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Update method generate_cluster_tracks() #290

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merged 6 commits into from
Dec 20, 2023
Merged

Update method generate_cluster_tracks() #290

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93eb33d
Update method generate_cluster_tracks()
stefanhahmann Dec 18, 2023
5dd0eb0
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Dec 18, 2023
71f94be
Add deprecation comment to generate_cluster_image_()
stefanhahmann Dec 18, 2023
c31ffd8
Remove flatten() from np.unique, since it is not required to get uniq…
stefanhahmann Dec 18, 2023
906f77a
revert to more robust function
Cryaaa Dec 19, 2023
f736d3b
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Dec 19, 2023
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29 changes: 18 additions & 11 deletions napari_clusters_plotter/_utilities.py
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Original file line number Diff line number Diff line change
Expand Up @@ -241,17 +241,20 @@ def update_properties_list(widget, exclude_list):

def generate_cluster_tracks(analysed_layer, plot_cluster_name):
features = analysed_layer.features
label_id_list_per_timepoint = [
features["label"].tolist() for i in range(analysed_layer.data.shape[0])
]
prediction_lists_per_timepoint = [
features[plot_cluster_name].tolist()
for i in range(analysed_layer.data.shape[0])
]
label_id_lists_per_timepoint = list()
prediction_lists_per_timepoint = list()

for i in range(analysed_layer.data.shape[0]):
labels_of_timeframe = np.unique(analysed_layer.data[i].flatten())
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filtered_features = features[features["label"].isin(labels_of_timeframe)]
label_id_lists_per_timepoint.append(filtered_features["label"].tolist())
prediction_lists_per_timepoint.append(
filtered_features[plot_cluster_name].tolist()
)

cluster_data = dask_cluster_image_timelapse(
analysed_layer.data,
label_id_list_per_timepoint,
label_id_lists_per_timepoint,
prediction_lists_per_timepoint,
)

Expand Down Expand Up @@ -284,6 +287,9 @@ def generate_cluster_image_(label_image, label_list, predictionlist):
Generates a clusters image from a label image and a list of cluster predictions,
where each label value corresponds to the cluster identity.
It is assumed that len(predictionlist) == max(label_image)

Deprecated, use generate_cluster_image instead

Parameters
----------
label_image: ndarray or dask array
Expand Down Expand Up @@ -314,6 +320,9 @@ def generate_cluster_image(label_image, label_list, predictionlist):
where each label value corresponds to the cluster identity.
It is assumed that len(predictionlist) == max(label_image)

This function is recommended instead of generate_cluster_image_ as it is faster,
because it does not use skimage.util.map_array

Parameters
----------
label_image: ndarray or dask array
Expand All @@ -327,9 +336,7 @@ def generate_cluster_image(label_image, label_list, predictionlist):
"""

predictionlist_new = np.array(predictionlist) + 1
plist = np.zeros(
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int(max([label_image.max(), np.max(label_list)])) + 1, dtype=np.uint32
)
plist = np.zeros(int(np.max(label_image)) + 1, dtype=np.uint32)
plist[label_list] = predictionlist_new

predictionlist_new = plist
Expand Down
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