Add data sources viz to dashboard

src/entropice/dashboard/plots/source_data.py

@@ -0,0 +1,798 @@
+"""Plotting functions for source data visualizations (AlphaEarth, ArcticDEM, ERA5)."""
+
+import antimeridian
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+import pydeck as pdk
+import streamlit as st
+import xarray as xr
+from shapely.geometry import shape
+
+from entropice.dashboard.plots.colors import get_cmap
+
+
+def _fix_hex_geometry(geom):
+    """Fix hexagon geometry crossing the antimeridian."""
+    try:
+        return shape(antimeridian.fix_shape(geom))
+    except ValueError as e:
+        st.error(f"Error fixing geometry: {e}")
+        return geom
+
+
+def render_alphaearth_overview(ds: xr.Dataset):
+    """Render overview statistics for AlphaEarth embeddings data.
+
+    Args:
+        ds: xarray Dataset containing AlphaEarth embeddings.
+
+    """
+    st.subheader("📊 AlphaEarth Embeddings Statistics")
+
+    # Overall statistics
+    col1, col2, col3, col4 = st.columns(4)
+
+    with col1:
+        st.metric("Total Cells", f"{len(ds['cell_ids']):,}")
+
+    with col2:
+        st.metric("Embedding Dimensions", f"{len(ds['band'])}")
+
+    with col3:
+        st.metric("Years Available", f"{len(ds['year'])}")
+
+    with col4:
+        st.metric("Aggregations", f"{len(ds['agg'])}")
+
+    # Show temporal coverage
+    st.markdown("**Temporal Coverage:**")
+    years = sorted(ds["year"].values)
+    st.write(f"Years: {min(years)} - {max(years)}")
+
+    # Show aggregations
+    st.markdown("**Available Aggregations:**")
+    aggs = ds["agg"].to_numpy()
+    st.write(", ".join(str(a) for a in aggs))
+
+
+@st.fragment
+def render_alphaearth_plots(ds: xr.Dataset):
+    """Render interactive plots for AlphaEarth embeddings data.
+
+    Args:
+        ds: xarray Dataset containing AlphaEarth embeddings.
+
+    """
+    st.markdown("---")
+    st.markdown("**Embedding Distribution by Band**")
+
+    embeddings_data = ds["embeddings"]
+
+    # Select year and aggregation for visualization
+    col1, col2 = st.columns(2)
+    with col1:
+        selected_year = st.selectbox("Select Year", options=sorted(ds["year"].values), key="stats_year")
+    with col2:
+        selected_agg = st.selectbox("Select Aggregation", options=ds["agg"].values, key="stats_agg")
+
+    # Get data for selected year and aggregation
+    year_agg_data = embeddings_data.sel(year=selected_year, agg=selected_agg)
+
+    # Calculate statistics for each band
+    band_stats = []
+    for band_idx in range(len(ds["band"])):
+        band_data = year_agg_data.isel(band=band_idx).values.flatten()
+        band_data = band_data[~np.isnan(band_data)]  # Remove NaN values
+
+        if len(band_data) > 0:
+            band_stats.append(
+                {
+                    "Band": band_idx,
+                    "Mean": float(np.mean(band_data)),
+                    "Std": float(np.std(band_data)),
+                    "Min": float(np.min(band_data)),
+                    "25%": float(np.percentile(band_data, 25)),
+                    "Median": float(np.median(band_data)),
+                    "75%": float(np.percentile(band_data, 75)),
+                    "Max": float(np.max(band_data)),
+                }
+            )
+
+    band_df = pd.DataFrame(band_stats)
+
+    # Create plot showing distribution across bands
+    fig = go.Figure()
+
+    # Add min/max range first (background)
+    fig.add_trace(
+        go.Scatter(
+            x=band_df["Band"],
+            y=band_df["Min"],
+            mode="lines",
+            line={"color": "lightgray", "width": 1, "dash": "dash"},
+            name="Min/Max Range",
+            showlegend=True,
+        )
+    )
+
+    fig.add_trace(
+        go.Scatter(
+            x=band_df["Band"],
+            y=band_df["Max"],
+            mode="lines",
+            fill="tonexty",
+            fillcolor="rgba(200, 200, 200, 0.1)",
+            line={"color": "lightgray", "width": 1, "dash": "dash"},
+            showlegend=False,
+        )
+    )
+
+    # Add std band
+    fig.add_trace(
+        go.Scatter(
+            x=band_df["Band"],
+            y=band_df["Mean"] - band_df["Std"],
+            mode="lines",
+            line={"width": 0},
+            showlegend=False,
+            hoverinfo="skip",
+        )
+    )
+
+    fig.add_trace(
+        go.Scatter(
+            x=band_df["Band"],
+            y=band_df["Mean"] + band_df["Std"],
+            mode="lines",
+            fill="tonexty",
+            fillcolor="rgba(31, 119, 180, 0.2)",
+            line={"width": 0},
+            name="±1 Std",
+        )
+    )
+
+    # Add mean line on top
+    fig.add_trace(
+        go.Scatter(
+            x=band_df["Band"],
+            y=band_df["Mean"],
+            mode="lines+markers",
+            name="Mean",
+            line={"color": "#1f77b4", "width": 2},
+            marker={"size": 4},
+        )
+    )
+
+    fig.update_layout(
+        title=f"Embedding Statistics per Band (Year: {selected_year}, Aggregation: {selected_agg})",
+        xaxis_title="Band",
+        yaxis_title="Embedding Value",
+        height=450,
+        hovermode="x unified",
+    )
+
+    st.plotly_chart(fig, use_container_width=True)
+
+    # Band statistics
+    with st.expander("📈 Statistics by Embedding Band", expanded=False):
+        st.markdown("Statistics aggregated across all years and aggregations for each embedding dimension:")
+
+        # Calculate statistics for each band
+        band_stats = []
+        for band_idx in range(min(10, len(ds["band"]))):  # Show first 10 bands
+            band_data = embeddings_data.isel(band=band_idx)
+            band_stats.append(
+                {
+                    "Band": int(band_idx),
+                    "Mean": float(band_data.mean().values),
+                    "Std": float(band_data.std().values),
+                    "Min": float(band_data.min().values),
+                    "Max": float(band_data.max().values),
+                }
+            )
+
+        band_df = pd.DataFrame(band_stats)
+        st.dataframe(band_df, use_container_width=True, hide_index=True)
+
+        if len(ds["band"]) > 10:
+            st.info(f"Showing first 10 of {len(ds['band'])} embedding dimensions")
+
+
+def render_arcticdem_overview(ds: xr.Dataset):
+    """Render overview statistics for ArcticDEM terrain data.
+
+    Args:
+        ds: xarray Dataset containing ArcticDEM data.
+
+    """
+    st.subheader("🏔️ ArcticDEM Terrain Statistics")
+
+    # Overall statistics
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        st.metric("Total Cells", f"{len(ds['cell_ids']):,}")
+
+    with col2:
+        st.metric("Variables", f"{len(ds.data_vars)}")
+
+    with col3:
+        st.metric("Aggregations", f"{len(ds['aggregations'])}")
+
+    # Show available variables
+    st.markdown("**Available Variables:**")
+    variables = list(ds.data_vars)
+    st.write(", ".join(variables))
+
+    # Show aggregations
+    st.markdown("**Available Aggregations:**")
+    aggs = ds["aggregations"].to_numpy()
+    st.write(", ".join(str(a) for a in aggs))
+
+    # Statistics by variable
+    st.markdown("---")
+    st.markdown("**Variable Statistics (across all aggregations)**")
+
+    var_stats = []
+    for var_name in ds.data_vars:
+        var_data = ds[var_name]
+        var_stats.append(
+            {
+                "Variable": var_name,
+                "Mean": float(var_data.mean().values),
+                "Std": float(var_data.std().values),
+                "Min": float(var_data.min().values),
+                "Max": float(var_data.max().values),
+                "Missing %": float((var_data.isnull().sum() / var_data.size * 100).values),
+            }
+        )
+
+    stats_df = pd.DataFrame(var_stats)
+    st.dataframe(stats_df, use_container_width=True, hide_index=True)
+
+
+@st.fragment
+def render_arcticdem_plots(ds: xr.Dataset):
+    """Render interactive plots for ArcticDEM terrain data.
+
+    Args:
+        ds: xarray Dataset containing ArcticDEM data.
+
+    """
+    st.markdown("---")
+    st.markdown("**Variable Distributions**")
+
+    variables = list(ds.data_vars)
+
+    # Select a variable to visualize
+    selected_var = st.selectbox("Select variable to visualize", options=variables, key="arcticdem_var_select")
+
+    if selected_var:
+        var_data = ds[selected_var]
+
+        # Create histogram
+        fig = go.Figure()
+
+        for agg in ds["aggregations"].to_numpy():
+            agg_data = var_data.sel(aggregations=agg).to_numpy().flatten()
+            agg_data = agg_data[~np.isnan(agg_data)]
+
+            fig.add_trace(
+                go.Histogram(
+                    x=agg_data,
+                    name=str(agg),
+                    opacity=0.7,
+                    nbinsx=50,
+                )
+            )
+
+        fig.update_layout(
+            title=f"Distribution of {selected_var} by Aggregation",
+            xaxis_title=selected_var,
+            yaxis_title="Count",
+            barmode="overlay",
+            height=400,
+        )
+
+        st.plotly_chart(fig, use_container_width=True)
+
+
+def render_era5_overview(ds: xr.Dataset, temporal_type: str):
+    """Render overview statistics for ERA5 climate data.
+
+    Args:
+        ds: xarray Dataset containing ERA5 data.
+        temporal_type: One of 'yearly', 'seasonal', 'shoulder'.
+
+    """
+    st.subheader(f"🌡️ ERA5 Climate Statistics ({temporal_type.capitalize()})")
+
+    # Overall statistics
+    has_agg = "aggregations" in ds.dims
+    col1, col2, col3, col4 = st.columns(4)
+
+    with col1:
+        st.metric("Total Cells", f"{len(ds['cell_ids']):,}")
+
+    with col2:
+        st.metric("Variables", f"{len(ds.data_vars)}")
+
+    with col3:
+        st.metric("Time Steps", f"{len(ds['time'])}")
+
+    with col4:
+        if has_agg:
+            st.metric("Aggregations", f"{len(ds['aggregations'])}")
+        else:
+            st.metric("Aggregations", "1")
+
+    # Show available variables
+    st.markdown("**Available Variables:**")
+    variables = list(ds.data_vars)
+    st.write(", ".join(variables))
+
+    # Show temporal range
+    st.markdown("**Temporal Range:**")
+    time_values = pd.to_datetime(ds["time"].values)
+    st.write(f"{time_values.min().strftime('%Y-%m-%d')} to {time_values.max().strftime('%Y-%m-%d')}")
+
+    if has_agg:
+        st.markdown("**Available Aggregations:**")
+        aggs = ds["aggregations"].to_numpy()
+        st.write(", ".join(str(a) for a in aggs))
+
+    # Statistics by variable
+    st.markdown("---")
+    st.markdown("**Variable Statistics (across all time steps and aggregations)**")
+
+    var_stats = []
+    for var_name in ds.data_vars:
+        var_data = ds[var_name]
+        var_stats.append(
+            {
+                "Variable": var_name,
+                "Mean": float(var_data.mean().values),
+                "Std": float(var_data.std().values),
+                "Min": float(var_data.min().values),
+                "Max": float(var_data.max().values),
+                "Missing %": float((var_data.isnull().sum() / var_data.size * 100).values),
+            }
+        )
+
+    stats_df = pd.DataFrame(var_stats)
+    st.dataframe(stats_df, use_container_width=True, hide_index=True)
+
+
+@st.fragment
+def render_era5_plots(ds: xr.Dataset, temporal_type: str):
+    """Render interactive plots for ERA5 climate data.
+
+    Args:
+        ds: xarray Dataset containing ERA5 data.
+        temporal_type: One of 'yearly', 'seasonal', 'shoulder'.
+
+    """
+    st.markdown("---")
+    st.markdown("**Temporal Trends**")
+
+    variables = list(ds.data_vars)
+    has_agg = "aggregations" in ds.dims
+
+    selected_var = st.selectbox(
+        "Select variable to visualize", options=variables, key=f"era5_{temporal_type}_var_select"
+    )
+
+    if selected_var:
+        var_data = ds[selected_var]
+
+        # Calculate mean over space for each time step
+        if has_agg:
+            # Average over aggregations first, then over cells
+            time_series = var_data.mean(dim=["cell_ids", "aggregations"])
+        else:
+            time_series = var_data.mean(dim="cell_ids")
+
+        time_df = pd.DataFrame({"Time": pd.to_datetime(ds["time"].to_numpy()), "Value": time_series.to_numpy()})
+
+        fig = go.Figure()
+
+        fig.add_trace(
+            go.Scatter(
+                x=time_df["Time"],
+                y=time_df["Value"],
+                mode="lines+markers",
+                name=selected_var,
+                line={"width": 2},
+            )
+        )
+
+        fig.update_layout(
+            title=f"Temporal Trend of {selected_var} (Spatial Mean)",
+            xaxis_title="Time",
+            yaxis_title=selected_var,
+            height=400,
+            hovermode="x unified",
+        )
+
+        st.plotly_chart(fig, use_container_width=True)
+
+
+@st.fragment
+def render_alphaearth_map(ds: xr.Dataset, targets: gpd.GeoDataFrame, grid: str):
+    """Render interactive pydeck map for AlphaEarth embeddings.
+
+    Args:
+        ds: xarray Dataset containing AlphaEarth data.
+        targets: GeoDataFrame with geometry for each cell.
+        grid: Grid type ('hex' or 'healpix').
+
+    """
+    st.subheader("🗺️ AlphaEarth Spatial Distribution")
+
+    # Controls
+    col1, col2, col3, col4 = st.columns([2, 2, 2, 1])
+
+    with col1:
+        selected_year = st.selectbox("Year", options=sorted(ds["year"].values), key="alphaearth_year")
+
+    with col2:
+        selected_agg = st.selectbox("Aggregation", options=ds["agg"].values, key="alphaearth_agg")
+
+    with col3:
+        selected_band = st.selectbox("Band", options=list(range(len(ds["band"]))), key="alphaearth_band")
+
+    with col4:
+        opacity = st.slider("Opacity", 0.1, 1.0, 0.7, 0.1, key="alphaearth_opacity")
+
+    # Extract data for selected parameters
+    data_values = ds["embeddings"].sel(year=selected_year, agg=selected_agg).isel(band=selected_band)
+
+    # Create GeoDataFrame
+    gdf = targets.copy()
+    gdf = gdf[gdf["cell_id"].isin(ds["cell_ids"].values)]
+    gdf = gdf.set_index("cell_id")
+
+    # Add values
+    values_df = data_values.to_dataframe(name="value")
+    gdf = gdf.join(values_df, how="inner")
+
+    # Convert to WGS84 first
+    gdf_wgs84 = gdf.to_crs("EPSG:4326")
+
+    # Fix geometries after CRS conversion
+    if grid == "hex":
+        gdf_wgs84["geometry"] = gdf_wgs84["geometry"].apply(_fix_hex_geometry)
+
+    # Normalize values for color mapping
+    values = gdf_wgs84["value"].to_numpy()
+    vmin, vmax = np.nanpercentile(values, [2, 98])  # Use percentiles to avoid outliers
+    normalized = np.clip((values - vmin) / (vmax - vmin), 0, 1)
+
+    # Apply colormap
+    cmap = get_cmap("embeddings")
+    colors = [cmap(val) for val in normalized]
+    gdf_wgs84["fill_color"] = [[int(c[0] * 255), int(c[1] * 255), int(c[2] * 255)] for c in colors]
+
+    # Create GeoJSON
+    geojson_data = []
+    for _, row in gdf_wgs84.iterrows():
+        feature = {
+            "type": "Feature",
+            "geometry": row["geometry"].__geo_interface__,
+            "properties": {
+                "value": float(row["value"]),
+                "fill_color": row["fill_color"],
+            },
+        }
+        geojson_data.append(feature)
+
+    # Create pydeck layer
+    layer = pdk.Layer(
+        "GeoJsonLayer",
+        geojson_data,
+        opacity=opacity,
+        stroked=True,
+        filled=True,
+        get_fill_color="properties.fill_color",
+        get_line_color=[80, 80, 80],
+        line_width_min_pixels=0.5,
+        pickable=True,
+    )
+
+    view_state = pdk.ViewState(latitude=70, longitude=0, zoom=2, pitch=0)
+
+    deck = pdk.Deck(
+        layers=[layer],
+        initial_view_state=view_state,
+        tooltip={"html": "<b>Value:</b> {value}", "style": {"backgroundColor": "steelblue", "color": "white"}},
+        map_style="https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json",
+    )
+
+    st.pydeck_chart(deck)
+
+    # Show statistics
+    st.caption(f"Min: {vmin:.4f} | Max: {vmax:.4f} | Mean: {np.nanmean(values):.4f} | Std: {np.nanstd(values):.4f}")
+
+
+@st.fragment
+def render_arcticdem_map(ds: xr.Dataset, targets: gpd.GeoDataFrame, grid: str):
+    """Render interactive pydeck map for ArcticDEM terrain data.
+
+    Args:
+        ds: xarray Dataset containing ArcticDEM data.
+        targets: GeoDataFrame with geometry for each cell.
+        grid: Grid type ('hex' or 'healpix').
+
+    """
+    st.subheader("🗺️ ArcticDEM Spatial Distribution")
+
+    # Controls
+    variables = list(ds.data_vars)
+    col1, col2, col3 = st.columns([3, 3, 1])
+
+    with col1:
+        selected_var = st.selectbox("Variable", options=variables, key="arcticdem_var")
+
+    with col2:
+        selected_agg = st.selectbox("Aggregation", options=ds["aggregations"].values, key="arcticdem_agg")
+
+    with col3:
+        opacity = st.slider("Opacity", 0.1, 1.0, 0.7, 0.1, key="arcticdem_opacity")
+
+    # Extract data for selected parameters
+    data_values = ds[selected_var].sel(aggregations=selected_agg)
+
+    # Create GeoDataFrame
+    gdf = targets.copy()
+    gdf = gdf[gdf["cell_id"].isin(ds["cell_ids"].values)]
+    gdf = gdf.set_index("cell_id")
+
+    # Add values
+    values_df = data_values.to_dataframe(name="value")
+    gdf = gdf.join(values_df, how="inner")
+
+    # Add all aggregation values for tooltip
+    if len(ds["aggregations"]) > 1:
+        for agg in ds["aggregations"].values:
+            agg_data = ds[selected_var].sel(aggregations=agg).to_dataframe(name=f"agg_{agg}")
+            # Drop the aggregations column to avoid conflicts
+            if "aggregations" in agg_data.columns:
+                agg_data = agg_data.drop(columns=["aggregations"])
+            gdf = gdf.join(agg_data, how="left")
+
+    # Convert to WGS84 first
+    gdf_wgs84 = gdf.to_crs("EPSG:4326")
+
+    # Fix geometries after CRS conversion
+    if grid == "hex":
+        gdf_wgs84["geometry"] = gdf_wgs84["geometry"].apply(_fix_hex_geometry)
+
+    # Normalize values for color mapping
+    values = gdf_wgs84["value"].values
+    values_clean = values[~np.isnan(values)]
+
+    if len(values_clean) > 0:
+        vmin, vmax = np.nanpercentile(values_clean, [2, 98])
+        normalized = np.clip((values - vmin) / (vmax - vmin), 0, 1)
+
+        # Apply colormap
+        cmap = get_cmap(f"arcticdem_{selected_var}")
+        colors = [cmap(val) if not np.isnan(val) else (0.5, 0.5, 0.5, 0.5) for val in normalized]
+        gdf_wgs84["fill_color"] = [[int(c[0] * 255), int(c[1] * 255), int(c[2] * 255)] for c in colors]
+
+        # Create GeoJSON
+        geojson_data = []
+        for _, row in gdf_wgs84.iterrows():
+            properties = {
+                "value": float(row["value"]) if not np.isnan(row["value"]) else None,
+                "fill_color": row["fill_color"],
+            }
+            # Add all aggregation values if available
+            if len(ds["aggregations"]) > 1:
+                for agg in ds["aggregations"].values:
+                    agg_col = f"agg_{agg}"
+                    if agg_col in row.index:
+                        properties[agg_col] = float(row[agg_col]) if not np.isnan(row[agg_col]) else None
+
+            feature = {
+                "type": "Feature",
+                "geometry": row["geometry"].__geo_interface__,
+                "properties": properties,
+            }
+            geojson_data.append(feature)
+
+        # Create pydeck layer
+        layer = pdk.Layer(
+            "GeoJsonLayer",
+            geojson_data,
+            opacity=opacity,
+            stroked=True,
+            filled=True,
+            get_fill_color="properties.fill_color",
+            get_line_color=[80, 80, 80],
+            line_width_min_pixels=0.5,
+            pickable=True,
+        )
+
+        view_state = pdk.ViewState(latitude=70, longitude=0, zoom=2, pitch=0)
+
+        # Build tooltip HTML for ArcticDEM
+        if len(ds["aggregations"]) > 1:
+            tooltip_lines = [f"<b>{selected_var} (selected: {selected_agg}):</b> {{value}}<br/>"]
+            tooltip_lines.append("<b>All aggregations:</b><br/>")
+            for agg in ds["aggregations"].values:
+                tooltip_lines.append(f"&nbsp;&nbsp;{agg}: {{agg_{agg}}}<br/>")
+            tooltip_html = "".join(tooltip_lines)
+        else:
+            tooltip_html = f"<b>{selected_var}:</b> {{value}}"
+
+        deck = pdk.Deck(
+            layers=[layer],
+            initial_view_state=view_state,
+            tooltip={"html": tooltip_html, "style": {"backgroundColor": "steelblue", "color": "white"}},
+            map_style="https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json",
+        )
+
+        st.pydeck_chart(deck)
+
+        # Show statistics
+        st.caption(
+            f"Min: {vmin:.2f} | Max: {vmax:.2f} | Mean: {np.nanmean(values_clean):.2f} | "
+            f"Std: {np.nanstd(values_clean):.2f}"
+        )
+    else:
+        st.warning("No valid data available for selected parameters")
+
+
+@st.fragment
+def render_era5_map(ds: xr.Dataset, targets: gpd.GeoDataFrame, grid: str, temporal_type: str):
+    """Render interactive pydeck map for ERA5 climate data.
+
+    Args:
+        ds: xarray Dataset containing ERA5 data.
+        targets: GeoDataFrame with geometry for each cell.
+        grid: Grid type ('hex' or 'healpix').
+        temporal_type: One of 'yearly', 'seasonal', 'shoulder'.
+
+    """
+    st.subheader("🗺️ ERA5 Spatial Distribution")
+
+    # Controls
+    variables = list(ds.data_vars)
+    has_agg = "aggregations" in ds.dims
+
+    if has_agg:
+        col1, col2, col3, col4 = st.columns([2, 2, 2, 1])
+    else:
+        col1, col2, col3 = st.columns([3, 3, 1])
+
+    with col1:
+        selected_var = st.selectbox("Variable", options=variables, key=f"era5_{temporal_type}_var")
+
+    with col2:
+        # Convert time to readable format
+        time_values = pd.to_datetime(ds["time"].values)
+        time_options = {str(t): t for t in time_values}
+        selected_time = st.selectbox("Time", options=list(time_options.keys()), key=f"era5_{temporal_type}_time")
+
+    if has_agg:
+        with col3:
+            selected_agg = st.selectbox(
+                "Aggregation", options=ds["aggregations"].values, key=f"era5_{temporal_type}_agg"
+            )
+        with col4:
+            opacity = st.slider("Opacity", 0.1, 1.0, 0.7, 0.1, key=f"era5_{temporal_type}_opacity")
+    else:
+        with col3:
+            opacity = st.slider("Opacity", 0.1, 1.0, 0.7, 0.1, key=f"era5_{temporal_type}_opacity")
+
+    # Extract data for selected parameters
+    time_val = time_options[selected_time]
+    if has_agg:
+        data_values = ds[selected_var].sel(time=time_val, aggregations=selected_agg)
+    else:
+        data_values = ds[selected_var].sel(time=time_val)
+
+    # Create GeoDataFrame
+    gdf = targets.copy()
+    gdf = gdf[gdf["cell_id"].isin(ds["cell_ids"].values)]
+    gdf = gdf.set_index("cell_id")
+
+    # Add values
+    values_df = data_values.to_dataframe(name="value")
+    gdf = gdf.join(values_df, how="inner")
+
+    # Add all aggregation values for tooltip if has_agg
+    if has_agg and len(ds["aggregations"]) > 1:
+        for agg in ds["aggregations"].values:
+            agg_data = ds[selected_var].sel(time=time_val, aggregations=agg).to_dataframe(name=f"agg_{agg}")
+            # Drop dimension columns to avoid conflicts
+            cols_to_drop = [col for col in ["aggregations", "time"] if col in agg_data.columns]
+            if cols_to_drop:
+                agg_data = agg_data.drop(columns=cols_to_drop)
+            gdf = gdf.join(agg_data, how="left")
+
+    # Convert to WGS84 first
+    gdf_wgs84 = gdf.to_crs("EPSG:4326")
+
+    # Fix geometries after CRS conversion
+    if grid == "hex":
+        gdf_wgs84["geometry"] = gdf_wgs84["geometry"].apply(_fix_hex_geometry)
+
+    # Normalize values for color mapping
+    values = gdf_wgs84["value"].values
+    values_clean = values[~np.isnan(values)]
+
+    if len(values_clean) > 0:
+        vmin, vmax = np.nanpercentile(values_clean, [2, 98])
+        normalized = np.clip((values - vmin) / (vmax - vmin), 0, 1)
+
+        # Apply colormap - use variable-specific colors
+        cmap = get_cmap(f"era5_{selected_var}")
+        colors = [cmap(val) if not np.isnan(val) else (0.5, 0.5, 0.5, 0.5) for val in normalized]
+        gdf_wgs84["fill_color"] = [[int(c[0] * 255), int(c[1] * 255), int(c[2] * 255)] for c in colors]
+
+        # Create GeoJSON
+        geojson_data = []
+        for _, row in gdf_wgs84.iterrows():
+            properties = {
+                "value": float(row["value"]) if not np.isnan(row["value"]) else None,
+                "fill_color": row["fill_color"],
+            }
+            # Add all aggregation values if available
+            if has_agg and len(ds["aggregations"]) > 1:
+                for agg in ds["aggregations"].values:
+                    agg_col = f"agg_{agg}"
+                    if agg_col in row.index:
+                        properties[agg_col] = float(row[agg_col]) if not np.isnan(row[agg_col]) else None
+
+            feature = {
+                "type": "Feature",
+                "geometry": row["geometry"].__geo_interface__,
+                "properties": properties,
+            }
+            geojson_data.append(feature)
+
+        # Create pydeck layer
+        layer = pdk.Layer(
+            "GeoJsonLayer",
+            geojson_data,
+            opacity=opacity,
+            stroked=True,
+            filled=True,
+            get_fill_color="properties.fill_color",
+            get_line_color=[80, 80, 80],
+            line_width_min_pixels=0.5,
+            pickable=True,
+        )
+
+        view_state = pdk.ViewState(latitude=70, longitude=0, zoom=2, pitch=0)
+
+        # Build tooltip HTML for ERA5
+        if has_agg and len(ds["aggregations"]) > 1:
+            tooltip_lines = [f"<b>{selected_var} (selected: {selected_agg}):</b> {{value}}<br/>"]
+            tooltip_lines.append("<b>All aggregations:</b><br/>")
+            for agg in ds["aggregations"].values:
+                tooltip_lines.append(f"&nbsp;&nbsp;{agg}: {{agg_{agg}}}<br/>")
+            tooltip_html = "".join(tooltip_lines)
+        else:
+            tooltip_html = f"<b>{selected_var}:</b> {{value}}"
+
+        deck = pdk.Deck(
+            layers=[layer],
+            initial_view_state=view_state,
+            tooltip={"html": tooltip_html, "style": {"backgroundColor": "steelblue", "color": "white"}},
+            map_style="https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json",
+        )
+
+        st.pydeck_chart(deck)
+
+        # Show statistics
+        st.caption(
+            f"Min: {vmin:.4f} | Max: {vmax:.4f} | Mean: {np.nanmean(values_clean):.4f} | "
+            f"Std: {np.nanstd(values_clean):.4f}"
+        )
+    else:
+        st.warning("No valid data available for selected parameters")

src/entropice/dashboard/training_data_page.py

@@ -2,8 +2,19 @@
 
 import streamlit as st
 
+from entropice.dashboard.plots.source_data import (
+    render_alphaearth_map,
+    render_alphaearth_overview,
+    render_alphaearth_plots,
+    render_arcticdem_map,
+    render_arcticdem_overview,
+    render_arcticdem_plots,
+    render_era5_map,
+    render_era5_overview,
+    render_era5_plots,
+)
 from entropice.dashboard.plots.training_data import render_all_distribution_histograms, render_spatial_map
-from entropice.dashboard.utils.data import load_all_training_data
+from entropice.dashboard.utils.data import load_all_training_data, load_source_data
 from entropice.dataset import DatasetEnsemble
 
 
@@ -107,30 +118,134 @@ def render_training_data_page():
         # Display dataset ID in a styled container
         st.info(f"**Dataset ID:** `{ensemble.id()}`")
 
-        # Load training data for all three tasks
+        # Create tabs for different data views
-        train_data_dict = load_all_training_data(ensemble)
+        tab_names = ["📊 Labels"]
 
-        # Calculate total samples (use binary as reference)
+        # Add tabs for each member
-        total_samples = len(train_data_dict["binary"])
+        for member in ensemble.members:
-        train_samples = (train_data_dict["binary"].split == "train").sum().item()
+            if member == "AlphaEarth":
-        test_samples = (train_data_dict["binary"].split == "test").sum().item()
+                tab_names.append("🌍 AlphaEarth")
+            elif member == "ArcticDEM":
+                tab_names.append("🏔️ ArcticDEM")
+            elif member.startswith("ERA5"):
+                # Group ERA5 temporal variants
+                if "🌡️ ERA5" not in tab_names:
+                    tab_names.append("🌡️ ERA5")
 
-        st.success(f"Loaded {total_samples} samples ({train_samples} train, {test_samples} test) for all three tasks")
+        tabs = st.tabs(tab_names)
 
-        # Render distribution histograms
+        # Labels tab
-        st.markdown("---")
+        with tabs[0]:
-        render_all_distribution_histograms(train_data_dict)
+            st.markdown("### Target Labels Distribution and Spatial Visualization")
 
-        st.markdown("---")
+            # Load training data for all three tasks
+            with st.spinner("Loading training data for all tasks..."):
+                train_data_dict = load_all_training_data(ensemble)
 
-        # Render spatial map (as a fragment for efficient re-rendering)
+            # Calculate total samples (use binary as reference)
-        # Extract geometries from the X.data dataframe (which has geometry as a column)
+            total_samples = len(train_data_dict["binary"])
-        # The index should be cell_id
+            train_samples = (train_data_dict["binary"].split == "train").sum().item()
-        binary_dataset = train_data_dict["binary"]
+            test_samples = (train_data_dict["binary"].split == "test").sum().item()
-        assert "geometry" in binary_dataset.dataset.columns, "Geometry column missing in dataset"
 
-        render_spatial_map(train_data_dict)
+            st.success(
+                f"Loaded {total_samples} samples ({train_samples} train, {test_samples} test) for all three tasks"
+            )
+
+            # Render distribution histograms
+            st.markdown("---")
+            render_all_distribution_histograms(train_data_dict)
+
+            st.markdown("---")
+
+            # Render spatial map
+            binary_dataset = train_data_dict["binary"]
+            assert "geometry" in binary_dataset.dataset.columns, "Geometry column missing in dataset"
+
+            render_spatial_map(train_data_dict)
+
+            st.balloons()
+
+        # AlphaEarth tab
+        tab_idx = 1
+        if "AlphaEarth" in ensemble.members:
+            with tabs[tab_idx]:
+                st.markdown("### AlphaEarth Embeddings Analysis")
+
+                with st.spinner("Loading AlphaEarth data..."):
+                    alphaearth_ds, targets = load_source_data(ensemble, "AlphaEarth")
+
+                st.success(f"Loaded AlphaEarth data with {len(alphaearth_ds['cell_ids'])} cells")
+
+                render_alphaearth_overview(alphaearth_ds)
+                render_alphaearth_plots(alphaearth_ds)
+
+                st.markdown("---")
+
+                render_alphaearth_map(alphaearth_ds, targets, ensemble.grid)
+
+                st.balloons()
+
+            tab_idx += 1
+
+        # ArcticDEM tab
+        if "ArcticDEM" in ensemble.members:
+            with tabs[tab_idx]:
+                st.markdown("### ArcticDEM Terrain Analysis")
+
+                with st.spinner("Loading ArcticDEM data..."):
+                    arcticdem_ds, targets = load_source_data(ensemble, "ArcticDEM")
+
+                st.success(f"Loaded ArcticDEM data with {len(arcticdem_ds['cell_ids'])} cells")
+
+                render_arcticdem_overview(arcticdem_ds)
+                render_arcticdem_plots(arcticdem_ds)
+
+                st.markdown("---")
+
+                render_arcticdem_map(arcticdem_ds, targets, ensemble.grid)
+
+                st.balloons()
+
+            tab_idx += 1
+
+        # ERA5 tab (combining all temporal variants)
+        era5_members = [m for m in ensemble.members if m.startswith("ERA5")]
+        if era5_members:
+            with tabs[tab_idx]:
+                st.markdown("### ERA5 Climate Data Analysis")
+
+                # Let user select which ERA5 temporal aggregation to view
+                era5_options = {
+                    "ERA5-yearly": "Yearly",
+                    "ERA5-seasonal": "Seasonal (Winter/Summer)",
+                    "ERA5-shoulder": "Shoulder Seasons (JFM/AMJ/JAS/OND)",
+                }
+
+                available_era5 = {k: v for k, v in era5_options.items() if k in era5_members}
+
+                selected_era5 = st.selectbox(
+                    "Select ERA5 temporal aggregation",
+                    options=list(available_era5.keys()),
+                    format_func=lambda x: available_era5[x],
+                    key="era5_temporal_select",
+                )
+
+                if selected_era5:
+                    temporal_type = selected_era5.split("-")[1]  # 'yearly', 'seasonal', or 'shoulder'
+
+                    with st.spinner(f"Loading {selected_era5} data..."):
+                        era5_ds, targets = load_source_data(ensemble, selected_era5)
+
+                    st.success(f"Loaded {selected_era5} data with {len(era5_ds['cell_ids'])} cells")
+
+                    render_era5_overview(era5_ds, temporal_type)
+                    render_era5_plots(era5_ds, temporal_type)
+
+                    st.markdown("---")
+
+                    render_era5_map(era5_ds, targets, ensemble.grid, temporal_type)
+
+                    st.balloons()
 
-        # Add more components and visualizations as needed for training data.
     else:
         st.info("Configure the dataset settings in the sidebar and click 'Load Dataset' to begin.")

src/entropice/dashboard/utils/data.py

@@ -99,3 +99,23 @@ def load_all_training_data(e: DatasetEnsemble) -> dict[str, CategoricalTrainingD
         "count": e.create_cat_training_dataset("count"),
         "density": e.create_cat_training_dataset("density"),
     }
+
+
+@st.cache_data
+def load_source_data(e: DatasetEnsemble, source: str):
+    """Load raw data from a specific source (AlphaEarth, ArcticDEM, or ERA5).
+
+    Args:
+        e: DatasetEnsemble object.
+        source: One of 'AlphaEarth', 'ArcticDEM', 'ERA5-yearly', 'ERA5-seasonal', 'ERA5-shoulder'.
+
+    Returns:
+        xarray.Dataset with the raw data for the specified source.
+
+    """
+    targets = e._read_target()
+
+    # Load the member data lazily to get metadata
+    ds = e._read_member(source, targets, lazy=False)
+
+    return ds, targets