%load_ext autoreload
%autoreload 2
from flyvis import EnsembleView
ensemble = EnsembleView("flow/0000")
Loading ensemble: 0%| | 0/50 [00:00<?, ?it/s]
[2024-12-08 19:44:37] ensemble:166 Loaded 50 networks.
a
task_error = ensemble.task_error()
embedding_and_clustering = ensemble.clustering("T4c")
[2024-12-08 19:44:39] clustering:835 Loaded T4c embedding and clustering from ../flyvis/data/results/flow/0000/umap_and_clustering
embeddingplot = embedding_and_clustering.plot(
task_error=task_error.values, colors=task_error.colors
)
b
import numpy as np
import matplotlib.pyplot as plt
from flyvis.analysis.visualization import plt_utils
from flyvis.analysis.moving_bar_responses import plot_angular_tuning
cluster_indices = ensemble.cluster_indices("T4c")
r = ensemble.moving_edge_responses()
r['responses'] /= np.abs(r['responses']).max(dim=('frame', 'sample'))
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cluster_indices = ensemble.cluster_indices("T4c")
colors = ensemble.task_error().colors
fig, axes = plt.subplots(
1, len(cluster_indices), subplot_kw={"projection": "polar"}, figsize=[2, 1]
)
for cluster_id, indices in cluster_indices.items():
plot_angular_tuning(
r.sel(network_id=indices),
"T4c",
intensity=1,
colors=colors[indices],
zorder=ensemble.zorder()[indices],
groundtruth=True if cluster_id == 0 else False,
fig=fig,
ax=axes[cluster_id],
)
plt_utils.add_cluster_marker(
fig, axes[cluster_id], marker=plt_utils.get_marker(cluster_id)
)
e
for cluster_id, indices in cluster_indices.items():
with ensemble.select_items(indices):
fig, ax = ensemble.flash_response_index(
cell_types=["Mi1", "Tm3", "Mi4", "Mi9", "CT1(M10)"], figsize=[1, 1]
)
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