bug fix: dir_save must be specified to do visualizations

RichieHakim · Aug 10, 2024 · 799c9e6 · 799c9e6
1 parent c67c02c
commit 799c9e6
Showing 1 changed file with 69 additions and 69 deletions.
diff --git a/roicat/pipelines.py b/roicat/pipelines.py
@@ -306,75 +306,75 @@ def choose_clustering_method(method='automatic', n_sessions_switch=8, n_sessions
         )
 
 
-    ## Visualize results
-    ### Save some figures
-
-    #### Save FOV_images as .png files
-    def save_image(array, path, normalize=True):
-        ## Use PIL to save the image
-        from PIL import Image
-        Path(path).parent.mkdir(parents=True, exist_ok=True)
-        Image.fromarray((np.array(array / array.max() if normalize else array) * 255).astype(np.uint8)).save(path)
-    [save_image(array, str(Path(dir_save).resolve() / 'visualization' / 'FOV_images' / f'FOV_images_{ii}.png') ) for ii, array in enumerate(data.FOV_images)]
-    [save_image(array, str(Path(dir_save).resolve() / 'visualization' / 'FOV_images_aligned_geometric' / f'FOV_images_aligned_geometric_{ii}.png') ) for ii, array in enumerate(aligner.ims_registered_geo)]
-    [save_image(array, str(Path(dir_save).resolve() / 'visualization' / 'FOV_images_aligned_nonrigid' / f'FOV_images_aligned_nonrigid_{ii}.png') ) for ii, array in enumerate(aligner.ims_registered_nonrigid)]
-    [save_image(array, str(Path(dir_save).resolve() / 'visualization' / 'ROIs' / f'ROIs_{ii}.png') ) for ii, array in enumerate(data.get_maxIntensityProjection_spatialFootprints())]
-    [save_image(array, str(Path(dir_save).resolve() / 'visualization' / 'ROIs_aligned' / f'ROIs_aligned_{ii}.png') ) for ii, array in enumerate(aligner.get_ROIsAligned_maxIntensityProjection(normalize=True))]
-    [save_image(array, str(Path(dir_save).resolve() / 'visualization' / 'ROIs_aligned_blurred' / f'ROIs_aligned_blurred_{ii}.png') ) for ii, array in enumerate(blurrer.get_ROIsBlurred_maxIntensityProjection())]
-
-    #### Save some sample ROI images
-    [save_image(array, str(Path(dir_save).resolve() / 'visualization' / 'ROIs_sample' / f'ROIs_sample_{ii}.png') ) for ii, array in enumerate(roinet.ROI_images_rs[:100])]
-
-    #### Save the similarity graphy blocks
-    fig = sim.visualize_blocks()
-    (Path(dir_save).resolve() / 'visualization' / 'similarity_graph').mkdir(parents=True, exist_ok=True)
-    fig.savefig(str(Path(dir_save).resolve() / 'visualization' / 'similarity_graph' / 'blocks.png'))
-
-    #### Save the similarity / distance plots for the given conjunctive distance matrix kwargs
-    fig = clusterer.plot_distSame(kwargs_makeConjunctiveDistanceMatrix=kwargs_makeConjunctiveDistanceMatrix_best)
-    (Path(dir_save).resolve() / 'visualization' / 'clustering').mkdir(parents=True, exist_ok=True)
-    fig.savefig(str(Path(dir_save).resolve() / 'visualization' / 'clustering' / 'dist.png'))
-    fig, axs = clusterer.plot_similarity_relationships(
-        plots_to_show=[1,2,3], 
-        max_samples=100000,  ## Make smaller if it is running too slow
-        kwargs_scatter={'s':1, 'alpha':0.2},
-        kwargs_makeConjunctiveDistanceMatrix=kwargs_makeConjunctiveDistanceMatrix_best
-    )
-    (Path(dir_save).resolve() / 'visualization' / 'clustering').mkdir(parents=True, exist_ok=True)
-    fig.savefig(str(Path(dir_save).resolve() / 'visualization' / 'clustering' / 'similarity_relationships.png'))
-
-    #### Save the clustering results
-    fig, axs = tracking.clustering.plot_quality_metrics(
-        quality_metrics=quality_metrics, 
-        labels=labels_squeezed, 
-        n_sessions=data.n_sessions,
-    )
-    (Path(dir_save).resolve() / 'visualization' / 'clustering').mkdir(parents=True, exist_ok=True)
-    fig.savefig(str(Path(dir_save).resolve() / 'visualization' / 'clustering' / 'quality_metrics.png'))
-
-    ### Save a gif of the ROIs
-    FOV_clusters = visualization.compute_colored_FOV(
-        spatialFootprints=[r.power(1.0) for r in results['ROIs']['ROIs_aligned']],  ## Spatial footprint sparse arrays
-        FOV_height=results['ROIs']['frame_height'],
-        FOV_width=results['ROIs']['frame_width'],
-        labels=results["clusters"]["labels_bySession"],  ## cluster labels
-    #     labels=(np.array(results["clusters"]["labels"])!=-1).astype(np.int64),  ## cluster labels
-    #     alphas_labels=confidence*1.5,  ## Set brightness of each cluster based on some 1-D array
-    #     alphas_labels=(clusterer.quality_metrics['cluster_silhouette'] > 0) * (clusterer.quality_metrics['cluster_intra_means'] > 0.4),
-    #     alphas_sf=clusterer.quality_metrics['sample_silhouette'],  ## Set brightness of each ROI based on some 1-D array
-    )
-    helpers.save_gif(
-        array=helpers.add_text_to_images(
-            images=[(f * 255).astype(np.uint8) for f in FOV_clusters], 
-            text=[[f"{ii}",] for ii in range(len(FOV_clusters))], 
-            font_size=3,
-            line_width=10,
-            position=(30, 90),
-        ), 
-        path=str(Path(dir_save).resolve() / 'visualization' / 'FOV_clusters.gif'),
-        frameRate=10.0,
-        loop=0,
-    )
+        ## Visualize results
+        ### Save some figures
+        
+        #### Save FOV_images as .png files
+        def save_image(array, path, normalize=True):
+            ## Use PIL to save the image
+            from PIL import Image
+            Path(path).parent.mkdir(parents=True, exist_ok=True)
+            Image.fromarray((np.array(array / array.max() if normalize else array) * 255).astype(np.uint8)).save(path)
+        [save_image(array, str(Path(dir_save).resolve() / 'visualization' / 'FOV_images' / f'FOV_images_{ii}.png') ) for ii, array in enumerate(data.FOV_images)]
+        [save_image(array, str(Path(dir_save).resolve() / 'visualization' / 'FOV_images_aligned_geometric' / f'FOV_images_aligned_geometric_{ii}.png') ) for ii, array in enumerate(aligner.ims_registered_geo)]
+        [save_image(array, str(Path(dir_save).resolve() / 'visualization' / 'FOV_images_aligned_nonrigid' / f'FOV_images_aligned_nonrigid_{ii}.png') ) for ii, array in enumerate(aligner.ims_registered_nonrigid)]
+        [save_image(array, str(Path(dir_save).resolve() / 'visualization' / 'ROIs' / f'ROIs_{ii}.png') ) for ii, array in enumerate(data.get_maxIntensityProjection_spatialFootprints())]
+        [save_image(array, str(Path(dir_save).resolve() / 'visualization' / 'ROIs_aligned' / f'ROIs_aligned_{ii}.png') ) for ii, array in enumerate(aligner.get_ROIsAligned_maxIntensityProjection(normalize=True))]
+        [save_image(array, str(Path(dir_save).resolve() / 'visualization' / 'ROIs_aligned_blurred' / f'ROIs_aligned_blurred_{ii}.png') ) for ii, array in enumerate(blurrer.get_ROIsBlurred_maxIntensityProjection())]
+        
+        #### Save some sample ROI images
+        [save_image(array, str(Path(dir_save).resolve() / 'visualization' / 'ROIs_sample' / f'ROIs_sample_{ii}.png') ) for ii, array in enumerate(roinet.ROI_images_rs[:100])]
+        
+        #### Save the similarity graphy blocks
+        fig = sim.visualize_blocks()
+        (Path(dir_save).resolve() / 'visualization' / 'similarity_graph').mkdir(parents=True, exist_ok=True)
+        fig.savefig(str(Path(dir_save).resolve() / 'visualization' / 'similarity_graph' / 'blocks.png'))
+        
+        #### Save the similarity / distance plots for the given conjunctive distance matrix kwargs
+        fig = clusterer.plot_distSame(kwargs_makeConjunctiveDistanceMatrix=kwargs_makeConjunctiveDistanceMatrix_best)
+        (Path(dir_save).resolve() / 'visualization' / 'clustering').mkdir(parents=True, exist_ok=True)
+        fig.savefig(str(Path(dir_save).resolve() / 'visualization' / 'clustering' / 'dist.png'))
+        fig, axs = clusterer.plot_similarity_relationships(
+            plots_to_show=[1,2,3], 
+            max_samples=100000,  ## Make smaller if it is running too slow
+            kwargs_scatter={'s':1, 'alpha':0.2},
+            kwargs_makeConjunctiveDistanceMatrix=kwargs_makeConjunctiveDistanceMatrix_best
+        )
+        (Path(dir_save).resolve() / 'visualization' / 'clustering').mkdir(parents=True, exist_ok=True)
+        fig.savefig(str(Path(dir_save).resolve() / 'visualization' / 'clustering' / 'similarity_relationships.png'))
+        
+        #### Save the clustering results
+        fig, axs = tracking.clustering.plot_quality_metrics(
+            quality_metrics=quality_metrics, 
+            labels=labels_squeezed, 
+            n_sessions=data.n_sessions,
+        )
+        (Path(dir_save).resolve() / 'visualization' / 'clustering').mkdir(parents=True, exist_ok=True)
+        fig.savefig(str(Path(dir_save).resolve() / 'visualization' / 'clustering' / 'quality_metrics.png'))
+        
+        ### Save a gif of the ROIs
+        FOV_clusters = visualization.compute_colored_FOV(
+            spatialFootprints=[r.power(1.0) for r in results['ROIs']['ROIs_aligned']],  ## Spatial footprint sparse arrays
+            FOV_height=results['ROIs']['frame_height'],
+            FOV_width=results['ROIs']['frame_width'],
+            labels=results["clusters"]["labels_bySession"],  ## cluster labels
+        #     labels=(np.array(results["clusters"]["labels"])!=-1).astype(np.int64),  ## cluster labels
+        #     alphas_labels=confidence*1.5,  ## Set brightness of each cluster based on some 1-D array
+        #     alphas_labels=(clusterer.quality_metrics['cluster_silhouette'] > 0) * (clusterer.quality_metrics['cluster_intra_means'] > 0.4),
+        #     alphas_sf=clusterer.quality_metrics['sample_silhouette'],  ## Set brightness of each ROI based on some 1-D array
+        )
+        helpers.save_gif(
+            array=helpers.add_text_to_images(
+                images=[(f * 255).astype(np.uint8) for f in FOV_clusters], 
+                text=[[f"{ii}",] for ii in range(len(FOV_clusters))], 
+                font_size=3,
+                line_width=10,
+                position=(30, 90),
+            ), 
+            path=str(Path(dir_save).resolve() / 'visualization' / 'FOV_clusters.gif'),
+            frameRate=10.0,
+            loop=0,
+        )