cleanup, fixed warnings

docs/source/conf.py

@@ -130,7 +130,7 @@
 
 
 # Hide some classes that are not production ready yet
-def skip(app, what, name, obj, do_skip, options):
+def skip(_app, _what, name, _obj, do_skip, _options):
     if name in ("WFSController", "Gain"):
         return True
     return do_skip
@@ -142,7 +142,7 @@ def visit_citation(self, node):
     self.add(f'<a name="{id}"></a>')
 
 
-def visit_label(self, node):
+def visit_label(_self, _node):
     """Patch-in function for markdown builder to support citations."""
     pass
 

examples/micro_manager_microscope.py

@@ -11,7 +11,7 @@
 import astropy.units as u
 import numpy as np
 
-from openwfs.simulation import Microscope, StaticSource
+from openwfs.simulation import Microscope, StaticSource, Camera
 
 specimen_resolution = (1024, 1024)  # height × width in pixels of the specimen image
 specimen_pixel_size = 60 * u.nm  # resolution (pixel size) of the specimen image
@@ -36,12 +36,13 @@
 )
 
 # simulate shot noise in an 8-bit camera with auto-exposure:
-cam = mic.get_camera(
+cam = Camera(
+    mic,
     shot_noise=True,
     digital_max=255,
     data_shape=camera_resolution,
     pixel_size=camera_pixel_size,
 )
 
 # construct dictionary of objects to expose to Micro-Manager
-devices = {"camera": cam, "stage": mic.stage}
+devices = {"camera": cam, "stage": mic.xy_stage}

examples/sample_microscope.py

@@ -10,7 +10,7 @@
 
 import set_path  # noqa - needed for setting the module search path to find openwfs
 from openwfs.plot_utilities import grab_and_show, imshow
-from openwfs.simulation import Microscope, StaticSource
+from openwfs.simulation import Microscope, StaticSource, Camera
 
 specimen_resolution = (1024, 1024)  # height × width in pixels of the specimen image
 specimen_pixel_size = 60 * u.nm  # resolution (pixel size) of the specimen image
@@ -36,7 +36,8 @@
 )
 
 # simulate shot noise in an 8-bit camera with auto-exposure:
-cam = mic.get_camera(
+cam = Camera(
+    mic,
     shot_noise=True,
     digital_max=255,
     data_shape=camera_resolution,

examples/troubleshooter_demo.py

@@ -13,7 +13,7 @@
 
 from openwfs.algorithms import StepwiseSequential, troubleshoot
 from openwfs.processors import SingleRoi
-from openwfs.simulation import SLM, Microscope, Shutter
+from openwfs.simulation import SLM, Microscope, Shutter, Camera
 from openwfs.utilities import set_pixel_size
 
 # === Define virtual devices for a WFS simulation ===
@@ -43,7 +43,7 @@
 )
 
 # Simulate a camera device with gaussian noise and shot noise
-cam = sim.get_camera(analog_max=1e4, shot_noise=True, gaussian_noise_std=4.0)
+cam = Camera(sim, analog_max=1e4, shot_noise=True, gaussian_noise_std=4.0)
 
 # Define feedback as circular region of interest in the center of the frame
 roi_detector = SingleRoi(cam, radius=0.1)

openwfs/algorithms/dual_reference.py

@@ -197,6 +197,7 @@ def _compute_cobasis(self):
         if self.phase_patterns is None:
             raise "The phase_patterns must be set before computing the cobasis."
 
+        # TODO: simplify, integrate in calling function, fix warnings
         cobasis = [None, None]
         for side in range(2):
             p = np.prod(self._shape)  # Number of SLM pixels

openwfs/algorithms/troubleshoot.py

@@ -132,7 +132,8 @@ def pearson_correlation(a: np.ndarray, b: np.ndarray, noise_var: np.ndarray = 0.
     by subtracting the noise variance from the signal variance.
 
     Args:
-        a, b: Real valued arrays.
+        a: real-valued input array.
+        b: real-valued input array.
         noise_var: Variance of uncorrelated noise to compensate for.
     """
     a_dev = a - a.mean()  # Deviations from mean a
@@ -396,7 +397,7 @@ class WFSTroubleshootResult:
 
     Attributes:
         fidelity_non_modulated: The estimated fidelity reduction factor due to the presence of non-modulated light.
-        phase_calibration_ratio: A ratio indicating the correctness of the SLM phase response. An incorrect phase
+        fidelity_phase_calibration: A ratio indicating the correctness of the SLM phase response. An incorrect phase
             response produces a value < 1.
         wfs_result (WFSResult): Object containing the analyzed result of running the WFS algorithm.
         feedback_before: Feedback from before running the WFS algorithm, with a flat wavefront.

openwfs/devices/camera.py

@@ -163,7 +163,7 @@ def duration(self) -> Quantity[u.ms]:
         """Returns the exposure time in milliseconds if software triggering is used.
         Returns ∞ if hardware triggering is used.
         TODO: implement hardware triggering."""
-        return self.exposure_time.to(u.ms)
+        return self.exposure.to(u.ms)
 
     @property
     def exposure(self) -> u.Quantity[u.ms]:

openwfs/simulation/microscope.py

@@ -9,16 +9,8 @@
 
 from ..core import Processor, Detector
 from ..plot_utilities import imshow  # noqa - for debugging
-from ..processors import TransformProcessor
-from ..simulation.mockdevices import XYStage, Camera, StaticSource
-from ..utilities import (
-    project,
-    place,
-    Transform,
-    get_pixel_size,
-    patterns,
-    CoordinateType,
-)
+from ..simulation.mockdevices import XYStage, StaticSource
+from ..utilities import project, place, Transform, get_pixel_size, patterns
 
 
 class Microscope(Processor):
@@ -251,33 +243,3 @@ def pixel_size(self) -> Quantity:
     def data_shape(self):
         """Returns the shape of the image in the image plane"""
         return self._data_shape
-
-    def get_camera(
-        self,
-        *,
-        transform: Optional[Transform] = None,
-        data_shape: Optional[tuple[int, int]] = None,
-        pixel_size: Optional[CoordinateType] = None,
-        **kwargs
-    ) -> Detector:
-        """
-        Returns a simulated camera that observes the microscope image.
-
-        The camera is a MockCamera object that simulates an AD-converter with optional noise.
-        shot noise and readout noise (see MockCamera for options).
-        In addition to the inputs accepted by the MockCamera constructor (data_shape, analog_max, shot_noise, etc.),
-        it is also possible to specify a transform, to mimic the (mis)alignment of the camera.
-
-        Args:
-            transform ():
-            **kwargs ():
-
-        Returns:
-
-        """
-        if transform is None and data_shape is None and pixel_size is None:
-            src = self
-        else:
-            src = TransformProcessor(self, data_shape=data_shape, pixel_size=pixel_size, transform=transform)
-
-        return Camera(src, **kwargs)

openwfs/simulation/mockdevices.py

@@ -328,10 +328,6 @@ def data_shape(self, value):
     def exposure(self) -> Quantity[u.ms]:
         return self.duration
 
-    @exposure.setter
-    def exposure(self, value: Quantity[u.ms]):
-        self.duration = value.to(u.ms)
-
 
 class XYStage(Actuator):
     """

openwfs/utilities/utilities.py

@@ -293,15 +293,20 @@ def project(
 
     The input image is scaled so that the pixel sizes match those of the output,
     and cropped/zero-padded so that the data shape matches that of the output.
-    Optionally, an additional transformation can be specified, e.g., to scale or translate the source image.
-    This transformation is specified as a 2x3 transformation matrix in homogeneous coordinates.
+    Optionally, an additional :class:`~Transform` can be specified, e.g., to scale or translate the source image.
 
     Args:
-        source (np.ndarray): input image.
-            Must have the pixel_size set (see set_pixel_size)
-        transform: transformation to appy to the source image before placing it in the output
-        out (np.ndarray): optional array where the output image is stored in.
-            If specified, `out_shape` is ignored.
+        source: input image.
+        source_extent: extent of the source image in some physical unit.
+            If not given (``None``), the extent metadata of the input image is used.
+            see :func:`~get_extent`.
+        transform: optional transformed (rotate, translate, etc.)
+            to appy to the source image before placing it in the output
+        out: optional array where the output image is stored in.
+        out_extent: extent of the output image in some physical unit.
+            If not given, the extent metadata of the out image is used.
+        out_shape: shape of the output image.
+            This value is ignored if `out` is specified.
 
     Returns:
         np.ndarray: the projected image (`out` if specified, otherwise a new array)

tests/test_algorithms_troubleshoot.py

@@ -15,6 +15,7 @@
     measure_modulated_light_dual_phase_stepping,
 )
 from ..openwfs.processors import SingleRoi
+from ..openwfs.simulation import Camera
 from ..openwfs.simulation import SimulatedWFS, StaticSource, SLM, Microscope
 
 
@@ -214,7 +215,7 @@ def test_fidelity_phase_calibration_ssa_with_noise(n_y, n_x, phase_steps, gaussi
         aberrations=aberration,
         wavelength=800 * u.nm,
     )
-    cam = sim.get_camera(analog_max=1e4, gaussian_noise_std=gaussian_noise_std)
+    cam = Camera(sim, analog_max=1e4, gaussian_noise_std=gaussian_noise_std)
     roi_detector = SingleRoi(cam, radius=0)  # Only measure that specific point
 
     # Define and run WFS algorithm
@@ -253,7 +254,7 @@ def test_measure_modulated_light_dual_phase_stepping_with_noise(num_blocks, phas
     # Aberration and image source
     img = np.zeros((64, 64), dtype=np.int16)
     img[32, 32] = 100
-    src = StaticSource(img, 200 * u.nm)
+    src = StaticSource(img, pixel_size=200 * u.nm)
 
     # SLM, simulation, camera, ROI detector
     slm = SLM(shape=(100, 100))
@@ -264,7 +265,7 @@ def test_measure_modulated_light_dual_phase_stepping_with_noise(num_blocks, phas
         numerical_aperture=1.0,
         wavelength=800 * u.nm,
     )
-    cam = sim.get_camera(analog_max=1e4, gaussian_noise_std=gaussian_noise_std)
+    cam = Camera(sim, analog_max=1e4, gaussian_noise_std=gaussian_noise_std)
     roi_detector = SingleRoi(cam, radius=0)  # Only measure that specific point
 
     # Measure the amount of modulated light (no non-modulated light present)
@@ -316,7 +317,7 @@ def test_measure_modulated_light_dual_phase_stepping_with_noise(
         non_modulated_field_fraction=non_modulated_field,
     )
     sim = Microscope(source=src, incident_field=slm.field, wavelength=800 * u.nm)
-    cam = sim.get_camera(analog_max=1e3, gaussian_noise_std=gaussian_noise_std)
+    cam = Camera(sim, analog_max=1e3, gaussian_noise_std=gaussian_noise_std)
     roi_detector = SingleRoi(cam, radius=0)  # Only measure that specific point
 
     # Measure the amount of modulated light (no non-modulated light present)

tests/test_processors.py

@@ -12,7 +12,7 @@
 )
 def test_croppers():
     img = sk.data.camera()
-    src = StaticSource(img, 50 * u.nm)
+    src = StaticSource(img, pixel_size=50 * u.nm)
     roi = select_roi(src, "disk")
     assert roi.mask_type == "disk"
 

tests/test_simulation.py

@@ -38,8 +38,7 @@ def test_microscope_without_magnification(shape):
 
     # construct microscope
     sim = Microscope(source=src, magnification=1, numerical_aperture=1, wavelength=800 * u.nm)
-
-    cam = sim.get_camera()
+    cam = Camera(sim)
     img = cam.read()
     assert img[256, 256] == 2**16 - 1
 
@@ -138,7 +137,7 @@ def test_slm_tilt():
 
     new_location = signal_location + shift
 
-    cam = sim.get_camera()
+    cam = Camera(sim)
     img = cam.read(immediate=True)
     max_pos = np.unravel_index(np.argmax(img), img.shape)
     assert np.all(max_pos == new_location)
@@ -172,7 +171,7 @@ def test_microscope_wavefront_shaping(caplog):
         wavelength=800 * u.nm,
     )
 
-    cam = sim.get_camera(analog_max=100)
+    cam = Camera(sim, analog_max=100)
     roi_detector = SingleRoi(cam, pos=signal_location, radius=0)  # Only measure that specific point
 
     alg = StepwiseSequential(feedback=roi_detector, slm=slm, phase_steps=3, n_x=3, n_y=3)

tests/test_wfs.py

@@ -13,16 +13,16 @@
 )
 from ..openwfs.algorithms.troubleshoot import field_correlation
 from ..openwfs.algorithms.utilities import WFSController
+from ..openwfs.plot_utilities import plot_field
 from ..openwfs.processors import SingleRoi
-from ..openwfs.simulation.mockdevices import GaussianNoise
 from ..openwfs.simulation import SimulatedWFS, StaticSource, SLM, Microscope
-from ..openwfs.plot_utilities import plot_field
+from ..openwfs.simulation.mockdevices import GaussianNoise, Camera
 
 
 @pytest.mark.parametrize("shape", [(4, 7), (10, 7), (20, 31)])
 @pytest.mark.parametrize("noise", [0.0, 0.1])
 @pytest.mark.parametrize("algorithm", ["ssa", "fourier"])
-def test_multi_target_algorithms(shape, noise: float, algorithm: str):
+def test_multi_target_algorithms(shape: tuple[int, int], noise: float, algorithm: str):
     """
     Test the multi-target capable algorithms (SSA and Fourier dual ref).
 
@@ -165,7 +165,7 @@ def test_fourier_microscope():
     img[signal_location] = 100
     slm_shape = (1000, 1000)
 
-    src = StaticSource(img, 400 * u.nm)
+    src = StaticSource(img, pixel_size=400 * u.nm)
     slm = SLM(shape=(1000, 1000))
     sim = Microscope(
         source=src,
@@ -175,7 +175,7 @@ def test_fourier_microscope():
         aberrations=aberration,
         wavelength=800 * u.nm,
     )
-    cam = sim.get_camera(analog_max=100)
+    cam = Camera(sim, analog_max=100)
     roi_detector = SingleRoi(cam, pos=(250, 250))  # Only measure that specific point
     alg = FourierDualReference(feedback=roi_detector, slm=slm, slm_shape=slm_shape, k_radius=1.5, phase_steps=3)
     controller = WFSController(alg)
@@ -231,7 +231,6 @@ def test_phase_shift_correction():
     # compute the phase pattern to optimize the intensity in target 0
     optimised_wf = -np.angle(t)
     sim.slm.set_phases(0)
-    before = sim.read()
 
     optimised_wf -= 5
     signals = []
@@ -376,7 +375,7 @@ def test_simple_genetic(population_size: int, elite_size: int):
 
 @pytest.mark.parametrize("basis_str", ("plane_wave", "hadamard"))
 @pytest.mark.parametrize("shape", ((8, 8), (16, 4)))
-def test_dual_reference_ortho_split(basis_str: str, shape):
+def test_dual_reference_ortho_split(basis_str: str, shape: tuple[int, int]):
     """Test dual reference with an orthonormal phase-only basis.
     Two types of bases are tested: plane waves and Hadamard"""
     do_debug = False
@@ -426,6 +425,9 @@ def test_dual_reference_ortho_split(basis_str: str, shape):
     result = alg.execute()
 
     if do_debug:
+        # Plot the modes
+        import matplotlib.pyplot as plt
+
         plt.figure()
         for m in range(N):
             plt.subplot(*modes_shape[0:2], m + 1)
@@ -512,6 +514,9 @@ def test_dual_reference_non_ortho_split():
     t_field = np.exp(1j * np.angle(result.t))
 
     if do_debug:
+        # Plot the modes
+        import matplotlib.pyplot as plt
+
         plt.figure()
         for m in range(M):
             plt.subplot(N2, N1, m + 1)