Merge pull request #4 from MilagrosMarin/main

Fix deprecated library to run KPMS + add `task_mode` + complete functionality of the tutorial in codespaces

Author: ttngu207

.devcontainer/Dockerfile

@@ -31,7 +31,9 @@ COPY ./ /tmp/element-moseq/
 
 RUN \
     # pipeline dependencies
-    apt-get install gcc g++ ffmpeg libsm6 libxext6 libgl1 libegl1 -y && \
+    apt-get update && \
+    apt-get install -y gcc ffmpeg graphviz && \
+    pip install ipywidgets && \
     pip install --no-cache-dir -e /tmp/element-moseq[elements,tests] && \
     # clean up
     rm -rf /tmp/element-moseq/ && \
@@ -52,4 +54,4 @@ ENV DATABASE_PREFIX neuro_
 USER vscode
 CMD bash -c "sudo rm /var/run/docker.pid; sudo dockerd"
 
-ENV LD_LIBRARY_PATH="/lib:/opt/conda/lib"
+ENV LD_LIBRARY_PATH="/lib:/opt/conda/lib"

.devcontainer/docker-compose.yaml

@@ -6,7 +6,7 @@ services:
     build:
       context: ..
       dockerfile: ./.devcontainer/Dockerfile
-    # image: datajoint/element_moseq:latest
+    #image: datajoint/element_moseq:latest
     extra_hosts:
       - fakeservices.datajoint.io:127.0.0.1
     environment:
@@ -23,3 +23,4 @@ services:
     privileged: true # only because of dind
 volumes:
   docker_data:
+

CHANGELOG.md

@@ -3,6 +3,14 @@
 Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and 
 [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) convention.
 
+## [0.2.0] - 2024-08-16
++ Add - `load` functions and new secondary attributes for tutorial purposes
++ Add - `outbox` results in the public s3 bucket to be mounted in Codespaces
++ Update - tutorial content
++ Fix - `scipy.linalg` deprecation in latest release by adjusting version in `setup.py`
++ Update -  `pre_kappa` and `full_kappa` to integer to simplify equality comparisons
++ Update - `images` of the pipeline 
+
 ## [0.1.1] - 2024-03-21
 
 + Update - Schemas and tables renaming

element_moseq/moseq_infer.py

@@ -58,7 +58,7 @@ def activate(
     )
 
 
-# -------------- Functions required by the element-moseq ---------------
+# -------------- Functions required by element-moseq ---------------
 
 
 def get_kpms_root_data_dir() -> list:
@@ -87,7 +87,7 @@ def get_kpms_processed_data_dir() -> Optional[str]:
 
     Method in parent namespace should provide a string to a directory where KPMS output
     files will be stored. If unspecified, output files will be stored in the
-    session directory 'videos' folder, per DeepLabCut default.
+    session directory 'videos' folder, per Keypoint-MoSeq default.
     """
     if hasattr(_linking_module, "get_kpms_processed_data_dir"):
         return _linking_module.get_kpms_processed_data_dir()
@@ -197,14 +197,15 @@ class InferenceTask(dj.Manual):
     """
 
     definition = """
-    -> VideoRecording                              # `VideoRecording` key
-    -> Model                                       # `Model` key 
+    -> VideoRecording                                       # `VideoRecording` key
+    -> Model                                                # `Model` key 
     ---
-    -> PoseEstimationMethod                        # Pose estimation method used for the specified `recording_id`
-    keypointset_dir               : varchar(1000)  # Keypointset directory for the specified VideoRecording
-    inference_output_dir=''       : varchar(1000)  # Optional. Sub-directory where the results will be stored
-    inference_desc=''             : varchar(1000)  # Optional. User-defined description of the inference task
-    num_iterations=NULL           : int            # Optional. Number of iterations to use for the model inference. If null, the default number internally is 50.
+    -> PoseEstimationMethod                                 # Pose estimation method used for the specified `recording_id`
+    keypointset_dir               : varchar(1000)           # Keypointset directory for the specified VideoRecording
+    inference_output_dir=''       : varchar(1000)           # Optional. Sub-directory where the results will be stored
+    inference_desc=''             : varchar(1000)           # Optional. User-defined description of the inference task
+    num_iterations=NULL           : int                     # Optional. Number of iterations to use for the model inference. If null, the default number internally is 50.
+    task_mode='load'              : enum('load', 'trigger') # Task mode for the inference task
     """
 
 
@@ -305,12 +306,14 @@ def make(self, key):
             num_iterations,
             model_id,
             pose_estimation_method,
+            task_mode,
         ) = (InferenceTask & key).fetch1(
             "keypointset_dir",
             "inference_output_dir",
             "num_iterations",
             "model_id",
             "pose_estimation_method",
+            "task_mode",
         )
 
         kpms_root = get_kpms_root_data_dir()
@@ -322,7 +325,7 @@ def make(self, key):
         )
         keypointset_dir = find_full_path(kpms_root, keypointset_dir)
 
-        inference_output_dir = model_dir / inference_output_dir
+        inference_output_dir = os.path.join(model_dir, inference_output_dir)
 
         if not os.path.exists(inference_output_dir):
             os.makedirs(model_dir / inference_output_dir)
@@ -366,55 +369,98 @@ def make(self, key):
                 f"No valid `kpms_dj_config` found in the parent model directory {model_dir.parent}"
             )
 
-        start_time = datetime.utcnow()
-        results = apply_model(
-            model=model,
-            data=data,
-            metadata=metadata,
-            pca=pca,
-            project_dir=model_dir.parent.as_posix(),
-            model_name=Path(model_dir).name,
-            results_path=(inference_output_dir / "results.h5").as_posix(),
-            return_model=False,
-            num_iters=num_iterations
-            or 50.0,  # default internal value in the keypoint-moseq function
-            **kpms_dj_config,
-        )
-        end_time = datetime.utcnow()
+        if task_mode == "trigger":
+            start_time = datetime.utcnow()
+            results = apply_model(
+                model=model,
+                data=data,
+                metadata=metadata,
+                pca=pca,
+                project_dir=model_dir.parent.as_posix(),
+                model_name=Path(model_dir).name,
+                results_path=(inference_output_dir / "results.h5").as_posix(),
+                return_model=False,
+                num_iters=num_iterations
+                or 50,  # default internal value in the keypoint-moseq function
+                **kpms_dj_config,
+            )
+            end_time = datetime.utcnow()
 
-        duration_seconds = (end_time - start_time).total_seconds()
+            duration_seconds = (end_time - start_time).total_seconds()
 
-        save_results_as_csv(
-            results=results,
-            save_dir=(inference_output_dir / "results_as_csv").as_posix(),
-        )
+            save_results_as_csv(
+                results=results,
+                save_dir=(inference_output_dir / "results_as_csv").as_posix(),
+            )
 
-        fig, _ = plot_syllable_frequencies(
-            results=results, path=inference_output_dir.as_posix()
-        )
-        fig.savefig(inference_output_dir / "syllable_frequencies.png")
-        plt.close(fig)
-
-        generate_trajectory_plots(
-            coordinates=coordinates,
-            results=results,
-            output_dir=(inference_output_dir / "trajectory_plots").as_posix(),
-            **kpms_dj_config,
-        )
+            fig, _ = plot_syllable_frequencies(
+                results=results, path=inference_output_dir.as_posix()
+            )
+            fig.savefig(inference_output_dir / "syllable_frequencies.png")
+            plt.close(fig)
+
+            generate_trajectory_plots(
+                coordinates=coordinates,
+                results=results,
+                output_dir=(inference_output_dir / "trajectory_plots").as_posix(),
+                **kpms_dj_config,
+            )
 
-        sampled_instances = generate_grid_movies(
-            coordinates=coordinates,
-            results=results,
-            output_dir=(inference_output_dir / "grid_movies").as_posix(),
-            **kpms_dj_config,
-        )
+            sampled_instances = generate_grid_movies(
+                coordinates=coordinates,
+                results=results,
+                output_dir=(inference_output_dir / "grid_movies").as_posix(),
+                **kpms_dj_config,
+            )
 
-        plot_similarity_dendrogram(
-            coordinates=coordinates,
-            results=results,
-            save_path=(inference_output_dir / "similarity_dendogram").as_posix(),
-            **kpms_dj_config,
-        )
+            plot_similarity_dendrogram(
+                coordinates=coordinates,
+                results=results,
+                save_path=(inference_output_dir / "similarity_dendogram").as_posix(),
+                **kpms_dj_config,
+            )
+
+        else:
+            from keypoint_moseq import (
+                load_results,
+                filter_centroids_headings,
+                get_syllable_instances,
+                sample_instances,
+            )
+
+            # load results
+            results = load_results(
+                project_dir=Path(inference_output_dir).parent,
+                model_name=Path(inference_output_dir).parts[-1],
+            )
+
+            # extract sampled_instances
+            ## extract syllables from results
+            syllables = {k: v["syllable"] for k, v in results.items()}
+
+            ## extract and smooth centroids and headings
+            centroids = {k: v["centroid"] for k, v in results.items()}
+            headings = {k: v["heading"] for k, v in results.items()}
+
+            filter_size = 9  # default value
+            centroids, headings = filter_centroids_headings(
+                centroids, headings, filter_size=filter_size
+            )
+
+            # sample instances for each syllable
+            syllable_instances = get_syllable_instances(
+                syllables, min_duration=3, min_frequency=0.005
+            )
+
+            sampled_instances = sample_instances(
+                syllable_instances=syllable_instances,
+                num_samples=4 * 6,  # minimum rows * cols
+                coordinates=coordinates,
+                centroids=centroids,
+                headings=headings,
+            )
+
+            duration_seconds = None
 
         self.insert1({**key, "inference_duration": duration_seconds})