added small plotting func

Author: timtreis

src/squidpy/exp/__init__.py

@@ -9,4 +9,7 @@
 from . import im
 from .im._qc import qc_sharpness
 
-__all__ = ["qc_sharpness"]
+from . import pl
+from .pl._qc import qc_sharpness_metrics
+
+__all__ = ["qc_sharpness", "qc_sharpness_metrics"]

src/squidpy/exp/pl/__init__.py

@@ -0,0 +1,3 @@
+from ._qc import qc_sharpness_metrics
+
+__all__ = ["qc_sharpness_metrics"]

src/squidpy/exp/pl/_qc.py

@@ -0,0 +1,165 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from typing import Optional, Tuple
+from spatialdata._logging import logger as logg
+from squidpy.exp.im._qc import SHARPNESS_METRICS
+
+
+
+def qc_sharpness_metrics(
+    sdata,
+    image_key: str,
+    metrics: Optional[SHARPNESS_METRICS | list[SHARPNESS_METRICS]] = None,
+    figsize: Optional[Tuple[int, int]] = None,
+    return_fig: bool = False,
+    **kwargs
+) -> Optional[plt.Figure]:
+    """
+    Plot a summary view of raw sharpness metrics from qc_sharpness results.
+    
+    Automatically scans adata.uns for calculated metrics and plots the raw sharpness values.
+    Creates a multi-panel plot: one panel per calculated sharpness metric.
+    Each panel shows: spatial view, histogram, and statistics.
+    
+    Parameters
+    ----------
+    sdata : SpatialData
+        SpatialData object containing QC results.
+    image_key : str
+        Image key used in qc_sharpness function.
+    metrics : SHARPNESS_METRICS or list of SHARPNESS_METRICS, optional
+        Specific metrics to plot. If None, plots all calculated sharpness metrics.
+        Use SHARPNESS_METRICS enum values.
+    figsize : tuple, optional
+        Figure size (width, height). Auto-calculated if None.
+    return_fig : bool
+        Whether to return the figure object. Default is False.
+    **kwargs
+        Additional arguments passed to render_shapes().
+        
+    Returns
+    -------
+    fig : matplotlib.Figure or None
+        The matplotlib figure object if return_fig=True, otherwise None.
+    """
+    import matplotlib.pyplot as plt
+    
+    # Expected keys
+    table_key = f"qc_img_{image_key}_sharpness"
+    shapes_key = f"qc_img_{image_key}_sharpness_grid"
+    
+    if table_key not in sdata.tables:
+        raise ValueError(f"No QC data found for image '{image_key}'. Run sq.exp.im.qc_sharpness() first.")
+    
+    adata = sdata.tables[table_key]
+    
+    # Check if qc_sharpness metadata exists
+    if "qc_sharpness" not in adata.uns:
+        raise ValueError(f"No qc_sharpness metadata found. Run sq.exp.im.qc_sharpness() first.")
+    
+    # Get calculated metrics from metadata
+    calculated_metrics = adata.uns["qc_sharpness"]["metrics"]
+    
+    if not calculated_metrics:
+        raise ValueError(f"No sharpness metrics found in metadata.")
+    
+    # Filter for specific metrics if requested
+    if metrics is not None:
+        # Convert metrics to list if single metric provided
+        metrics_list = metrics if isinstance(metrics, list) else [metrics]
+        # Convert enum to string names using the same logic as main function
+        metrics_to_plot = []
+        for metric in metrics_list:
+            metric_name = metric.name.lower() if isinstance(metric, SHARPNESS_METRICS) else metric
+            if metric_name not in calculated_metrics:
+                raise ValueError(f"Metric '{metric_name}' not found. Available: {calculated_metrics}")
+            metrics_to_plot.append(metric_name)
+    else:
+        metrics_to_plot = calculated_metrics
+    
+    logg.info(f"Plotting {len(metrics_to_plot)} sharpness metrics: {metrics_to_plot}")
+    
+    # Create subplots: 3 columns, one row per metric
+    n_metrics = len(metrics_to_plot)
+    ncols = 3  # spatial, histogram, stats
+    nrows = n_metrics
+    
+    if figsize is None:
+        figsize = (12, 4 * nrows)  # 12 width for 3 columns, 4 height per row
+    
+    fig, axes = plt.subplots(nrows, ncols, figsize=figsize)
+    
+    # Ensure axes is always 2D array for consistent indexing
+    if nrows == 1:
+        axes = axes.reshape(1, -1)
+    if ncols == 1:
+        axes = axes.reshape(-1, 1)
+    
+    # Plot each metric
+    for i, metric_name in enumerate(metrics_to_plot):
+        # Find the metric in adata.var_names and get raw values
+        var_name = f"sharpness_{metric_name}"
+        if var_name not in adata.var_names:
+            logg.warning(f"Variable '{var_name}' not found in adata.var_names. Skipping.")
+            continue
+        
+        # Get metric index and raw values
+        metric_idx = list(adata.var_names).index(var_name)
+        raw_values = adata.X[:, metric_idx]
+        
+        # Get axes for this metric (row i, columns 0, 1, 2)
+        ax_spatial = axes[i, 0]
+        ax_hist = axes[i, 1]
+        ax_stats = axes[i, 2]
+        
+        # Panel 1: Spatial plot
+        try:
+            (
+                sdata
+                .pl.render_shapes(shapes_key, color=var_name, **kwargs)
+                .pl.show(ax=ax_spatial, title=f"{metric_name.replace('_', ' ').title()}")
+                )
+        except Exception as e:
+            logg.warning(f"Error plotting spatial view for {metric_name}: {e}")
+            ax_spatial.text(0.5, 0.5, f"Error plotting\n{metric_name}", 
+                    ha='center', va='center', transform=ax_spatial.transAxes)
+            ax_spatial.set_title(f"{metric_name.replace('_', ' ').title()}")
+        
+        # Panel 2: Histogram
+        ax_hist.hist(raw_values, bins=50, alpha=0.7, edgecolor='black')
+        ax_hist.set_xlabel(f"{metric_name.replace('_', ' ').title()}")
+        ax_hist.set_ylabel('Count')
+        ax_hist.set_title('Distribution')
+        ax_hist.grid(True, alpha=0.3)
+        
+        # Panel 3: Statistics
+        ax_stats.axis('off')
+        stats_text = f"""
+        Raw {metric_name.replace('_', ' ').title()} Statistics:
+        
+        Count: {len(raw_values):,}
+        Mean: {np.mean(raw_values):.4f}
+        Std: {np.std(raw_values):.4f}
+        Min: {np.min(raw_values):.4f}
+        Max: {np.max(raw_values):.4f}
+        
+        Percentiles:
+        5%: {np.percentile(raw_values, 5):.4f}
+        25%: {np.percentile(raw_values, 25):.4f}
+        50%: {np.percentile(raw_values, 50):.4f}
+        75%: {np.percentile(raw_values, 75):.4f}
+        95%: {np.percentile(raw_values, 95):.4f}
+        
+        Non-zero: {np.count_nonzero(raw_values):,}
+        Zero: {np.sum(raw_values == 0):,}
+        """
+        
+        ax_stats.text(
+            0.05, 0.95, stats_text.strip(), 
+            transform=ax_stats.transAxes, fontsize=9, 
+            verticalalignment='top', fontfamily='monospace'
+        )
+    
+    plt.tight_layout()
+
+    return fig if return_fig else None