Initial ECFlow deployment

Amortized Bayesian inference for mechanism identification and parameter
estimation in electrochemistry (CV) and catalysis (TPD).

- v7 targeted calibration checkpoints (EC + TPD)
- Gradio web UI with CSV and image upload
- Demo data for all 12 mechanisms

Made-with: Cursor

.gitignore

@@ -0,0 +1,2 @@
+__pycache__/
+*.pyc

README.md

@@ -1,12 +1,33 @@
 ---
-title: Ecflow
-emoji: 👀
-colorFrom: red
-colorTo: blue
+title: ECFlow
+emoji: ⚡
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 6.9.0
+sdk_version: "4.44.0"
 app_file: app.py
 pinned: false
+license: mit
+short_description: Bayesian inference for CV & TPD analysis
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ECFlow — Amortized Bayesian Inference for Electrochemistry & Catalysis
+
+Upload cyclic voltammetry (CV) or temperature-programmed desorption (TPD) data to automatically:
+
+1. **Identify the reaction mechanism** from 6 candidates per domain
+2. **Infer kinetic parameters** with full Bayesian posterior uncertainty
+
+Inference takes ~50 ms on CPU. Accepts CSV files or plot images.
+
+## Supported Mechanisms
+
+**Electrochemistry (CV):** Nernst, Butler-Volmer, Marcus-Hush-Chidsey, Adsorption, EC, Langmuir-Hinshelwood
+
+**Catalysis (TPD):** First-order, Second-order, LH Surface, Mars-van Krevelen, Coverage-dependent, Diffusion-limited
+
+## Citation
+
+```
+[Citation to be added upon publication]
+```

app.py

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+"""
+ECFlow — Amortized Bayesian Inference for Electrochemistry & Catalysis
+
+Gradio web interface for mechanism classification and parameter inference
+from cyclic voltammetry (CV) and temperature-programmed desorption (TPD) data.
+"""
+
+import os
+import sys
+import json
+import tempfile
+from pathlib import Path
+
+import numpy as np
+import gradio as gr
+
+
+
+from inference import ECFlowPredictor
+from preprocessing import (
+    nondimensionalize_cv,
+    estimate_E0,
+    parse_cv_csv,
+    parse_tpd_csv,
+)
+from plotting import (
+    plot_mechanism_probs,
+    plot_posteriors,
+    plot_parameter_table,
+    plot_reconstruction,
+    plot_concentration_profiles,
+)
+
+# ---------------------------------------------------------------------------
+# Model paths (relative to repo root)
+# ---------------------------------------------------------------------------
+REPO_ROOT = Path(__file__).resolve().parent
+
+EC_CHECKPOINT = REPO_ROOT / "checkpoints" / "ec_best.pt"
+TPD_CHECKPOINT = REPO_ROOT / "checkpoints" / "tpd_best.pt"
+
+# Allow override via environment variables
+EC_CHECKPOINT = Path(os.environ.get("ECFLOW_EC_CHECKPOINT", str(EC_CHECKPOINT)))
+TPD_CHECKPOINT = Path(os.environ.get("ECFLOW_TPD_CHECKPOINT", str(TPD_CHECKPOINT)))
+
+predictor = None
+
+
+def get_predictor():
+    global predictor
+    if predictor is None:
+        ec_ckpt = str(EC_CHECKPOINT) if EC_CHECKPOINT.exists() else None
+        tpd_ckpt = str(TPD_CHECKPOINT) if TPD_CHECKPOINT.exists() else None
+        predictor = ECFlowPredictor(
+            ec_checkpoint=ec_ckpt,
+            tpd_checkpoint=tpd_ckpt,
+            device="cpu",
+        )
+    return predictor
+
+
+# =========================================================================
+# CV Analysis
+# =========================================================================
+
+def analyze_cv(files, scan_rates_text, E0_V, T_K, A_cm2,
+               C_mM, D_cm2s, n_electrons, n_samples):
+    """Analyze CV data from potentiostat CSV files.
+
+    Accepts CSV files with columns for potential (V) and current (A/mA/µA).
+    If the CSV includes a Time (s) column, the scan rate is auto-detected.
+    Otherwise, scan rates must be provided.
+    """
+    if not files:
+        return _ec_error("Please upload at least one CSV file.")
+
+    scan_rates_text = scan_rates_text.strip() if scan_rates_text else ""
+
+    user_rates = None
+    if scan_rates_text:
+        try:
+            user_rates = [float(s.strip()) for s in scan_rates_text.split(",")]
+        except ValueError:
+            return _ec_error("Invalid scan rates. Enter comma-separated numbers in V/s.")
+        if len(files) != len(user_rates):
+            return _ec_error(
+                f"Number of files ({len(files)}) must match number of "
+                f"scan rates ({len(user_rates)}).")
+
+    C_molcm3 = float(C_mM) * 1e-6 if C_mM else 1e-6
+    n = int(n_electrons) if n_electrons else 1
+    T = float(T_K) if T_K else 298.15
+    A = float(A_cm2) if A_cm2 else 0.0707
+
+    parsed_data = []
+    scan_rates = []
+    for idx, f in enumerate(files):
+        content = Path(f.name).read_text()
+        parsed = parse_cv_csv(content)
+        parsed_data.append(parsed)
+
+        if user_rates is not None:
+            v = user_rates[idx]
+        elif "scan_rate_Vs" in parsed:
+            v = parsed["scan_rate_Vs"]
+        else:
+            return _ec_error(
+                f"Cannot determine scan rate for file '{Path(f.name).name}'. "
+                "Either provide scan rates (V/s) or upload CSV files that "
+                "include a Time (s) column.")
+        scan_rates.append(v)
+
+    if E0_V:
+        e0 = float(E0_V)
+        e0_source = "user"
+    else:
+        e0_estimates = [estimate_E0(p["E_V"], p["i_A"]) for p in parsed_data]
+        e0 = float(np.median(e0_estimates))
+        e0_source = "auto"
+
+    D = float(D_cm2s) if D_cm2s else 1e-5
+
+    potentials, fluxes, sigmas_list = [], [], []
+    for idx, (parsed, v) in enumerate(zip(parsed_data, scan_rates)):
+        E, i_A = parsed["E_V"], parsed["i_A"]
+
+        theta, flux, sigma = nondimensionalize_cv(
+            E, i_A, v, e0, T, A, C_molcm3, D, n
+        )
+        potentials.append(theta)
+        fluxes.append(flux)
+        sigmas_list.append(sigma)
+
+    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":
+        preproc_info += (
+            f"E₀ auto-estimated as {e0:.4f} V from peak midpoints. "
+            "For better accuracy, provide E₀ under Advanced parameters."
+        )
+    else:
+        preproc_info += f"E₀ = {e0:.4f} V (user-provided)."
+
+    result_list = list(result)
+    if isinstance(result_list[1], str):
+        result_list[1] += preproc_info
+    return tuple(result_list)
+
+
+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.
+
+    Extracts the CV curve via image digitization, then nondimensionalizes
+    and runs inference identically to the CSV path.
+    Axis bounds are auto-detected via OCR if not provided.
+    """
+    if image is None:
+        return _ec_error("Please upload an image.")
+
+    try:
+        from digitizer import digitize_plot, auto_detect_axis_bounds
+    except ImportError:
+        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 (V/s).")
+    try:
+        v_Vs = float(scan_rate_text)
+    except ValueError:
+        return _ec_error("Invalid scan rate. Enter a number in V/s.")
+
+    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"
+
+    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}")
+
+    # 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"
+
+    if E0_V is not None and E0_V != 0:
+        e0 = float(E0_V)
+        e0_source = "user"
+    else:
+        e0 = float(estimate_E0(E_V, i_A))
+        e0_source = "auto"
+
+    D = 1e-5
+    T = 298.15
+    A = 0.0707
+    C_molcm3 = 1e-6
+    n = 1
+
+    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 = "\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."
+    else:
+        preproc_info += f"E₀ = {e0:.4f} V (user-provided)."
+
+    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."""
+    return None, msg, None, gr.update(choices=[], value=None), None, None, None
+
+
+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))
+    top_mech = result["predicted_mechanism"]
+    recon = pred.reconstruct_ec(result, potentials, fluxes, sigmas)
+
+    fig_probs = plot_mechanism_probs(result["mechanism_probs"], domain="ec")
+    summary = _build_summary_text(result, recon=recon, domain="ec")
+
+    sorted_mechs = sorted(result["mechanism_probs"].items(), key=lambda x: -x[1])
+    mech_choices = [f"{m} ({p:.1%})" for m, p in sorted_mechs]
+
+    state = {
+        "result": result,
+        "potentials": [p.tolist() for p in potentials],
+        "fluxes": [f.tolist() for f in fluxes],
+        "sigmas": sigmas,
+    }
+
+    fig_post, fig_recon, fig_conc = _render_ec_mechanism(
+        top_mech, result, recon, sigmas
+    )
+
+    return (
+        fig_probs, summary, state,
+        gr.update(choices=mech_choices, value=mech_choices[0]),
+        fig_post, fig_recon, fig_conc,
+    )
+
+
+def _render_ec_mechanism(mech, result, recon, sigmas):
+    """Render posteriors, reconstruction, and concentration for one EC mechanism."""
+    stats = result["parameter_stats"].get(mech)
+    samples = result["posterior_samples"].get(mech)
+
+    fig_posteriors = None
+    if stats and samples is not None:
+        fig_posteriors = plot_posteriors(samples, stats["names"], mech, domain="ec")
+
+    fig_recon = None
+    fig_conc = None
+    if recon is not None:
+        scan_labels = [f"\u03c3 = {s:.2f}" for s in sigmas] if sigmas else None
+        fig_recon = plot_reconstruction(
+            recon["observed"], recon["reconstructed"], domain="ec",
+            nrmses=recon.get("nrmse"), r2s=recon.get("r2"),
+            scan_labels=scan_labels,
+        )
+        conc_curves = recon.get("concentrations")
+        if conc_curves:
+            fig_conc = plot_concentration_profiles(conc_curves, scan_labels=scan_labels)
+
+    return fig_posteriors, fig_recon, fig_conc
+
+
+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
+
+    mech = mech_choice.split(" (")[0]
+    result = state["result"]
+    potentials = [np.array(p) for p in state["potentials"]]
+    fluxes = [np.array(f) for f in state["fluxes"]]
+    sigmas = state["sigmas"]
+
+    pred = get_predictor()
+    recon = pred.reconstruct_ec(result, potentials, fluxes, sigmas, mechanism=mech)
+    return _render_ec_mechanism(mech, result, recon, sigmas)
+
+
+# =========================================================================
+# TPD Analysis
+# =========================================================================
+
+def analyze_tpd(files, heating_rates_text, n_samples):
+    """Analyze TPD data."""
+    if not files:
+        return _tpd_error("Please upload at least one CSV file.")
+
+    temperatures, rates = [], []
+    csv_betas = []
+    for f in files:
+        content = Path(f.name).read_text()
+        parsed = parse_tpd_csv(content)
+        temperatures.append(parsed["T_K"])
+        rates.append(parsed["signal"])
+        if "beta_Ks" in parsed:
+            csv_betas.append(parsed["beta_Ks"])
+
+    heating_rates_text = heating_rates_text.strip() if heating_rates_text else ""
+    if heating_rates_text:
+        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.")
+        if len(files) != len(betas):
+            return _tpd_error(
+                f"Number of files ({len(files)}) must match heating rates ({len(betas)}).")
+    elif len(csv_betas) == len(files):
+        betas = csv_betas
+    else:
+        return _tpd_error(
+            "Please enter the heating rate (β in K/s) for each file. "
+            "This value is critical for correct inference. "
+            "Alternatively, include a 'Time (s)' column in your CSV so β can be computed automatically.")
+
+    return _run_tpd_analysis(temperatures, rates, betas, n_samples)
+
+
+def analyze_tpd_image(image, heating_rates_text, threshold, n_samples,
+                      x_min, x_max, y_min, y_max):
+    """Analyze TPD from an uploaded plot image.
+
+    Axis bounds are auto-detected via OCR if not provided.
+    """
+    if image is None:
+        return _tpd_error("Please upload an image.")
+
+    try:
+        from digitizer import digitize_plot, auto_detect_axis_bounds
+    except ImportError:
+        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 (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.")
+
+    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'.")
+
+    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([x_data], [y_data], betas[:1], n_samples)
+
+
+def _tpd_error(msg=""):
+    """Return empty outputs for TPD error cases."""
+    return None, msg, None, gr.update(choices=[], value=None), None, None
+
+
+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))
+    top_mech = result["predicted_mechanism"]
+    recon = pred.reconstruct_tpd(result, temperatures, rates, betas)
+
+    fig_probs = plot_mechanism_probs(result["mechanism_probs"], domain="tpd")
+    summary = _build_summary_text(result, recon=recon, domain="tpd")
+
+    sorted_mechs = sorted(result["mechanism_probs"].items(), key=lambda x: -x[1])
+    mech_choices = [f"{m} ({p:.1%})" for m, p in sorted_mechs]
+
+    state = {
+        "result": result,
+        "temperatures": [t.tolist() for t in temperatures],
+        "rates": [r.tolist() for r in rates],
+        "betas": betas,
+    }
+
+    fig_post, fig_recon = _render_tpd_mechanism(top_mech, result, recon, betas)
+
+    return (
+        fig_probs, summary, state,
+        gr.update(choices=mech_choices, value=mech_choices[0]),
+        fig_post, fig_recon,
+    )
+
+
+def _render_tpd_mechanism(mech, result, recon, betas):
+    """Render posteriors and reconstruction for one TPD mechanism."""
+    stats = result["parameter_stats"].get(mech)
+    samples = result["posterior_samples"].get(mech)
+
+    fig_posteriors = None
+    if stats and samples is not None:
+        fig_posteriors = plot_posteriors(samples, stats["names"], mech, domain="tpd")
+
+    fig_recon = None
+    if recon is not None:
+        scan_labels = [f"\u03b2 = {b:.2f} K/s" for b in betas] if betas else None
+        fig_recon = plot_reconstruction(
+            recon["observed"], recon["reconstructed"], domain="tpd",
+            nrmses=recon.get("nrmse"), r2s=recon.get("r2"),
+            scan_labels=scan_labels,
+        )
+
+    return fig_posteriors, fig_recon
+
+
+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
+
+    mech = mech_choice.split(" (")[0]
+    result = state["result"]
+    temperatures = [np.array(t) for t in state["temperatures"]]
+    rates = [np.array(r) for r in state["rates"]]
+    betas = state["betas"]
+
+    pred = get_predictor()
+    recon = pred.reconstruct_tpd(result, temperatures, rates, betas, mechanism=mech)
+    return _render_tpd_mechanism(mech, result, recon, betas)
+
+
+# =========================================================================
+# Shared helpers
+# =========================================================================
+
+def _build_summary_text(result, recon=None, domain="ec"):
+    """Build a markdown summary of inference results."""
+    mech = result["predicted_mechanism"]
+    prob = result["mechanism_probs"][mech]
+
+    lines = [
+        f"## Predicted Mechanism: **{mech}** ({prob:.1%} confidence)\n",
+    ]
+
+    stats = result["parameter_stats"].get(mech)
+    if stats:
+        lines.append("### Parameter Estimates (90% Credible Interval)\n")
+        lines.append("| Parameter | Mean | 90% CI |")
+        lines.append("|-----------|------|--------|")
+        for i, name in enumerate(stats["names"]):
+            mean = stats["mean"][i]
+            q05 = stats["q05"][i]
+            q95 = stats["q95"][i]
+            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:
+        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)
+
+
+def download_results(result_text):
+    """Create a downloadable JSON from the summary."""
+    if not result_text:
+        return None
+    tmp = tempfile.NamedTemporaryFile(suffix=".json", delete=False, mode="w")
+    tmp.write(result_text)
+    tmp.close()
+    return tmp.name
+
+
+# =========================================================================
+# Gradio UI
+# =========================================================================
+
+def _build_ec_output_section(prefix):
+    """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("---")
+    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="View mechanism (select to explore alternatives)",
+        choices=[],
+        interactive=True,
+    )
+    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.
+
+    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("---")
+    gr.Markdown(
+        "### Mechanism Details\n"
+        "Select a mechanism below to view its parameter posteriors "
+        "and signal reconstruction."
+    )
+    mech_dd = gr.Dropdown(
+        label="View mechanism (select to explore alternatives)",
+        choices=[],
+        interactive=True,
+    )
+    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",
+    ) as app:
+        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():
+            # =================================================================
+            # Tab 1: CV Analysis
+            # =================================================================
+            with gr.Tab("CV Analysis"):
+                with gr.Tabs():
+                    # --- CSV upload mode (primary) ---
+                    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"
+                            "If your CSV includes a **Time (s)** column, the scan rate is "
+                            "detected automatically. Otherwise, enter scan rates below."
+                        )
+                        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_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.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_probs, cv_summary, cv_state,
+                            cv_mech_dd, cv_posteriors,
+                            cv_recon, cv_conc,
+                        ]
+                        cv_btn.click(
+                            analyze_cv,
+                            inputs=[
+                                cv_files, cv_rates, cv_E0, cv_T,
+                                cv_A, cv_C, cv_D, cv_n, cv_nsamples,
+                            ],
+                            outputs=ec_outputs,
+                        )
+                        cv_mech_dd.change(
+                            _on_ec_mechanism_change,
+                            inputs=[cv_mech_dd, cv_state],
+                            outputs=[cv_posteriors, cv_recon, cv_conc],
+                        )
+
+                    # --- Image mode ---
+                    with gr.Tab("From Image"):
+                        gr.Markdown(
+                            "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."
+                        )
+                        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="",
+                                )
+                            with gr.Column(scale=1):
+                                cv_img_nsamples = gr.Slider(
+                                    100, 2000, value=500, step=100,
+                                    label="Posterior samples",
+                                )
+                                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_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, 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,
+                            ],
+                            outputs=ec_img_outputs,
+                        )
+                        cv_img_mech_dd.change(
+                            _on_ec_mechanism_change,
+                            inputs=[cv_img_mech_dd, cv_img_state],
+                            outputs=[cv_img_posteriors, cv_img_recon, cv_img_conc],
+                        )
+
+            # =================================================================
+            # Tab 2: TPD Analysis
+            # =================================================================
+            with gr.Tab("TPD Analysis"):
+                with gr.Tabs():
+                    # --- CSV mode ---
+                    with gr.Tab("CSV Data"):
+                        gr.Markdown(
+                            "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():
+                            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_probs, tpd_summary, tpd_state,
+                         tpd_mech_dd, tpd_posteriors,
+                         tpd_recon) = _build_tpd_output_section("tpd")
+
+                        tpd_outputs = [
+                            tpd_probs, tpd_summary, tpd_state,
+                            tpd_mech_dd, tpd_posteriors, tpd_recon,
+                        ]
+                        tpd_btn.click(
+                            analyze_tpd,
+                            inputs=[tpd_files, tpd_betas, tpd_nsamples],
+                            outputs=tpd_outputs,
+                        )
+                        tpd_mech_dd.change(
+                            _on_tpd_mechanism_change,
+                            inputs=[tpd_mech_dd, tpd_state],
+                            outputs=[tpd_posteriors, tpd_recon],
+                        )
+
+                    # --- Image mode ---
+                    with gr.Tab("From Image"):
+                        gr.Markdown(
+                            "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."
+                        )
+                        with gr.Row():
+                            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_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, tpd_img_betas,
+                                tpd_img_threshold, tpd_img_nsamples,
+                                tpd_img_xmin, tpd_img_xmax,
+                                tpd_img_ymin, tpd_img_ymax,
+                            ],
+                            outputs=tpd_img_outputs,
+                        )
+                        tpd_img_mech_dd.change(
+                            _on_tpd_mechanism_change,
+                            inputs=[tpd_img_mech_dd, tpd_img_state],
+                            outputs=[tpd_img_posteriors, tpd_img_recon],
+                        )
+
+            # =================================================================
+            # Tab 3: About
+            # =================================================================
+            with gr.Tab("About"):
+                gr.Markdown("""
+## 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 |
+
+### Catalysis (TPD) Mechanisms
+
+| 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 |
+
+### 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:
+```
+[Citation to be added upon publication]
+```
+""")
+
+    return app
+
+
+# =========================================================================
+# Entry point
+# =========================================================================
+
+if __name__ == "__main__":
+    app = build_app()
+    app.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True,
+        theme=gr.themes.Soft(
+            primary_hue="blue",
+            secondary_hue="purple",
+        ),
+    )

checkpoints/ec_best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:875cfed256127e9a3d1fd63cf37f53fd911333e4bd606f5693605503b4e3c7d3
+size 24782085

checkpoints/ec_norm_stats.json

@@ -0,0 +1,14 @@
+{
+  "potential": [
+    -0.2561522126197815,
+    6.610905647277832
+  ],
+  "flux": [
+    -0.16024018824100494,
+    0.4030846059322357
+  ],
+  "time": [
+    55.81125259399414,
+    97.78398132324219
+  ]
+}

checkpoints/ec_theta_stats.json

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+{
+  "Nernst": {
+    "mean": [
+      0.0,
+      0.0,
+      0.0
+    ],
+    "std": [
+      1.0,
+      1.0,
+      1.0
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+      0.24877089262008667,
+      0.5012289881706238,
+      0.001087042037397623
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+      0.11590524017810822,
+      0.17334586381912231
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+    "count": 50000
+  },
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+    "mean": [
+      0.24597173929214478,
+      1.2555487155914307,
+      6.6473007791501004e-06
+    ],
+    "std": [
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+      0.17297042906284332
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+    "std": [
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+      0.5781700611114502
+    ],
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+    "mean": [
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+    "std": [
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+    ],
+    "count": 25000
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+      0.49786701798439026,
+      0.5003064870834351,
+      -0.0005569184431806207
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+    "std": [
+      1.2338130474090576,
+      0.11521629244089127,
+      0.8666039705276489,
+      0.8665307760238647,
+      0.17345362901687622
+    ],
+    "count": 25000
+  }
+}

checkpoints/tpd_best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:656bfaa0b3b0c3cfdbb13f528961966bfb135baec10141aa08dbe698628568b2
+size 27588293

checkpoints/tpd_norm_stats.json

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+{
+  "temperature": [
+    706.4283447265625,
+    208.69534301757812
+  ],
+  "rate": [
+    0.07081261277198792,
+    0.20301415026187897
+  ]
+}

checkpoints/tpd_theta_stats.json

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+      1.1530061960220337,
+      4364.41845703125,
+      0.20163801312446594
+    ],
+    "count": 25000
+  }
+}

demo_data/ec_Ads_11mVs.csv

@@ -0,0 +1,613 @@
+Time (s),E (V),I (A)
+0.000000,0.48865084,8.6305661053e-10
+0.121492,0.48736620,-8.0962613013e-09
+0.242985,0.48608160,1.8415062300e-09
+0.364477,0.48479696,-2.0238478375e-09
+0.485970,0.48351233,1.6334221169e-09
+0.607462,0.48222769,-7.3551231405e-09
+0.728955,0.48094306,-9.6052419080e-10
+0.850447,0.47965845,1.8721567114e-09
+0.971940,0.47837382,-1.3129958911e-10
+1.093432,0.47708918,3.2303755498e-09
+1.214924,0.47580455,1.2841098554e-09
+1.336417,0.47451992,6.7288956893e-10
+1.457909,0.47323531,-5.1005728569e-09
+1.579402,0.47195067,-1.9477836790e-09
+1.700894,0.47066604,-2.8932885217e-09
+1.822387,0.46938141,-9.9166240880e-10
+1.943879,0.46809677,1.1036684876e-08
+2.065372,0.46681216,-1.5510896768e-09
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demo_data/ec_Ads_metadata.json

@@ -0,0 +1,32 @@
+{
+  "mechanism": "Ads",
+  "n_scans": 3,
+  "scan_rates_Vs": [
+    0.010573734343051911,
+    0.24367923736572267,
+    4.024525451660156
+  ],
+  "physical_params": {
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+    "T_K": 298.15,
+    "A_cm2": 0.0707,
+    "C_mM": 1.0,
+    "D_cm2s": 1e-05,
+    "n_electrons": 1
+  },
+  "true_params_dimless": {
+    "sigma": 8.719717822841444,
+    "theta_i": 9.288707976005638,
+    "theta_v": -6.010308548417381,
+    "cycles": 1.0,
+    "kinetics": "Ads",
+    "K0": 0.4485674957444803,
+    "alpha": 0.5293986596997461,
+    "Gamma_sat": 1.2656920004857595
+  },
+  "csv_files": [
+    "ec_Ads_11mVs.csv",
+    "ec_Ads_244mVs.csv",
+    "ec_Ads_4025mVs.csv"
+  ]
+}

demo_data/ec_BV_11mVs.csv

@@ -0,0 +1,673 @@
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demo_data/ec_BV_metadata.json

@@ -0,0 +1,35 @@
+{
+  "mechanism": "BV",
+  "n_scans": 3,
+  "scan_rates_Vs": [
+    0.010573734343051911,
+    0.24367923736572267,
+    4.024525451660156
+  ],
+  "physical_params": {
+    "E0_V": 0.25,
+    "T_K": 298.15,
+    "A_cm2": 0.0707,
+    "C_mM": 1.0,
+    "D_cm2s": 1e-05,
+    "n_electrons": 1
+  },
+  "true_params_dimless": {
+    "sigma": 8.719717822841444,
+    "C_A_bulk": 1.0,
+    "C_B_bulk": 0.0,
+    "dA": 1.0,
+    "dB": 1.2541572204674802,
+    "theta_i": 3.6881732361108934,
+    "theta_v": -13.090140187264856,
+    "cycles": 1.0,
+    "kinetics": "BV",
+    "K0": 5.810825216518235,
+    "alpha": 0.5204656070504309
+  },
+  "csv_files": [
+    "ec_BV_11mVs.csv",
+    "ec_BV_244mVs.csv",
+    "ec_BV_4025mVs.csv"
+  ]
+}

demo_data/ec_EC_10mVs.csv

@@ -0,0 +1,673 @@
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demo_data/ec_EC_metadata.json

@@ -0,0 +1,36 @@
+{
+  "mechanism": "EC",
+  "n_scans": 3,
+  "scan_rates_Vs": [
+    0.010000000149011612,
+    0.12726666927337646,
+    7.638240051269531
+  ],
+  "physical_params": {
+    "E0_V": 0.25,
+    "T_K": 298.15,
+    "A_cm2": 0.0707,
+    "C_mM": 1.0,
+    "D_cm2s": 1e-05,
+    "n_electrons": 1
+  },
+  "true_params_dimless": {
+    "sigma": 8.719717822841444,
+    "C_A_bulk": 1.0,
+    "C_B_bulk": 0.0,
+    "dA": 1.0,
+    "dB": 1.2541572204674802,
+    "theta_i": 3.6881732361108934,
+    "theta_v": -13.090140187264856,
+    "cycles": 1.0,
+    "kinetics": "EC",
+    "K0": 5.810825216518235,
+    "alpha": 0.5204656070504309,
+    "kc": 11.173370442593823
+  },
+  "csv_files": [
+    "ec_EC_10mVs.csv",
+    "ec_EC_127mVs.csv",
+    "ec_EC_7638mVs.csv"
+  ]
+}

demo_data/ec_LH_10mVs.csv

@@ -0,0 +1,673 @@
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demo_data/ec_LH_metadata.json

@@ -0,0 +1,37 @@
+{
+  "mechanism": "LH",
+  "n_scans": 3,
+  "scan_rates_Vs": [
+    0.010000000149011612,
+    0.2415423631668091,
+    7.443257904052735
+  ],
+  "physical_params": {
+    "E0_V": 0.25,
+    "T_K": 298.15,
+    "A_cm2": 0.0707,
+    "C_mM": 1.0,
+    "D_cm2s": 1e-05,
+    "n_electrons": 1
+  },
+  "true_params_dimless": {
+    "sigma": 8.719717822841444,
+    "C_A_bulk": 1.0,
+    "C_B_bulk": 0.0,
+    "dA": 1.0,
+    "dB": 1.2541572204674802,
+    "theta_i": 3.6881732361108934,
+    "theta_v": -13.090140187264856,
+    "cycles": 1.0,
+    "kinetics": "LH",
+    "K0": 5.810825216518235,
+    "alpha": 0.5204656070504309,
+    "KA_eq": 3.808564587276162,
+    "KB_eq": 1.3265647159051492
+  },
+  "csv_files": [
+    "ec_LH_10mVs.csv",
+    "ec_LH_242mVs.csv",
+    "ec_LH_7443mVs.csv"
+  ]
+}

demo_data/ec_MHC_11mVs.csv

@@ -0,0 +1,673 @@
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demo_data/ec_MHC_metadata.json

@@ -0,0 +1,36 @@
+{
+  "mechanism": "MHC",
+  "n_scans": 3,
+  "scan_rates_Vs": [
+    0.010573734343051911,
+    0.24367923736572267,
+    4.024525451660156
+  ],
+  "physical_params": {
+    "E0_V": 0.25,
+    "T_K": 298.15,
+    "A_cm2": 0.0707,
+    "C_mM": 1.0,
+    "D_cm2s": 1e-05,
+    "n_electrons": 1
+  },
+  "true_params_dimless": {
+    "sigma": 8.719717822841444,
+    "C_A_bulk": 1.0,
+    "C_B_bulk": 0.0,
+    "dA": 1.0,
+    "dB": 1.2541572204674802,
+    "theta_i": 3.6881732361108934,
+    "theta_v": -13.090140187264856,
+    "cycles": 1.0,
+    "kinetics": "MHC",
+    "K0": 5.810825216518235,
+    "alpha": 0.5,
+    "reorg_e": 21.220031606830634
+  },
+  "csv_files": [
+    "ec_MHC_11mVs.csv",
+    "ec_MHC_244mVs.csv",
+    "ec_MHC_4025mVs.csv"
+  ]
+}

demo_data/ec_Nernst_10000mVs.csv

@@ -0,0 +1,673 @@
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demo_data/ec_Nernst_metadata.json

@@ -0,0 +1,36 @@
+{
+  "mechanism": "Nernst",
+  "n_scans": 3,
+  "scan_rates_Vs": [
+    0.011645200848579408,
+    0.35159897804260254,
+    10.0
+  ],
+  "physical_params": {
+    "E0_V": 0.25,
+    "T_K": 298.15,
+    "A_cm2": 0.0707,
+    "C_mM": 1.0,
+    "D_cm2s": 1e-05,
+    "n_electrons": 1
+  },
+  "true_params_dimless": {
+    "sigma": 8.719717822841444,
+    "C_A_bulk": 1.0,
+    "C_B_bulk": 0.0,
+    "dA": 1.0,
+    "dB": 1.2541572204674802,
+    "theta_i": 3.6881732361108934,
+    "theta_v": -13.090140187264856,
+    "cycles": 1.0,
+    "kinetics": "Nernst",
+    "K0": 1000000.0,
+    "alpha": 0.5,
+    "E0_offset": 0.0
+  },
+  "csv_files": [
+    "ec_Nernst_12mVs.csv",
+    "ec_Nernst_352mVs.csv",
+    "ec_Nernst_10000mVs.csv"
+  ]
+}

demo_data/tpd_DiffLimited_1.csv

@@ -0,0 +1,501 @@
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demo_data/tpd_DiffLimited_metadata.json

@@ -0,0 +1,24 @@
+{
+  "mechanism": "DiffLimited",
+  "betas_Ks": [
+    0.3162277638912201,
+    1.6846271753311157,
+    31.62277603149414
+  ],
+  "n_rates": 3,
+  "true_params": {
+    "mechanism": "DiffLimited",
+    "Ed": 23369.582400663046,
+    "nu": 160197624009364.56,
+    "D0": 175.0557535242827,
+    "E_diff": 12198.817834034686,
+    "theta_0": 0.5502601671297983,
+    "T_start": 364.6834143679138,
+    "T_end": 1099.1759914138238
+  },
+  "csv_files": [
+    "tpd_DiffLimited_1.csv",
+    "tpd_DiffLimited_2.csv",
+    "tpd_DiffLimited_3.csv"
+  ]
+}

demo_data/tpd_FirstOrderCovDep_1.csv

@@ -0,0 +1,501 @@
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demo_data/tpd_FirstOrderCovDep_metadata.json

@@ -0,0 +1,23 @@
+{
+  "mechanism": "FirstOrderCovDep",
+  "betas_Ks": [
+    0.3162277638912201,
+    2.212759494781494,
+    16.846271514892578
+  ],
+  "n_rates": 3,
+  "true_params": {
+    "mechanism": "FirstOrderCovDep",
+    "Ed0": 23369.582400663046,
+    "alpha_cov": 1594.6989199486932,
+    "nu": 160197624009364.56,
+    "theta_0": 0.5589947991241528,
+    "T_start": 364.6834143679138,
+    "T_end": 1099.1759914138238
+  },
+  "csv_files": [
+    "tpd_FirstOrderCovDep_1.csv",
+    "tpd_FirstOrderCovDep_2.csv",
+    "tpd_FirstOrderCovDep_3.csv"
+  ]
+}

demo_data/tpd_FirstOrder_1.csv

@@ -0,0 +1,501 @@
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demo_data/tpd_FirstOrder_metadata.json

@@ -0,0 +1,22 @@
+{
+  "mechanism": "FirstOrder",
+  "betas_Ks": [
+    0.3162277638912201,
+    2.597219944000244,
+    22.127595901489258
+  ],
+  "n_rates": 3,
+  "true_params": {
+    "mechanism": "FirstOrder",
+    "Ed": 23369.582400663046,
+    "nu": 160197624009364.56,
+    "theta_0": 0.15471468757226675,
+    "T_start": 364.6834143679138,
+    "T_end": 1099.1759914138238
+  },
+  "csv_files": [
+    "tpd_FirstOrder_1.csv",
+    "tpd_FirstOrder_2.csv",
+    "tpd_FirstOrder_3.csv"
+  ]
+}

demo_data/tpd_LH_Surface_1.csv

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demo_data/tpd_LH_Surface_metadata.json

@@ -0,0 +1,23 @@
+{
+  "mechanism": "LH_Surface",
+  "betas_Ks": [
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+    2.212759494781494,
+    16.846271514892578
+  ],
+  "n_rates": 3,
+  "true_params": {
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+    "nu": 160197624009364.56,
+    "theta_A0": 0.15471468757226675,
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+    "T_start": 364.6834143679138,
+    "T_end": 1099.1759914138238
+  },
+  "csv_files": [
+    "tpd_LH_Surface_1.csv",
+    "tpd_LH_Surface_2.csv",
+    "tpd_LH_Surface_3.csv"
+  ]
+}

demo_data/tpd_MvK_1.csv

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