diff --git a/deepof/utils.py b/deepof/utils.py
index 478267b96372f23f3e83d8f8ad2f8cf9814af885..1997dae0adf630457c32d3d05a614b4a7d9f30ef 100644
--- a/deepof/utils.py
+++ b/deepof/utils.py
@@ -90,8 +90,10 @@ def compute_dist(
- result (pd.DataFrame): pandas.DataFrame with the
absolute distances between a pair of body parts"""
- a, b = pair_array[:, :2], pair_array[:, 2:]
+ lim = 2 if pair_array.shape[1] == 4 else 1
+ a, b = pair_array[:, :lim], pair_array[:, lim:]
ab = a - b
+
dist = np.sqrt(np.einsum("...i,...i", ab, ab))
return pd.DataFrame(dist * arena_abs / arena_rel)
@@ -199,18 +201,21 @@ def align_trajectories(data: np.array, mode: str = "all") -> np.array:
center_time = (data.shape[1] - 1) // 2
angles = np.arctan2(data[:, center_time, 0], data[:, center_time, 1])
elif mode == "all":
- data = data.reshape(-1, dshape[-1])
+ data = data.reshape(-1, dshape[-1], order="C")
angles = np.arctan2(data[:, 0], data[:, 1])
+ elif mode == "none":
+ data = data.reshape(-1, dshape[-1], order="C")
+ angles = np.zeros(data.shape[0])
aligned_trajs = np.zeros(data.shape)
for frame in range(data.shape[0]):
aligned_trajs[frame] = rotate(
- data[frame].reshape([-1, 2]), angles[frame],
- ).reshape(data.shape[1:])
+ data[frame].reshape([-1, 2], order="C"), angles[frame],
+ ).reshape(data.shape[1:], order="C")
- if mode == "all":
- aligned_trajs = aligned_trajs.reshape(dshape)
+ if mode == "all" or mode == "none":
+ aligned_trajs = aligned_trajs.reshape(dshape, order="C")
return aligned_trajs
@@ -436,33 +441,52 @@ def climb_wall(
return climbing
-def rolling_speed(dframe, typ, pause=10, rounds=5, order=1):
- """Returns the average speed over 10 frames in pixels per frame"""
-
- s = dframe.shape[0]
+def rolling_speed(
+ dframe: pd.DatetimeIndex, window: int = 10, rounds: int = 10, deriv: int = 1
+) -> pd.DataFrame:
+ """Returns the average speed over n frames in pixels per frame
+ Parameters:
+ - dframe (pandas.DataFrame): position over time dataframe
+ - pause (int): frame-length of the averaging window
+ - rounds (int): float rounding decimals
+ - deriv (int): position derivative order; 1 for speed,
+ 2 for acceleration, 3 for jerk, etc
- if typ == "coords":
- bp = dframe.shape[1] / 2 if order == 1 else dframe.shape[1]
- d = 2 if order == 1 else 1
+ Returns:
+ - speeds (pd.DataFrame): containing 2D speeds for each body part
+ in the original data or their consequent derivatives"""
+
+ original_shape = dframe.shape
+ body_parts = dframe.columns.levels[0]
+ speeds = pd.DataFrame
+
+ for der in range(deriv):
+ distances = np.concatenate(
+ [
+ np.array(dframe).reshape([-1, (2 if der == 0 else 1)], order="F"),
+ np.array(dframe.shift()).reshape(
+ [-1, (2 if der == 0 else 1)], order="F"
+ ),
+ ],
+ axis=1,
+ )
- else:
- bp = dframe.shape[1]
- d = 1
+ distances = np.array(compute_dist(distances))
+ distances = distances.reshape(
+ [original_shape[0], original_shape[1] // 2], order="F"
+ )
+ distances = pd.DataFrame(distances, index=dframe.index)
+ speeds = np.round(distances.rolling(window).mean(), rounds)
+ speeds[np.isnan(speeds)] = 0.0
- distances = np.linalg.norm(
- np.array(dframe).reshape(s, int(bp), d)
- - np.array(dframe.shift()).reshape(s, int(bp), d),
- axis=2,
- )
+ dframe = speeds
- distances = pd.DataFrame(distances, index=dframe.index)
- speeds = np.round(distances.rolling(pause).mean(), rounds)
- speeds[np.isnan(speeds)] = 0.0
+ speeds.columns = body_parts
return speeds
-def huddle(pos_dict, fnum, tol, tol2, mouse="B"):
+def huddle(pos_dict, tol, tol2, mouse="B"):
"""Returns true when the specified mouse is huddling"""
return (
@@ -700,8 +724,8 @@ def Tag_video(
)
# Compute speed on a rolling window
- tagdict["bspeed"] = rolling_speed(dframe["B_Center"], pause=speedpause)
- tagdict["wspeed"] = rolling_speed(dframe["W_Center"], pause=speedpause)
+ tagdict["bspeed"] = rolling_speed(dframe["B_Center"], window=speedpause)
+ tagdict["wspeed"] = rolling_speed(dframe["W_Center"], window=speedpause)
if any([show, save]):
# Loop over the frames in the video
diff --git a/tests/test_examples/Tables/test_csv_table.csv b/tests/test_examples/Tables/test_csv_table.csv
new file mode 100644
index 0000000000000000000000000000000000000000..eeecd1136e0b705839bd451ddb084e4deb38cd93
--- /dev/null
+++ b/tests/test_examples/Tables/test_csv_table.csv
@@ -0,0 +1,101 @@
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diff --git a/tests/test_examples/Tables/test_h5_table.h5 b/tests/test_examples/Tables/test_h5_table.h5
index 39996cc0ff6d7c0a38c3a13045f2c0849721b4f1..bea9524f1d469d4fb6900c954e3bfa6d5d7bba90 100644
Binary files a/tests/test_examples/Tables/test_h5_table.h5 and b/tests/test_examples/Tables/test_h5_table.h5 differ
diff --git a/tests/test_examples/test_video_circular_arena.mp4 b/tests/test_examples/test_video_circular_arena.mp4
deleted file mode 100644
index 9b27dd1de4ef8b73a56b255b7d1b961da8cd14b9..0000000000000000000000000000000000000000
Binary files a/tests/test_examples/test_video_circular_arena.mp4 and /dev/null differ
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 864a4c0cfe61e99c6b25469a13c8d9ddfc3edd4f..3ac96d9191444375c45fafac236b695a753efe92 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -10,6 +10,12 @@ from deepof.utils import *
import deepof.preprocess
import pytest
+# AUXILIARY FUNCTIONS #
+
+
+def autocorr(x, t=1):
+ return np.round(np.corrcoef(np.array([x[:-t], x[t:]]))[0, 1], 5)
+
# QUALITY CONTROL AND PREPROCESSING #
@@ -215,6 +221,7 @@ def test_angle_trio(array):
def test_rotate(p):
assert np.allclose(rotate(p, 2 * np.pi), p)
assert np.allclose(rotate(p, np.pi), -p)
+ assert np.allclose(rotate(p, 0), p)
@settings(deadline=None)
@@ -230,16 +237,18 @@ def test_rotate(p):
min_value=1, max_value=10, allow_nan=False, allow_infinity=False
),
),
- mode_idx=st.integers(min_value=0, max_value=1),
+ mode_idx=st.integers(min_value=0, max_value=2),
)
def test_align_trajectories(data, mode_idx):
- mode = ["center", "all"][mode_idx]
+ mode = ["center", "all", "none"][mode_idx]
aligned = align_trajectories(data, mode)
assert aligned.shape == data.shape
if mode == "center":
assert np.allclose(aligned[:, (data.shape[1] - 1) // 2, 0], 0)
elif mode == "all":
assert np.allclose(aligned[:, :, 0], 0)
+ elif mode == "none":
+ assert np.allclose(aligned, data)
@settings(deadline=None)
@@ -305,9 +314,6 @@ def test_smooth_mult_trajectory(alpha, series):
smoothed1 = smooth_mult_trajectory(series, alpha1)
smoothed2 = smooth_mult_trajectory(series, alpha2)
- def autocorr(x, t=1):
- return np.round(np.corrcoef(np.array([x[:-t], x[t:]]))[0, 1], 5)
-
assert autocorr(smoothed1) >= autocorr(series)
assert autocorr(smoothed2) >= autocorr(series)
assert autocorr(smoothed2) <= autocorr(smoothed1)
@@ -347,14 +353,14 @@ def test_close_single_contact(pos_dframe, tol):
index=range_indexes(min_size=5),
columns=columns(["X1", "y1", "X2", "y2", "X3", "y3", "X4", "y4"], dtype=float),
rows=st.tuples(
- st.floats(min_value=1, max_value=10, allow_nan=False, allow_infinity=False),
- st.floats(min_value=1, max_value=10, allow_nan=False, allow_infinity=False),
- st.floats(min_value=1, max_value=10, allow_nan=False, allow_infinity=False),
- st.floats(min_value=1, max_value=10, allow_nan=False, allow_infinity=False),
- st.floats(min_value=1, max_value=10, allow_nan=False, allow_infinity=False),
- st.floats(min_value=1, max_value=10, allow_nan=False, allow_infinity=False),
- st.floats(min_value=1, max_value=10, allow_nan=False, allow_infinity=False),
- st.floats(min_value=1, max_value=10, allow_nan=False, allow_infinity=False),
+ st.floats(min_value=1, max_value=10),
+ st.floats(min_value=1, max_value=10),
+ st.floats(min_value=1, max_value=10),
+ st.floats(min_value=1, max_value=10),
+ st.floats(min_value=1, max_value=10),
+ st.floats(min_value=1, max_value=10),
+ st.floats(min_value=1, max_value=10),
+ st.floats(min_value=1, max_value=10),
),
),
tol=st.floats(min_value=0.01, max_value=4.98),
@@ -422,3 +428,43 @@ def test_climb_wall(arena, tol):
with pytest.raises(NotImplementedError):
climb_wall("", arena, prun["test"], tol1, nose="Nose")
+
+
+@settings(deadline=None)
+@given(
+ dframe=data_frames(
+ index=range_indexes(min_size=50),
+ columns=columns(["X1", "y1", "X2", "y2"], dtype=float),
+ rows=st.tuples(
+ st.floats(min_value=1, max_value=10),
+ st.floats(min_value=1, max_value=10),
+ st.floats(min_value=1, max_value=10),
+ st.floats(min_value=1, max_value=10),
+ ),
+ ),
+ sampler=st.data(),
+)
+def test_rolling_speed(dframe, sampler):
+
+ dframe *= np.random.uniform(0, 1, dframe.shape)
+
+ order1 = sampler.draw(st.integers(min_value=1, max_value=3))
+ order2 = sampler.draw(st.integers(min_value=order1, max_value=3))
+ window2 = sampler.draw(st.integers(min_value=10, max_value=25))
+
+ idx = pd.MultiIndex.from_product(
+ [["bpart1", "bpart2"], ["X", "y"]], names=["bodyparts", "coords"],
+ )
+ dframe.columns = idx
+
+ speeds1 = rolling_speed(dframe, 5, 10, order1)
+ speeds2 = rolling_speed(dframe, 5, 10, order2)
+ speeds3 = rolling_speed(dframe, window2, 10, order1)
+
+ assert speeds1.shape[0] == dframe.shape[0]
+ assert speeds1.shape[1] == dframe.shape[1] // 2
+ assert np.all(np.std(speeds1) >= np.std(speeds2))
+ for i in range(speeds1.shape[1]):
+ assert autocorr(np.array(speeds1.iloc[:, i])) <= autocorr(
+ np.array(speeds3.iloc[:, i])
+ )