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Post-hoc Grid Heatmap

Functions for creating grid-based heatmaps of behavioral states and metrics.

Overview

This module provides tools to:

  • Create spatial heatmaps on grid-based representations
  • Visualize state probabilities across maze locations
  • Generate multi-panel heatmap displays
  • Compare spatial patterns between groups

Functions

compass_labyrinth.post_hoc_analysis.level_1.grid_heatmap

HEATMAP REPRESENTATIONS Author: Shreya Bangera Goal: ├── Grid-wise probability heatmaps showing the proportion of time spent in a specific HMM state across all sessions. ├── Interactive version of the visualization available too.

NODE_TYPE_MAPPING module-attribute 

NODE_TYPE_MAPPING = {
    "decision_reward": DECISION_REWARD,
    "nondecision_reward": NONDECISION_REWARD,
    "corner_reward": CORNER_REWARD,
    "decision_nonreward": DECISION_NONREWARD,
    "nondecision_nonreward": NONDECISION_NONREWARD,
    "corner_nonreward": CORNER_NONREWARD,
    "entry_zone": ENTRY_ZONE_NODES,
    "target_zone": TARGET_ZONE_NODES,
    "decision_3way": DECISION_3WAY,
    "decision_4way": DECISION_4WAY,
}

compute_state_proportion 

compute_state_proportion(
    df: DataFrame, genotype_name: str, hmm_state: int = 2
) -> pd.DataFrame

Compute state proportions and return filtered dataframe for given genotype and HMM state.

Parameters:

  • df (DataFrame) –

    DataFrame containing columns: 'Genotype', 'Grid Number', 'HMM_State', 'x', 'y'.

  • genotype_name (str) –

    Name of the genotype to filter.

  • hmm_state (int, default: 2 ) –

    HMM state to filter.

Returns:

  • DataFrame –

    DataFrame with computed proportions for the specified genotype and HMM state.

Source code in src/compass_labyrinth/post_hoc_analysis/level_1/grid_heatmap.py 
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def compute_state_proportion(
    df: pd.DataFrame,
    genotype_name: str,
    hmm_state: int = 2,
) -> pd.DataFrame:
    """
    Compute state proportions and return filtered dataframe for given genotype and HMM state.

    Parameters
    -----------
    df : pd.DataFrame
        DataFrame containing columns: 'Genotype', 'Grid Number', 'HMM_State', 'x', 'y'.
    genotype_name : str
        Name of the genotype to filter.
    hmm_state : int
        HMM state to filter.

    Returns
    --------
    pd.DataFrame
        DataFrame with computed proportions for the specified genotype and HMM state.
    """
    st_cnt = df.groupby(["Genotype", "Grid Number", "HMM_State"]).size().rename("cnt").reset_index()
    gn_cnt = df.groupby(["Genotype", "Grid Number"]).size().rename("tot").reset_index()
    x_y = df.groupby(["Genotype", "Grid Number"]).agg({"x": "mean", "y": "mean"}).reset_index()
    state_count = st_cnt.merge(gn_cnt, on=["Genotype", "Grid Number"], how="left")
    state_count["prop"] = state_count["cnt"] / state_count["tot"]
    state_count = state_count.merge(x_y, on=["Genotype", "Grid Number"], how="left")

    return state_count[(state_count.Genotype == genotype_name) & (state_count["HMM_State"] == hmm_state)].reset_index(
        drop=True
    )

create_grid_geodata 

create_grid_geodata(
    config: dict, grid_filename: str
) -> gpd.GeoDataFrame

Load the shapefile grid as GeoDataFrame.

Source code in src/compass_labyrinth/post_hoc_analysis/level_1/grid_heatmap.py 
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def create_grid_geodata(config: dict, grid_filename: str) -> gpd.GeoDataFrame:
    """Load the shapefile grid as GeoDataFrame."""
    gridfile = Path(config["project_path_full"]) / "data" / "grid_files" / grid_filename
    return gpd.read_file(gridfile)

map_points_to_grid 

map_points_to_grid(
    df_points: DataFrame, grid: GeoDataFrame
) -> gpd.GeoDataFrame

Map mean x, y points to the grid.

Source code in src/compass_labyrinth/post_hoc_analysis/level_1/grid_heatmap.py 
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def map_points_to_grid(df_points: pd.DataFrame, grid: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
    """Map mean x, y points to the grid."""
    points = [Point(xy) for xy in zip(df_points["x"], df_points["y"])]
    pnt_gpd = gpd.GeoDataFrame(geometry=points, index=np.arange(len(points)), crs=grid.crs)
    pointInPolys = gpd.tools.sjoin(pnt_gpd, grid, predicate="within", how="left")
    return pointInPolys

merge_state_proportions_to_grid 

merge_state_proportions_to_grid(
    grid: GeoDataFrame, df_props: DataFrame
) -> gpd.GeoDataFrame

Merge state proportions to shapefile grid polygons.

Source code in src/compass_labyrinth/post_hoc_analysis/level_1/grid_heatmap.py 
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def merge_state_proportions_to_grid(
    grid: gpd.GeoDataFrame,
    df_props: pd.DataFrame,
) -> gpd.GeoDataFrame:
    """Merge state proportions to shapefile grid polygons."""
    prop_by_grid = df_props[["Grid Number", "prop"]].copy()
    prop_by_grid = prop_by_grid.rename(columns={"prop": "State1_Proportion"})
    return grid.merge(
        prop_by_grid,
        left_on="FID",
        right_on="Grid Number",
        how="left",
    )

plot_grid_heatmap 

plot_grid_heatmap(
    config: dict,
    grid: GeoDataFrame,
    genotype_name: str,
    highlight_grids: str | None = None,
    target_grids: str | None = None,
    cmap: str = "RdBu",
    save_fig: bool = True,
    show_fig: bool = True,
    return_fig: bool = False,
) -> None | plt.Figure

Plot grid heatmap for state proportions.

Parameters:

  • config (dict) –

    Configuration dictionary containing project details.

  • grid (GeoDataFrame) –

    GeoDataFrame of the grid with state proportions merged.

  • genotype_name (str) –

    Name of the genotype to be plotted.

  • highlight_grids (str | None, default: None ) –

    Node type to highlight (e.g., "decision_reward").

  • target_grids (str | None, default: None ) –

    Node type to mark as target (e.g., "target_zone").

  • cmap (str, default: 'RdBu' ) –

    Colormap to use for the heatmap.

  • save_fig (bool, default: True ) –

    Whether to save the figure.

  • show_fig (bool, default: True ) –

    Whether to display the figure.

  • return_fig (bool, default: False ) –

    Whether to return the figure object.

Returns:

  • fig ( Figure | None ) –

    Matplotlib Figure object if return_fig is True, else None.

Source code in src/compass_labyrinth/post_hoc_analysis/level_1/grid_heatmap.py 
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def plot_grid_heatmap(
    config: dict,
    grid: gpd.GeoDataFrame,
    genotype_name: str,
    highlight_grids: str | None = None,
    target_grids: str | None = None,
    cmap: str = "RdBu",
    save_fig: bool = True,
    show_fig: bool = True,
    return_fig: bool = False,
) -> None | plt.Figure:
    """
    Plot grid heatmap for state proportions.

    Parameters
    -----------
    config : dict
        Configuration dictionary containing project details.
    grid : gpd.GeoDataFrame
        GeoDataFrame of the grid with state proportions merged.
    genotype_name : str
        Name of the genotype to be plotted.
    highlight_grids : str | None
        Node type to highlight (e.g., "decision_reward").
    target_grids : str | None
        Node type to mark as target (e.g., "target_zone").
    cmap : str
        Colormap to use for the heatmap.
    save_fig : bool
        Whether to save the figure.
    show_fig : bool
        Whether to display the figure.
    return_fig : bool
        Whether to return the figure object.

    Returns
    --------
    fig : plt.Figure | None
        Matplotlib Figure object if return_fig is True, else None.
    """
    fig, ax = plt.subplots(1, 1, figsize=(10, 8))
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.1)

    grid.plot(
        column="State1_Proportion",
        cmap=cmap,
        linewidth=0.8,
        ax=ax,
        vmin=0,
        vmax=1,
        edgecolor="0.8",
        legend=True,
        cax=cax,
        alpha=1.0,
    )

    if highlight_grids is not None:
        highlight_grids_values = NODE_TYPE_MAPPING[highlight_grids]
        edge_subset = grid[grid["FID"].isin(highlight_grids_values)]
        edge_subset.plot(ax=ax, edgecolor="black", facecolor="none", linewidth=2)
    if target_grids is not None:
        target_grids_values = NODE_TYPE_MAPPING[target_grids]
        target_nodes = grid[grid["FID"].isin(target_grids_values)]
        target_nodes.plot(ax=ax, edgecolor="yellow", facecolor="none", linewidth=5)

    ax.set_title(f"{genotype_name} (Sternum Tracking)", fontsize=14)
    plt.tight_layout()

    # Save figure
    if save_fig:
        save_path = Path(config["project_path_full"]) / "figures" / f"{genotype_name}_grid_heatmap.png"
        plt.savefig(save_path, bbox_inches="tight", dpi=300)
        print(f"Figure saved at: {save_path}")

    # Show figure
    if show_fig:
        plt.show()

    # Return figure
    if return_fig:
        return fig

plot_all_genotype_heatmaps 

plot_all_genotype_heatmaps(
    config: dict,
    df_hmm: DataFrame,
    grid_filename: str,
    highlight_grids: str | None = None,
    target_grids: str | None = None,
    hmm_state: int = 1,
    cmap: str = "RdBu",
    save_fig: bool = True,
    show_fig: bool = True,
    return_fig: bool = False,
) -> None | plt.Figure

Plot grid heatmaps for all genotypes.

Parameters:

  • config (dict) –

    Configuration dictionary containing project details.

  • df_hmm (DataFrame) –

    DataFrame containing HMM data with 'Genotype' and 'Grid Number' columns.

  • grid_filename (str) –

    Filename of the grid shapefile.

  • highlight_grids (str | None, default: None ) –

    Node type to highlight (e.g., "decision_reward").

  • target_grids (str | None, default: None ) –

    Node type to mark as target (e.g., "target_zone").

  • hmm_state (int, default: 1 ) –

    HMM state to filter.

  • cmap (str, default: 'RdBu' ) –

    Colormap to use for the heatmap.

  • save_fig (bool, default: True ) –

    Whether to save the figure.

  • show_fig (bool, default: True ) –

    Whether to display the figure.

  • return_fig (bool, default: False ) –

    Whether to return the figure object.

Returns:

  • fig ( Figure | None ) –

    Matplotlib Figure object if return_fig is True, else None.

Source code in src/compass_labyrinth/post_hoc_analysis/level_1/grid_heatmap.py 
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def plot_all_genotype_heatmaps(
    config: dict,
    df_hmm: pd.DataFrame,
    grid_filename: str,
    highlight_grids: str | None = None,
    target_grids: str | None = None,
    hmm_state: int = 1,
    cmap: str = "RdBu",
    save_fig: bool = True,
    show_fig: bool = True,
    return_fig: bool = False,
) -> None | plt.Figure:
    """
    Plot grid heatmaps for all genotypes.

    Parameters
    -----------
    config : dict
        Configuration dictionary containing project details.
    df_hmm : pd.DataFrame
        DataFrame containing HMM data with 'Genotype' and 'Grid Number' columns.
    grid_filename : str
        Filename of the grid shapefile.
    highlight_grids : str | None
        Node type to highlight (e.g., "decision_reward").
    target_grids : str | None
        Node type to mark as target (e.g., "target_zone").
    hmm_state : int
        HMM state to filter.
    cmap : str
        Colormap to use for the heatmap.
    save_fig : bool
        Whether to save the figure.
    show_fig : bool
        Whether to display the figure.
    return_fig : bool
        Whether to return the figure object.

    Returns
    --------
    fig : plt.Figure | None
        Matplotlib Figure object if return_fig is True, else None.
    """
    genotypes = sorted(df_hmm["Genotype"].unique())
    n_genotypes = len(genotypes)
    n_cols = math.ceil(n_genotypes**0.5)
    n_rows = math.ceil(n_genotypes / n_cols)

    grid = create_grid_geodata(config, grid_filename)

    fig, axs = plt.subplots(n_rows, n_cols, figsize=(6 * n_cols, 5 * n_rows))
    axs = axs.flatten() if isinstance(axs, np.ndarray) else [axs]

    for i, genotype in enumerate(genotypes):
        # Step 1: Compute state proportions
        state_df = compute_state_proportion(df_hmm, genotype, hmm_state)

        # Step 2: Map mean (x, y) points to grid polygons
        pointInPolys = map_points_to_grid(state_df, grid)

        # Step 3: Merge with grid
        grid_mapped = merge_state_proportions_to_grid(grid, state_df)

        # Step 4: Plot on subplot
        ax = axs[i]
        divider = make_axes_locatable(ax)
        cax = divider.append_axes("right", size="5%", pad=0.1)

        grid_mapped.plot(
            column="State1_Proportion",
            cmap=cmap,
            linewidth=0.8,
            ax=ax,
            vmin=0,
            vmax=1,
            edgecolor="0.8",
            legend=True,
            cax=cax,
            alpha=1.0,
        )

        if highlight_grids is not None:
            highlight_grids_values = NODE_TYPE_MAPPING[highlight_grids]
            edge_subset = grid_mapped[grid_mapped["FID"].isin(highlight_grids_values)]
            edge_subset.plot(ax=ax, edgecolor="black", facecolor="none", linewidth=2)

        if target_grids is not None:
            target_grids_values = NODE_TYPE_MAPPING[target_grids]
            target_subset = grid_mapped[grid_mapped["FID"].isin(target_grids_values)]
            target_subset.plot(ax=ax, edgecolor="yellow", facecolor="none", linewidth=5)

        ax.set_title(f"{genotype} (Sternum Tracking)", fontsize=12)
        ax.axis("off")

    # Hide unused axes
    for j in range(len(genotypes), len(axs)):
        fig.delaxes(axs[j])

    fig.suptitle(f"HMM State {hmm_state} Proportion Across Genotypes", fontsize=16)
    plt.tight_layout(rect=[0, 0, 1, 0.96])

    # Save figure
    if save_fig:
        save_path = Path(config["project_path_full"]) / "figures" / "all_genotypes_grid_heatmap.pdf"
        plt.savefig(save_path, bbox_inches="tight", dpi=300)
        print(f"Figure saved at: {save_path}")

    # Show figure
    if show_fig:
        plt.show()

    # Return figure
    if return_fig:
        return fig

get_grid_centroids 

get_grid_centroids(grid)
Source code in src/compass_labyrinth/post_hoc_analysis/level_1/grid_heatmap.py 
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def get_grid_centroids(grid):
    grid["x"] = grid.geometry.centroid.x
    grid["y"] = grid.geometry.centroid.y
    return {row["FID"]: (row["x"], row["y"]) for _, row in grid.iterrows()}

overlay_trajectory_lines_plotly 

overlay_trajectory_lines_plotly(
    fig: Figure,
    df_hmm: DataFrame,
    genotype_name: str,
    grid_centroids: dict,
    top_percent: float = 0.1,
)

Overlay trajectory lines on the interactive heatmap.

Parameters:

  • fig (Figure) –

    Plotly Figure object to overlay lines on.

  • df_hmm (DataFrame) –

    DataFrame containing HMM data with 'Genotype' and 'Grid Number' columns.

  • genotype_name (str) –

    Name of the genotype to filter.

  • grid_centroids (dict) –

    Dictionary mapping grid numbers to their centroid coordinates.

  • top_percent (float, default: 0.1 ) –

    Top percentage of transitions to visualize.

Returns:

  • None –
Source code in src/compass_labyrinth/post_hoc_analysis/level_1/grid_heatmap.py 
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def overlay_trajectory_lines_plotly(
    fig: go.Figure,
    df_hmm: pd.DataFrame,
    genotype_name: str,
    grid_centroids: dict,
    top_percent: float = 0.1,
):
    """
    Overlay trajectory lines on the interactive heatmap.

    Parameters
    -----------
    fig : go.Figure
        Plotly Figure object to overlay lines on.
    df_hmm : pd.DataFrame
        DataFrame containing HMM data with 'Genotype' and 'Grid Number' columns.
    genotype_name : str
        Name of the genotype to filter.
    grid_centroids : dict
        Dictionary mapping grid numbers to their centroid coordinates.
    top_percent : float
        Top percentage of transitions to visualize.

    Returns
    --------
    None
    """
    df_geno = df_hmm[df_hmm["Genotype"] == genotype_name].copy()
    df_geno["Grid.Next"] = df_geno["Grid Number"].shift(-1)
    df_geno["Grid.Prev"] = df_geno["Grid Number"]
    transitions = df_geno[["Grid.Prev", "Grid.Next"]].dropna()
    transitions = transitions[transitions["Grid.Prev"] != transitions["Grid.Next"]].astype(int)
    transitions["pair"] = list(zip(transitions["Grid.Prev"], transitions["Grid.Next"]))
    trans_counts = transitions["pair"].value_counts()

    top_n = int(len(trans_counts) * top_percent)
    trans_counts = trans_counts.iloc[:top_n]

    max_freq = trans_counts.max()
    min_freq = trans_counts.min()

    for (g1, g2), freq in trans_counts.items():
        if g1 in grid_centroids and g2 in grid_centroids:
            pt1, pt2 = grid_centroids[g1], grid_centroids[g2]
            if np.any(np.isnan(pt1)) or np.any(np.isnan(pt2)):
                continue
            # Smooth interpolation
            x_vals = np.array([pt1[0], (pt1[0] + pt2[0]) / 2, pt2[0]])
            y_vals = np.array([pt1[1], (pt1[1] + pt2[1]) / 2 + 2, pt2[1]])  # add slight curve
            spline = make_interp_spline([0, 1, 2], np.column_stack([x_vals, y_vals]), k=2)
            smooth = spline(np.linspace(0, 2, 20))
            xs, ys = smooth[:, 0], smooth[:, 1]

            line_alpha = np.interp(freq, [min_freq, max_freq], [0.2, 1.0])
            line_width = np.interp(freq, [min_freq, max_freq], [1, 10])

            fig.add_trace(
                go.Scatter(
                    x=xs.tolist(),
                    y=ys.tolist(),
                    mode="lines",
                    line=dict(color="black", width=line_width),
                    opacity=line_alpha,
                    hoverinfo="skip",
                    showlegend=False,
                )
            )

plot_interactive_heatmap 

plot_interactive_heatmap(
    config: dict,
    grid_mapped: GeoDataFrame,
    genotype_name: str,
    decision_grids: str | None = None,
    target_grids: str | None = None,
    entry_node_id: int | None = 47,
    save_fig: bool = True,
    show_fig: bool = True,
    return_fig: bool = False,
) -> None | go.Figure

Plot interactive heatmap for state proportions.

Parameters:

  • config (dict) –

    Configuration dictionary containing project details.

  • grid_mapped (GeoDataFrame) –

    GeoDataFrame of the grid with state proportions merged.

  • genotype_name (str) –

    Name of the genotype to be plotted.

  • decision_grids (str | None, default: None ) –

    Node type to highlight (e.g., "decision_reward").

  • target_grids (str | None, default: None ) –

    Node type to mark as target (e.g., "target_zone").

  • entry_node_id (int, default: 47 ) –

    ID of the entry node (default is 47).

  • save_fig (bool, default: True ) –

    Whether to save the figure.

  • show_fig (bool, default: True ) –

    Whether to display the figure.

  • return_fig (bool, default: False ) –

    Whether to return the figure object.

Returns:

  • fig ( Figure | None ) –

    Plotly Figure object if return_fig is True, else None.

Source code in src/compass_labyrinth/post_hoc_analysis/level_1/grid_heatmap.py 
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def plot_interactive_heatmap(
    config: dict,
    grid_mapped: gpd.GeoDataFrame,
    genotype_name: str,
    decision_grids: str | None = None,
    target_grids: str | None = None,
    entry_node_id: int | None = 47,
    save_fig: bool = True,
    show_fig: bool = True,
    return_fig: bool = False,
) -> None | go.Figure:
    """
    Plot interactive heatmap for state proportions.

    Parameters
    -----------
    config : dict
        Configuration dictionary containing project details.
    grid_mapped : gpd.GeoDataFrame
        GeoDataFrame of the grid with state proportions merged.
    genotype_name : str
        Name of the genotype to be plotted.
    decision_grids : str | None
        Node type to highlight (e.g., "decision_reward").
    target_grids : str | None
        Node type to mark as target (e.g., "target_zone").
    entry_node_id : int
        ID of the entry node (default is 47).
    save_fig : bool
        Whether to save the figure.
    show_fig : bool
        Whether to display the figure.
    return_fig : bool
        Whether to return the figure object.

    Returns
    --------
    fig : go.Figure | None
        Plotly Figure object if return_fig is True, else None.
    """
    fig = go.Figure()

    # Add grid cells as filled polygons
    for _, row in grid_mapped.iterrows():
        if row.geometry is None or row["State1_Proportion"] is None:
            continue
        x = list(row.geometry.exterior.xy[0])
        y = list(row.geometry.exterior.xy[1])
        color_val = row["State1_Proportion"]
        fillcolor = sample_colorscale("RdBu", [color_val])[0]  # RdBu reversed = blue to red
        fig.add_trace(
            go.Scatter(
                x=x,
                y=y,
                mode="lines",
                fill="toself",
                # fillcolor=f'rgba({255 * (1 - row["State1_Proportion"])},0,{255 * row["State1_Proportion"]},0.7)',
                fillcolor=fillcolor,
                line=dict(width=0.5, color="gray"),
                hoverinfo="text",
                text=f"Grid: {row['FID']}<br>Proportion: {row['State1_Proportion']:.2f}",
                showlegend=False,
            )
        )

    # Highlight decision nodes in black
    for fid in NODE_TYPE_MAPPING.get(decision_grids, []):
        if fid in grid_mapped["FID"].values:
            poly = grid_mapped.loc[grid_mapped["FID"] == fid, "geometry"].values[0]
            x = list(poly.exterior.xy[0])
            y = list(poly.exterior.xy[1])
            fig.add_trace(
                go.Scatter(
                    x=x,
                    y=y,
                    mode="lines",
                    line=dict(color="yellow", width=2),
                    name="Decision Node",
                    showlegend=False,
                )
            )

    # Highlight decision nodes in black
    if entry_node_id is not None:
        if entry_node_id in grid_mapped["FID"].values:
            poly = grid_mapped.loc[grid_mapped["FID"] == entry_node_id, "geometry"].values[0]
            x = list(poly.exterior.xy[0])
            y = list(poly.exterior.xy[1])
            fig.add_trace(
                go.Scatter(
                    x=x,
                    y=y,
                    mode="lines",
                    line=dict(color="yellow", width=5),
                    name="Entry Node",
                    showlegend=False,
                )
            )

    # Highlight target nodes in yellow
    for fid in NODE_TYPE_MAPPING.get(target_grids, []):
        if fid in grid_mapped["FID"].values:
            poly = grid_mapped.loc[grid_mapped["FID"] == fid, "geometry"].values[0]
            x = list(poly.exterior.xy[0])
            y = list(poly.exterior.xy[1])
            fig.add_trace(
                go.Scatter(
                    x=x,
                    y=y,
                    mode="lines",
                    line=dict(color="yellow", width=5),
                    name="Target Node",
                    showlegend=False,
                )
            )

    fig.update_layout(
        title=f"{genotype_name}: Grid Heatmap with Trajectories",
        xaxis=dict(showgrid=False, visible=False),
        yaxis=dict(scaleanchor="x", showgrid=False, visible=False),
        height=800,
        margin=dict(l=0, r=0, t=40, b=0),
    )

    # Add annotations above Grid Number 47 and below 84
    for fid, label, valign in [(47, "entry_zone", "top"), (84, "target_zone", "bottom")]:
        if fid in grid_mapped["FID"].values:
            poly = grid_mapped.loc[grid_mapped["FID"] == fid, "geometry"].values[0]
            centroid_x = poly.centroid.x
            centroid_y = poly.centroid.y

            offset = 55 if valign == "top" else -55

            fig.add_annotation(
                x=centroid_x,
                y=centroid_y + offset,
                text=label,
                showarrow=False,
                font=dict(color="black", size=14, family="Arial"),
                xanchor="center",
            )

    # Save figure
    if save_fig:
        save_path = Path(config["project_path_full"]) / "figures" / f"{genotype_name}_interactive_grid_heatmap.html"
        fig.write_html(save_path)
        print(f"Figure saved at: {save_path}")

    # Show figure
    if show_fig:
        fig.show()

    # Return figure
    if return_fig:
        return fig

plot_all_genotype_interactive_heatmaps 

plot_all_genotype_interactive_heatmaps(
    config: dict,
    df_hmm: DataFrame,
    grid_filename: str,
    hmm_state: int = 2,
    decision_grids: str | None = None,
    target_grids: str | None = None,
    entry_node_id: int | None = 47,
    top_percent: float = 0.1,
    save_fig: bool = True,
    show_fig: bool = True,
    return_fig: bool = False,
) -> None | go.Figure

Plot interactive grid heatmaps for all genotypes.

Parameters:

  • config (dict) –

    Configuration dictionary containing project details.

  • df_hmm (DataFrame) –

    DataFrame containing HMM data with 'Genotype' and 'Grid Number' columns.

  • grid_filename (str) –

    Filename of the grid shapefile.

  • hmm_state (int, default: 2 ) –

    HMM state to filter.

  • decision_grids (str | None, default: None ) –

    Node type to highlight (e.g., "decision_reward").

  • target_grids (str | None, default: None ) –

    Node type to mark as target (e.g., "target_zone").

  • entry_node_id (int | None, default: 47 ) –

    ID of the entry node (default is 47).

  • top_percent (float, default: 0.1 ) –

    Top percentage of transitions to visualize.

  • save_fig (bool, default: True ) –

    Whether to save the figure.

  • show_fig (bool, default: True ) –

    Whether to display the figure.

  • return_fig (bool, default: False ) –

    Whether to return the figure object.

Returns:

  • fig ( Figure | None ) –

    Plotly Figure object if return_fig is True, else None.

Source code in src/compass_labyrinth/post_hoc_analysis/level_1/grid_heatmap.py 
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def plot_all_genotype_interactive_heatmaps(
    config: dict,
    df_hmm: pd.DataFrame,
    grid_filename: str,
    hmm_state: int = 2,
    decision_grids: str | None = None,
    target_grids: str | None = None,
    entry_node_id: int | None = 47,
    top_percent: float = 0.1,
    save_fig: bool = True,
    show_fig: bool = True,
    return_fig: bool = False,
) -> None | go.Figure:
    """
    Plot interactive grid heatmaps for all genotypes.

    Parameters
    -----------
    config : dict
        Configuration dictionary containing project details.
    df_hmm : pd.DataFrame
        DataFrame containing HMM data with 'Genotype' and 'Grid Number' columns.
    grid_filename : str
        Filename of the grid shapefile.
    hmm_state : int
        HMM state to filter.
    decision_grids : str | None
        Node type to highlight (e.g., "decision_reward").
    target_grids : str | None
        Node type to mark as target (e.g., "target_zone").
    entry_node_id : int | None
        ID of the entry node (default is 47).
    top_percent : float
        Top percentage of transitions to visualize.
    save_fig : bool
        Whether to save the figure.
    show_fig : bool
        Whether to display the figure.
    return_fig : bool
        Whether to return the figure object.

    Returns
    --------
    fig : go.Figure | None
        Plotly Figure object if return_fig is True, else None.
    """
    genotypes = sorted(df_hmm["Genotype"].unique())
    n = len(genotypes)
    n_cols = math.ceil(math.sqrt(n))
    n_rows = math.ceil(n / n_cols)

    grid = create_grid_geodata(config, grid_filename)
    grid_centroids = get_grid_centroids(grid)

    # Create subplot figure
    subplot_fig = make_subplots(
        rows=n_rows,
        cols=n_cols,
        subplot_titles=genotypes,
        horizontal_spacing=0.05,
        vertical_spacing=0.08,
    )

    subplot_idx = 0
    for i in range(n_rows):
        for j in range(n_cols):
            if subplot_idx >= n:
                break

            genotype = genotypes[subplot_idx]
            subplot_idx += 1
            row = i + 1
            col = j + 1

            # 1. Compute state proportions
            state_df = compute_state_proportion(df_hmm, genotype, hmm_state)

            # 2. Merge into grid
            grid_mapped = merge_state_proportions_to_grid(grid, state_df)

            # 3. Generate interactive heatmap figure
            fig = plot_interactive_heatmap(
                config=config,
                grid_mapped=grid_mapped,
                decision_grids=decision_grids,
                target_grids=target_grids,
                genotype_name=genotype,
                entry_node_id=entry_node_id,
                save_fig=False,
                show_fig=False,
                return_fig=True,
            )

            # 4. Add trajectory lines
            overlay_trajectory_lines_plotly(
                fig=fig,
                df_hmm=df_hmm,
                genotype_name=genotype,
                grid_centroids=grid_centroids,
                top_percent=top_percent,
            )

            # 5. Add traces to subplot
            for trace in fig.data:
                subplot_fig.add_trace(trace, row=row, col=col)

    # Final layout improvements
    subplot_fig.update_layout(
        title_text=f"Genotype-wise Grid Heatmaps (HMM State {hmm_state})",
        height=600 * n_rows,
        width=600 * n_cols,
        showlegend=False,
        margin=dict(l=10, r=10, t=80, b=10),
        title_x=0.5,
    )

    # Save figure
    if save_fig:
        save_path = Path(config["project_path_full"]) / "figures" / "all_genotypes_interactive_grid_heatmap.html"
        subplot_fig.write_html(save_path)
        print(f"Figure saved at: {save_path}")

    # Show figure
    if show_fig:
        subplot_fig.show()

    # Return figure
    if return_fig:
        return subplot_fig