Pint definitions

.github/workflows/python-app.yml

@@ -15,13 +15,10 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
         os: [ubuntu-latest, macos-latest, windows-latest]
         install-deeplay: ["", "deeplay"]
-        install-tensorflow: ["", "tensorflow"]
-        exclude:
-          - python-version: "3.11"
-            install-tensorflow: "tensorflow"
+
     if:
     steps:
       - uses: actions/checkout@v3
@@ -38,10 +35,6 @@ jobs:
         if: ${{ matrix.install-deeplay == 'deeplay' }}
         run: |
           python -m pip install deeplay
-      - name: Install tensorflow
-        if: ${{ matrix.install-tensorflow == 'tensorflow' }}
-        run: |
-          python -m pip install tensorflow==2.10 tensorflow-probability tensorflow-datasets
       - name: Lint with flake8
         run: |
           # stop the build if there are Python syntax errors or undefined names

_src/source/utils.rst

@@ -21,11 +21,6 @@ hasmethod
 
 .. autofunction:: deeptrack.utils.hasmethod
 
-isiterable
-^^^^^^^^^^
-
-.. autofunction:: deeptrack.utils.isiterable
-
 kwarg_has_default
 ^^^^^^^^^^^^^^^^^
 

deeptrack/__init__.py

@@ -1,13 +1,21 @@
 # flake8: noqa
-from pint import UnitRegistry, Context
-from .backend.pint_definition import pint_definitions
 import lazy_import
-import importlib
+from pint import UnitRegistry
 
+'''from .backend.pint_definition import pint_definitions
+units = UnitRegistry(pint_definitions.split("\n"))'''#TBE
 
-units = UnitRegistry(pint_definitions.split("\n"))
+# Create a UnitRegistry and add custom units.
+units = UnitRegistry()
+units.define("img_pixel = 1 * micrometer = ipx")
+units.define("xpixel = 1 * micrometer = xpx")
+units.define("ypixel = 1 * micrometer = ypx")
+units.define("zpixel = 1 * micrometer = zpx")
+units.define("simulation_xpixel = 1 * micrometer = sxpx")
+units.define("simulation_ypixel = 1 * micrometer = sypx")
+units.define("simulation_zpixel = 1 * micrometer = szpx")
 
-# Check if tensorflow is installed without importing it
+'''# Check if tensorflow is installed without importing it
 import pkg_resources
 
 installed = [pkg.key for pkg in pkg_resources.working_set]
@@ -23,7 +31,7 @@
     HAS_TORCH = False
 
 if HAS_TENSORFLOW and HAS_TORCH:
-    import torch # torch must be imported before tensorflow
+    import torch # torch must be imported before tensorflow'''#TBE
 
 from deeptrack.features import *
 from deeptrack.aberrations import *

deeptrack/features.py

@@ -10,7 +10,7 @@
 import random
 
 import numpy as np
-from pint.quantity import Quantity
+from pint import Quantity
 # import tensorflow as tf
 import skimage
 import skimage.measure
@@ -2313,7 +2313,7 @@ def _resample_volume_position(self, volume):
             if "position" in pdict and "_position_sampler" in pdict:
                 new_position = pdict["_position_sampler"]()
                 if isinstance(new_position, Quantity):
-                    new_position = new_position.to("pixel").magnitude
+                    new_position = new_position.to("img_pixel").magnitude
                 pdict["position"] = new_position
 
         return volume

deeptrack/math.py

@@ -1,11 +1,16 @@
-""" Mathematical oprations and structures
+"""Mathematical operations and structures.
 
-Classses
---------
+This module provides classes and utilities to perform common mathematical 
+operations and transformations on images, including clipping, normalization, 
+blurring, and pooling. These are implemented as subclasses of `Feature` for 
+seamless integration with the  feature-based design of the library.
+
+Classes
+-------
 Clip
-    Clip the input within a minimum and a maximum value.
+    Clip the input values within a specified minimum and maximum range.
 NormalizeMinMax
-    Min-max image normalization.
+    Perform min-max normalization on images.
 """
 
 from typing import Callable, List

deeptrack/models/utils.py

@@ -21,9 +21,9 @@
         ImportWarning,
     )
 
-import pkg_resources
+'''import pkg_resources
 
-installed_pkg = [pkg.key for pkg in pkg_resources.working_set]
+installed_pkg = [pkg.key for pkg in pkg_resources.working_set]'''#TBE
 
 __all__ = [
     "compile",
@@ -96,8 +96,8 @@ def _get_norm_by_name(x):
     """Returns a normalization layer by name."""
     if hasattr(layers, x):
         return getattr(layers, x)
-    elif "tensorflow-addons" in installed_pkg and hasattr(tfa.layers, x):
-        return getattr(tfa.layers, x)
+        '''elif "tensorflow-addons" in installed_pkg and hasattr(tfa.layers, x):
+            return getattr(tfa.layers, x)'''#TBE
     else:
         raise ValueError(f"Unknown normalization {x}.")
 

deeptrack/optics.py

@@ -16,7 +16,7 @@
 """
 
 
-from pint.quantity import Quantity
+from pint import Quantity
 from deeptrack.backend.units import (
     ConversionTable,
     create_context,

deeptrack/properties.py

@@ -2,10 +2,7 @@
 """
 
 import numpy as np
-from .utils import (
-    isiterable,
-    get_kwarg_names,
-)
+from .utils import get_kwarg_names
 
 
 from .backend.core import DeepTrackNode
@@ -58,7 +55,7 @@ def create_action(self, sampling_rule, **dependencies):
         if isinstance(sampling_rule, (tuple, np.ndarray)):
             return lambda _ID=(): sampling_rule
 
-        if isiterable(sampling_rule):
+        if hasattr(sampling_rule, "__next__"):
             # If it's iterable, return the next value
             def wrapped_iterator():
                 while True:

deeptrack/scatterers.py

@@ -65,7 +65,7 @@ class Scatterer(Feature):
         A default value of the characteristic of the particle. Used by
         optics unless a more direct property is set (eg. `refractive_index`
         for `Brightfield` and `intensity` for `Fluorescence`).
-    position_unit : "meter" or "pixel"
+    position_unit : "meter" or "img_pixel"
         The unit of the provided position property.
 
     Other Parameters
@@ -80,7 +80,7 @@ class Scatterer(Feature):
     __list_merge_strategy__ = MERGE_STRATEGY_APPEND
     __distributed__ = False
     __conversion_table__ = ConversionTable(
-        position=(u.pixel, u.pixel),
+        position=(u.img_pixel, u.img_pixel),
         z=(u.zpixel, u.zpixel),
         voxel_size=(u.meter, u.meter),
     )
@@ -90,7 +90,7 @@ def __init__(
         position: PropertyLike[ArrayLike[float]] = (32, 32),
         z: PropertyLike[float] = 0.0,
         value: PropertyLike[float] = 1.0,
-        position_unit: PropertyLike[str] = "pixel",
+        position_unit: PropertyLike[str] = "img_pixel",
         upsample: PropertyLike[int] = 1,
         voxel_size=None,
         pixel_size=None,
@@ -529,7 +529,7 @@ class MieScatterer(Scatterer):
         collection_angle=(u.radian, u.radian),
         wavelength=(u.meter, u.meter),
         offset_z=(u.meter, u.meter),
-        coherence_length=(u.meter, u.pixel),
+        coherence_length=(u.meter, u.img_pixel),
     )
 
     def __init__(

deeptrack/test/test_aberrations.py

@@ -15,7 +15,7 @@
 
 class TestAberrations(unittest.TestCase):
 
-    particle = PointParticle(position=(32, 32), position_unit="pixel", intensity=1)
+    particle = PointParticle(position=(32, 32), position_unit="img_pixel", intensity=1)
 
     def testGaussianApodization(self):
         aberrated_optics = Fluorescence(

deeptrack/test/test_features.py

@@ -946,7 +946,7 @@ def test_NonOverlapping_resample_volume_position(self):
         )
 
         positions_no_unit = [1, 2]
-        positions_with_unit = [1 * units.px, 2 * units.px]
+        positions_with_unit = [1 * units.ipx, 2 * units.ipx]
 
         positions_no_unit_iter = iter(positions_no_unit)
         positions_with_unit_iter = iter(positions_with_unit)
@@ -963,7 +963,7 @@ def test_NonOverlapping_resample_volume_position(self):
         self.assertEqual(volume_1.get_property("position"), positions_no_unit[0])
         self.assertEqual(
             volume_2.get_property("position"),
-            positions_with_unit[0].to("px").magnitude,
+            positions_with_unit[0].to("ipx").magnitude,
         )
 
         nonOverlapping._resample_volume_position(volume_1)
@@ -972,7 +972,7 @@ def test_NonOverlapping_resample_volume_position(self):
         self.assertEqual(volume_1.get_property("position"), positions_no_unit[1])
         self.assertEqual(
             volume_2.get_property("position"),
-            positions_with_unit[1].to("px").magnitude,
+            positions_with_unit[1].to("ipx").magnitude,
         )
 
     def test_NonOverlapping_check_volumes_non_overlapping(self):
@@ -1170,53 +1170,53 @@ def test_NonOverlapping_check_non_overlapping(self):
 
         # Two spheres at the same position
         volume_test0_a = scatterers.Sphere(
-            radius=5 * units.px, position=(0, 0, 0) * units.px
+            radius=5 * units.ipx, position=(0, 0, 0) * units.ipx
         )()
         volume_test0_b = scatterers.Sphere(
-            radius=5 * units.px, position=(0, 0, 0) * units.px
+            radius=5 * units.ipx, position=(0, 0, 0) * units.ipx
         )()
 
         # Two spheres of the same size, one under the other
         volume_test1_a = scatterers.Sphere(
-            radius=5 * units.px, position=(0, 0, 0) * units.px
+            radius=5 * units.ipx, position=(0, 0, 0) * units.ipx
         )()
         volume_test1_b = scatterers.Sphere(
-            radius=5 * units.px, position=(0, 0, 10) * units.px
+            radius=5 * units.ipx, position=(0, 0, 10) * units.ipx
         )()
 
         # Two spheres of the same size, one under the other, but with a
         # spacing of 1
         volume_test2_a = scatterers.Sphere(
-            radius=5 * units.px, position=(0, 0, 0) * units.px
+            radius=5 * units.ipx, position=(0, 0, 0) * units.ipx
         )()
         volume_test2_b = scatterers.Sphere(
-            radius=5 * units.px, position=(0, 0, 11) * units.px
+            radius=5 * units.ipx, position=(0, 0, 11) * units.ipx
         )()
 
         # Two spheres of the same size, one under the other, but with a
         # spacing of -1
         volume_test3_a = scatterers.Sphere(
-            radius=5 * units.px, position=(0, 0, 0) * units.px
+            radius=5 * units.ipx, position=(0, 0, 0) * units.ipx
         )()
         volume_test3_b = scatterers.Sphere(
-            radius=5 * units.px, position=(0, 0, 9) * units.px
+            radius=5 * units.ipx, position=(0, 0, 9) * units.ipx
         )()
 
         # Two spheres of the same size, diagonally next to each other
         volume_test4_a = scatterers.Sphere(
-            radius=5 * units.px, position=(0, 0, 0) * units.px
+            radius=5 * units.ipx, position=(0, 0, 0) * units.ipx
         )()
         volume_test4_b = scatterers.Sphere(
-            radius=5 * units.px, position=(6, 6, 6) * units.px
+            radius=5 * units.ipx, position=(6, 6, 6) * units.ipx
         )()
 
         # Two spheres of the same size, diagonally next to each other, but
         # with a spacing of 1
         volume_test5_a = scatterers.Sphere(
-            radius=5 * units.px, position=(0, 0, 0) * units.px
+            radius=5 * units.ipx, position=(0, 0, 0) * units.ipx
         )()
         volume_test5_b = scatterers.Sphere(
-            radius=5 * units.px, position=(7, 7, 7) * units.px
+            radius=5 * units.ipx, position=(7, 7, 7) * units.ipx
         )()
 
         # Run tests

deeptrack/test/test_generators.py

@@ -28,7 +28,7 @@ def test_Generator(self):
         )
         scatterer = PointParticle(
             intensity=100,
-            position_unit="pixel",
+            position_unit="img_pixel",
             position=lambda: np.random.rand(2) * 128,
         )
         imaged_scatterer = optics(scatterer)
@@ -59,7 +59,7 @@ def test_ContinuousGenerator(self):
         )
         scatterer = PointParticle(
             intensity=100,
-            position_unit="pixel",
+            position_unit="img_pixel",
             position=lambda: np.random.rand(2) * 128,
         )
         imaged_scatterer = optics(scatterer)
@@ -89,12 +89,12 @@ def test_MultiInputs_ContinuousGenerator(self):
         )
         scatterer_A = PointParticle(
             intensity=100,
-            position_unit="pixel",
+            position_unit="img_pixel",
             position=lambda: np.random.rand(2) * 128,
         )
         scatterer_B = PointParticle(
             intensity=10,
-            position_unit="pixel",
+            position_unit="img_pixel",
             position=lambda: np.random.rand(2) * 128,
         )
         imaged_scatterer_A = optics(scatterer_A)
@@ -135,7 +135,7 @@ def test_CappedContinuousGenerator(self):
         )
         scatterer = PointParticle(
             intensity=100,
-            position_unit="pixel",
+            position_unit="img_pixel",
             position=lambda: np.random.rand(2) * 128,
         )
         imaged_scatterer = optics(scatterer)