Merge pull request #28 from IvoVellekoop/further-revisions

Further revisions

.github/workflows/pytest.yml

@@ -10,15 +10,23 @@ jobs:
         python-version: ["3.9", "3.10", "3.11"]
 
     steps:
-      - uses: actions/checkout@v4
+      - name: Check out the code
+        uses: actions/checkout@v4
+
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-      - name: Install dependencies
+
+      - name: Install Poetry
         run: |
           python -m pip install --upgrade pip
-          pip install .[dev]
-      - name: Test with pytest
+          pip install poetry
+
+      - name: Install dependencies
+        run: |
+          poetry install --with dev
+
+      - name: Run tests
         run: |
-          pytest
+          poetry run pytest

docs/source/conf.py

@@ -34,13 +34,9 @@
 # basic project information
 project = "OpenWFS"
 copyright = "2023-, Ivo Vellekoop, Daniël W. S. Cox, and Jeroen H. Doornbos, University of Twente"
-author = (
-    "Jeroen H. Doornbos, Daniël W. S. Cox, Tom Knop, Harish Sasikumar, Ivo M. Vellekoop"
-)
+author = "Jeroen H. Doornbos, Daniël W. S. Cox, Tom Knop, Harish Sasikumar, Ivo M. Vellekoop"
 release = "0.1.0rc2"
-html_title = (
-    "OpenWFS - a library for conducting and simulating wavefront shaping experiments"
-)
+html_title = "OpenWFS - a library for conducting and simulating wavefront shaping experiments"
 #   \renewenvironment{sphinxtheindex}{\setbox0\vbox\bgroup\begin{theindex}}{\end{theindex}}
 
 # latex configuration
@@ -167,23 +163,17 @@ def setup(app):
 def source_read(app, docname, source):
     if docname == "readme" or docname == "conclusion":
         if (app.builder.name == "latex") == (docname == "conclusion"):
-            source[0] = source[0].replace(
-                "%endmatter%", ".. include:: acknowledgements.rst"
-            )
+            source[0] = source[0].replace("%endmatter%", ".. include:: acknowledgements.rst")
         else:
             source[0] = source[0].replace("%endmatter%", "")
 
 
 def builder_inited(app):
     if app.builder.name == "html":
-        exclude_patterns.extend(
-            ["conclusion.rst", "index_latex.rst", "index_markdown.rst"]
-        )
+        exclude_patterns.extend(["conclusion.rst", "index_latex.rst", "index_markdown.rst"])
         app.config.master_doc = "index"
     elif app.builder.name == "latex":
-        exclude_patterns.extend(
-            ["auto_examples/*", "index_markdown.rst", "index.rst", "api*"]
-        )
+        exclude_patterns.extend(["auto_examples/*", "index_markdown.rst", "index.rst", "api*"])
         app.config.master_doc = "index_latex"
     elif app.builder.name == "markdown":
         include_patterns.clear()

docs/source/readme.rst

@@ -1,22 +1,15 @@
-.. _root-label:
-
-OpenWFS
-=====================================
-
-..
-    NOTE: README.MD IS AUTO-GENERATED FROM DOCS/SOURCE/README.RST. DO NOT EDIT README.MD DIRECTLY.
-
-.. only:: html
-
-    .. image:: https://readthedocs.org/projects/openwfs/badge/?version=latest
-       :target: https://openwfs.readthedocs.io/en/latest/?badge=latest
-       :alt: Documentation Status
-
 .. only:: markdown
+
+   .. raw:: html
+
+      [![PyTest Status](https://github.com/IvoVellekoop/openwfs/actions/workflows/pytest.yml/badge.svg)](https://github.com/IvoVellekoop/openwfs/actions/workflows/pytest.yml)
+      [![Black Code Style Status](https://github.com/IvoVellekoop/openwfs/actions/workflows/black.yml/badge.svg)](https://github.com/IvoVellekoop/openwfs/actions/workflows/black.yml)</p>
 
-   [![PyTest](https://github.com/IvoVellekoop/openwfs/actions/workflows/pytest.yml/badge.svg)](https://github.com/IvoVellekoop/openwfs/actions/workflows/pytest.yml)
-   [![Black](https://github.com/IvoVellekoop/openwfs/actions/workflows/black.yml/badge.svg)](https://github.com/IvoVellekoop/openwfs/actions/workflows/black.yml)
-
+..
+   NOTE: README.MD IS AUTO-GENERATED FROM DOCS/SOURCE/README.RST. DO NOT EDIT README.MD DIRECTLY. 
+    
+..
+      
 What is wavefront shaping?
 --------------------------------
 

examples/slm_demo.py

@@ -34,9 +34,7 @@
 p4.phases = 1
 p4.additive_blend = False
 
-pf.phases = patterns.lens(
-    100, f=1 * u.m, wavelength=0.8 * u.um, extent=(10 * u.mm, 10 * u.mm)
-)
+pf.phases = patterns.lens(100, f=1 * u.m, wavelength=0.8 * u.um, extent=(10 * u.mm, 10 * u.mm))
 rng = np.random.default_rng()
 for n in range(200):
     random_data = rng.random([10, 10], np.float32) * 2.0 * np.pi

examples/troubleshooter_demo.py

@@ -56,7 +56,5 @@
 roi_background = SingleRoi(cam, radius=10)
 
 # Run WFS troubleshooter and output a report to the console
-trouble = troubleshoot(
-    algorithm=alg, background_feedback=roi_background, frame_source=cam, shutter=shutter
-)
+trouble = troubleshoot(algorithm=alg, background_feedback=roi_background, frame_source=cam, shutter=shutter)
 trouble.report()

openwfs/algorithms/basic_fourier.py

@@ -52,14 +52,14 @@ def __init__(
         self.k_step = k_step
         self._slm_shape = slm_shape
         group_mask = np.zeros(slm_shape, dtype=bool)
-        group_mask[:, slm_shape[1] // 2:] = True
+        group_mask[:, slm_shape[1] // 2 :] = True
         super().__init__(
             feedback=feedback,
             slm=slm,
             phase_patterns=None,
             group_mask=group_mask,
             phase_steps=phase_steps,
-            amplitude='uniform',
+            amplitude="uniform",
             iterations=iterations,
             optimized_reference=optimized_reference,
             analyzer=analyzer,

openwfs/algorithms/custom_iter_dual_reference.py

@@ -70,9 +70,7 @@ def __init__(
                 A, B, A, B, A. Should be at least 2
             analyzer: The function used to analyze the phase stepping data. Must return a WFSResult object. Defaults to `analyze_phase_stepping`
         """
-        if (phase_patterns[0].shape[0:2] != group_mask.shape) or (
-            phase_patterns[1].shape[0:2] != group_mask.shape
-        ):
+        if (phase_patterns[0].shape[0:2] != group_mask.shape) or (phase_patterns[1].shape[0:2] != group_mask.shape):
             raise ValueError("The phase patterns and group mask must all have the same shape.")
         if iterations < 2:
             raise ValueError("The number of iterations must be at least 2.")
@@ -93,8 +91,7 @@ def __init__(
 
         # Pre-compute the conjugate modes for reconstruction
         self.modes = [
-            np.exp(-1j * self.phase_patterns[side]) * np.expand_dims(self.masks[side], axis=2)
-            for side in range(2)
+            np.exp(-1j * self.phase_patterns[side]) * np.expand_dims(self.masks[side], axis=2) for side in range(2)
         ]
 
     def execute(self, capture_intermediate_results: bool = False, progress_bar=None) -> WFSResult:
@@ -123,9 +120,7 @@ def execute(self, capture_intermediate_results: bool = False, progress_bar=None)
             None,
             None,
         ]  # The two latest results. Used for computing fidelity factors.
-        intermediate_results = np.zeros(
-            self.iterations
-        )  # List to store feedback from full patterns
+        intermediate_results = np.zeros(self.iterations)  # List to store feedback from full patterns
 
         # Prepare progress bar
         if progress_bar:
@@ -138,9 +133,7 @@ def execute(self, capture_intermediate_results: bool = False, progress_bar=None)
         # Switch the phase sets back and forth multiple times
         for it in range(self.iterations):
             side = it % 2  # pick set A or B for phase stepping
-            ref_phases = -np.angle(
-                t_full
-            )  # use the best estimate so far to construct an optimized reference
+            ref_phases = -np.angle(t_full)  # use the best estimate so far to construct an optimized reference
             side_mask = self.masks[side]
             # Perform WFS experiment on one side, keeping the other side sized at the ref_phases
             result = self._single_side_experiment(
@@ -201,9 +194,7 @@ def execute(self, capture_intermediate_results: bool = False, progress_bar=None)
         result.intermediate_results = intermediate_results
         return result
 
-    def _single_side_experiment(
-        self, mod_phases: nd, ref_phases: nd, mod_mask: nd, progress_bar=None
-    ) -> WFSResult:
+    def _single_side_experiment(self, mod_phases: nd, ref_phases: nd, mod_mask: nd, progress_bar=None) -> WFSResult:
         """
         Conducts experiments on one part of the SLM.
 

openwfs/algorithms/dual_reference.py

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Optional, Union
 
 import numpy as np
 from numpy import ndarray as nd
@@ -38,7 +38,7 @@ def __init__(
         feedback: Detector,
         slm: PhaseSLM,
         phase_patterns: Optional[tuple[nd, nd]],
-        amplitude: Optional[tuple[nd, nd] | str],
+        amplitude: Optional[Union[tuple[nd, nd], str]],
         group_mask: nd,
         phase_steps: int = 4,
         iterations: int = 2,
@@ -89,9 +89,7 @@ def __init__(
         if iterations < 2:
             raise ValueError("The number of iterations must be at least 2.")
         if not optimized_reference and iterations != 2:
-            raise ValueError(
-                "When not using an optimized reference, the number of iterations must be 2."
-            )
+            raise ValueError("When not using an optimized reference, the number of iterations must be 2.")
 
         self.slm = slm
         self.feedback = feedback
@@ -108,7 +106,7 @@ def __init__(
             ~mask,
             mask,
         )  # self.masks[0] is True for group A, self.masks[1] is True for group B
-        self.amplitude = amplitude      # Note: when 'uniform' is passed, the shape of self.masks[0] is used.
+        self.amplitude = amplitude  # Note: when 'uniform' is passed, the shape of self.masks[0] is used.
         self.phase_patterns = phase_patterns
 
     @property
@@ -121,19 +119,17 @@ def amplitude(self, value):
             self._amplitude = None
             return
 
-        if value == 'uniform':
+        if value == "uniform":
             self._amplitude = tuple(
-                (np.ones(shape=self._shape) / np.sqrt(self.masks[side].sum())).astype(np.float32) for side in range(2))
+                (np.ones(shape=self._shape) / np.sqrt(self.masks[side].sum())).astype(np.float32) for side in range(2)
+            )
             return
 
         if value[0].shape != self._shape or value[1].shape != self._shape:
-            raise ValueError(
-                "The amplitude and group mask must all have the same shape."
-            )
+            raise ValueError("The amplitude and group mask must all have the same shape.")
 
         self._amplitude = value
 
-
     @property
     def phase_patterns(self) -> tuple[nd, nd]:
         return self._phase_patterns
@@ -148,26 +144,14 @@ def phase_patterns(self, value):
         if not self.optimized_reference:
             # find the modes in A and B that correspond to flat wavefronts with phase 0
             try:
-                a0_index = next(
-                    i
-                    for i in range(value[0].shape[2])
-                    if np.allclose(value[0][:, :, i], 0)
-                )
-                b0_index = next(
-                    i
-                    for i in range(value[1].shape[2])
-                    if np.allclose(value[1][:, :, i], 0)
-                )
+                a0_index = next(i for i in range(value[0].shape[2]) if np.allclose(value[0][:, :, i], 0))
+                b0_index = next(i for i in range(value[1].shape[2]) if np.allclose(value[1][:, :, i], 0))
                 self.zero_indices = (a0_index, b0_index)
             except StopIteration:
-                raise (
-                    "For multi-target optimization, the both sets must contain a flat wavefront with phase 0."
-                )
+                raise ("For multi-target optimization, the both sets must contain a flat wavefront with phase 0.")
 
         if (value[0].shape[0:2] != self._shape) or (value[1].shape[0:2] != self._shape):
-            raise ValueError(
-                "The phase patterns and group mask must all have the same shape."
-            )
+            raise ValueError("The phase patterns and group mask must all have the same shape.")
 
         self._phase_patterns = (
             value[0].astype(np.float32),
@@ -200,7 +184,7 @@ def _compute_cobasis(self):
         denotes the matrix inverse, and ⁺ denotes the Moore-Penrose pseudo-inverse.
         """
         if self.phase_patterns is None:
-            raise('The phase_patterns must be set before computing the cobasis.')
+            raise ("The phase_patterns must be set before computing the cobasis.")
 
         cobasis = [None, None]
         for side in range(2):
@@ -215,9 +199,7 @@ def _compute_cobasis(self):
 
         self._cobasis = cobasis
 
-    def execute(
-        self, *, capture_intermediate_results: bool = False, progress_bar=None
-    ) -> WFSResult:
+    def execute(self, *, capture_intermediate_results: bool = False, progress_bar=None) -> WFSResult:
         """
         Executes the blind focusing dual reference algorithm and compute the SLM transmission matrix.
             capture_intermediate_results: When True, measures the feedback from the optimized wavefront after each iteration.
@@ -237,9 +219,7 @@ def execute(
 
         # Initialize storage lists
         results_all = [None] * self.iterations  # List to store all results
-        intermediate_results = np.zeros(
-            self.iterations
-        )  # List to store feedback from full patterns
+        intermediate_results = np.zeros(self.iterations)  # List to store feedback from full patterns
 
         # Prepare progress bar
         if progress_bar:
@@ -284,9 +264,7 @@ def execute(
             relative = results_all[0].t[self.zero_indices[0], ...] + np.conjugate(
                 results_all[1].t[self.zero_indices[1], ...]
             )
-            factor = (relative / np.abs(relative)).reshape(
-                (1, *self.feedback.data_shape)
-            )
+            factor = (relative / np.abs(relative)).reshape((1, *self.feedback.data_shape))
 
         t_full = self.compute_t_set(results_all[0].t, self.cobasis[0]) + self.compute_t_set(
             factor * results_all[1].t, self.cobasis[1]
@@ -304,9 +282,7 @@ def execute(
         result.intermediate_results = intermediate_results
         return result
 
-    def _single_side_experiment(
-        self, mod_phases: nd, ref_phases: nd, mod_mask: nd, progress_bar=None
-    ) -> WFSResult:
+    def _single_side_experiment(self, mod_phases: nd, ref_phases: nd, mod_mask: nd, progress_bar=None) -> WFSResult:
         """
         Conducts experiments on one part of the SLM.
 
@@ -322,9 +298,7 @@ def _single_side_experiment(
             WFSResult: An object containing the computed SLM transmission matrix and related data.
         """
         num_modes = mod_phases.shape[2]
-        measurements = np.zeros(
-            (num_modes, self.phase_steps, *self.feedback.data_shape)
-        )
+        measurements = np.zeros((num_modes, self.phase_steps, *self.feedback.data_shape))
 
         for m in range(num_modes):
             phases = ref_phases.copy()

openwfs/algorithms/genetic.py

@@ -61,9 +61,7 @@ def __init__(
         self.elite_size = elite_size
         self.generations = generations
         self.generator = generator or np.random.default_rng()
-        self.mutation_count = round(
-            (population_size - elite_size) * np.prod(shape) * mutation_probability
-        )
+        self.mutation_count = round((population_size - elite_size) * np.prod(shape) * mutation_probability)
 
     def _generate_random_phases(self, shape):
         return self.generator.random(size=shape, dtype=np.float32) * (2 * np.pi)

openwfs/algorithms/ssa.py

@@ -48,9 +48,7 @@ def execute(self) -> WFSResult:
             WFSResult: An object containing the computed transmission matrix and statistics.
         """
         phase_pattern = np.zeros((self.n_y, self.n_x), "float32")
-        measurements = np.zeros(
-            (self.n_y, self.n_x, self.phase_steps, *self.feedback.data_shape)
-        )
+        measurements = np.zeros((self.n_y, self.n_x, self.phase_steps, *self.feedback.data_shape))
 
         for y in range(self.n_y):
             for x in range(self.n_x):