From 2c977b3e1b3bd9344cbc69bc62a02acfad00a07b Mon Sep 17 00:00:00 2001 From: joshuahwu Date: Tue, 25 Jul 2023 22:28:32 -0400 Subject: [PATCH] added tutorials, contributing.md, codeowner, data folder, results folder --- .github/CODEOWNERS | 1 + .gitignore | 7 +- CONTRIBUTING.md | 0 configs/param_configs/fitsne.yaml | 26 - configs/tutorial.yaml | 20 +- data/demo_meta.csv | 3 + environment.yml | 2 +- src/dappy/DataStruct.py | 5 +- src/dappy/analysis.py | 121 +++- src/dappy/preprocess.py | 3 +- src/dappy/read.py | 15 +- src/dappy/utils.py | 12 +- src/dappy/visualization.py | 9 +- .../run.py => tutorials/automated_pipeline.py | 2 +- tutorials/tutorial.ipynb | 515 ++++++++++++++---- 15 files changed, 554 insertions(+), 187 deletions(-) create mode 100644 .github/CODEOWNERS create mode 100644 CONTRIBUTING.md delete mode 100644 configs/param_configs/fitsne.yaml create mode 100644 data/demo_meta.csv rename src/dappy/run.py => tutorials/automated_pipeline.py (98%) diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 0000000..bde2deb --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1 @@ +# * @joshuahwu diff --git a/.gitignore b/.gitignore index ac1bcfd..999308f 100644 --- a/.gitignore +++ b/.gitignore @@ -9,7 +9,6 @@ Desktop.ini # Recycle Bin used on file shares $RECYCLE.BIN/ -*.mat *.sh *.out *.p @@ -22,22 +21,28 @@ $RECYCLE.BIN/ *.txt *.mat +*.h5 *.mp4 *.yaml *.png *.pyc *./dappy/__pycache__/ +!/data/demo_meta.csv !/tutorials/ /tutorials/* !/tutorials/tutorial.ipynb +!/tutorials/automated_pipeline.py /configs/* +/configs/param_configs/ +/configs/param_configs/* !/configs/tutorial.yaml /dappy/wandb/ /dappy/models/ /dappy/artifcats/ +/results/* *.egg-info # Windows shortcuts diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..e69de29 diff --git a/configs/param_configs/fitsne.yaml b/configs/param_configs/fitsne.yaml deleted file mode 100644 index d5753b9..0000000 --- a/configs/param_configs/fitsne.yaml +++ /dev/null @@ -1,26 +0,0 @@ -label: 'fitsne' - -analysis: 'embed' - -downsample: 10 - -column: 'Condition' -density_by_column: ['label'] - -filter_still: False - -single_embed: - method: 'fitsne' - perplexity: 50 - lr: 'auto' - sigma: 15 - -transform_embed: - method: 'knn' - k: 5 - sigma: 15 - -skeleton_vids: False - -save_embedder: True -load_embedder: null \ No newline at end of file diff --git a/configs/tutorial.yaml b/configs/tutorial.yaml index 692acd5..7d0b834 100644 --- a/configs/tutorial.yaml +++ b/configs/tutorial.yaml @@ -1,36 +1,26 @@ # Folder path of data location -data_path: '/home/exx/Desktop/GitHub/CAPTURE_data/ensemble_healthy/' - -# File path of predictions file -pose_path: '/home/exx/Desktop/GitHub/CAPTURE_data/ensemble_healthy/predictions.mat' - -# File path of metadata file -meta_path: '/home/exx/Desktop/GitHub/CAPTURE_data/ensemble_healthy/metadata.csv' +data_path: '../data/' # Output folder of all plots -out_path: '/home/exx/Desktop/GitHub/results/ensemble_healthy/' - -# Path of list of behavior heuristics -heuristics_path: '/home/exx/Desktop/GitHub/dappy/src/dappy/behavior_heuristics.py' +out_path: '../results/tutorial/' # File path of skeletal specifications -skeleton_path: '/home/exx/Desktop/GitHub/dappy/src/dappy/skeletons.py' +skeleton_path: '../src/dappy/skeletons.py' # Key of skeleton in the skeletons file skeleton_name: 'mouse20_notail' label: 'fitsne' - -analysis: 'embed' - downsample: 10 +# Parameters for t-SNE embedding single_embed: method: 'fitsne' perplexity: 50 lr: 'auto' sigma: 15 +# Parameters for embedding new data into an existing t-SNE embedding transform_embed: method: 'knn' k: 5 diff --git a/data/demo_meta.csv b/data/demo_meta.csv new file mode 100644 index 0000000..b98a363 --- /dev/null +++ b/data/demo_meta.csv @@ -0,0 +1,3 @@ +AnimalID,Sex,Strain,Condition +A0,Male,Adora2a-Cre,Baseline +A1,Female,Adora2a-Cre,Baseline \ No newline at end of file diff --git a/environment.yml b/environment.yml index a65487e..5efe355 100644 --- a/environment.yml +++ b/environment.yml @@ -7,7 +7,7 @@ channels: dependencies: - python=3.8 - numpy - - faiss-gpu=1.6.5 + - faiss-gpu=1.7.1 - matplotlib - seaborn - hdf5storage diff --git a/src/dappy/DataStruct.py b/src/dappy/DataStruct.py index e631b91..21452f3 100644 --- a/src/dappy/DataStruct.py +++ b/src/dappy/DataStruct.py @@ -163,7 +163,7 @@ def __init__( joint_names: List[str], colors: Union[np.ndarray, List[Tuple[float, float, float, float]]], links: Union[np.ndarray, List[Tuple[int, int]]], - angles: Union[np.ndarray, List[Tuple[int, int, int]]], + angles: Optional[Union[np.ndarray, List[Tuple[int, int, int]]]] = None, ): """Initializes instance of Connectivity class @@ -183,7 +183,8 @@ def __init__( self.joint_names = joint_names self.colors = self._check_type(colors, np.float32) self.links = self._check_type(links, np.uint16) - self.angles = self._check_type(angles, np.uint16) + if angles is not None: + self.angles = self._check_type(angles, np.uint16) def _check_type( self, diff --git a/src/dappy/analysis.py b/src/dappy/analysis.py index 8a3b25a..36b5da4 100644 --- a/src/dappy/analysis.py +++ b/src/dappy/analysis.py @@ -12,9 +12,92 @@ from sklearn.ensemble import RandomForestRegressor import seaborn as sns from dappy.embed import Watershed - +import faiss +import time +from scipy.sparse import csr_matrix +from scipy.sparse.csgraph import dijkstra, minimum_spanning_tree from scipy.spatial import distance +def get_nn_graph(X: np.ndarray, k: int = 5, weighted: bool = True): + X = np.ascontiguousarray(X, dtype=np.float32) + + # max_k = 20 + print("Building NN Graph") + start_time = time.time() + index = faiss.IndexFlatL2(X.shape[1]) + index.add(X) + distances, indices = index.search(X, k=k+1) + distances, indices = distances[:, 1:], indices[:, 1:] + row = np.tile(np.arange(X.shape[0])[:, None], k) + + # min_distances, min_indices = distances[:, :k], indices[:,:k] + # min_row = row = np.tile(np.arange(X.shape[0])[:, None], k) + if weighted: + nn_graph = csr_matrix( + (distances.flatten(), (row.flatten(), indices.flatten())), + shape=(X.shape[0], X.shape[0]), + ) + + # min_graph = csr_matrix( + # (min_distances.flatten(), (min_row.flatten(), min_indices.flatten())), + # shape=(X.shape[0], X.shape[0]), + # ) + else: + nn_graph = csr_matrix( + (np.ones(distances.flatten().shape), (row.flatten(), indices.flatten())), + shape=(X.shape[0], X.shape[0]), + ) + # min_graph = csr_matrix( + # (np.ones(min_distances.flatten()), (min_row.flatten(), min_indices.flatten())), + # shape=(X.shape[0], X.shape[0]), + # ) + + print("NN Time: " + str(time.time() - start_time)) + + # # Get minimum spanning tree to ensure full connectivity in graph + # start_time = time.time() + # min_span_tree = minimum_spanning_tree(nn_graph) + # min_span_tree.data = min_span_tree.data.astype(X.dtype) + # print("Minimum Spanning Tree Time: " + str(time.time() - start_time)) + + # # Get union between minimum spanning tree and nn graph + # min_span_tree_insert = min_span_tree - nn_graph + # min_span_tree_insert.data = np.where(min_span_tree_insert.data < 0, 1, 0) + # graph = ( + # min_span_tree + # - min_span_tree.multiply(min_span_tree_insert) + # + nn_graph.multiply(min_span_tree_insert) + # ) + + return nn_graph + + +def get_pose_geodesic( + pose: np.ndarray, + graph: csr_matrix, + START_FRAME: int, + END_FRAME: int, +): + print("Calculating Dijkstra") + path_indices = dijkstra( + csgraph=graph, directed=False, indices=END_FRAME, return_predecessors=True + )[1] + + print("Finding pose geodesic") + geodesic_pose, geodesic_indices = [], [] + curr_frame = START_FRAME + + while path_indices[curr_frame] > 0: + geodesic_pose += [pose[curr_frame : curr_frame + 1, ...]] + geodesic_indices += [curr_frame] + curr_frame = path_indices[curr_frame] + + if curr_frame != END_FRAME: + print("Broken graph") + + geodesic_pose = np.concatenate(geodesic_pose, axis=0) + + return geodesic_pose, geodesic_indices def cluster_freq_from_data(data: np.ndarray, watershed: Watershed): """ @@ -63,12 +146,6 @@ def lstsq(freq: np.ndarray, y: np.ndarray, filepath: str): m = np.linalg.lstsq(np.delete(freq, i, axis=0), np.delete(y, i))[0] pred_y[i] = freq[i, :] @ m - plt.scatter(y, pred_y) - plt.xlabel("Real Fluorescence") - plt.ylabel("Predicted Fluorescence") - plt.savefig("".join([filepath, "lstsq.png"])) - plt.close() - print("R2 Score " + str(r2_score(y, pred_y))) return pred_y @@ -85,16 +162,16 @@ def elastic_net(freq: np.ndarray, y: np.ndarray, filepath: str): regr.fit(scaler.transform(temp_lesion), np.log2(np.delete(y, i))) pred_y[i] = regr.predict(scaler.transform(freq[i, :][None, :])) - sns.set(rc={'figure.figsize':(6,5)}) - f = plt.figure() - # import pdb; pdb.set_trace() - plt.plot(np.linspace(y.min(), y.max(), 100), np.linspace(y.min(),y.max(),100), markersize=0, color='k', label="y = x") - plt.legend(loc="upper center") - plt.scatter(y, 2**pred_y, s=30) - plt.xlabel("Real Fluorescence") - plt.ylabel("Predicted Fluorescence") - plt.savefig("".join([filepath, "elastic.png"])) - plt.close() + # sns.set(rc={'figure.figsize':(6,5)}) + # f = plt.figure() + # # import pdb; pdb.set_trace() + # plt.plot(np.linspace(y.min(), y.max(), 100), np.linspace(y.min(),y.max(),100), markersize=0, color='k', label="y = x") + # plt.legend(loc="upper center") + # plt.scatter(y, 2**pred_y, s=30) + # plt.xlabel("Real Fluorescence") + # plt.ylabel("Predicted Fluorescence") + # plt.savefig("".join([filepath, "elastic.png"])) + # plt.close() print("R2 Score " + str(r2_score(y, 2**pred_y))) return pred_y, @@ -148,11 +225,11 @@ def random_forest(freq: np.ndarray, y: np.ndarray, filepath: str): rf_regr.fit(np.delete(freq, i, axis=0), np.delete(y, i)) pred_y[i] = rf_regr.predict(freq[i, :][None, :]) - plt.scatter(y, pred_y) - plt.xlabel("Real Fluorescence") - plt.ylabel("Predicted Fluorescence") - plt.savefig("".join([filepath, "rforest.png"])) - plt.close() + # plt.scatter(y, pred_y) + # plt.xlabel("Real Fluorescence") + # plt.ylabel("Predicted Fluorescence") + # plt.savefig("".join([filepath, "rforest.png"])) + # plt.close() print("R2 Score " + str(r2_score(y, pred_y))) return diff --git a/src/dappy/preprocess.py b/src/dappy/preprocess.py index 414aa91..fbf0787 100644 --- a/src/dappy/preprocess.py +++ b/src/dappy/preprocess.py @@ -220,8 +220,7 @@ def anipose_med_filt( for _, i in enumerate(tqdm(np.unique(exp_id))): pose_exp = pose[exp_id == i, :, :] - # dxyz = get_frame_diff(pose_exp, time=1, idx_center=False) - # vel = + pose_error = pose_exp - scp_ndi.median_filter( pose_exp, (filter_len, 1, 1) ) # Median filter 5 frames repeat the ends of video diff --git a/src/dappy/read.py b/src/dappy/read.py index bb8ddb3..f744f21 100644 --- a/src/dappy/read.py +++ b/src/dappy/read.py @@ -96,7 +96,7 @@ def pose_mat( path: str, connectivity: Connectivity, dtype: Optional[Type[Union[np.float64, np.float32]]] = np.float32, -): +) -> np.ndarray: ## TODO: Use output docstrings """Reads 3D pose data from .mat files. @@ -183,6 +183,19 @@ def connectivity(path: str, skeleton_name: str): return connectivity +def connectivity_config(path: str): + skeleton_config = config(path) + + joint_names = skeleton_config["LABELS"] + colors = skeleton_config["COLORS"] + links = skeleton_config["SEGMENTS"] + + connectivity = Connectivity( + joint_names=joint_names, colors=colors, links=links + ) + + return connectivity + def features_h5( path, dtype: Optional[Type[Union[np.float64, np.float32]]] = np.float32 diff --git a/src/dappy/utils.py b/src/dappy/utils.py index 76bb1f0..481b597 100644 --- a/src/dappy/utils.py +++ b/src/dappy/utils.py @@ -3,15 +3,18 @@ import numpy as np from typing import Union, List + def by_id(func): @functools.wraps(func) - def wrapper(pose: np.ndarray, ids:Union[np.ndarray, List], **kwargs): + def wrapper(pose: np.ndarray, ids: Union[np.ndarray, List], **kwargs): for _, i in enumerate(tqdm(np.unique(ids))): - pose_exp = pose[ids == i,:,:] - pose[ids == i ,:,:] = func(pose_exp, **kwargs) + pose_exp = pose[ids == i, :, :] + pose[ids == i, :, :] = func(pose_exp, **kwargs) return pose + return wrapper + def rolling_window(data:np.ndarray, window:int): """ Returns a view of data windowed (data.shape, window) @@ -36,6 +39,7 @@ def rolling_window(data:np.ndarray, window:int): np.lib.stride_tricks.as_strided(d_pad, shape=shape, strides=strides), 0, 1 ) + def get_frame_diff(x: np.ndarray, time: int, idx_center: bool = True): """ IN: @@ -81,4 +85,4 @@ def standard_scale(features, clip=None): features = np.clip(features / feat_std[feat_std != 0], -clip, clip) labels = [label for i, label in enumerate(labels) if feat_std[i] != 0] - return features, labels \ No newline at end of file + return features, labels diff --git a/src/dappy/visualization.py b/src/dappy/visualization.py index 3b3fe6d..c72cb63 100644 --- a/src/dappy/visualization.py +++ b/src/dappy/visualization.py @@ -910,6 +910,9 @@ def pose3D_arena( VID_NAME: str = "0.mp4", SAVE_ROOT: str = "./test/pose_vids/", ): + if isinstance(frames, int): + frames = [frames] + pose_3d, limits, links, COLORS = _init_vid3D( pose, connectivity, np.array(frames,dtype=int), centered, N_FRAMES, SAVE_ROOT ) @@ -983,6 +986,8 @@ def pose3D_grid( VID_NAME: str = "0.mp4", SAVE_ROOT: str = "./test/pose_vids/", ): + if isinstance(frames, int): + frames = [frames] # Reshape pose and other variables pose_3d, limits, links, COLOR = _init_vid3D( pose, connectivity, np.array(frames,dtype=int), centered, N_FRAMES, SAVE_ROOT @@ -992,7 +997,7 @@ def pose3D_grid( writer = FFMpegWriter(fps=fps) # Set up figure cols = min(4, len(frames)) - rows = int(len(frames) / 4) + rows = int(len(frames) / 4) + 1 figsize = (cols * 5, rows * 5) fig = plt.figure(figsize=figsize) @@ -1041,6 +1046,8 @@ def pose3D_features( VID_NAME: str = "0.mp4", SAVE_ROOT: str = "./test/skeleton_vids/", ): + if isinstance(frames, int): + frames = [frames] # Reshape pose and other variables pose_3d, limits, links_expand, COLOR = _init_vid3D( pose, connectivity, frames, N_FRAMES, SAVE_ROOT diff --git a/src/dappy/run.py b/tutorials/automated_pipeline.py similarity index 98% rename from src/dappy/run.py rename to tutorials/automated_pipeline.py index 262a2f7..387a366 100644 --- a/src/dappy/run.py +++ b/tutorials/automated_pipeline.py @@ -5,7 +5,7 @@ from dappy.embed import Watershed, Embed from pathlib import Path - +#TODO: Probably be like a demo/notebook, don't maintain this def standard_features( pose, connectivity, diff --git a/tutorials/tutorial.ipynb b/tutorials/tutorial.ipynb index 98d3ab8..5d1f9ea 100644 --- a/tutorials/tutorial.ipynb +++ b/tutorials/tutorial.ipynb @@ -7,10 +7,10 @@ "source": [ "## Unsupervised Behavioral Phenotyping with 3D Skeletal Pose\n", "Joshua Wu\n", - "\n", + "Duke University Biomedical Engineering\n", "Timothy Dunn Lab\n", "\n", - "14 October 2022" + "25 July, 2023" ] }, { @@ -26,7 +26,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook implements a Python version of [CAPTURE (Marshall, 2020)](https://www.cell.com/neuron/fulltext/S0896-6273(20)30894-1?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0896627320308941%3Fshowall%3Dtrue), which was based on earlier work [MotionMapper (Berman, 2014)](https://royalsocietypublishing.org/doi/full/10.1098/rsif.2014.0672) for the analysis of behavioral data, which can interface with future frameworks." + "This notebook implements a Python version of [CAPTURE (Marshall, 2020)](https://www.cell.com/neuron/fulltext/S0896-6273(20)30894-1?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0896627320308941%3Fshowall%3Dtrue), which was based on earlier work [MotionMapper (Berman, 2014)](https://royalsocietypublishing.org/doi/full/10.1098/rsif.2014.0672) for the analysis of behavioral data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get this notebook to run, please download the [demo dataset](https://duke.box.com/v/demo-mouse-poses) into the `/dappy/data/` directory." ] }, { @@ -39,17 +46,17 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ - "from dappy import features, read, write\n", - "import dappy.DataStruct as ds\n", - "import dappy.visualization as vis\n", + "from dappy import read, write\n", + "from dappy import visualization as vis\n", "import numpy as np\n", "import time\n", "from IPython.display import Video\n", - "from dappy.embed import Watershed, Embed\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, @@ -58,119 +65,299 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Load pose predictions and keypoint connectivity information" + "Load pose predictions, keypoint connectivity information, and metadata." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "analysis_key = \"tutorial\"\n", - "config = read.config(\"../../configs/\" + analysis_key + \".yaml\")\n", + "config = read.config(\"../configs/\" + analysis_key + \".yaml\")\n", "\n", - "pose, ids = read.pose_h5(config[\"data_path\"] + \"pose_aligned.h5\")\n", + "pose, ids = read.pose_h5(config[\"data_path\"] + \"demo_mouse.h5\")\n", "\n", "connectivity = read.connectivity(\n", " path=config[\"skeleton_path\"], skeleton_name=config[\"skeleton_name\"]\n", ")\n", "\n", - "meta, meta_by_frame = read.meta(config[\"meta_path\"], id=ids)" + "meta, meta_by_frame = read.meta(config[\"data_path\"] + \"demo_meta.csv\", id=ids)\n", + "\n", + "Path(config[\"out_path\"]).mkdir(parents=True, exist_ok=True)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Plot some skeletons together" + "`pose` shape (# frames x # keypoints x 3 coordinates)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pose shape (# frames x # keypoints x 3 coordinates): \n", + "(648000, 18, 3)\n" + ] + } + ], + "source": [ + "print(\"Pose shape (# frames x # keypoints x 3 coordinates): \")\n", + "print(pose.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`meta` contains categorical information on recording sessions in `pose`. Here, we have loaded in two sessions. Each frame of the `pose` has a session id label in `ids`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id AnimalID Sex Strain Condition\n", + "0 0 A0 Male Adora2a-Cre Baseline\n", + "1 1 A1 Female Adora2a-Cre Baseline\n", + "\n", + "[0 0 0 ... 1 1 1]\n" + ] + } + ], + "source": [ + "print(meta)\n", + "print(\"\\n\" + str(ids))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`connectivity` contains key information indicating keypoint labels, connectivity, etc." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "keypoint labels\n", + "['Snout', 'EarR', 'EarL', 'SpineF', 'SpineM', 'Tail_base_', 'Forepaw_R', 'Wrist_R', 'ForeLimb_R', 'Forepaw_L', 'Wrist_L', 'Forelimb_L', 'Hindpaw_R', 'Ankel_R', 'Hindlimb_R', 'Hindpaw_L', 'Ankel_L', 'Hindlimb_L']\n", + "\n", + " Keypoint connections\n", + "[[ 0 1]\n", + " [ 1 3]\n", + " [ 0 2]\n", + " [ 2 3]\n", + " [ 2 1]\n", + " [ 0 3]\n", + " [ 4 3]\n", + " [ 5 4]\n", + " [ 6 7]\n", + " [ 7 8]\n", + " [ 8 3]\n", + " [ 9 10]\n", + " [10 11]\n", + " [11 3]\n", + " [12 13]\n", + " [13 14]\n", + " [14 5]\n", + " [15 16]\n", + " [16 17]\n", + " [17 5]]\n" + ] + } + ], + "source": [ + "print(\"keypoint labels\")\n", + "print(connectivity.joint_names)\n", + "print(\"\\n Keypoint connections\")\n", + "print(connectivity.links)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot 150 frames from each session." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/200 [00:00 1\u001b[0m vis\u001b[39m.\u001b[39;49mskeleton_vid3D(\n\u001b[1;32m 2\u001b[0m pose,\n\u001b[1;32m 3\u001b[0m connectivity,\n\u001b[1;32m 4\u001b[0m frames\u001b[39m=\u001b[39;49m[\u001b[39m1000\u001b[39;49m, \u001b[39m500000\u001b[39;49m, \u001b[39m200000\u001b[39;49m],\n\u001b[1;32m 5\u001b[0m N_FRAMES\u001b[39m=\u001b[39;49m\u001b[39m200\u001b[39;49m,\n\u001b[1;32m 6\u001b[0m dpi\u001b[39m=\u001b[39;49m\u001b[39m100\u001b[39;49m,\n\u001b[1;32m 7\u001b[0m VID_NAME\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mvid_raw.mp4\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m 8\u001b[0m SAVE_ROOT\u001b[39m=\u001b[39;49mconfig[\u001b[39m\"\u001b[39;49m\u001b[39mout_path\u001b[39;49m\u001b[39m\"\u001b[39;49m],\n\u001b[1;32m 9\u001b[0m )\n\u001b[1;32m 11\u001b[0m Video(config[\u001b[39m\"\u001b[39m\u001b[39mout_path\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mvis_vid_raw.mp4\u001b[39m\u001b[39m\"\u001b[39m, width\u001b[39m=\u001b[39m\u001b[39m600\u001b[39m, height\u001b[39m=\u001b[39m\u001b[39m600\u001b[39m)\n", - "File \u001b[0;32m/hpc/group/tdunn/joshwu/dappy/src/dappy/visualization.py:891\u001b[0m, in \u001b[0;36mskeleton_vid3D\u001b[0;34m(pose, connectivity, frames, N_FRAMES, fps, dpi, VID_NAME, SAVE_ROOT)\u001b[0m\n\u001b[1;32m 886\u001b[0m \u001b[39mfor\u001b[39;00m color, (index_from, index_to) \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(COLOR, links_expand):\n\u001b[1;32m 887\u001b[0m xs, ys, zs \u001b[39m=\u001b[39m [\n\u001b[1;32m 888\u001b[0m np\u001b[39m.\u001b[39marray([kpts_3d[index_from, j], kpts_3d[index_to, j]])\n\u001b[1;32m 889\u001b[0m \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m3\u001b[39m)\n\u001b[1;32m 890\u001b[0m ]\n\u001b[0;32m--> 891\u001b[0m ax_3d\u001b[39m.\u001b[39;49mplot3D(xs, ys, zs, c\u001b[39m=\u001b[39;49mcolor, lw\u001b[39m=\u001b[39;49m\u001b[39m2\u001b[39;49m)\n\u001b[1;32m 893\u001b[0m ax_3d\u001b[39m.\u001b[39mset_xlim(\u001b[39m*\u001b[39mlimits[\u001b[39m0\u001b[39m, :])\n\u001b[1;32m 894\u001b[0m ax_3d\u001b[39m.\u001b[39mset_ylim(\u001b[39m*\u001b[39mlimits[\u001b[39m1\u001b[39m, :])\n", - "File \u001b[0;32m/hpc/group/tdunn/joshwu/miniconda3/envs/capture/lib/python3.8/site-packages/mpl_toolkits/mplot3d/axes3d.py:1497\u001b[0m, in \u001b[0;36mAxes3D.plot\u001b[0;34m(self, xs, ys, zdir, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1494\u001b[0m \u001b[39m# Match length\u001b[39;00m\n\u001b[1;32m 1495\u001b[0m zs \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mbroadcast_to(zs, np\u001b[39m.\u001b[39mshape(xs))\n\u001b[0;32m-> 1497\u001b[0m lines \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mplot(xs, ys, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1498\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m lines:\n\u001b[1;32m 1499\u001b[0m art3d\u001b[39m.\u001b[39mline_2d_to_3d(line, zs\u001b[39m=\u001b[39mzs, zdir\u001b[39m=\u001b[39mzdir)\n", - "File \u001b[0;32m/hpc/group/tdunn/joshwu/miniconda3/envs/capture/lib/python3.8/site-packages/matplotlib/axes/_axes.py:1632\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1390\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1391\u001b[0m \u001b[39mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1392\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1629\u001b[0m \u001b[39m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1630\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1631\u001b[0m kwargs \u001b[39m=\u001b[39m cbook\u001b[39m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[39m.\u001b[39mLine2D)\n\u001b[0;32m-> 1632\u001b[0m lines \u001b[39m=\u001b[39m [\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_lines(\u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39mdata, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)]\n\u001b[1;32m 1633\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m lines:\n\u001b[1;32m 1634\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madd_line(line)\n", - "File \u001b[0;32m/hpc/group/tdunn/joshwu/miniconda3/envs/capture/lib/python3.8/site-packages/matplotlib/axes/_base.py:312\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m this \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m args[\u001b[39m0\u001b[39m],\n\u001b[1;32m 311\u001b[0m args \u001b[39m=\u001b[39m args[\u001b[39m1\u001b[39m:]\n\u001b[0;32m--> 312\u001b[0m \u001b[39myield from\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_plot_args(this, kwargs)\n", - "File \u001b[0;32m/hpc/group/tdunn/joshwu/miniconda3/envs/capture/lib/python3.8/site-packages/matplotlib/axes/_base.py:538\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mlist\u001b[39m(result)\n\u001b[1;32m 537\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 538\u001b[0m \u001b[39mreturn\u001b[39;00m [l[\u001b[39m0\u001b[39m] \u001b[39mfor\u001b[39;00m l \u001b[39min\u001b[39;00m result]\n", - "File \u001b[0;32m/hpc/group/tdunn/joshwu/miniconda3/envs/capture/lib/python3.8/site-packages/matplotlib/axes/_base.py:538\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mlist\u001b[39m(result)\n\u001b[1;32m 537\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 538\u001b[0m \u001b[39mreturn\u001b[39;00m [l[\u001b[39m0\u001b[39m] \u001b[39mfor\u001b[39;00m l \u001b[39min\u001b[39;00m result]\n", - "File \u001b[0;32m/hpc/group/tdunn/joshwu/miniconda3/envs/capture/lib/python3.8/site-packages/matplotlib/axes/_base.py:531\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 529\u001b[0m labels \u001b[39m=\u001b[39m [label] \u001b[39m*\u001b[39m n_datasets\n\u001b[0;32m--> 531\u001b[0m result \u001b[39m=\u001b[39m (make_artist(x[:, j \u001b[39m%\u001b[39;49m ncx], y[:, j \u001b[39m%\u001b[39;49m ncy], kw,\n\u001b[1;32m 532\u001b[0m {\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs, \u001b[39m'\u001b[39;49m\u001b[39mlabel\u001b[39;49m\u001b[39m'\u001b[39;49m: label})\n\u001b[1;32m 533\u001b[0m \u001b[39mfor\u001b[39;00m j, label \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(labels))\n\u001b[1;32m 535\u001b[0m \u001b[39mif\u001b[39;00m return_kwargs:\n\u001b[1;32m 536\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mlist\u001b[39m(result)\n", - "File \u001b[0;32m/hpc/group/tdunn/joshwu/miniconda3/envs/capture/lib/python3.8/site-packages/matplotlib/axes/_base.py:351\u001b[0m, in \u001b[0;36m_process_plot_var_args._makeline\u001b[0;34m(self, x, y, kw, kwargs)\u001b[0m\n\u001b[1;32m 349\u001b[0m default_dict \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getdefaults(\u001b[39mset\u001b[39m(), kw)\n\u001b[1;32m 350\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_setdefaults(default_dict, kw)\n\u001b[0;32m--> 351\u001b[0m seg \u001b[39m=\u001b[39m mlines\u001b[39m.\u001b[39;49mLine2D(x, y, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkw)\n\u001b[1;32m 352\u001b[0m \u001b[39mreturn\u001b[39;00m seg, kw\n", - "File \u001b[0;32m/hpc/group/tdunn/joshwu/miniconda3/envs/capture/lib/python3.8/site-packages/matplotlib/lines.py:370\u001b[0m, in \u001b[0;36mLine2D.__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_drawstyle(drawstyle)\n\u001b[1;32m 369\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_color \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m--> 370\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_color(color)\n\u001b[1;32m 371\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_marker \u001b[39m=\u001b[39m MarkerStyle(marker, fillstyle)\n\u001b[1;32m 373\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_markevery \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n", - "File \u001b[0;32m/hpc/group/tdunn/joshwu/miniconda3/envs/capture/lib/python3.8/site-packages/matplotlib/lines.py:1030\u001b[0m, in \u001b[0;36mLine2D.set_color\u001b[0;34m(self, color)\u001b[0m\n\u001b[1;32m 1022\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mset_color\u001b[39m(\u001b[39mself\u001b[39m, color):\n\u001b[1;32m 1023\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1024\u001b[0m \u001b[39m Set the color of the line.\u001b[39;00m\n\u001b[1;32m 1025\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1028\u001b[0m \u001b[39m color : color\u001b[39;00m\n\u001b[1;32m 1029\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1030\u001b[0m mcolors\u001b[39m.\u001b[39;49m_check_color_like(color\u001b[39m=\u001b[39;49mcolor)\n\u001b[1;32m 1031\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_color \u001b[39m=\u001b[39m color\n\u001b[1;32m 1032\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstale \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n", - "File \u001b[0;32m/hpc/group/tdunn/joshwu/miniconda3/envs/capture/lib/python3.8/site-packages/matplotlib/colors.py:130\u001b[0m, in \u001b[0;36m_check_color_like\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems():\n\u001b[1;32m 129\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m is_color_like(v):\n\u001b[0;32m--> 130\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mv\u001b[39m!r}\u001b[39;00m\u001b[39m is not a valid value for \u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", - "\u001b[0;31mValueError\u001b[0m: array([3. , 1.5651001, 0. , 1.5 ], dtype=float32) is not a valid value for color" + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 150/150 [00:27<00:00, 5.43it/s]\n" ] }, { "data": { - "image/png": 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", + "text/html": [ + "" + ], "text/plain": [ - "
" + "" ] }, + "execution_count": 18, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "vis.skeleton_vid3D(\n", + "vis.pose3D_arena(\n", " pose,\n", " connectivity,\n", - " frames=[1000, 500000, 200000],\n", - " N_FRAMES=200,\n", + " frames=[1000, 500000],\n", + " N_FRAMES=150,\n", " dpi=100,\n", - " VID_NAME=\"vid_raw.mp4\",\n", + " VID_NAME=\"raw.mp4\",\n", " SAVE_ROOT=config[\"out_path\"],\n", ")\n", "\n", - "Video(config[\"out_path\"] + \"vis_vid_raw.mp4\", width=600, height=600)" + "Video(config[\"out_path\"] + \"vis_raw.mp4\", width=600, height=600)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "All other features will be egocentric so we will center and lock the front spine onto the x-z axis by rotation." + "Skeletons across sessions may not be aligned worldviews. The following code will estimate the floor plane for each session, and rotate to the x-y plane." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/2 [00:00\n", + " Your browser does not support the video element.\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "pose = features.rotate_spine(features.center_spine(pose))\n", + "from dappy import preprocess\n", "\n", - "vis.skeleton_vid3D(\n", - " pose,\n", + "pose_aligned = preprocess.align_floor_by_id(pose=pose, ids=ids, foot_id=12, head_id=0)\n", + "\n", + "vis.pose3D_arena(\n", + " pose_aligned,\n", " connectivity,\n", - " frames=[50000],\n", - " N_FRAMES=200,\n", + " frames=[1000, 500000],\n", + " N_FRAMES=150,\n", " dpi=100,\n", - " VID_NAME=\"vid_centered.mp4\",\n", + " VID_NAME=\"aligned.mp4\",\n", " SAVE_ROOT=config[\"out_path\"],\n", - ")" + ")\n", + "\n", + "Video(config[\"out_path\"] + \"vis_aligned.mp4\", width=600, height=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use the following code to save the new aligned poses for easy access later." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "from dappy import write\n", + "\n", + "# write.pose_h5(pose_aligned, ids, config[\"data_path\"] + \"pose_aligned.h5\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this analysis, we would like to prevent divergence of behavioral representations due to global position. Thus, we will generate an egocentric representation of pose for downstream feature calculation. \n", + "\n", + "Here, we center the mid-spine to $(0,0,0)$, and rotate the front-spine to the $x+$ direction." ] }, { @@ -179,7 +366,20 @@ "metadata": {}, "outputs": [], "source": [ - "Video(config[\"out_path\"] + \"vis_vid_centered.mp4\", width=600, height=600)" + "# Provide the mid-spine and the mid-spine -> front-spine indices.\n", + "pose = features.rotate_spine(features.center_spine(pose_aligned, keypt_idx=4), keypt_idx=[4, 3])\n", + "\n", + "vis.skeleton_vid3D(\n", + " pose,\n", + " connectivity,\n", + " frames=[50000],\n", + " N_FRAMES=150,\n", + " dpi=100,\n", + " VID_NAME=\"centered.mp4\",\n", + " SAVE_ROOT=config[\"out_path\"],\n", + ")\n", + "\n", + "Video(config[\"out_path\"] + \"vis_centered.mp4\", width=600, height=600)" ] }, { @@ -187,7 +387,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Using this centered and spine-locked pose transformation, we can calculate relative velocities of all keypoints. We leave out the mid spine since it is zeroed." + "In this package, we provide functionality for easily calculating features of interest. \n", + "\n", + "Using this centered and spine-locked pose transformation, we can calculate relative velocities of all keypoints. We leave out the mid spine since it is centered." ] }, { @@ -196,6 +398,8 @@ "metadata": {}, "outputs": [], "source": [ + "from dappy import features\n", + "\n", "# Getting relative velocities\n", "rel_vel, rel_vel_labels = features.get_velocities(\n", " pose,\n", @@ -211,9 +415,53 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we calculate joint angles.\n", - "\n", - "Hopefully, informative joint angles are preselected in `skeletons.py`." + "You can also calculate joint angles of interest as specified in `skeletons.py`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1 3]\n", + " [ 0 2 3]\n", + " [ 0 3 4]\n", + " [ 1 3 4]\n", + " [ 2 3 4]\n", + " [ 3 4 5]\n", + " [ 1 3 8]\n", + " [ 2 3 8]\n", + " [ 0 3 8]\n", + " [ 3 8 7]\n", + " [ 8 7 6]\n", + " [ 1 3 11]\n", + " [ 2 3 11]\n", + " [ 0 3 11]\n", + " [ 3 11 10]\n", + " [11 10 9]\n", + " [ 4 5 14]\n", + " [ 5 14 13]\n", + " [14 13 12]\n", + " [ 4 5 17]\n", + " [ 5 17 16]\n", + " [17 16 15]\n", + " [ 0 3 6]\n", + " [ 0 3 7]\n", + " [ 0 3 9]\n", + " [ 0 3 10]\n", + " [ 4 5 12]\n", + " [ 4 5 13]\n", + " [ 4 5 15]\n", + " [ 4 5 16]]\n" + ] + } + ], + "source": [ + "print(connectivity.angles)" ] }, { @@ -231,9 +479,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, we are going to save the egocentric x, y, z coordinates as its own set of features\n", + "These velocity and angle calculations are just for demonstration, we will not use velocities or angles for the analysis in this tutorial.\n", "\n", - "This code does not calculate anything - it just reshapes the pose and generates labels for each feature." + "We will just rearrange egocentric x, y, z coordinates of each keypoint into its own set of features. This code does not calculate anything - it just reshapes the pose and generates labels for each feature." ] }, { @@ -243,7 +491,10 @@ "outputs": [], "source": [ "# Reshape pose to get egocentric pose features\n", - "ego_pose, ego_pose_labels = features.get_ego_pose(pose, connectivity.joint_names)" + "features, labels = features.get_ego_pose(pose, connectivity.joint_names)\n", + "\n", + "# Clear some memory\n", + "del angles, rel_vel, angel_labels, rel_vel_labels" ] }, { @@ -251,7 +502,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Merge features together and clear some memory" + "Write features to or read features from `.h5` file." ] }, { @@ -260,9 +511,19 @@ "metadata": {}, "outputs": [], "source": [ - "# Collect all features together\n", - "features = np.concatenate([ego_pose, angles], axis=1)\n", - "labels = ego_pose_labels + angle_labels" + "# Write\n", + "# write.features_h5(features, labels, path=config[\"out_path\"] + \"postural_feats.h5\")\n", + "\n", + "# Read\n", + "# features, labels = read.features_h5(path=config[\"out_path\"] + \"postural_feats.h5\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's now time for principal component analysis (PCA). PCA is a dimensionality reduction technique which generates orthogonal axes of high variance upon which to project our data. There are many implementations of PCA, but we will use Facebook's Fast Randomized PCA package (`fbpca`), which is significantly faster than most other implementations." ] }, { @@ -271,36 +532,52 @@ "metadata": {}, "outputs": [], "source": [ - "# Save or read kinematic/wavelet features from h5 file\n", - "write.features_h5(\n", - " features, labels, path=\"\".join([config[\"out_path\"], \"postural_feats.h5\"])\n", + "t = time.time()\n", + "pc_feats, pc_labels = features.pca(\n", + " features, labels, categories=[\"ego_euc\"], n_pcs=5, method=\"fbpca\"\n", ")\n", - "# features, labels = read_h5(path = ''.join([pstruct.out_path,'postural_feats.h5']))" + "print(\"PCA time: \" + str(time.time() - t))\n", + "\n", + "del features, labels" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "It's now time for principal component analysis (PCA). PCA is a dimensionality reduction technique which generates orthogonal axes of high variance upon which to project our data. There are many implementations of PCA, but we will use Facebook's Fast Randomized PCA package (`fbpca`), which is significantly faster than most other implementations.\n", + "Although velocities are calculated over rolling windows, the featurization we have so far still lacks the ability to capture complex temporal signals.\n", "\n", - "We calculate PCA separately on each feature category to preserve variance and balance the categories. This is in lieu of z-transforming (mean-centering and unit variance) every feature. ** Discussion" + "To address this, we can leverage the frequency domain through a Morlet wavelet transformation.\n", + "\n", + "Let's see first what a Morlet wavelet looks like." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "t = time.time()\n", - "pc_feats, pc_labels = features.pca(\n", - " features, labels, categories=[\"ego_euc\", \"ang\"], n_pcs=5, method=\"fbpca\"\n", - ")\n", - "print(\"PCA time: \" + str(time.time() - t))\n", - "\n", - "del features, labels" + "from scipy import signal\n", + "M = 100\n", + "w0 = 5\n", + "s = w0*90/(2*np.pi*25)\n", + "morlet_wavelet = signal.morlet2(M, s, w0)\n", + "plt.plot(morlet_wavelet.imag, label='Imaginary')\n", + "plt.plot(morlet_wavelet.real, label='Real')\n", + "plt.legend()\n", + "plt.show()" ] }, { @@ -319,7 +596,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We now use PCA to reduce the dimensions of the new wavelet features, and consolidate with previous PC scores." + "We now use PCA to reduce the dimensions of the new wavelet features, and consolidate with the previous PC scores. Each frame is now associated with a vector of features corresponding to the PC scores of egocentric keypoint coordinates and local frequency information." ] }, { @@ -328,13 +605,15 @@ "metadata": {}, "outputs": [], "source": [ + "# PCA on wavelet features\n", "pc_wlet, pc_wlet_labels = features.pca(\n", " wlet_feats,\n", " wlet_labels,\n", - " categories=[\"wlet_ego_euc\", \"wlet_ang\"],\n", + " categories=[\"wlet_ego_euc\"],\n", " n_pcs=5,\n", " method=\"fbpca\",\n", ")\n", + "\n", "del wlet_feats, wlet_labels\n", "pc_feats = np.hstack((pc_feats, pc_wlet))\n", "pc_labels += pc_wlet_labels\n", @@ -347,9 +626,17 @@ "metadata": {}, "outputs": [], "source": [ - "write.features_h5(\n", - " pc_feats, pc_labels, path=\"\".join([config[\"out_path\"], \"pca_feats.h5\"])\n", - ")" + "# Optionally save full PC features to file\n", + "# write.features_h5(\n", + "# pc_feats, pc_labels, path=\"\".join([config[\"out_path\"], \"pca_feats.h5\"])\n", + "# )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We encapsulate all relevant data to store in a data object." ] }, { @@ -361,16 +648,24 @@ "data_obj = ds.DataStruct(\n", " pose=pose,\n", " id=ids,\n", - " id_full=ids,\n", " meta=meta,\n", " meta_by_frame=meta_by_frame,\n", " connectivity=connectivity,\n", ")\n", "\n", "data_obj.features = pc_feats\n", + "# Downsampling data, appears to be necessary in order to \n", + "# discover granular structure in embedding\n", "data_obj = data_obj[:: config[\"downsample\"], :]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using t-SNE, frames are projected onto a 2D embedding for clustering and visualization." + ] + }, { "cell_type": "code", "execution_count": null, @@ -387,16 +682,10 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Watershed clustering\n", - "data_obj.ws = Watershed(\n", - " sigma=config[\"single_embed\"][\"sigma\"], max_clip=1, log_out=True, pad_factor=0.05\n", - ")\n", - "data_obj.data.loc[:, \"Cluster\"] = data_obj.ws.fit_predict(data=data_obj.embed_vals)" + "The histogram of the 2D embedding is smoothed with a Gaussian, and segmented by the watershed algorithm to determine cluster assignments." ] }, { @@ -405,14 +694,28 @@ "metadata": {}, "outputs": [], "source": [ + "# Watershed clustering\n", + "data_obj.ws = Watershed(\n", + " sigma=config[\"single_embed\"][\"sigma\"], max_clip=1, log_out=True, pad_factor=0.05\n", + ")\n", + "data_obj.data.loc[:, \"Cluster\"] = data_obj.ws.fit_predict(data=data_obj.embed_vals)\n", + "\n", + "# Plot density\n", "vis.density(\n", " data_obj.ws.density,\n", " data_obj.ws.borders,\n", - " filepath=\"\".join([config[\"out_path\"], config[\"label\"], \"/density.png\"]),\n", + " filepath=config[\"out_path\"] + \"/density.png\",\n", " show=True,\n", ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Within the embedding, we can visualize the density of each animal separately." + ] + }, { "cell_type": "code", "execution_count": null, @@ -421,23 +724,13 @@ "source": [ "vis.density_cat(\n", " data=data_obj,\n", - " column='id',\n", + " column=\"id\",\n", " watershed=data_obj.ws,\n", " n_col=4,\n", - " filepath=\"\".join(\n", - " [config[\"out_path\"], config[\"label\"], \"/density_id.png\"]\n", - " ),\n", - " show=True\n", + " filepath=config[\"out_path\"] + \"/density_id.png\",\n", + " show=True,\n", ")" ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Time to run the rest of the analysis" - ] } ], "metadata": { @@ -456,7 +749,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.8.12" }, "orig_nbformat": 4, "vscode": {