Feature/replace nan value

datalab/adapters_plotpy/objects/signal.py

@@ -15,7 +15,7 @@
 import numpy as np
 from guidata.dataset import restore_dataset, update_dataset
 from plotpy.builder import make
-from plotpy.items import CurveItem
+from plotpy.items import CurveItem, ErrorBarCurveItem
 from sigima.objects import SignalObj
 
 from datalab.adapters_plotpy.objects.base import (
@@ -245,7 +245,12 @@ def update_item(self, item: CurveItem, data_changed: bool = True) -> None:
                     and isinstance(dx, np.ndarray)
                     and isinstance(dy, np.ndarray)
                 )
-                item.set_data(x.real, y.real, dx.real, dy.real)
+                if isinstance(item, ErrorBarCurveItem):
+                    item.set_data(x.real, y.real, dx.real, dy.real)
+                else:
+                    # xydata has 4 rows but dx/dy are all NaN (no real
+                    # error bars) — the plot item is a plain CurveItem
+                    item.set_data(x.real, y.real)
         item.param.label = o.title
         apply_downsampling(item)
         # Reapply linewidth with smart clamping (data size may have changed)

datalab/gui/actionhandler.py

@@ -1086,6 +1086,7 @@ def create_first_actions(self):
             with self.new_menu(_("Level adjustment"), icon_name="level_adjustment.svg"):
                 self.action_for("normalize")
                 self.action_for("clip")
+                self.action_for("replace_special_values")
                 self.new_action(
                     _("Offset correction"),
                     triggered=self.panel.processor.compute_offset_correction,

datalab/gui/processor/image.py

@@ -36,6 +36,9 @@
 from datalab.objectmodel import get_uuid
 from datalab.utils.qthelpers import create_progress_bar, qt_try_except
 from datalab.widgets import imagebackground
+from datalab.widgets.replacespecialvalues import (
+    ReplaceSpecialValuesImageParamDL,
+)
 
 
 class ImageProcessor(BaseProcessor[ImageROI, ROI2DParam]):
@@ -279,6 +282,12 @@ def register_processing(self) -> None:
             icon_name="normalize.svg",
         )
         self.register_1_to_1(sipi.clip, _("Clipping"), sipb.ClipParam, "clip.svg")
+        self.register_1_to_1(
+            sipi.replace_special_values,
+            _("Replace special values"),
+            ReplaceSpecialValuesImageParamDL,
+            "replace_nan.svg",
+        )
         self.register_1_to_1(
             sipi.offset_correction,
             _("Offset correction"),

datalab/gui/processor/signal.py

@@ -40,6 +40,9 @@
     signaldeltax,
     signalpeak,
 )
+from datalab.widgets.replacespecialvalues import (
+    ReplaceSpecialValuesSignalParamDL,
+)
 
 
 class SignalProcessor(BaseProcessor[SignalROI, ROI1DParam]):
@@ -236,6 +239,12 @@ def register_processing(self) -> None:
         self.register_1_to_1(
             sips.clip, _("Clipping"), sigima_base.ClipParam, "clip.svg"
         )
+        self.register_1_to_1(
+            sips.replace_special_values,
+            _("Replace special values"),
+            ReplaceSpecialValuesSignalParamDL,
+            "replace_nan.svg",
+        )
         self.register_1_to_1(
             sips.offset_correction,
             _("Offset correction"),

datalab/locale/fr/LC_MESSAGES/datalab.po

@@ -1677,7 +1677,6 @@ msgstr "Enregistrer l'historique dans un fichier HDF5..."
 msgid "Save history to a standalone .dlhist file"
 msgstr "Enregistrer l'historique dans un fichier .dlhist autonome"
 
-#, python-format
 msgid "Duplicate selected history action/session"
 msgstr "Dupliquer l'objet %s sélectionné"
 
@@ -2169,6 +2168,9 @@ msgstr "Normaliser"
 msgid "Clipping"
 msgstr "Écrêtage"
 
+msgid "Replace special values"
+msgstr "Remplacer les valeurs spéciales"
+
 msgid "Add Gaussian noise"
 msgstr "Ajouter du bruit gaussien"
 
@@ -3772,6 +3774,21 @@ msgstr "Des journaux de bord ont été générés lors de la dernière session."
 msgid "Log files are currently empty."
 msgstr "Les journaux de bord sont vides."
 
+msgid "Kernel / Mask preview"
+msgstr "Aperçu du noyau / masque"
+
+#, python-brace-format
+msgid "{label} — Kernel / Mask preview"
+msgstr "{label} — Aperçu du noyau / masque"
+
+#, python-brace-format
+msgid "±{n} points"
+msgstr "±{n} points"
+
+#, python-brace-format
+msgid "±{n} rows × ±{n} columns"
+msgstr "±{n} lignes × ±{n} colonnes"
+
 msgid "Signal baseline selection"
 msgstr "Sélection de la ligne de base du signal"
 
@@ -4113,6 +4130,9 @@ msgstr "Image"
 msgid "Dimensions"
 msgstr "Dimensions"
 
+msgid "This image uses an integer data type, so it cannot contain NaN or infinite values. Replace special values is therefore not applicable."
+msgstr "Cette image utilise un type de données entier, elle ne peut donc pas contenir de valeurs NaN ou infinies. Le remplacement des valeurs spéciales n'est donc pas applicable."
+
 msgid "Minimum value"
 msgstr "Valeur minimum"
 
@@ -4160,3 +4180,4 @@ msgstr "Aucun contour n'a été trouvé pour la plage de niveaux sélectionnée.
 
 msgid "Show contour plot..."
 msgstr "Afficher le tracé de contours..."
+

datalab/tests/features/image/replace_special_values_app_test.py

@@ -0,0 +1,108 @@
+# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
+
+"""
+Replace special values application test for images.
+
+This test verifies DataLab processor integration for ``replace_special_values``.
+Algorithm correctness (including Inf handling) is covered by Sigima unit tests.
+Only NaN values are injected here to avoid Qt plot instability with Inf data.
+"""
+
+# pylint: disable=invalid-name  # Allows short reference names like x, y, ...
+# guitest: show
+
+from __future__ import annotations
+
+import numpy as np
+import sigima.params
+from qtpy import QtWidgets as QW
+from sigima.enums import ReplacementStrategyImage as S
+from sigima.objects import ImageObj, create_image
+
+from datalab.tests import datalab_test_app_context
+from datalab.widgets.replacespecialvalues import ReplaceSpecialValuesImageParamDL
+
+
+def test_replace_special_values_image_app():
+    """Test image replace special values through DataLab processor."""
+    with datalab_test_app_context(console=False) as win:
+        panel = win.imagepanel
+
+        # Use NaN-only data: Inf values destabilize Qt image plot rendering/teardown
+        data = np.array(
+            [
+                [1.0, np.nan, 3.0],
+                [4.0, 5.0, np.nan],
+                [7.0, 8.0, 9.0],
+            ]
+        )
+        image = create_image("Image with NaN values", data)
+        panel.add_object(image)
+        panel.objview.select_objects([image])
+
+        param = sigima.params.ReplaceSpecialValuesImageParam.create(
+            nan_strategy=S.CONSTANT,
+            nan_constant_value=10.0,
+        )
+        panel.processor.run_feature("replace_special_values", param, edit=False)
+
+        result_objects = panel.objview.get_sel_objects()
+        assert len(result_objects) == 1
+        result = result_objects[0]
+        assert isinstance(result, ImageObj)
+        assert not np.isnan(result.data).any()
+        np.testing.assert_array_equal(
+            result.data,
+            np.array(
+                [
+                    [1.0, 10.0, 3.0],
+                    [4.0, 5.0, 10.0],
+                    [7.0, 8.0, 9.0],
+                ]
+            ),
+        )
+
+
+def test_replace_special_values_integer_image_app_noop():
+    """Integer images should keep their data unchanged and emit a warning path."""
+    with datalab_test_app_context(console=False) as win:
+        panel = win.imagepanel
+
+        data = np.arange(9, dtype=np.uint16).reshape(3, 3)
+        image = create_image("Integer image", data)
+        panel.add_object(image)
+        panel.objview.select_objects([image])
+
+        param = sigima.params.ReplaceSpecialValuesImageParam.create(
+            nan_strategy=S.CONSTANT,
+            nan_constant_value=10.0,
+        )
+        panel.processor.run_feature("replace_special_values", param, edit=False)
+
+        result_objects = panel.objview.get_sel_objects()
+        assert len(result_objects) == 1
+        result = result_objects[0]
+        assert isinstance(result, ImageObj)
+        assert result is not image
+        assert result.data.dtype == np.uint16
+        np.testing.assert_array_equal(result.data, data)
+
+
+def test_replace_special_values_integer_image_dialog_disabled():
+    """The custom dialog should inform the user and disable validation."""
+    with datalab_test_app_context(console=False):
+        data = np.arange(9, dtype=np.uint16).reshape(3, 3)
+        image = create_image("Integer image", data)
+
+        param = ReplaceSpecialValuesImageParamDL.create()
+        param.update_from_obj(image)
+
+        dlg = param.create_dialog()
+        ok_button = dlg.findChild(QW.QDialogButtonBox).button(QW.QDialogButtonBox.Ok)
+        assert ok_button is not None
+        assert not ok_button.isEnabled()
+        dlg.close()
+
+
+if __name__ == "__main__":
+    test_replace_special_values_image_app()

datalab/tests/features/signal/replace_special_values_app_test.py

@@ -0,0 +1,51 @@
+# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
+
+"""
+Replace special values application test for signals.
+
+This test verifies DataLab processor integration for ``replace_special_values``.
+Algorithm correctness (including Inf handling) is covered by Sigima unit tests.
+Only NaN values are injected here to avoid Qt plot instability with Inf data.
+"""
+
+# pylint: disable=invalid-name  # Allows short reference names like x, y, ...
+# guitest: show
+
+from __future__ import annotations
+
+import numpy as np
+import sigima.params
+from sigima.enums import ReplacementStrategySignal as S
+from sigima.objects import SignalObj, create_signal
+
+from datalab.tests import datalab_test_app_context
+
+
+def test_replace_special_values_signal_app():
+    """Test signal replace special values through DataLab processor."""
+    with datalab_test_app_context(console=False) as win:
+        panel = win.signalpanel
+
+        # Use NaN-only data: Inf values destabilize Qt signal plot rendering
+        x = np.arange(5, dtype=float)
+        y = np.array([1.0, np.nan, 3.0, np.nan, 5.0])
+        sig = create_signal("Signal with NaN values", x, y)
+        panel.add_object(sig)
+        panel.objview.select_objects([sig])
+
+        param = sigima.params.ReplaceSpecialValuesSignalParam.create(
+            nan_strategy=S.CONSTANT,
+            nan_constant_value=10.0,
+        )
+        panel.processor.run_feature("replace_special_values", param, edit=False)
+
+        result_objects = panel.objview.get_sel_objects()
+        assert len(result_objects) == 1
+        result = result_objects[0]
+        assert isinstance(result, SignalObj)
+        assert not np.isnan(result.y).any()
+        np.testing.assert_array_equal(result.y, [1.0, 10.0, 3.0, 10.0, 5.0])
+
+
+if __name__ == "__main__":
+    test_replace_special_values_signal_app()