Merge pull request #385 from BiAPoL/v0.9.0_support_tracking_layer

Support tracking data

Author: jo-mueller

napari_clusters_plotter/tgmm-mini-spot.csv

@@ -40,7 +40,8 @@ dependencies = [
     "napari-skimage-regionprops>=0.3.1",
     "scikit-image",
     "scipy",
-    "biaplotter>=0.2.0"
+    "biaplotter>=0.2.0",
+    "imagecodecs"
 ]
 
 

pyproject.toml

@@ -5,12 +5,13 @@
     DimensionalityReductionWidget,
 )
 from ._new_plotter_widget import PlotterWidget
-from ._sample_data import bbbc_1_dataset, cells3d_curvatures
+from ._sample_data import bbbc_1_dataset, cells3d_curvatures, tgmm_mini_dataset
 
 __all__ = [
     "PlotterWidget",
     "DimensionalityReductionWidget",
     "ClusteringWidget",
     "bbbc_1_dataset",
+    "tgmm_mini_dataset",
     "cells3d_curvatures",
 ]

src/napari_clusters_plotter/__init__.py

@@ -7,6 +7,7 @@
     Points,
     Shapes,
     Surface,
+    Tracks,
     Vectors,
 )
 from qtpy.QtWidgets import (
@@ -21,13 +22,7 @@
 
 class BaseWidget(QWidget):
 
-    input_layer_types = [
-        Labels,
-        Points,
-        Surface,
-        Vectors,
-        Shapes,
-    ]
+    input_layer_types = [Labels, Points, Surface, Vectors, Shapes, Tracks]
 
     def __init__(self, napari_viewer):
         super().__init__()

src/napari_clusters_plotter/_algorithm_widget.py

@@ -372,14 +372,30 @@ def _on_update_layer_selection(
         self._update_feature_selection(None)
 
         for layer in self.layers:
-            layer.events.features.connect(self._update_feature_selection)
+            event_attr = getattr(layer.events, "features", None) or getattr(
+                layer.events, "properties", None
+            )
+            if event_attr:
+                event_attr.connect(self._update_feature_selection)
+            else:
+                Warning(
+                    f"Layer {layer.name} does not have events.features or events.properties"
+                )
 
     def _clean_up(self):
         """In case of empty layer selection"""
 
         # disconnect the events from the layers
         for layer in self.viewer.layers.selection:
-            layer.events.features.disconnect(self._update_feature_selection)
+            event_attr = getattr(layer.events, "features", None) or getattr(
+                layer.events, "properties", None
+            )
+            if event_attr:
+                event_attr.disconnect(self._update_feature_selection)
+            else:
+                Warning(
+                    f"Layer {layer.name} does not have events.features or events.properties"
+                )
 
         # reset the selected layers
         self.layers = []
@@ -512,6 +528,10 @@ def _apply_layer_color(layer, colors):
     elif isinstance(layer, napari.layers.Shapes):
         layer.face_color = colors
 
+    elif isinstance(layer, napari.layers.Tracks):
+        layer._track_colors = colors
+        layer.events.color_by()
+
     elif isinstance(layer, napari.layers.Labels):
 
         colors = np.insert(colors, 0, [0, 0, 0, 0], axis=0)

src/napari_clusters_plotter/_new_plotter_widget.py

@@ -4,6 +4,41 @@
 from typing import List
 
 
+def tgmm_mini_dataset() -> List["LayerData"]:  # noqa: F821
+    import pandas as pd
+    from skimage.io import imread
+
+    path = Path(__file__).parent / "sample_data" / "tracking_data"
+    data = pd.read_csv(path / Path("tgmm-mini-tracks-layer-data.csv"))
+    features = pd.read_csv(
+        path / Path("tgmm-mini-spot.csv"),
+        skiprows=[1, 2],
+        low_memory=False,
+        encoding="utf-8",
+    )
+    tracking_label_image = imread(path / Path("tgmm-mini.tif"))
+
+    layer_data_tuple_tracks = (
+        data,
+        {
+            "name": "tgmm-mini-tracks",
+            "features": features,
+        },
+        "tracks",
+    )
+
+    layer_data_tuple_labels = (
+        tracking_label_image,
+        {
+            "name": "tgmm-mini-labels",
+            "features": features,
+        },
+        "labels",
+    )
+
+    return [layer_data_tuple_tracks, layer_data_tuple_labels]
+
+
 def bbbc_1_dataset() -> List["LayerData"]:  # noqa: F821
     import numpy as np
     import pandas as pd
@@ -78,8 +113,9 @@ def cells3d_curvatures() -> List["LayerData"]:  # noqa: F821
         (vertices, faces),
         {
             "name": "cells_3d_mitotic_nucleus_surface_curvatures",
-            'features': hks,},
-         "surface",
+            "features": hks,
+        },
+        "surface",
     ]
 
     layer_data_nuclei = (

src/napari_clusters_plotter/_sample_data.py

@@ -81,6 +81,38 @@ def create_multi_point_layer(n_samples: int = 100):
     return layer, layer2
 
 
+def create_multi_tracks_layer(n_samples: int = 100):
+    from napari.layers import Tracks
+
+    points1, points2 = create_multi_point_layer(n_samples=n_samples)
+
+    tracks1 = points1.data.copy()
+    tracks2 = points2.data.copy()
+
+    # insert empty track id column
+    tracks1 = np.insert(tracks1, 0, 0, axis=1)
+    tracks2 = np.insert(tracks2, 0, 0, axis=1)
+
+    for t in range(int(points1.data[:, 0].max() + 1)):
+        # set the track id for each point
+        tracks1[tracks1[:, 1] == t, 0] = np.arange(
+            len(tracks1[tracks1[:, 1] == t]), dtype=int
+        )
+
+    for t in range(int(points2.data[:, 0].max() + 1)):
+        # set the track id for each point
+        tracks2[tracks2[:, 1] == t, 0] = np.arange(
+            len(tracks2[tracks2[:, 1] == t]), dtype=int
+        )
+
+    tracks1 = Tracks(tracks1, features=points1.features, name="tracks1")
+    tracks2 = Tracks(
+        tracks2, features=points2.features, name="tracks2", translate=(0, 0, 2)
+    )
+
+    return tracks1, tracks2
+
+
 def create_multi_vectors_layer(n_samples: int = 100):
     from napari.layers import Vectors
 
@@ -258,6 +290,7 @@ def test_mixed_layers(make_napari_viewer):
         create_multi_vectors_layer,
         create_multi_surface_layer,
         create_multi_shapes_layers,
+        create_multi_tracks_layer,
     ],
 )
 def test_cluster_memorization(make_napari_viewer, create_sample_layers):
@@ -318,6 +351,7 @@ def test_multiscale_plotter(make_napari_viewer):
         create_multi_vectors_layer,
         create_multi_surface_layer,
         create_multi_shapes_layers,
+        create_multi_tracks_layer,
     ],
 )
 def test_categorical_handling(make_napari_viewer, create_sample_layers):

src/napari_clusters_plotter/_tests/test_plotter.py

@@ -3,15 +3,13 @@
 from napari_clusters_plotter._sample_data import (
     bbbc_1_dataset,
     cells3d_curvatures,
+    tgmm_mini_dataset,
 )
 
 
 @pytest.mark.parametrize(
     "sample_data_function",
-    [
-        bbbc_1_dataset,
-        cells3d_curvatures,
-    ],
+    [bbbc_1_dataset, cells3d_curvatures, tgmm_mini_dataset],
 )
 def test_bbbc_1_sample_data(make_napari_viewer, sample_data_function):
     from napari.layers import Layer

src/napari_clusters_plotter/_tests/test_sample_data.py

@@ -21,6 +21,10 @@ contributions:
     python_name: napari_clusters_plotter:cells3d_curvatures
     title: Load cells3d mitotic nucleus surface curvatures
 
+  - id: napari-clusters-plotter.tgmm_mini_data
+    python_name: napari_clusters_plotter:tgmm_mini_dataset
+    title: Load tgmm mini dataset
+
   menus:
     napari/layers/visualize:
       - submenu: features_submenu
@@ -47,6 +51,9 @@ contributions:
   - command: napari-clusters-plotter.bbbc_sample_data
     key: bbbc1
     display_name: BBBC 1 dataset & segmentations
+  - command: napari-clusters-plotter.tgmm_mini_data
+    key: tgmm_mini
+    display_name: TGMM mini dataset (tracks and segmentations)
   - command: napari-clusters-plotter.cells3d_curvatures
     key: cells3d_curvatures
     display_name: Cells3D mitotic nucleus surface curvatures

src/napari_clusters_plotter/napari.yaml

@@ -0,0 +1,9 @@
+Janelia Open-Source Software
+Copyright © 2018 Howard Hughes Medical Institute
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+Neither the name of HHMI nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.