Update logo, favicon, and overview images (neuroinformatics-unit#632)

* update logo and favicon images

* updated overview image

Author: niksirbi

docs/source/_static/movement_favicon_512px.png

@@ -123,7 +123,7 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 html_theme = "pydata_sphinx_theme"
 html_title = "movement"
-html_logo = "_static/movement_logo_transparent_no-text.png"
+html_logo = "_static/movement_favicon_512px.png"
 html_favicon = "_static/movement_favicon_64px.png"
 # Customize the theme
 html_theme_options = {

docs/source/_static/movement_favicon_64px.png

@@ -8,6 +8,7 @@
     compute_speed,
     compute_time_derivative,
     compute_velocity,
+    detect_u_turns,
 )
 from movement.kinematics.orientation import (
     compute_forward_vector,
@@ -26,4 +27,5 @@
     "compute_forward_vector",
     "compute_head_direction_vector",
     "compute_forward_vector_angle",
+    "detect_u_turns",
 ]

docs/source/_static/movement_logo_transparent.png

@@ -13,11 +13,13 @@
 import warnings
 from typing import Literal
 
+import numpy as np
 import xarray as xr
 
+from movement.kinematics.orientation import compute_forward_vector
 from movement.utils.logging import logger
 from movement.utils.reports import report_nan_values
-from movement.utils.vector import compute_norm
+from movement.utils.vector import compute_norm, compute_signed_angle_2d
 from movement.validators.arrays import validate_dims_coords
 
 
@@ -379,3 +381,68 @@ def _compute_scaled_path_length(
     valid_proportion = valid_segments / (data.sizes["time"] - 1)
     # return scaled path length
     return compute_norm(displacement).sum(dim="time") / valid_proportion
+
+
+def detect_u_turns(
+    data: xr.DataArray,
+    use_direction: Literal["forward_vector", "displacement"] = "displacement",
+    u_turn_threshold: float = np.pi * 5 / 6,  # 150 degrees in radians
+    camera_view: Literal["top_down", "bottom_up"] = "bottom_up",
+) -> xr.DataArray:
+    """Detect U-turn behavior in a trajectory.
+
+    This function computes the directional change between consecutive time
+    frames and accumulates the rotation angles. If the accumulated angle
+    exceeds a specified threshold, a U-turn is detected.
+
+    Parameters
+    ----------
+    data : xarray.DataArray
+        The trajectory data, which must contain the 'time' and 'space' (x, y).
+    use_direction : Literal["forward_vector", "displacement"], optional
+        Method to compute direction vectors, default is `"displacement"`:
+        - `"forward_vector"`: Computes the forward direction vector.
+        - `"displacement"`: Computes displacement vectors.
+    u_turn_threshold : float, optional
+        The angle threshold (in radians) to detect U-turn. Default is (`5π/6`).
+    camera_view : Literal["top_down", "bottom_up"], optional
+        Specifies the camera perspective used for computing direction vectors.
+
+    Returns
+    -------
+    xarray.DataArray
+        A boolean scalar DataArray indicating whether a U-turn has occurred.
+
+    """
+    # Compute direction vectors
+    if use_direction == "forward_vector":
+        direction_vectors = compute_forward_vector(
+            data, "left_ear", "right_ear", camera_view=camera_view
+        )
+    elif use_direction == "displacement":
+        if "keypoints" in data.dims:
+            raise ValueError(
+                "Displacement expects single keypoint data "
+                "and must not include the 'keypoints' dimension."
+            )
+        direction_vectors = compute_displacement(data)
+    else:
+        raise ValueError(
+            "The parameter `use_direction` must be one of `forward_vector` "
+            f" or `displacement`, but got {use_direction}."
+        )
+
+    # Compute angle between vectors
+    angles = compute_signed_angle_2d(
+        direction_vectors.shift(time=1), direction_vectors
+    )
+    angles = angles.fillna(0)
+
+    # Accumulate angles over time and compute range
+    cumulative_rotation = angles.cumsum(dim="time")
+    rotation_range = cumulative_rotation.max(
+        dim="time"
+    ) - cumulative_rotation.min(dim="time")
+
+    # Return scalar boolean as xarray.DataArray
+    return xr.DataArray(rotation_range >= u_turn_threshold)

docs/source/_static/movement_logo_transparent_no-text.png

@@ -318,3 +318,71 @@ def test_path_length_nan_warn_threshold(
             position, nan_warn_threshold=nan_warn_threshold
         )
         assert result.name == "path_length"
+
+
+@pytest.fixture
+def valid_data_array_for_u_turn_detection():
+    """Return a position data array for an individual with 3 keypoints
+    (left ear, right ear, and nose), tracked for 4 frames, in x-y space.
+    """
+    time = [0, 1, 2, 3]
+    keypoints = ["left_ear", "right_ear", "nose"]
+    space = ["x", "y"]
+
+    ds = xr.DataArray(
+        [
+            [[-1, 0], [1, 0], [0, 1]],  # time 0
+            [[0, 2], [0, 0], [1, 1]],  # time 1
+            [[2, 1], [0, 1], [1, 0]],  # time 2
+            [[1, -1], [1, 1], [0, 0]],  # time 3
+        ],
+        dims=["time", "keypoints", "space"],
+        coords={
+            "time": time,
+            "keypoints": keypoints,
+            "space": space,
+        },
+    )
+    return ds
+
+
+def test_detect_u_turns(valid_data_array_for_u_turn_detection):
+    """Test that U-turn detection works correctly using a mock dataset."""
+    # Forward vector method
+    u_turn_forward_vector = kinematics.detect_u_turns(
+        valid_data_array_for_u_turn_detection, use_direction="forward_vector"
+    )
+    assert u_turn_forward_vector.item() is True
+
+    # Displacement method (nose-only)
+    nose_data = valid_data_array_for_u_turn_detection.sel(
+        keypoints="nose"
+    ).drop_vars("keypoints")
+    u_turn_displacement = kinematics.detect_u_turns(
+        nose_data, use_direction="displacement"
+    )
+    assert u_turn_displacement.item() is True
+
+    # Stricter threshold - displacement should return False
+    strict_displacement = kinematics.detect_u_turns(
+        nose_data, use_direction="displacement", u_turn_threshold=np.pi * 7 / 6
+    )
+    assert strict_displacement.item() is False
+
+    # Stricter threshold - forward vector still returns True
+    strict_forward_vector = kinematics.detect_u_turns(
+        valid_data_array_for_u_turn_detection,
+        use_direction="forward_vector",
+        u_turn_threshold=np.pi * 7 / 6,
+    )
+    assert strict_forward_vector.item() is True
+
+    # Invalid use_direction check
+    with pytest.raises(
+        ValueError,
+        match="must be one of `forward_vector`.*but got invalid_direction",
+    ):
+        kinematics.detect_u_turns(
+            valid_data_array_for_u_turn_detection,
+            use_direction="invalid_direction",
+        )