changed examples

yfukai · Jul 4, 2022 · 796a6e2 · 796a6e2
1 parent 0308d96
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Showing 4 changed files with 13 additions and 9 deletions.
diff --git a/docs/usage.rst b/docs/usage.rst
@@ -5,4 +5,4 @@ Example usage: tracking pre-detected spots
 
 .. literalinclude:: ../examples/lap_tracking_example.py
    :language: python
-   :emphasize-lines: 29-35
+   :emphasize-lines: 29-37
diff --git a/examples/brownian_motion_tracking.py b/examples/brownian_motion_tracking.py
@@ -4,7 +4,7 @@
 import numpy as np
 from matplotlib import pyplot as plt
 
-from laptrack import laptrack
+from laptrack import LapTrack
 
 track_length = 100
 track_count = 10
@@ -41,7 +41,8 @@
 plt.legend()
 
 spots = [np.array([pos[t] for pos in brownian_poss]) for t in range(track_length)]
-tree = laptrack(spots)
+lt = LapTrack()
+tree = lt.predict(spots)
 #%% # noqa:
 for edge in tree.edges():
     if (edge[0][0] + 1 != edge[1][0]) or (edge[0][1] != edge[1][1]):

diff --git a/examples/lap_tracking_example.py b/examples/lap_tracking_example.py
@@ -2,7 +2,7 @@
 
 import pandas as pd
 
-from laptrack import laptrack
+from laptrack import LapTrack
 
 script_path = path.dirname(path.realpath(__file__))
 filename = "../tests/data/trackmate_tracks_with_splitting_spots.csv"
@@ -26,13 +26,15 @@
 # ]
 
 max_distance = 15
-track_tree = laptrack(
-    coords,
+lt = LapTrack(
     track_dist_metric="sqeuclidean",
     splitting_dist_metric="sqeuclidean",
     track_cost_cutoff=max_distance**2,
     splitting_cost_cutoff=max_distance**2,
 )
+track_tree = lt.predict(
+    coords,
+)
 
 for edge in track_tree.edges():
     print(edge)

diff --git a/examples/napari_example.py b/examples/napari_example.py
@@ -6,7 +6,7 @@
 from matplotlib import pyplot as plt
 from skimage.feature import blob_log
 
-from laptrack import laptrack
+from laptrack import LapTrack
 
 #%% # noqa:
 viewer = napari.Viewer()
@@ -65,7 +65,8 @@
 
 # %%
 spots_for_tracking = [spots[spots[:, 0] == j][:, 1:] for j in range(track_length)]
-track_tree = laptrack(spots_for_tracking)
+lt = LapTrack()
+track_tree = lt.predict(spots_for_tracking)
 
 tracks = []
 for i, segment in enumerate(nx.connected_components(track_tree)):
@@ -105,7 +106,7 @@
     for j in np.sort(np.unique(extracted_spots[:, 0]))
 ]
 
-test_track = laptrack(extracted_spots_for_tracking, gap_closing_cost_cutoff=False)
+test_track = lt.predict(extracted_spots_for_tracking, gap_closing_cost_cutoff=False)
 for edge in test_track.edges():
     _e = np.array(edge)
     pos = [extracted_spots_for_tracking[frame][ind] for frame, ind in edge]