app.py

import gradio as gr
import numpy as np
import plotly.graph_objects as go
from pathlib import Path
from scipy.integrate import trapezoid
import scipy.signal as signal

# =============================================================================
# CONFIGURATION
# =============================================================================

OUTPUT_IEEG = Path("consolidated_ieeg.npz")
OUTPUT_LCMV = Path("consolidated_lcmv.npz")

RUN_MAP = {"c": "eyes_closed", "o": "eyes_open", "l": "left_hand", "r": "right_hand"}

# PSD Parameters
SFREQ_DEFAULT = 500.0
PSD_WINDOW_SEC = 2.0
FMAX = 50

FREQ_BANDS = {
    'Delta': (1, 4), 'Theta': (4, 8), 'Alpha': (8, 12),
    'Low_Beta': (12, 20), 'High_Beta': (20, 30),
    'Low_Gamma': (30, 50), 'High_Gamma': (50, 100),
}

# Patterns
STN_PATTERNS = ["STN-L", "STN-R", "STN_L", "STN_R", "Left-STN", "Right-STN"]
GPI_PATTERNS = ["GPi-L", "GPi-R", "GPi_L", "GPi_R", "pGP-lh", "pGP-rh", "L-GPi", "R-GPi", "GPI-L", "GPI-R"]
M1_L_PATTERNS = ["ECOG-8-9-L", "ECOG-10-11-L", "M1-L", "Left-M1"]
M1_R_PATTERNS = ["ECOG-8-9-R", "ECOG-10-11-R", "M1-R", "Right-M1"]

ATLAS_LABELS = {
    "STN": "STN (DiFuMo-223)",
    "L_GPi": "L-GPi (GT pGP-lh)",
    "R_GPi": "R-GPi (GT pGP-rh)",
}

COLORS = {
    "IEEG":  "#1f77b4",
    "LCMV":  "#d62728",
    "STN":   "#ff7f0e",
    "L_GPi": "#2ca02c",
    "R_GPi": "#9467bd",
}

# Global Data Handles
ALL_IEEG_DATA = None
ALL_LCMV_DATA = None

# =============================================================================
# CORE LOGIC
# =============================================================================

def compute_psd(time_series, sfreq=SFREQ_DEFAULT, fmax=FMAX):
    ts = np.real(time_series).astype(np.float64)
    window_size = int(PSD_WINDOW_SEC * sfreq)
    if len(ts) < window_size:
        window_size = max(int(len(ts)*0.8), 100) 
    
    nyq = sfreq * 0.5
    if nyq <= 0.5: nyq = 0.51
        
    b, a = signal.butter(4, 0.5 / nyq, btype='high')
    filtered = signal.filtfilt(b, a, ts)
    
    freqs, psd = signal.welch(filtered, fs=sfreq, window='hann', nperseg=window_size,
                               noverlap=window_size // 2, detrend='constant')
    mask = (freqs >= 1.0) & (freqs <= fmax)
    freqs, psd = freqs[mask], psd[mask]
    
    if len(freqs) == 0:
        return np.array([1, 10]), np.log10(np.array([1e-10, 1e-10]) + 1e-12)

    psd_log = np.log10(psd + 1e-12)
    return freqs.astype(np.float32), psd_log.astype(np.float32)

def load_data():
    global ALL_IEEG_DATA, ALL_LCMV_DATA
    if ALL_IEEG_DATA is None or ALL_LCMV_DATA is None:
        if not OUTPUT_IEEG.exists() or not OUTPUT_LCMV.exists():
            raise FileNotFoundError("Consolidated files missing. Please run consolidation first.")
        
        ALL_IEEG_DATA = np.load(OUTPUT_IEEG, allow_pickle=True)
        ALL_LCMV_DATA = np.load(OUTPUT_LCMV, allow_pickle=True)

def get_consolidated_ieeg(subj_id, run_code):
    global ALL_IEEG_DATA
    meta_key = f"meta_{subj_id}_{run_code}"
    if meta_key not in ALL_IEEG_DATA.files:
        return None, None
    meta = ALL_IEEG_DATA[meta_key].item()
    channels = {}
    prefix = f"{subj_id}_{run_code}_"
    for key in ALL_IEEG_DATA.files:
        if key.startswith(prefix) and key != meta_key:
            channels[key.replace(prefix, "")] = ALL_IEEG_DATA[key]
    return channels, meta

def get_consolidated_lcmv(subj_id):
    global ALL_LCMV_DATA
    meta_key = f"meta_{subj_id}"
    if meta_key not in ALL_LCMV_DATA.files:
        return None, None
    meta = ALL_LCMV_DATA[meta_key].item()
    rois = {}
    prefix = f"{subj_id}_"
    for key in ALL_LCMV_DATA.files:
        if key.startswith(prefix) and key != meta_key:
            rois[key.replace(prefix, "")] = ALL_LCMV_DATA[key]
    return rois, meta

def find_channel(channels_dict, patterns):
    if channels_dict is None:
        return None, None
    for pattern in patterns:
        if pattern in channels_dict:
            return pattern, channels_dict[pattern]
        for key in channels_dict.keys():
            if pattern.lower() in key.lower():
                return key, channels_dict[key]
    return None, None

def create_interactive_plot(roi_name, ieeg_signal, ieeg_sfreq, ch_used,
                            source_signal, source_sfreq, source_label, source_color,
                            subject_id, run_id):
    
    freqs_ieeg, psd_ieeg = compute_psd(ieeg_signal, sfreq=ieeg_sfreq)
    freqs_src, psd_src = compute_psd(source_signal, sfreq=source_sfreq)

    fig = go.Figure()

    fig.add_trace(go.Scatter(
        x=freqs_ieeg, y=psd_ieeg,
        mode='lines', name=f'iEEG ({ch_used})',
        line=dict(color=COLORS["IEEG"], width=3),
        hovertemplate=f'<b>iEEG</b><br>Freq: %{{x:.2f}} Hz<br>PSD: %{{y:.2f}}<extra></extra>'
    ))

    fig.add_trace(go.Scatter(
        x=freqs_src, y=psd_src,
        mode='lines', name=source_label,
        line=dict(color=source_color, width=3, dash='dash'),
        hovertemplate=f'<b>{source_label}</b><br>Freq: %{{x:.2f}} Hz<br>PSD: %{{y:.2f}}<extra></extra>'
    ))

    shapes = []
    n_bands = len(FREQ_BANDS)
    band_colors = [f"rgba(31, 119, 180, {0.1 + (i/n_bands)*0.2})" for i in range(n_bands)]

    for i, (band, (fmin, fmax)) in enumerate(FREQ_BANDS.items()):
        band_low = max(fmin, min(freqs_ieeg))
        band_high = min(fmax, max(freqs_ieeg))
        if band_low < band_high:
            shapes.append(dict(
                type="rect", xref="x", yref="paper",
                x0=band_low, x1=band_high, y0=0, y1=1,
                fillcolor=band_colors[i], opacity=0.3, layer="below", line_width=0
            ))

    title_text = f"{subject_id} | Run: {run_id} | ROI: {roi_name}<br><sup>{source_label} vs iEEG</sup>"
    
    fig.update_layout(
        title=dict(text=title_text, font=dict(size=14, family="Arial")),
        xaxis_title="Frequency (Hz)",
        yaxis_title="PSD (log₁₀)",
        xaxis=dict(range=[1, FMAX], type="linear"),
        yaxis_type="linear",
        hovermode="x unified",
        legend=dict(x=0, y=1, bgcolor="rgba(255,255,255,0.8)"),
        shapes=shapes,
        template="plotly_white",
        height=600,
        margin=dict(l=50, r=50, t=60, b=50)
    )
    
    return fig

def generate_all_plots(subj_id, run_code):
    """Generates all valid plots for a subject/run and returns a dictionary."""
    try:
        load_data()
    except FileNotFoundError as e:
        return {}, str(e)

    cond = RUN_MAP.get(run_code, "unknown")
    ieeg_ch, ieeg_meta = get_consolidated_ieeg(subj_id, run_code)
    lcmv_rois, lcmv_meta = get_consolidated_lcmv(subj_id)
    
    plots_dict = {}
    logs = [f"Processing {subj_id} | Condition: {cond}"]

    if ieeg_ch is None or lcmv_rois is None:
        return plots_dict, f"No data found for {subj_id} (Run: {run_code})."

    ieeg_sfreq = ieeg_meta.get('sfreq', SFREQ_DEFAULT)
    lcmv_sfreq = lcmv_meta.get('sfreq', SFREQ_DEFAULT)

    # Detect Electrodes
    stn_l_ch, stn_l_sig = find_channel(ieeg_ch, STN_PATTERNS)
    stn_r_ch, stn_r_sig = find_channel(ieeg_ch, [p.replace("-L","-R").replace("_L","_R") for p in STN_PATTERNS])
    gpi_l_ch, gpi_l_sig = find_channel(ieeg_ch, GPI_PATTERNS)
    
    gpi_r_ch, gpi_r_sig = None, None
    if gpi_l_ch:
         right_patterns = [gpi_l_ch.replace("L","R").replace("l","r").replace("lh","rh")]
         right_patterns.extend([p.replace("-L","-R").replace("_L","_R") for p in GPI_PATTERNS])
         gpi_r_ch, gpi_r_sig = find_channel(ieeg_ch, right_patterns)

    m1_l_ch, m1_l_sig = find_channel(ieeg_ch, M1_L_PATTERNS)
    m1_r_ch, m1_r_sig = find_channel(ieeg_ch, M1_R_PATTERNS)

    def add_plot(name, sig, ch, roi_key, label, color):
        if sig is not None and ch is not None and roi_key in lcmv_rois:
            fig = create_interactive_plot(name, sig, ieeg_sfreq, ch, lcmv_rois[roi_key], lcmv_sfreq, label, color, subj_id, run_code)
            key = f"{name} vs {label}"
            plots_dict[key] = fig
            logs.append(f"✅ Found: {key}")

    # M1
    add_plot("L_M1", m1_l_sig, m1_l_ch, f"L_M1_{cond}", "LCMV MNI voxel", COLORS["LCMV"])
    add_plot("R_M1", m1_r_sig, m1_r_ch, f"R_M1_{cond}", "LCMV MNI voxel", COLORS["LCMV"])

    # STN
    if stn_l_sig is not None:
        add_plot("L_STN", stn_l_sig, stn_l_ch, f"L_STN_{cond}", "LCMV MNI voxel", COLORS["LCMV"])
        if f"STN_{cond}" in lcmv_rois:
            add_plot("L_STN", stn_l_sig, stn_l_ch, f"STN_{cond}", ATLAS_LABELS["STN"], COLORS["STN"])
    
    if stn_r_sig is not None:
        add_plot("R_STN", stn_r_sig, stn_r_ch, f"R_STN_{cond}", "LCMV MNI voxel", COLORS["LCMV"])
        if f"STN_{cond}" in lcmv_rois:
            add_plot("R_STN", stn_r_sig, stn_r_ch, f"STN_{cond}", ATLAS_LABELS["STN"], COLORS["STN"])

    # GPi (Fallback)
    if gpi_l_sig is not None and stn_l_sig is None:
        add_plot("L_GPi", gpi_l_sig, gpi_l_ch, f"L_GPi_{cond}", "LCMV MNI voxel (GPi)", COLORS["LCMV"])
        if f"L_GPi_{cond}" in lcmv_rois:
            add_plot("L_GPi", gpi_l_sig, gpi_l_ch, f"L_GPi_{cond}", ATLAS_LABELS["L_GPi"], COLORS["L_GPi"])

    if gpi_r_sig is not None and stn_r_sig is None:
        add_plot("R_GPi", gpi_r_sig, gpi_r_ch, f"R_GPi_{cond}", "LCMV MNI voxel (GPi)", COLORS["LCMV"])
        if f"R_GPi_{cond}" in lcmv_rois:
            add_plot("R_GPi", gpi_r_sig, gpi_r_ch, f"R_GPi_{cond}", ATLAS_LABELS["R_GPi"], COLORS["R_GPi"])

    if not plots_dict:
        logs.append("⚠️ No matching electrode/ROI pairs found.")
    
    return plots_dict, "\n".join(logs)

def get_available_subjects():
    if not OUTPUT_LCMV.exists():
        return []
    data = np.load(OUTPUT_LCMV, allow_pickle=True)
    subjects = set()
    for key in data.files:
        if key.startswith("meta_"):
            subjects.add(key.replace("meta_", ""))
    return sorted(list(subjects))

# =============================================================================
# GRADIO INTERFACE
# =============================================================================

# Note: 'theme' parameter removed from constructor for Gradio 5.0+ compatibility
with gr.Blocks(title="Interactive iEEG-LCMV Viewer") as demo:
    gr.Markdown("# Interactive iEEG & LCMV Viewer")
    gr.Markdown("Select a subject and condition to generate available comparisons. Then choose specific plots to visualize.")

    # State to store generated plots for the current selection
    current_plots_state = gr.State({})

    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### 1. Select Data")
            btn_refresh = gr.Button("🔄 Refresh Subjects")
            subject_dropdown = gr.Dropdown(label="Subject", choices=[], interactive=True)
            run_dropdown = gr.Dropdown(
                label="Condition", 
                choices=["c", "o", "l", "r"], 
                value="c",
                info="c: Eyes Closed, o: Eyes Open, l: Left Hand, r: Right Hand"
            )
            btn_generate = gr.Button("🔍 Find Available Plots", variant="primary")
            
            gr.Markdown("### 2. Choose Visualization")
            plot_selector = gr.Dropdown(label="Select Plot to View", choices=[], interactive=True)
            
            gr.Markdown("### Log")
            val_log = gr.Textbox(label="Status", lines=6, interactive=False)

        with gr.Column(scale=3):
            gr.Markdown("### PSD Comparison")
            plot_display = gr.Plot(label="Interactive Plot", show_label=False)

    # Event Handlers
    
    def refresh_subjects():
        subs = get_available_subjects()
        return gr.Dropdown(choices=subs, value=subs[0] if subs else None)

    def process_and_update_dropdown(subj, run):
        """Generates plots, updates state, log, dropdown options, and shows the first plot."""
        if not subj:
            return {}, "Please select a subject.", gr.Dropdown(choices=[], value=None), None
        
        plots_dict, log_msg = generate_all_plots(subj, run)
        choices = list(plots_dict.keys())
        
        if not choices:
            return plots_dict, log_msg, gr.Dropdown(choices=[], value=None), None
        
        initial_val = choices[0]
        initial_fig = plots_dict[initial_val]
        
        return plots_dict, log_msg, gr.Dropdown(choices=choices, value=initial_val), initial_fig

    def on_plot_selection(plots_dict, selected_key):
        """Updates only the plot when dropdown changes."""
        if not plots_dict or not selected_key:
            return None
        return plots_dict.get(selected_key)

    # Wire up events
    btn_refresh.click(fn=refresh_subjects, inputs=[], outputs=[subject_dropdown])
    demo.load(fn=refresh_subjects, inputs=[], outputs=[subject_dropdown])

    # When Generate is clicked: Update State, Log, Dropdown, AND Plot
    btn_generate.click(
        fn=process_and_update_dropdown,
        inputs=[subject_dropdown, run_dropdown],
        outputs=[current_plots_state, val_log, plot_selector, plot_display]
    )

    # When Dropdown changes: Update Plot Display only
    plot_selector.change(
        fn=on_plot_selection,
        inputs=[current_plots_state, plot_selector],
        outputs=[plot_display]
    )

if __name__ == "__main__":
    # Note: 'theme' parameter moved to launch() for Gradio 5.0+
    demo.launch(theme=gr.themes.Soft())