Merge pull request #65 from zoccoler/compute_pixel_coords_chunck_sizes

compute pixel coords chunck sizes (fix dask Error)

README.md

@@ -61,10 +61,6 @@ Alternatively, clone this repository and install the latest plugin development v
 
     pip install git+https://github.com/zoccoler/napari-flim-phasor-plotter.git
 
-_Optional, but we **strongly** recommend having the `devbio-napari` plugin bundle also installed for post-processing. This can be done with:_
-
-    mamba install -c conda-forge devbio-napari=0.10.0 scikit-image=0.24.0
-
 ## Usage
 
 ### Loading Raw FLIM Data
@@ -157,7 +153,7 @@ A common step is to select a single cluster of interest for further processing.
 
 To connect small isolated regions and remove small holes within the mask, we can use the `smooth_cluster_mask` function. This can be accessed via `Plugins -> FLIM phasor plotter -> Smooth Cluster Mask`. This will remove holes with an area smaller than the specified `fill area px` in total number of pixels and connect regions within a given `smooth radius`. Don't forget to select the layer containing the mask before running the function, because this function expects a layer with a single label (like a binary mask).
 
-Beyond this point, we can use other plugins, like the `devbio-napari` plugin bundle, to further process the mask. For example, we can perform instance segmentation on the mask via `Tools -> Segmentation / labeling -> Connectec component labeling (scikit-image, nsbatwm)`. We can also extract features from the objects via `Tools -> Measurement tables -> Objetct Features/Properties (scikit-image, nsr)`.
+Beyond this point, we can use other plugins, like the `napari-segment-blobs-and-things-with-membranes` and `napari-skimage-regionprops` plugins, to further process the mask. For example, we can perform instance segmentation on the mask via `Tools -> Segmentation / labeling -> Connectec component labeling (scikit-image, nsbatwm)`. We can also extract features from the objects via `Tools -> Measurement tables -> Objetct Features/Properties (scikit-image, nsr)`.
 
 Below is a demonstration of the post-processing steps:
 

setup.cfg

@@ -34,7 +34,7 @@ install_requires =
     magicgui
     qtpy
     napari>=0.4.19
-    napari-clusters-plotter>=0.8.0
+    napari-clusters-plotter>=0.8.1
     ptufile
     sdtfile
     natsort

src/napari_flim_phasor_plotter/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.1.1"
+__version__ = "0.1.2"
 
 from ._reader import napari_get_reader
 from ._sample_data import load_seminal_receptacle_image, load_hazelnut_image, load_hazelnut_z_stack, load_lifetime_cat_synthtetic_single_image

src/napari_flim_phasor_plotter/_widget.py

@@ -2,11 +2,13 @@
 from magicgui import magic_factory
 from magicgui.widgets import Container, PushButton, ComboBox, SpinBox
 from typing import List
+from importlib.metadata import version
 
 if TYPE_CHECKING:
     import napari
     import pandas
 
+napari_version = tuple(map(int, list(version("napari").split(".")[:2])))
 
 def connect_events(widget):
     '''
@@ -90,11 +92,16 @@ def make_flim_phasor_plot(image_layer: "napari.layers.Image",
 
     g_flat_masked = np.ravel(g[space_mask])
     s_flat_masked = np.ravel(s[space_mask])
+    t_coords, z_coords, y_coords, x_coords = np.where(space_mask)
     if isinstance(g, da.Array):
         g_flat_masked.compute_chunk_sizes()
         s_flat_masked.compute_chunk_sizes()
+    if isinstance(space_mask, da.Array):
+        t_coords.compute_chunk_sizes()
+        z_coords.compute_chunk_sizes()
+        y_coords.compute_chunk_sizes()
+        x_coords.compute_chunk_sizes()
 
-    t_coords, z_coords, y_coords, x_coords = np.where(space_mask)
     phasor_components = pd.DataFrame({
         'label': np.ravel(label_image[space_mask]),
         'G': g_flat_masked,
@@ -233,10 +240,13 @@ def manual_label_extract(cluster_labels_layer: "napari.layers.Labels", label_num
     unitary_dims = [i for i, size in enumerate(np.asarray(cluster_labels_layer.data).shape) if size == 1]
     labels_data = np.squeeze(np.asarray(cluster_labels_layer.data).copy())
     labels_data[labels_data != label_number] = 0
-    # TODO: update to use DirectLabelColormap once napari-clusters-plotter has this issue fixed
-    label_color = cluster_labels_layer.color
+    if napari_version >= (0, 5):
+        colormap = cluster_labels_layer.colormap
+    else:
+        label_color = cluster_labels_layer.color
+        colormap = DirectLabelColormap(color_dict=label_color)
     new_scale = np.array([scale for i, scale in enumerate(cluster_labels_layer.scale) if i not in unitary_dims])
-    return Labels(labels_data, colormap=DirectLabelColormap(color_dict=label_color), name=f'Cluster Label #{label_number}', scale=new_scale)
+    return Labels(labels_data, colormap=colormap, name=f'Cluster Label #{label_number}', scale=new_scale)
 
 def get_n_largest_cluster_labels(features_table: 'pandas.DataFrame', n: int=1, clustering_id: str='MANUAL_CLUSTER_ID') -> List[int]:
     """Get the labels of the n largest clusters in a features table
@@ -419,7 +429,10 @@ def smooth_cluster_mask(cluster_mask_layer: "napari.layers.Labels", fill_area_px
     labels_data = morphology.isotropic_opening(labels_data, smooth_radius)
     # Restore label number
     labels_data = labels_data.astype(cluster_mask_layer.data.dtype)*cluster_mask_layer.data.max()
-    # TODO: update to use DirectLabelColormap once napari-clusters-plotter has this issue fixed
-    label_color = cluster_mask_layer.color
+    if napari_version >= (0, 5):
+        colormap = cluster_mask_layer.colormap
+    else:
+        label_color = cluster_mask_layer.color
+        colormap = DirectLabelColormap(color_dict=label_color)
     new_scale = np.array([scale for i, scale in enumerate(cluster_mask_layer.scale) if i not in unitary_dims])
-    return Labels(labels_data, colormap=DirectLabelColormap(color_dict=label_color), scale=new_scale, name=cluster_mask_layer.name + ' smoothed')
+    return Labels(labels_data, colormap=colormap, scale=new_scale, name=cluster_mask_layer.name + ' smoothed')