Merge pull request #145 from yfukai/major_update_for_algorithm

Major update for algorithm

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

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]):

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)

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]

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "laptrack"
-version = "0.1.6"
+version = "0.1.7-alpha.2"
 description = "LapTrack"
 authors = ["Yohsuke Fukai <[email protected]>"]
 license = "BSD-3-Clause"
@@ -32,6 +32,7 @@ scipy = "^1.7.0"
 networkx = "^2.6.1"
 pandas = "^1.3.1"
 typing-extensions = "^3.10.0"
+pydantic = "^1.9.1"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.4"

src/laptrack/__init__.py

@@ -3,6 +3,6 @@
 __author__ = """Yohsuke T. Fukai"""
 __email__ = "[email protected]"
 
-from ._tracking import laptrack
+from ._tracking import laptrack, LapTrack, LapTrackMulti
 
-__all__ = ["laptrack"]
+__all__ = ["laptrack", "LapTrack", "LapTrackMulti"]

src/laptrack/_cost_matrix.py

@@ -2,7 +2,7 @@
 from typing import Union
 
 import numpy as np
-from scipy.sparse.coo import coo_matrix
+from scipy.sparse import coo_matrix
 
 from ._typing_utils import Float
 from ._typing_utils import Matrix
@@ -67,7 +67,7 @@ def build_segment_cost_matrix(
     alternative_cost_factor: Float = 1.05,
     alternative_cost_percentile: Float = 90,
     alternative_cost_percentile_interpolation: str = "lower",
-) -> coo_matrix:
+) -> Optional[coo_matrix]:
     """Build sparce array for segment-linking cost matrix.
 
     Parameters
@@ -141,7 +141,7 @@ def build_segment_cost_matrix(
                 # XXX seems numpy / mypy is over-strict here. Will fix later.
                 C.data,  # type: ignore
                 alternative_cost_percentile,
-                interpolation=alternative_cost_percentile_interpolation,
+                method=alternative_cost_percentile_interpolation,
             )
             * alternative_cost_factor
         )

src/laptrack/_optimization.py

@@ -1,7 +1,7 @@
 from typing import Tuple
 
 import lap
-from scipy.sparse.csr import csr_matrix
+from scipy.sparse import csr_matrix
 
 from ._typing_utils import FloatArray
 from ._typing_utils import Int