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Add cosine similarity
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@sfmig
sfmig committed Dec 2, 2024
1 parent f237dc3 commit 8e91cf9
Showing 1 changed file with 105 additions and 57 deletions.
162 changes: 105 additions & 57 deletions examples/compute_pairwise_distances.py
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from matplotlib import pyplot as plt

from movement import sample_data
from movement.kinematics import compute_pairwise_distances
from movement.kinematics import (
compute_forward_vector,
compute_pairwise_distances,
)

# %%
# Load sample dataset
Expand Down Expand Up @@ -60,6 +63,8 @@
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Get reference length

# Measure the long side of the box in pixels
# Note the lens is a bit distorted
# Should I use diagonal?
start_point = np.array([[209, 382]])
end_point = np.array([[213, 1022]])
Expand All @@ -84,37 +89,6 @@
ax.set_ylabel("y (pixels)")
ax.set_title("Reference length")

# Measure the long side of the box in pixels

# Note the lens is a bit distorted


# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Plot keypoints on sample frames

# time_sel = [5.0, 50.0, 100.0]

# # get colormap tab20
# cmap = plt.get_cmap("tab20")

# fig, axs = plt.subplots(len(time_sel), 1, figsize=(10, 15))
# for k in range(len(time_sel)):
# for kpt_i, kpt in enumerate(ds.coords["keypoints"].data):
# axs[k].scatter(
# x=ds.position.sel(keypoints=kpt, space="x", time=time_sel[k]),
# y=ds.position.sel(keypoints=kpt, space="y", time=time_sel[k]),
# s=10,
# label=f"{kpt}",
# color=cmap(kpt_i),
# )

# axs[k].axis("equal")
# axs[k].invert_yaxis()
# axs[k].set_xlabel("x (pixels)")
# axs[k].set_ylabel("y (pixels)")
# axs[k].set_title(f"Keypoints at {time_sel[k]} s")
# # axs[k].legend(bbox_to_anchor=(1.1, 1.05))


# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Compute distances between keypoints on different individuals
Expand All @@ -133,15 +107,15 @@
# from individual 1 to all keypoints on individual 2
print(inter_individual_kpt_distances.shape) # inter_individual_distances

# normalise with reference length?
inter_individual_kpt_distances_norm = (
inter_individual_kpt_distances / reference_length
)
# # normalise with reference length?
# inter_individual_kpt_distances_norm = (
# inter_individual_kpt_distances / reference_length
# )

# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Plot matrix of distances and keypoints
# Show different patterns / positions between the two animals
# Note that the colorbars vary across frames!
# Note that the colorbars vary across plots aka frames!

time_sel = [50.0, 100.0, 250.0]

Expand Down Expand Up @@ -170,12 +144,6 @@
label=f"{kpt}",
color=cmap(kpt_i),
)
# # connect matching keypoints
# axs[0].plot(
# ds.position.sel(keypoints=kpt, space="x", time=time_sel[k]),
# ds.position.sel(keypoints=kpt, space="y", time=time_sel[k]),
# 'r'
# )

# add text per individual
for ind in ds.coords["individuals"].data:
Expand Down Expand Up @@ -229,7 +197,11 @@
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# To get distance between homologous keypoints
# get the diagonal of the previous matrix at each frame
inter_individual_same_kpts = np.diagonal(inter_individual_kpt_distances)
inter_individual_same_kpts = np.diagonal(
inter_individual_kpt_distances,
axis1=1,
axis2=2,
)
print(inter_individual_same_kpts.shape) # (59999, 12)


Expand All @@ -240,45 +212,67 @@
inter_individual_same_kpts[:, k_i],
)

# # plot matrix as sparse matrix?
# # plot vectors on top of a given frame?
# for k in range(len(time_sel)):
# fig, axs = plt.subplots(1, 2, figsize=(13, 5))
# fig.subplots_adjust(wspace=0.5)

# # plot keypoints
# for kpt_i, kpt in enumerate(ds.coords["keypoints"].data):
# axs[0].scatter(
# x=ds.position.sel(keypoints=kpt, space="x", time=time_sel[k]),
# y=ds.position.sel(keypoints=kpt, space="y", time=time_sel[k]),
# s=10,
# label=f"{kpt}",
# color=cmap(kpt_i),
# )
# # connect matching keypoints
# axs[0].plot(
# ds.position.sel(keypoints=kpt, space="x", time=time_sel[k]),
# ds.position.sel(keypoints=kpt, space="y", time=time_sel[k]),
# "r",
# )

# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

# To get specific keypoints between individuals
# To get distance between specific keypoints on different individuals
# e.g. snout of individual 1 to tail base of individual 2
# yo can select the relevant keypoints along the dimensions
# you can select the relevant keypoint coordinates along the dimensions
# "individual1" and "individual2"

distance_snout_1_to_tail_2 = inter_individual_kpt_distances.sel(
individual1="snout", individual2="tailbase"
)

# plot distance over time
# plot distance from snout 1 to tailbase 2 over time
# plot in a short time window?
fig, ax = plt.subplots()
ax.plot(
distance_snout_1_to_tail_2.time, # seconds
distance_snout_1_to_tail_2 / reference_length,
)
ax.set_xlabel("time (seconds)")
ax.set_ylabel("distance normalised")
ax.set_ylabel("distance snout-to-tail normalised")


# plot vectors on top of a given frame?
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Compute distances between the keypoints of the same individual
# Compute distances between the keypoints on the same individual
# compute average bodylength = snout to tailbase

distance_snout_to_tailbase_all = compute_pairwise_distances(
ds.position,
dim="keypoints",
pairs={
"snout": "tailbase",
# this will set the dims of the output (individuals will be the
# coordinates)
# this will set the dims of the output
# (individuals will be the coordinates)
},
) # pixels

print(distance_snout_to_tailbase_all) # dimensions are snout and tailbase!

# within individual
# compute distances within individual
bodylength_individual_1 = distance_snout_to_tailbase_all.sel(
snout="individual1",
tailbase="individual1",
Expand All @@ -289,7 +283,7 @@
tailbase="individual2",
)

# across individuals
# compute distances across individuals
# (an alternative way to the above)
snout_1_to_tail_2 = distance_snout_to_tailbase_all.sel(
snout="individual1",
Expand All @@ -300,13 +294,14 @@
tailbase="individual1",
)

# check that this approach is equivalent to the previous one
np.testing.assert_almost_equal(
snout_1_to_tail_2.data, distance_snout_1_to_tail_2.data
)

# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Plot bodylength over time

# as a histogram instead?
for b_i, bodylength_data_array in enumerate(
[
bodylength_individual_1,
Expand All @@ -330,7 +325,60 @@
ax.set_ylabel("length (pixels)")

# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Try usage of 'all' and plot matrix of four quadrants
# Try usage of 'all' and plot distance matrix with four quadrants

# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Compute distances between centroids

distances_between_centroids = compute_pairwise_distances(
ds.centroid,
dim="individuals",
pairs={
"individual1": "individual2",
},
)

print(distances_between_centroids.shape) # (59999,)

# histogram
fig, ax = plt.subplots()
ax.hist(
distances_between_centroids,
)
ax.set_xlabel("distance (pixels)")
ax.set_ylabel("frames") # make it relative to the total number of frames?
ax.set_title("Distances between centroids")

# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Try a different metric, e.g cosine distance
# https://en.wikipedia.org/wiki/Cosine_similarity

# compute forward vector per individual

ds["head_vector"] = compute_forward_vector(
ds.position,
left_keypoint="leftear",
right_keypoint="rightear",
camera_view="top_down",
)

# compute cosine distance between forward vectors
# 1 - dot product of unit vectors
cosine_distance_head_vectors = compute_pairwise_distances(
ds.head_vector,
dim="individuals",
pairs={
"individual1": "individual2",
},
metric="cosine",
)

# plot histogram
# most of the time the vectors are antiparallel?
fig, ax = plt.subplots() # figsize=(3, 3))
ax.hist(
cosine_distance_head_vectors,
)
ax.set_xlabel("cosine distance")
ax.set_ylabel("frames")
# %%
# Try a different metric

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