Merge remote-tracking branch 'origin/master' into further-revisions

# Conflicts:
#	examples/sample_microscope.py
#	openwfs/algorithms/custom_iter_dual_reference.py

examples/micro_manager_microscope.py

@@ -1,41 +1,30 @@
-""" Sample microscope
+""" Micro-Manager simulated microscope
 =======================
-This script simulates a microscopic imaging system, generating a random noise image as a mock source and capturing it
-through a microscope with adjustable magnification, numerical aperture, and wavelength. It visualizes the original and
-processed images dynamically, demonstrating how changes in optical parameters affect image quality and resolution.
+This script simulates a microscope with a random noise image as a mock specimen.
+The numerical aperture, stage position, and other parameters can be modified through the Micro-Manager GUI.
+To use this script as a device in Micro-Manager, make sure you have the PyDevice adapter installed and
+select this script in the hardware configuration wizard for the PyDevice component.
 
-This script should be opened from the μManager microscope GUI software using the PyDevice plugin.
-To do so, add a PyDevice adapter to the μManager hardware configuration, and select this script as the device script.
+See the 'Sample Microscope' example for a microscope simulation that runs from Python directly.
 """
 
 import astropy.units as u
 import numpy as np
 
 from openwfs.simulation import Microscope, StaticSource
 
-# height × width, and resolution, of the specimen image
-specimen_size = (1024, 1024)
-specimen_resolution = 60 * u.nm
-
-# magnification from object plane to camera.
-magnification = 40
-
-# numerical aperture of the microscope objective
-numerical_aperture = 0.85
-
-# wavelength of the light, for computing diffraction.
-wavelength = 532.8 * u.nm
-
-# Size of the pixels on the camera
-pixel_size = 6.45 * u.um
-
-# number of pixels on the camera
-camera_resolution = (256, 256)
+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
+magnification = 40  # magnification from object plane to camera.
+numerical_aperture = 0.85  # numerical aperture of the microscope objective
+wavelength = 532.8 * u.nm  # wavelength of the light, for computing diffraction.
+camera_resolution = (256, 256)  # number of pixels on the camera
+camera_pixel_size = 6.45 * u.um  # Size of the pixels on the camera
 
 # Create a random noise image with a few bright spots
 src = StaticSource(
-    data=np.maximum(np.random.randint(-10000, 100, specimen_size, dtype=np.int16), 0),
-    pixel_size=specimen_resolution,
+    data=np.maximum(np.random.randint(-10000, 100, specimen_resolution, dtype=np.int16), 0),
+    pixel_size=specimen_pixel_size,
 )
 
 # Create a microscope with the given parameters
@@ -51,25 +40,8 @@
     shot_noise=True,
     digital_max=255,
     data_shape=camera_resolution,
-    pixel_size=pixel_size,
+    pixel_size=camera_pixel_size,
 )
 
-# expose the xy-stage of the microscope
-stage = mic.xy_stage
-
 # construct dictionary of objects to expose to Micro-Manager
 devices = {"camera": cam, "stage": stage}
-
-if __name__ == "__main__":
-    # When running this script directly (not from μManager)
-    # the code below shows how to operate the stage and change the numerical aperture of the microscope
-    import matplotlib.pyplot as plt
-
-    for i in range(20):
-        stage.x = stage.x + 2 * u.um
-        mic.numerical_aperture -= 0.025
-        plt.imshow(mic.read(), cmap="gray")
-        if i == 0:
-            plt.colorbar()
-        plt.show(block=False)
-        plt.pause(0.5)

examples/micro_manager_scanning_microscope.py

@@ -1,16 +1,17 @@
-"""
-Constructs a scanning microscope controller for use with Micro-Manager
-
-The microscope object can be loaded into Micro-Manager through the PyDevice
-device adapter.
+""" Micro-Manager simulated scanning microscope
+=======================
+This script simulates a scanning microscope with a pre-set image as a mock specimen.
+The scan parameters can be modified through the Micro-Manager GUI.
+To use this script as a device in Micro-Manager, make sure you have the PyDevice adapter installed and
+select this script in the hardware configuration wizard for the PyDevice component.
 """
 
 import astropy.units as u
-import skimage
 
 # add 'openwfs' to the search path. This is only needed when developing openwfs
 # otherwise it is just installed as a package
 import set_path  # noqa
+import skimage
 from openwfs.devices import ScanningMicroscope, Axis
 from openwfs.devices.galvo_scanner import InputChannel
 

examples/sample_microscope.py

@@ -11,40 +11,23 @@
 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.utilities import set_pixel_size
 
-# Parameters that can be altered
-
-img_size_x = 1024
-# Determines how wide the image is.
-
-img_size_y = 1024
-# Determines how high the image is.
-
-magnification = 40
-# magnification from object plane to camera.
-
-numerical_aperture = 0.85
-# numerical aperture of the microscope objective
-
-wavelength = 532.8 * u.nm
-# wavelength of the light, different wavelengths are possible, units can be adjusted accordingly.
-
-pixel_size = 6.45 * u.um
-# Size of the pixels on the camera
-
-camera_resolution = (256, 256)
-# number of pixels on the camera
-
-p_limit = 100
-# Number of iterations. Influences how quick the 'animation' is complete.
-
-# Code
-img = set_pixel_size(
-    np.maximum(np.random.randint(-10000, 100, (img_size_y, img_size_x), dtype=np.int16), 0),
-    60 * u.nm,
+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
+magnification = 40  # magnification from object plane to camera.
+numerical_aperture = 0.85  # numerical aperture of the microscope objective
+wavelength = 532.8 * u.nm  # wavelength of the light, for computing diffraction.
+camera_resolution = (256, 256)  # number of pixels on the camera
+camera_pixel_size = 6.45 * u.um  # Size of the pixels on the camera
+p_limit = 100  # Number steps in the animation
+
+# Create a random noise image with a few bright spots
+src = StaticSource(
+    data=np.maximum(np.random.randint(-10000, 100, specimen_resolution, dtype=np.int16), 0),
+    pixel_size=specimen_pixel_size,
 )
-src = StaticSource(img)
+
+# Create a microscope with the given parameters
 mic = Microscope(
     src,
     magnification=magnification,
@@ -57,15 +40,15 @@
     shot_noise=True,
     digital_max=255,
     data_shape=camera_resolution,
-    pixel_size=pixel_size,
+    pixel_size=camera_pixel_size,
 )
 devices = {"camera": cam, "stage": mic.xy_stage}
 
 if __name__ == "__main__":
     import matplotlib.pyplot as plt
 
     plt.subplot(1, 2, 1)
-    imshow(img)
+    imshow(src.data)
     plt.title("Original image")
     plt.subplot(1, 2, 2)
     plt.title("Scanned image")

examples/wfs_demonstration_experimental.py

@@ -2,6 +2,9 @@
 WFS demo experiment
 =====================
 This script demonstrates how to perform a wavefront shaping experiment using the openwfs library.
+It assumes that you have a genicam camera and an SLM connected to your computer.
+Please adjust the path to the camera driver and (when needed) the monitor id in the Camera and SLM objects.
+
 """
 
 import astropy.units as u
@@ -42,6 +45,3 @@
     plt.pause(1.0)
     slm.set_phases(0.0)
     plt.pause(1.0)
-
-# plt.show()
-# input("press any key")

openwfs/algorithms/custom_iter_dual_reference.py

@@ -82,7 +82,10 @@ def __init__(
         self.phase_steps = phase_steps
         self.iterations = iterations
         self.analyzer = analyzer
-        self.phase_patterns = (phase_patterns[0].astype(np.float32), phase_patterns[1].astype(np.float32))
+        self.phase_patterns = (
+            phase_patterns[0].astype(np.float32),
+            phase_patterns[1].astype(np.float32),
+        )
         mask = group_mask.astype(bool)
         self.masks = (~mask, mask)  # masks[0] is True for group A, mask[1] is True for group B
 
@@ -113,7 +116,10 @@ def execute(self, capture_intermediate_results: bool = False, progress_bar=None)
         # Initialize storage lists
         t_set_all = [None] * self.iterations
         results_all = [None] * self.iterations  # List to store all results
-        results_latest = [None, None]  # The two latest results. Used for computing fidelity factors.
+        results_latest = [
+            None,
+            None,
+        ]  # The two latest results. Used for computing fidelity factors.
         intermediate_results = np.zeros(self.iterations)  # List to store feedback from full patterns
 
         # Prepare progress bar
@@ -155,7 +161,10 @@ def execute(self, capture_intermediate_results: bool = False, progress_bar=None)
 
         # Compute average fidelity factors
         fidelity_noise = weighted_average(
-            results_latest[0].fidelity_noise, results_latest[1].fidelity_noise, results_latest[0].n, results_latest[1].n
+            results_latest[0].fidelity_noise,
+            results_latest[1].fidelity_noise,
+            results_latest[0].n,
+            results_latest[1].n,
         )
         fidelity_amplitude = weighted_average(
             results_latest[0].fidelity_amplitude,
@@ -172,7 +181,6 @@ def execute(self, capture_intermediate_results: bool = False, progress_bar=None)
 
         result = WFSResult(
             t=t_full,
-            t_f=None,
             n=self.modes[0].shape[2] + self.modes[1].shape[2],
             axis=2,
             fidelity_noise=fidelity_noise,

openwfs/algorithms/genetic.py

@@ -97,7 +97,7 @@ def execute(self, *, progress_bar=None) -> WFSResult:
 
             # Terminate after the specified number of generations, return the best wavefront
             if i >= self.generations:
-                return WFSResult(t=np.exp(-1.0j * population[sorted_indices[-1]]), t_f=None, axis=2)
+                return WFSResult(t=np.exp(-1.0j * population[sorted_indices[-1]]), axis=2)
 
             # We keep the elite individuals, and regenerate the rest by mixing the elite
             # For this mixing, the probability of selecting an individual is proportional to its measured intensity.

openwfs/algorithms/utilities.py

@@ -30,7 +30,6 @@ class WFSResult:
     def __init__(
         self,
         t: np.ndarray,
-        t_f: np.ndarray,
         axis: int,
         fidelity_noise: ArrayLike = 1.0,
         fidelity_amplitude: ArrayLike = 1.0,
@@ -60,7 +59,6 @@ def __init__(
 
         """
         self.t = t
-        self.t_f = t_f
         self.axis = axis
         self.fidelity_noise = np.atleast_1d(fidelity_noise)
         self.n = np.prod(t.shape[0:axis]) if n is None else n
@@ -107,7 +105,6 @@ def select_target(self, b) -> "WFSResult":
         """
         return WFSResult(
             t=self.t.reshape((*self.t.shape[0:2], -1))[:, :, b],
-            t_f=self.t_f.reshape((*self.t_f.shape[0:2], -1))[:, :, b],
             axis=self.axis,
             intensity_offset=self.intensity_offset[:][b],
             fidelity_noise=self.fidelity_noise[:][b],
@@ -139,7 +136,6 @@ def weighted_average(attribute):
 
         return WFSResult(
             t=weighted_average("t"),
-            t_f=weighted_average("t_f"),
             n=n,
             axis=axis,
             fidelity_noise=weighted_average("fidelity_noise"),
@@ -243,7 +239,6 @@ def analyze_phase_stepping(measurements: np.ndarray, axis: int, A: Optional[floa
 
     return WFSResult(
         t,
-        t_f=t_f,
         axis=axis,
         fidelity_amplitude=amplitude_factor,
         fidelity_noise=noise_factor,

openwfs/simulation/mockdevices.py

@@ -7,7 +7,7 @@
 
 from ..core import Detector, Processor, Actuator
 from ..processors import CropProcessor
-from ..utilities import ExtentType, get_pixel_size
+from ..utilities import ExtentType, get_pixel_size, set_pixel_size
 
 
 class StaticSource(Detector):
@@ -42,6 +42,8 @@ def __init__(
                 pixel_size = Quantity(extent) / data.shape
             else:
                 pixel_size = get_pixel_size(data)
+        else:
+            data = set_pixel_size(data, pixel_size)  # make sure the data array holds the pixel size
 
         if pixel_size is not None and (np.isscalar(pixel_size) or pixel_size.size == 1) and data.ndim > 1:
             pixel_size = pixel_size.repeat(data.ndim)