basic example with ndv viewer

example_ndv.py

@@ -0,0 +1,39 @@
+# sanity checks for ndv example
+
+try:
+    import ndv
+except ImportError:
+    raise ImportError("You need to `pip install ndv` to run this example.")
+from typing import cast
+
+from qtpy import QT6
+
+if not QT6:
+    raise ImportError("ndv will require QT>=6.4.")
+
+from careamics.utils import get_careamics_home
+from careamics_portfolio import PortfolioManager
+from napari.qt import get_stylesheet
+
+from careamics_napari.training_plugin import TrainPlugin, TrainPluginWrapper
+from careamics_napari.widgets import PredictDataWidget
+
+# create a viewer and your main gui
+viewer = ndv.ArrayViewer()
+scroll = TrainPluginWrapper(viewer)
+# apply the napari stylesheet
+scroll.setStyleSheet(get_stylesheet("dark"))
+scroll.show()
+
+#  get sample data
+files = PortfolioManager().denoising.N2V_SEM.download(path=get_careamics_home())
+
+# my gross hack to pre-populate the text fields with the sample files :)
+wdg = cast("TrainPlugin", scroll.widget())
+wdg.data_layers[0].train_images_folder.text_field.setText(files[-2])
+wdg.data_layers[0].val_images_folder.text_field.setText(files[-1])
+pred_wdg = wdg.prediction_widget.findChild(PredictDataWidget)
+pred_wdg.pred_images_folder.text_field.setText(files[-1])
+
+# run the Qt event loop
+ndv.run_app()

src/careamics_napari/training_plugin.py

@@ -2,7 +2,7 @@
 
 from pathlib import Path
 from queue import Queue
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING, Optional, Union
 
 from careamics import CAREamist
 from careamics.config.support import SupportedAlgorithm
@@ -45,6 +45,7 @@
 import numpy as np
 
 if TYPE_CHECKING:
+    import ndv
     import napari
 
 # at run time
@@ -101,7 +102,7 @@ class TrainPlugin(QWidget):
 
     def __init__(
         self: Self,
-        napari_viewer: Optional[napari.Viewer] = None,
+        napari_viewer: Union[napari.Viewer, "ndv.ArrayViewer"] = None,
     ) -> None:
         """Initialize the plugin.
 
@@ -345,21 +346,28 @@ def _update_from_prediction(self, update: PredictionUpdate) -> None:
                 )
 
         else:
-            if update.type == PredictionUpdateType.SAMPLE:
+            if update.type == PredictionUpdateType.SAMPLE and self.viewer is not None:
                 # add image to napari
                 # TODO keep scaling?
-                if self.viewer is not None:
-                    # value is eighter a numpy array or a list of numpy arrays with each sample/timepoint as an element
-                    if isinstance(update.value, list):
-                        # combine all samples
-                        samples = np.concatenate(update.value, axis=0)
-                    else:
-                        samples = update.value
-
-                    # reshape the prediction to match the input axes
-                    samples = reshape_prediction(samples, self.train_config_signal.axes, self.pred_config_signal.is_3d)
-
+                # value is eighter a numpy array or a list of numpy arrays with each sample/timepoint as an element
+                if isinstance(update.value, list):
+                    # combine all samples
+                    samples = np.concatenate(update.value, axis=0)
+                else:
+                    samples = update.value
+
+                # reshape the prediction to match the input axes
+                samples = reshape_prediction(samples, self.train_config_signal.axes, self.pred_config_signal.is_3d)
+
+                # if isinstance(self.viewer, napari.Viewer):
+                # elif isinstance(self.viewer, ndv.ArrayViewer):
+                if type(self.viewer).__module__.startswith("napari"):
                     self.viewer.add_image(samples, name="Prediction")
+                elif type(self.viewer).__module__.startswith("ndv"):
+                    # one could also instantiate and show new ndv.ArrayViewer here
+                    self.viewer.data = samples
+                    self.viewer.show()
+
             else:
                 self.pred_status.update(update)