{
"cells": [
{
"cell_type": "markdown",
"id": "5a232aef",
"metadata": {},
"source": [
"# Trajectory Preprocessing\n",
"\n",
"**The following notebook illustrate the `TrajPreprocessing` class how to run the preprocessing steps.**\n",
"- **Several preprocessing steps are available for the tail angle**:\n",
" - Interpolating missing values\n",
" - Apply 1€ filter\n",
"\n",
"- **The kinematic vigor is also computed from the speed and will also be useful for segmentation into bouts**:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Loading dependencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from megabouts.tracking_data import TrackingConfig, FullTrackingData, load_example_data\n",
"from megabouts.config import TrajPreprocessingConfig\n",
"from megabouts.preprocessing import TrajPreprocessing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* TrackingConfig and TrackingData similar to [tutorial_Loading_Data](./Loading_Data.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Load data and set tracking configuration\n",
"df_recording, fps, mm_per_unit = load_example_data(\"SLEAP_fulltracking\")\n",
"tracking_cfg = TrackingConfig(fps=fps, tracking=\"full_tracking\")\n",
"\n",
"# List of keypoints\n",
"keypoints = [\"left_eye\", \"right_eye\", \"tail0\", \"tail1\", \"tail2\", \"tail3\", \"tail4\"]\n",
"\n",
"# Place NaN where the score is below a threshold\n",
"thresh_score = 0.0\n",
"for kps in keypoints:\n",
" score_below_thresh = df_recording[\"instance.score\"] < thresh_score\n",
" df_recording.loc[\n",
" score_below_thresh | (df_recording[f\"{kps}.score\"] < thresh_score),\n",
" [f\"{kps}.x\", f\"{kps}.y\"],\n",
" ] = np.nan\n",
"\n",
"# Compute head and tail coordinates and convert to mm\n",
"head_x = ((df_recording[\"left_eye.x\"] + df_recording[\"right_eye.x\"]) / 2) * mm_per_unit\n",
"head_y = ((df_recording[\"left_eye.y\"] + df_recording[\"right_eye.y\"]) / 2) * mm_per_unit\n",
"tail_x = df_recording[[f\"tail{i}.x\" for i in range(5)]].values * mm_per_unit\n",
"tail_y = df_recording[[f\"tail{i}.y\" for i in range(5)]].values * mm_per_unit\n",
"\n",
"# Create FullTrackingData object\n",
"tracking_data = FullTrackingData.from_keypoints(\n",
" head_x=head_x.values, head_y=head_y.values, tail_x=tail_x, tail_y=tail_y\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run Preprocessing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Define preprocessing config"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"traj_preprocessing_cfg = TrajPreprocessingConfig(fps=tracking_cfg.fps)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Apply the trajectory preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"traj_df_input = tracking_data.traj_df\n",
"traj = TrajPreprocessing(traj_preprocessing_cfg).preprocess_traj_df(traj_df_input)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* traj.df contains information about the trajectory, the smooth values as well as the kinematic vigor:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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