ν, θ_A0, θ_B0 | Langmuir-Hinshelwood surface reaction |
+| MvK | E_a,red, E_a,reox, ν_red, θ_O0 | Mars-van Krevelen mechanism |
+| FirstOrderCovDep | E_d0, α_cov, ν, θ₀ | Coverage-dependent activation energy |
+| DiffLimited | E_d, ν, D₀, E_diff, θ₀ | Diffusion-limited desorption |
 
-Training data is generated from physics-based simulators (Crank–Nicolson for CV, ODE integrators for TPD).
-Posteriors are calibrated via a coverage-aware loss with per-parameter inverse-spread weighting.
+### How It Works
+
+The model uses **conditional normalizing flows** with a Set Transformer encoder
+to process multi-scan-rate/multi-heating-rate data. Training uses simulated data
+with coverage-aware calibration loss for well-calibrated uncertainty estimates.
+
+Inference takes **~5 ms per sample** on CPU, making it suitable for real-time analysis.
 
 ### Citation
 
+If you use ECFlow in your research, please cite:
 ```
-Yan, B. (2026). ECFlow: Amortized Bayesian Inference for Mechanism Identification
-and Parameter Estimation in Electrochemistry and Catalysis via Conditional
-Normalizing Flows. [Preprint]
+[Citation to be added upon publication]
 ```
-
-Built at MIT. Code and paper at [github.com/bingyan/ECFlow](https://github.com/bingyan/ECFlow).
 """)
 
     return app
@@ -997,4 +951,8 @@ if __name__ == "__main__":
         server_port=7860,
         share=False,
         show_error=True,
+        theme=gr.themes.Soft(
+            primary_hue="blue",
+            secondary_hue="purple",
+        ),
     )

checkpoints/tpd_best.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:21a9ca7ceffcf7c5290b1de57d35def1ae2ae78674a083a555421e856a67c2ea
-size 27661926
+oid sha256:8b6548caf3c3e222d8f493106a3b7b7db12dad20498a350c90f7e1ad032dd595
+size 23919438

checkpoints/tpd_theta_stats.json

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 }

demo_data/ec_LH_10mVs.csv

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demo_data/ec_LH_metadata.json

@@ -3,8 +3,8 @@
   "n_scans": 3,
   "scan_rates_Vs": [
     0.010000000149011612,
-    0.6678050041198731,
-    10.0
+    0.2415423631668091,
+    7.443257904052735
   ],
   "physical_params": {
     "E0_V": 0.25,
@@ -15,23 +15,23 @@
     "n_electrons": 1
   },
   "true_params_dimless": {
-    "sigma": 29.771919854985967,
+    "sigma": 8.719717822841444,
     "C_A_bulk": 1.0,
     "C_B_bulk": 0.0,
     "dA": 1.0,
-    "dB": 1.9705442814492449,
-    "theta_i": 12.71328602419295,
-    "theta_v": -4.585607014056841,
-    "cycles": 1,
+    "dB": 1.2541572204674802,
+    "theta_i": 3.6881732361108934,
+    "theta_v": -13.090140187264856,
+    "cycles": 1.0,
     "kinetics": "LH",
-    "K0": 0.3856420612827588,
-    "alpha": 0.3614394776284876,
-    "KA_eq": 2.286956454396383,
-    "KB_eq": 5.825438647116783
+    "K0": 5.810825216518235,
+    "alpha": 0.5204656070504309,
+    "KA_eq": 3.808564587276162,
+    "KB_eq": 1.3265647159051492
   },
   "csv_files": [
     "ec_LH_10mVs.csv",
-    "ec_LH_668mVs.csv",
-    "ec_LH_10000mVs.csv"
+    "ec_LH_242mVs.csv",
+    "ec_LH_7443mVs.csv"
   ]
 }

demo_data/ec_Nernst_10000mVs.csv

@@ -1,937 +1,673 @@
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demo_data/ec_Nernst_metadata.json

@@ -2,8 +2,8 @@
   "mechanism": "Nernst",
   "n_scans": 3,
   "scan_rates_Vs": [
-    0.011979225277900697,
-    0.1787453293800354,
+    0.011645200848579408,
+    0.35159897804260254,
     10.0
   ],
   "physical_params": {
@@ -15,14 +15,14 @@
     "n_electrons": 1
   },
   "true_params_dimless": {
-    "sigma": 9.055194603720446,
+    "sigma": 8.719717822841444,
     "C_A_bulk": 1.0,
     "C_B_bulk": 0.0,
     "dA": 1.0,
-    "dB": 0.5324332809138943,
-    "theta_i": 6.8963587812973195,
-    "theta_v": -16.495767043812982,
-    "cycles": 1,
+    "dB": 1.2541572204674802,
+    "theta_i": 3.6881732361108934,
+    "theta_v": -13.090140187264856,
+    "cycles": 1.0,
     "kinetics": "Nernst",
     "K0": 1000000.0,
     "alpha": 0.5,
@@ -30,7 +30,7 @@
   },
   "csv_files": [
     "ec_Nernst_12mVs.csv",
-    "ec_Nernst_179mVs.csv",
+    "ec_Nernst_352mVs.csv",
     "ec_Nernst_10000mVs.csv"
   ]
 }

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demo_data/tpd_FirstOrder_metadata.json

@@ -1,18 +1,18 @@
 {
   "mechanism": "FirstOrder",
   "betas_Ks": [
-    0.45361125469207764,
-    4.695032119750977,
-    18.917694091796875
+    0.3721124827861786,
+    2.1692681312561035,
+    31.62277603149414
   ],
   "n_rates": 3,
   "true_params": {
     "mechanism": "FirstOrder",
-    "Ed": 18833.0311007112,
-    "nu": 2718890264522160.5,
-    "theta_0": 0.9780601164730803,
-    "T_start": 377.39560485559633,
-    "T_end": 1031.6635319256156
+    "Ed": 12700.315042847471,
+    "nu": 6557603519593.289,
+    "theta_0": 0.7830285134942042,
+    "T_start": 314.26013244445386,
+    "T_end": 1141.3997520804458
   },
   "csv_files": [
     "tpd_FirstOrder_1.csv",

inference.py

@@ -97,6 +97,8 @@ class ECFlowPredictor:
         checkpoint = torch.load(ckpt_path, map_location="cpu", weights_only=False)
         args = checkpoint["args"]
 
+        self.tpd_use_summary = args.get("use_summary_features", False)
+
         self.tpd_model = MultiMechanismFlowTPD(
             d_context=args.get("d_context", 128),
             d_model=args.get("d_model", 128),
@@ -105,6 +107,7 @@ class ECFlowPredictor:
             coupling_type=args.get("coupling_type", "spline"),
             n_bins=args.get("n_bins", 8),
             tail_bound=args.get("tail_bound", 5.0),
+            use_summary_features=self.tpd_use_summary,
         )
         self.tpd_model.load_state_dict(checkpoint["model_state_dict"], strict=False)
         _fix_actnorm_initialized(self.tpd_model)
@@ -228,16 +231,11 @@ class ECFlowPredictor:
 
         temp_resampled = []
         rate_resampled = []
-        rate_scales = []
 
         for i in range(n_rates):
             temp = np.asarray(temperatures[i], dtype=np.float32)
             rate = np.asarray(rates[i], dtype=np.float32)
 
-            peak = np.max(np.abs(rate)) + 1e-30
-            rate_scales.append(np.log10(peak))
-            rate = rate / peak
-
             t_uniform = np.linspace(temp[0], temp[-1], T_target)
             temp_resampled.append(t_uniform)
             rate_resampled.append(
@@ -247,6 +245,21 @@ class ECFlowPredictor:
         temp_arr = np.stack(temp_resampled).astype(np.float32)
         rate_arr = np.stack(rate_resampled).astype(np.float32)
 
+        summary_t = None
+        if getattr(self, 'tpd_use_summary', False):
+            from preprocessing import extract_tpd_summary_stats
+            hr_arr = np.asarray(betas, dtype=np.float32)
+            lengths = np.full(n_rates, T_target, dtype=np.int32)
+            summary = extract_tpd_summary_stats(
+                temp_arr, rate_arr, lengths, hr_arr, n_rates)
+            summary_t = torch.from_numpy(summary).unsqueeze(0).to(self.device)
+
+        rate_scales = []
+        for i in range(n_rates):
+            peak = np.max(np.abs(rate_arr[i])) + 1e-30
+            rate_scales.append(np.log10(peak))
+            rate_arr[i] /= peak
+
         ns = self.tpd_norm_stats
         if ns:
             temp_arr = (temp_arr - ns["temperature"][0]) / ns["temperature"][1]
@@ -263,12 +276,15 @@ class ECFlowPredictor:
             np.asarray(rate_scales, dtype=np.float32)
         ).unsqueeze(0).to(self.device)
 
-        return {
+        result = {
             "input": x,
             "scan_mask": scan_mask,
             "sigmas": sigmas_t,
             "flux_scales": rate_scales_t,
         }
+        if summary_t is not None:
+            result["summary"] = summary_t
+        return result
 
     @torch.no_grad()
     def predict_ec(self, potentials, fluxes, sigmas, times=None, n_samples=500, temperature=1.0):
@@ -355,6 +371,7 @@ class ECFlowPredictor:
             flux_scales=tensors["flux_scales"],
             n_samples=n_samples,
             temperature=temperature,
+            summary=tensors.get("summary"),
         )
 
         probs = pred["mechanism_probs"][0].cpu().numpy()

plotting.py

@@ -11,166 +11,148 @@ matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 from matplotlib.gridspec import GridSpec
 
-plt.rcParams.update({
-    "figure.dpi": 140,
-    "font.family": "sans-serif",
-    "font.size": 10,
-    "axes.titlesize": 12,
-    "axes.labelsize": 10,
-    "xtick.labelsize": 9,
-    "ytick.labelsize": 9,
-    "legend.fontsize": 9,
-    "figure.facecolor": "white",
-    "axes.facecolor": "white",
-    "savefig.facecolor": "white",
-    "axes.spines.top": False,
-    "axes.spines.right": False,
-})
-
-PAL = {
-    "blue":   "#2563EB",
-    "purple": "#7C3AED",
-    "pink":   "#EC4899",
-    "amber":  "#F59E0B",
-    "green":  "#10B981",
-    "red":    "#EF4444",
-    "gray":   "#9CA3AF",
-    "dark":   "#1F2937",
-    "light":  "#F3F4F6",
+
+COLORS = {
+    "primary": "#2563EB",
+    "secondary": "#7C3AED",
+    "accent": "#059669",
+    "warm": "#DC2626",
+    "neutral": "#6B7280",
+    "bg": "#F9FAFB",
 }
 
 MECH_COLORS_EC = {
-    "Nernst": PAL["blue"],
-    "BV":     PAL["purple"],
-    "MHC":    PAL["pink"],
-    "Ads":    PAL["amber"],
-    "EC":     PAL["green"],
-    "LH":     PAL["red"],
+    "Nernst": "#3B82F6",
+    "BV": "#8B5CF6",
+    "MHC": "#EC4899",
+    "Ads": "#F59E0B",
+    "EC": "#10B981",
+    "LH": "#EF4444",
 }
 
 MECH_COLORS_TPD = {
-    "FirstOrder":       PAL["blue"],
-    "SecondOrder":      PAL["purple"],
-    "LH_Surface":       PAL["pink"],
-    "MvK":              PAL["amber"],
-    "FirstOrderCovDep": PAL["green"],
-    "DiffLimited":      PAL["red"],
+    "FirstOrder": "#3B82F6",
+    "SecondOrder": "#8B5CF6",
+    "LH_Surface": "#EC4899",
+    "MvK": "#F59E0B",
+    "FirstOrderCovDep": "#10B981",
+    "DiffLimited": "#EF4444",
 }
 
-MECH_FULL_NAMES_EC = {
-    "Nernst": "Nernst (reversible)",
-    "BV":     "BV (Butler–Volmer)",
-    "MHC":    "MHC (Marcus–Hush–Chidsey)",
-    "Ads":    "Ads (adsorption)",
-    "EC":     "EC (electrochemical–chemical)",
-    "LH":     "LH (Langmuir–Hinshelwood)",
-}
 
-MECH_FULL_NAMES_TPD = {
-    "FirstOrder":       "First-order desorption",
-    "SecondOrder":      "Second-order desorption",
-    "LH_Surface":       "LH surface reaction",
-    "MvK":              "MvK (Mars–van Krevelen)",
-    "FirstOrderCovDep": "Coverage-dependent",
-    "DiffLimited":      "Diffusion-limited",
-}
+def plot_mechanism_probs(probs_dict, domain="ec"):
+    """
+    Horizontal bar chart of mechanism classification probabilities.
 
+    Args:
+        probs_dict: {mechanism_name: probability}
+        domain: 'ec' or 'tpd'
 
-def plot_mechanism_probs(probs_dict, domain="ec"):
-    """Horizontal bar chart of mechanism classification probabilities."""
+    Returns:
+        matplotlib Figure
+    """
     colors = MECH_COLORS_EC if domain == "ec" else MECH_COLORS_TPD
-    full_names = MECH_FULL_NAMES_EC if domain == "ec" else MECH_FULL_NAMES_TPD
     names = list(probs_dict.keys())
     probs = [probs_dict[n] for n in names]
 
-    idx = np.argsort(probs)
-    names  = [names[i] for i in idx]
-    probs  = [probs[i] for i in idx]
-    bar_c  = [colors.get(n, PAL["gray"]) for n in names]
-    labels = [full_names.get(n, n) for n in names]
+    sorted_idx = np.argsort(probs)
+    names = [names[i] for i in sorted_idx]
+    probs = [probs[i] for i in sorted_idx]
+    bar_colors = [colors.get(n, COLORS["neutral"]) for n in names]
 
-    fig, ax = plt.subplots(figsize=(7, max(2.4, len(names) * 0.55)))
-    bars = ax.barh(range(len(names)), probs, color=bar_c,
-                   edgecolor="white", linewidth=0.6, height=0.65,
-                   zorder=3)
+    fig, ax = plt.subplots(figsize=(8, max(3, len(names) * 0.6)))
+    bars = ax.barh(range(len(names)), probs, color=bar_colors, edgecolor="white",
+                   linewidth=0.5, height=0.7)
 
     ax.set_yticks(range(len(names)))
-    ax.set_yticklabels(labels, fontweight="medium", fontsize=9)
-    ax.set_xlim(0, 1.12)
-    ax.set_xlabel("Probability")
-    ax.grid(axis="x", alpha=0.15, linestyle="-", zorder=0)
-    ax.set_axisbelow(True)
-
-    for bar, prob in zip(bars, probs):
-        if prob > 0.03:
-            ax.text(bar.get_width() + 0.015,
-                    bar.get_y() + bar.get_height() / 2,
-                    f"{prob:.1%}", va="center", fontsize=10,
-                    fontweight="bold", color=PAL["dark"])
-
-    fig.tight_layout(pad=1.0)
+    ax.set_yticklabels(names, fontsize=12, fontweight="medium")
+    ax.set_xlim(0, 1.05)
+    ax.set_xlabel("Probability", fontsize=12)
+    ax.set_title("Mechanism Classification", fontsize=14, fontweight="bold", pad=15)
+
+    for i, (bar, prob) in enumerate(zip(bars, probs)):
+        if prob > 0.05:
+            ax.text(bar.get_width() + 0.02, bar.get_y() + bar.get_height() / 2,
+                    f"{prob:.1%}", va="center", fontsize=11, fontweight="bold")
+
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+    ax.grid(axis="x", alpha=0.3, linestyle="--")
+    fig.tight_layout()
     return fig
 
 
 def plot_posteriors(samples, param_names, mechanism_name, domain="ec"):
-    """KDE + rug plots of posterior distributions for each parameter."""
-    from scipy.stats import gaussian_kde
-
+    """
+    Violin plots of posterior distributions for each parameter.
+
+    Args:
+        samples: [n_samples, D] array of posterior samples
+        param_names: list of parameter names
+        mechanism_name: name of the mechanism
+        domain: 'ec' or 'tpd'
+
+    Returns:
+        matplotlib Figure
+    """
     n_params = len(param_names)
-    fig, axes = plt.subplots(1, n_params,
-                             figsize=(max(4, 2.8 * n_params), 3.2))
+    fig, axes = plt.subplots(1, n_params, figsize=(max(4, 3 * n_params), 4.5))
     if n_params == 1:
         axes = [axes]
 
     colors = MECH_COLORS_EC if domain == "ec" else MECH_COLORS_TPD
-    color = colors.get(mechanism_name, PAL["blue"])
+    color = colors.get(mechanism_name, COLORS["primary"])
 
     for i, (ax, name) in enumerate(zip(axes, param_names)):
         data = samples[:, i]
-        q05, q50, q95 = np.quantile(data, [0.05, 0.5, 0.95])
-
-        try:
-            kde = gaussian_kde(data, bw_method="silverman")
-            xs = np.linspace(data.min() - 0.1 * data.ptp(),
-                             data.max() + 0.1 * data.ptp(), 300)
-            ys = kde(xs)
-            ax.fill_between(xs, ys, alpha=0.25, color=color, zorder=2)
-            ax.plot(xs, ys, color=color, linewidth=1.8, zorder=3)
-        except Exception:
-            ax.hist(data, bins=40, density=True, color=color, alpha=0.4)
-
-        ax.axvline(q50, color=PAL["dark"], linewidth=1.2, linestyle="-",
-                   label=f"median {q50:.3f}", zorder=4)
-        ax.axvspan(q05, q95, alpha=0.08, color=color, zorder=1)
-
-        n_rug = min(len(data), 200)
-        rug_idx = np.random.choice(len(data), n_rug, replace=False)
-        ax.plot(data[rug_idx], np.zeros(n_rug) - 0.02 * ax.get_ylim()[1],
-                "|", color=color, alpha=0.3, markersize=4, zorder=2)
-
-        ax.set_xlabel(_format_param_name(name))
-        ax.set_yticks([])
-        ax.spines["left"].set_visible(False)
-
-        ax.text(0.97, 0.95,
-                f"median {q50:.3f}\n90% CI [{q05:.3f}, {q95:.3f}]",
-                transform=ax.transAxes, fontsize=7.5, va="top", ha="right",
-                color=PAL["dark"], alpha=0.7,
-                bbox=dict(boxstyle="round,pad=0.3", fc="white", ec="none", alpha=0.8))
-
-    fig.suptitle(f"Posterior Distributions — {mechanism_name}",
-                 fontsize=13, fontweight="bold")
+
+        parts = ax.violinplot(data, positions=[0], showmeans=True,
+                              showmedians=True, showextrema=False)
+        for pc in parts["bodies"]:
+            pc.set_facecolor(color)
+            pc.set_alpha(0.6)
+        parts["cmeans"].set_color("black")
+        parts["cmedians"].set_color(COLORS["warm"])
+
+        q05, q95 = np.quantile(data, [0.05, 0.95])
+        ax.axhline(q05, color=COLORS["neutral"], linestyle="--", alpha=0.5, linewidth=0.8)
+        ax.axhline(q95, color=COLORS["neutral"], linestyle="--", alpha=0.5, linewidth=0.8)
+
+        ax.set_title(_format_param_name(name), fontsize=11, fontweight="medium")
+        ax.set_xticks([])
+        ax.spines["top"].set_visible(False)
+        ax.spines["right"].set_visible(False)
+        ax.spines["bottom"].set_visible(False)
+
+        mean_val = data.mean()
+        ax.text(0.5, 0.02, f"mean={mean_val:.3f}", transform=ax.transAxes,
+                ha="center", fontsize=9, color=COLORS["neutral"])
+
+    fig.suptitle(f"Parameter Posteriors — {mechanism_name}",
+                 fontsize=14, fontweight="bold")
     fig.tight_layout(rect=[0, 0, 1, 0.93])
     return fig
 
 
 def plot_reconstruction(observed_curves, recon_curves, domain="ec",
                         nrmses=None, r2s=None, scan_labels=None):
-    """Overlay of observed vs reconstructed signals."""
+    """
+    Overlay of observed vs reconstructed signals with optional metrics.
+
+    Args:
+        observed_curves: list of dicts with 'x' and 'y' arrays
+        recon_curves: list of dicts with 'x' and 'y' arrays (same length)
+        domain: 'ec' or 'tpd'
+        nrmses: optional list of NRMSE values per curve
+        r2s: optional list of R2 values per curve
+        scan_labels: optional list of label strings per curve
+
+    Returns:
+        matplotlib Figure
+    """
     n_curves = len(observed_curves)
-    ncols = min(n_curves, 3)
-    fig, axes = plt.subplots(1, ncols,
-                             figsize=(max(4.5, 4 * ncols), 3.8),
+    fig, axes = plt.subplots(1, min(n_curves, 4),
+                             figsize=(max(5, 4 * min(n_curves, 4)), 5),
                              squeeze=False)
     axes = axes[0]
 
@@ -185,42 +167,61 @@ def plot_reconstruction(observed_curves, recon_curves, domain="ec",
         obs = observed_curves[i]
         rec = recon_curves[i]
 
-        ax.plot(obs["x"], obs["y"], color=PAL["gray"], linewidth=1.6,
-                label="Observed", alpha=0.85, zorder=2)
-        ax.plot(rec["x"], rec["y"], color=PAL["blue"], linewidth=1.6,
-                label="Reconstructed", linestyle="--", zorder=3)
+        ax.plot(obs["x"], obs["y"], color=COLORS["neutral"], linewidth=1.5,
+                label="Observed", alpha=0.8)
+        ax.plot(rec["x"], rec["y"], color=COLORS["primary"], linewidth=1.5,
+                label="Reconstructed", linestyle="--")
 
-        ax.set_xlabel(xlabel)
-        if i == 0:
-            ax.set_ylabel(ylabel)
+        ax.set_xlabel(xlabel, fontsize=10)
         if i == 0:
-            ax.legend(framealpha=0.9, loc="best", handlelength=1.5)
+            ax.set_ylabel(ylabel, fontsize=10)
+        ax.legend(fontsize=8, framealpha=0.8, loc="best")
+        ax.spines["top"].set_visible(False)
+        ax.spines["right"].set_visible(False)
+
+        if scan_labels and i < len(scan_labels):
+            title = scan_labels[i]
+        elif domain == "ec":
+            title = f"Scan rate {i + 1}"
+        else:
+            title = f"Heating rate {i + 1}"
+        ax.set_title(title, fontsize=10)
 
-        parts = []
+        metrics_parts = []
         if nrmses and i < len(nrmses) and np.isfinite(nrmses[i]):
-            parts.append(f"NRMSE {nrmses[i]:.4f}")
+            metrics_parts.append(f"NRMSE={nrmses[i]:.4f}")
         if r2s and i < len(r2s) and np.isfinite(r2s[i]):
-            parts.append(f"R\u00b2 {r2s[i]:.4f}")
-        if parts:
-            metrics_str = "  |  ".join(parts)
-            title = (scan_labels[i] if scan_labels and i < len(scan_labels)
-                     else f"Curve {i + 1}")
-            ax.set_title(f"{title}    {metrics_str}", fontsize=10)
-
-    fig.suptitle("Signal Reconstruction", fontsize=13, fontweight="bold")
+            metrics_parts.append(f"R\u00b2={r2s[i]:.4f}")
+        if metrics_parts:
+            ax.text(0.02, 0.98, "  ".join(metrics_parts),
+                    transform=ax.transAxes, fontsize=8, va="top",
+                    color=COLORS["accent"], fontweight="bold",
+                    bbox=dict(boxstyle="round,pad=0.3", facecolor="white",
+                              alpha=0.8, edgecolor=COLORS["accent"]))
+
+    suptitle = "Signal Reconstruction"
+    if nrmses and r2s:
+        valid_nrmse = [v for v in nrmses if np.isfinite(v)]
+        valid_r2 = [v for v in r2s if np.isfinite(v)]
+        if valid_nrmse and valid_r2:
+            avg_nrmse = np.mean(valid_nrmse)
+            avg_r2 = np.mean(valid_r2)
+            suptitle += f"  (avg NRMSE={avg_nrmse:.4f}, avg R\u00b2={avg_r2:.4f})"
+
+    fig.suptitle(suptitle, fontsize=12, fontweight="bold")
     fig.tight_layout(rect=[0, 0, 1, 0.93])
     return fig
 
 
-def _add_sweep_arrows(ax, pot, y_ox, y_red, mid, show_labels=False):
-    """Add direction arrows for forward/reverse sweeps."""
+def _add_sweep_arrows(ax, pot, y_ox, y_red, mid):
+    """Add direction arrows and labels for forward/reverse sweeps on both species."""
     sweep_specs = [
         (slice(None, mid), "reductive \u2192", 16),
         (slice(mid, None), "\u2190 oxidative", -16),
     ]
     curves = [
-        (y_ox, PAL["blue"], 0.35, 0.65),
-        (y_red, PAL["red"], 0.35, 0.65),
+        (y_ox, COLORS["primary"], 0.35, 0.65),
+        (y_red, COLORS["warm"], 0.35, 0.65),
     ]
     for y_data, color, fwd_frac, rev_frac in curves:
         for segment, label, y_offset in sweep_specs:
@@ -231,7 +232,9 @@ def _add_sweep_arrows(ax, pot, y_ox, y_red, mid, show_labels=False):
                 continue
 
             frac = fwd_frac if y_offset > 0 else rev_frac
-            idx = max(2, min(int(n * frac), n - 3))
+            idx = int(n * frac)
+            idx = max(2, min(idx, n - 3))
+
             step = max(1, n // 30)
             i0 = max(0, idx - step)
             i1 = min(n - 1, idx + step)
@@ -240,25 +243,34 @@ def _add_sweep_arrows(ax, pot, y_ox, y_red, mid, show_labels=False):
                 "", xy=(x_seg[i1], y_seg[i1]),
                 xytext=(x_seg[i0], y_seg[i0]),
                 arrowprops=dict(arrowstyle="-|>", color=color,
-                                lw=1.6, mutation_scale=12),
+                                lw=1.8, mutation_scale=14),
             )
-            if show_labels:
-                ax.annotate(label, xy=(x_seg[idx], y_seg[idx]),
-                            xytext=(0, y_offset), textcoords="offset points",
-                            fontsize=7, color=color, fontstyle="italic",
-                            ha="center", va="center")
+
+            ax.annotate(label, xy=(x_seg[idx], y_seg[idx]),
+                        xytext=(0, y_offset), textcoords="offset points",
+                        fontsize=7.5, color=color, fontstyle="italic",
+                        ha="center", va="center")
 
 
 def plot_concentration_profiles(conc_curves, scan_labels=None):
-    """Plot surface concentration profiles vs potential."""
+    """
+    Plot surface concentration profiles (C_A and C_B) vs potential.
+
+    Args:
+        conc_curves: list of dicts with 'x' (potential), 'c_ox', 'c_red',
+                     or None for failed reconstructions
+        scan_labels: optional list of label strings per curve
+
+    Returns:
+        matplotlib Figure, or None if no valid data
+    """
     valid = [c for c in conc_curves if c is not None]
     if not valid:
         return None
 
     n_curves = len(conc_curves)
-    ncols = min(n_curves, 3)
-    fig, axes = plt.subplots(1, ncols,
-                             figsize=(max(4.5, 4 * ncols), 3.8),
+    fig, axes = plt.subplots(1, min(n_curves, 4),
+                             figsize=(max(5, 4 * min(n_curves, 4)), 5),
                              squeeze=False)
     axes = axes[0]
 
@@ -273,30 +285,46 @@ def plot_concentration_profiles(conc_curves, scan_labels=None):
         c_red = np.asarray(c["c_red"])
         mid = len(pot) // 2
 
-        ax.plot(pot[:mid], c_ox[:mid], color=PAL["blue"], lw=1.5, label="C$_A$ (ox)")
-        ax.plot(pot[:mid], c_red[:mid], color=PAL["red"], lw=1.5, label="C$_B$ (red)")
-        ax.plot(pot[mid:], c_ox[mid:], color=PAL["blue"], lw=1.5)
-        ax.plot(pot[mid:], c_red[mid:], color=PAL["red"], lw=1.5)
+        # Forward sweep (reductive): first half
+        ax.plot(pot[:mid], c_ox[:mid], color=COLORS["primary"], linewidth=1.5,
+                label="C$_A$ (ox)")
+        ax.plot(pot[:mid], c_red[:mid], color=COLORS["warm"], linewidth=1.5,
+                label="C$_B$ (red)")
+        # Reverse sweep (oxidative): second half
+        ax.plot(pot[mid:], c_ox[mid:], color=COLORS["primary"], linewidth=1.5)
+        ax.plot(pot[mid:], c_red[mid:], color=COLORS["warm"], linewidth=1.5)
 
-        _add_sweep_arrows(ax, pot, c_ox, c_red, mid, show_labels=False)
+        _add_sweep_arrows(ax, pot, c_ox, c_red, mid)
 
-        ax.set_xlabel("Potential (\u03b8)")
-        if i == 0:
-            ax.set_ylabel("Surface concentration")
+        ax.set_xlabel("Potential (\u03b8)", fontsize=10)
         if i == 0:
-            ax.legend(framealpha=0.9, loc="best", handlelength=1.5)
+            ax.set_ylabel("Surface concentration", fontsize=10)
+        ax.legend(fontsize=8, framealpha=0.8, loc="best")
+        ax.spines["top"].set_visible(False)
+        ax.spines["right"].set_visible(False)
 
-        title = (scan_labels[i] if scan_labels and i < len(scan_labels)
-                 else f"Curve {i + 1}")
-        ax.set_title(title, fontsize=10)
+        if scan_labels and i < len(scan_labels):
+            ax.set_title(scan_labels[i], fontsize=10)
+        else:
+            ax.set_title(f"Scan rate {i + 1}", fontsize=10)
 
-    fig.suptitle("Surface Concentration Profiles", fontsize=13, fontweight="bold")
+    fig.suptitle("Surface Concentration Profiles", fontsize=12,
+                 fontweight="bold")
     fig.tight_layout(rect=[0, 0, 1, 0.93])
     return fig
 
 
 def plot_parameter_table(param_stats, mechanism_name):
-    """Create a formatted parameter summary table as a figure."""
+    """
+    Create a formatted parameter summary table as a figure.
+
+    Args:
+        param_stats: dict with 'names', 'mean', 'std', 'q05', 'q95'
+        mechanism_name: name of the mechanism
+
+    Returns:
+        matplotlib Figure
+    """
     names = param_stats["names"]
     means = param_stats["mean"]
     stds = param_stats["std"]
@@ -304,33 +332,35 @@ def plot_parameter_table(param_stats, mechanism_name):
     q95s = param_stats["q95"]
 
     n = len(names)
-    fig, ax = plt.subplots(figsize=(7, max(1.8, 0.5 * n + 0.8)))
+    fig, ax = plt.subplots(figsize=(8, max(2, 0.6 * n + 1)))
     ax.axis("off")
 
     col_labels = ["Parameter", "Mean", "Std", "5th %ile", "95th %ile"]
-    cell_text = [
-        [_format_param_name(names[i]),
-         f"{means[i]:.4f}", f"{stds[i]:.4f}",
-         f"{q05s[i]:.4f}", f"{q95s[i]:.4f}"]
-        for i in range(n)
-    ]
+    cell_text = []
+    for i in range(n):
+        cell_text.append([
+            _format_param_name(names[i]),
+            f"{means[i]:.4f}",
+            f"{stds[i]:.4f}",
+            f"{q05s[i]:.4f}",
+            f"{q95s[i]:.4f}",
+        ])
 
     table = ax.table(cellText=cell_text, colLabels=col_labels,
                      loc="center", cellLoc="center")
     table.auto_set_font_size(False)
-    table.set_fontsize(10)
-    table.scale(1.0, 1.4)
+    table.set_fontsize(11)
+    table.scale(1.0, 1.5)
 
     for (row, col), cell in table.get_celld().items():
-        cell.set_edgecolor("#E5E7EB")
         if row == 0:
-            cell.set_facecolor("#EEF2FF")
+            cell.set_facecolor("#E5E7EB")
             cell.set_text_props(fontweight="bold")
         else:
-            cell.set_facecolor("white" if row % 2 else "#F9FAFB")
+            cell.set_facecolor("#F9FAFB" if row % 2 == 0 else "white")
 
     ax.set_title(f"Parameter Estimates — {mechanism_name}",
-                 fontsize=13, fontweight="bold", pad=16)
+                 fontsize=14, fontweight="bold", pad=20)
     fig.tight_layout()
     return fig
 
@@ -338,29 +368,29 @@ def plot_parameter_table(param_stats, mechanism_name):
 def _format_param_name(name):
     """Format parameter names for display."""
     replacements = {
-        "log10(K0)": "log\u2081\u2080(K\u2080)",
-        "log10(dB)": "log\u2081\u2080(d_B)",
-        "log10(dA)": "log\u2081\u2080(d_A)",
-        "log10(kc)": "log\u2081\u2080(k_c)",
-        "log10(reorg_e)": "log\u2081\u2080(\u03bb)",
-        "log10(Gamma_sat)": "log\u2081\u2080(\u0393_sat)",
-        "log10(KA_eq)": "log\u2081\u2080(K_A,eq)",
-        "log10(KB_eq)": "log\u2081\u2080(K_B,eq)",
-        "log10(nu)": "log\u2081\u2080(\u03bd)",
-        "log10(nu_red)": "log\u2081\u2080(\u03bd_red)",
-        "log10(D0)": "log\u2081\u2080(D\u2080)",
-        "E0_offset": "E\u2080 offset",
-        "alpha": "\u03b1",
-        "alpha_cov": "\u03b1_cov",
+        "log10(K0)": "log₁₀(K₀)",
+        "log10(dB)": "log₁₀(d_B)",
+        "log10(dA)": "log₁₀(d_A)",
+        "log10(kc)": "log₁₀(k_c)",
+        "log10(reorg_e)": "log₁₀(λ)",
+        "log10(Gamma_sat)": "log₁₀(Γ_sat)",
+        "log10(KA_eq)": "log₁₀(K_A,eq)",
+        "log10(KB_eq)": "log₁₀(K_B,eq)",
+        "log10(nu)": "log₁₀(ν)",
+        "log10(nu_red)": "log₁₀(ν_red)",
+        "log10(D0)": "log₁₀(D₀)",
+        "E0_offset": "E₀ offset",
+        "alpha": "α",
+        "alpha_cov": "α_cov",
         "Ed": "E_d (K)",
         "Ed0": "E_d0 (K)",
         "Ea": "E_a (K)",
         "Ea_red": "E_a,red (K)",
         "Ea_reox": "E_a,reox (K)",
         "E_diff": "E_diff (K)",
-        "theta_0": "\u03b8\u2080",
-        "theta_A0": "\u03b8_A0",
-        "theta_B0": "\u03b8_B0",
-        "theta_O0": "\u03b8_O0",
+        "theta_0": "θ₀",
+        "theta_A0": "θ_A0",
+        "theta_B0": "θ_B0",
+        "theta_O0": "θ_O0",
     }
     return replacements.get(name, name)

preprocessing.py

@@ -175,29 +175,23 @@ def parse_cv_csv(file_content, delimiter=None):
         ("current", 1.0), ("i", 1.0),
     ]
 
-    assigned = set()
     for idx, col in enumerate(header):
         if t_col is None:
             for pat in time_patterns:
                 if pat in col:
                     t_col = idx
-                    assigned.add(idx)
                     break
-            if idx in assigned:
+            if t_col == idx:
                 continue
         if e_col is None:
             for pat, scale in potential_patterns:
                 if pat in col:
                     e_col, e_scale = idx, scale
-                    assigned.add(idx)
                     break
-            if idx in assigned:
-                continue
         if i_col is None:
             for pat, scale in current_patterns:
                 if pat in col:
                     i_col, i_scale = idx, scale
-                    assigned.add(idx)
                     break
 
     if e_col is None or i_col is None:
@@ -409,3 +403,71 @@ def parse_dimensionless_cv_csv(file_content, delimiter=None):
         "theta": np.array(theta_vals, dtype=np.float32),
         "flux": np.array(flux_vals, dtype=np.float32),
     }
+
+
+# ── TPD summary feature extraction ──────────────────────────────────
+
+MAX_HEATING_RATES = 3
+TPD_FEATURES_PER_RATE = 6
+TPD_SUMMARY_DIM = MAX_HEATING_RATES * TPD_FEATURES_PER_RATE + MAX_HEATING_RATES  # 21
+
+
+def extract_tpd_summary_stats(temperature, rate, lengths, heating_rates, n_rates):
+    """Extract 21-dim hand-crafted summary statistics from raw TPD data.
+
+    Per heating rate (6 features): normalized peak rate, peak temperature,
+    half-peak width, normalized total desorption integral, asymmetry ratio
+    (left vs right half-width), log10(peak rate).
+    Plus log10(heating_rate) per curve.
+
+    Args:
+        temperature: [N, T] array of temperatures (K)
+        rate: [N, T] array of desorption rates
+        lengths: [N] array of valid lengths per curve
+        heating_rates: [N] array of heating rates (K/s)
+        n_rates: number of heating rates
+
+    Returns:
+        1-D array of shape (21,)
+    """
+    features = np.zeros(TPD_SUMMARY_DIM, dtype=np.float32)
+    for i in range(min(n_rates, MAX_HEATING_RATES)):
+        L = int(lengths[i])
+        temp = temperature[i, :L]
+        r = rate[i, :L]
+        peak_abs = np.max(np.abs(r)) + 1e-30
+
+        peak_rate = np.max(r)
+        idx_peak = np.argmax(r)
+        peak_temp = temp[idx_peak]
+
+        half_max = peak_rate / 2.0
+        above_half = r >= half_max
+        if np.any(above_half):
+            indices = np.where(above_half)[0]
+            half_width = temp[indices[-1]] - temp[indices[0]]
+            left_width = peak_temp - temp[indices[0]]
+            right_width = temp[indices[-1]] - peak_temp
+            asymmetry = (right_width - left_width) / (half_width + 1e-30)
+        else:
+            half_width = 0.0
+            asymmetry = 0.0
+
+        if L > 1:
+            integral = (np.trapezoid(r, temp)
+                        if hasattr(np, 'trapezoid') else np.trapz(r, temp))
+        else:
+            integral = 0.0
+
+        log_peak = np.log10(peak_abs)
+
+        offset = i * TPD_FEATURES_PER_RATE
+        features[offset + 0] = peak_rate / peak_abs
+        features[offset + 1] = peak_temp
+        features[offset + 2] = half_width
+        features[offset + 3] = integral / (peak_abs * (temp.max() - temp.min()) + 1e-30)
+        features[offset + 4] = asymmetry
+        features[offset + 5] = log_peak
+
+        features[MAX_HEATING_RATES * TPD_FEATURES_PER_RATE + i] = np.log10(heating_rates[i])
+    return features