diff --git a/README.md b/README.md
index 0e2daeb..16b673b 100644
--- a/README.md
+++ b/README.md
@@ -4,83 +4,54 @@
# What is wavefront shaping?
-Wavefront shaping (WFS) is a technique for controlling the propagation of light in arbitrarily complex structures,
-including strongly scattering materials [[1](#id76)]. In WFS, a spatial light modulator (SLM) is used to shape the phase
-and/or amplitude of the incident light. With a properly constructed wavefront, light can be made to focus
-through [[2](#id57)], or inside [[3](#id46)] scattering materials; or light can be shaped to have other desired
-properties, such as optimal sensitivity for specific measurements [[4](#id47)], specialized point-spread
-functions [[5](#id33)], spectral filtering [[6](#id69)],, or for functions like optical trapping [[7](#id36)].
+Wavefront shaping (WFS) is a technique for controlling the propagation of light in arbitrarily complex structures, including strongly scattering materials [[1](#id82)]. In WFS, a spatial light modulator (SLM) is used to shape the phase and/or amplitude of the incident light. With a properly constructed wavefront, light can be made to focus through [[2](#id61)], or inside [[3](#id50)] scattering materials; or light can be shaped to have other desired properties, such as optimal sensitivity for specific measurements [[4](#id51)], specialized point-spread functions [[5](#id37)], spectral filtering [[6](#id75)], or for functions like optical trapping [[7](#id40)].
-It stands out that an important driving force in WFS is the development of new algorithms, for example to account for
-sample movement [[8](#id35)], experimental conditions [[9](#id64)], to be optimally resilient to noise [[10](#id34)], or
-to use digital twin models to compute the required correction
-patterns [[11](#id56), [12](#id55), [13](#id72), [14](#id38)]. Much progress has been made towards developing fast and
-noise-resilient algorithms, or algorithms designed for specific towards the methodology of wavefront shaping, such as
-using algorithms based on Hadamard patterns, or Fourier-based approaches [[15](#id51)]. Fast techniques that enable
-wavefront shaping in dynamic samples [[16](#id59), [17](#id60)], and many potential applications have been developed and
-prototyped, including endoscopy [[12](#id55)], optical trapping [[18](#id61)], Raman scattering, [[19](#id45)], and
-deep-tissue imaging [[20](#id62)]. Applications extend beyond that of microscope imaging such as optimizing
-photoelectrochemical absorption [[21](#id63)] and tuning random lasers [[22](#id68)].
+It stands out that an important driving force in WFS is the development of new algorithms, for example, to account for sample movement [[8](#id39)], experimental conditions [[9](#id69)], to be optimally resilient to noise [[10](#id38)], or to use digital twin models to compute the required correction patterns [[11](#id60), [12](#id59), [13](#id77), [14](#id42)]. Much progress has been made towards developing fast and noise-resilient algorithms, or algorithms designed specifically for the methodology of wavefront shaping, such as using algorithms based on Hadamard patterns or Fourier-based approaches [[15](#id55)]. Fast techniques that enable wavefront shaping in dynamic samples [[16](#id64), [17](#id65)] have also been developed, and many potential applications have been prototyped, including endoscopy [[12](#id59)], optical trapping [[18](#id66)], Raman scattering [[19](#id49)], and deep-tissue imaging [[20](#id67)]. Applications extend beyond that of microscope imaging, such as in optimizing photoelectrochemical absorption [[21](#id68)] and tuning random lasers [[22](#id74)].
-With the development of these advanced algorithms, however, the complexity of WFS software is steadily increasing as the
-field matures, which hinders cooperation as well as end-user adoption. Code for controlling wavefront shaping tends to
-be complex and setup-specific, and developing this code typically requires detailed technical knowledge and low-level
-programming. A recent c++ based contribution [[23](#id67)], highlights the growing need for software based tools that
-enable use and development. Moreover, since many labs use their own in-house programs to control the experiments,
-sharing and re-using code between different research groups is troublesome.
+With the development of these advanced algorithms, however, the complexity of WFS software is steadily increasing as the field matures, which hinders cooperation as well as end-user adoption. Code for controlling wavefront shaping tends to be complex and setup-specific, and developing this code typically requires detailed technical knowledge and low-level programming. Moreover, since many labs use their own in-house programs to control the experiments, sharing and re-using code between different research groups is troublesome.
+
+Even though authors are increasingly sharing their code, for example for controlling spatial light modulators (SLMs) [[23](#id63)], or running genetic algorithms [[24](#id73)], a modular framework that combines all aspects of hardware control, simulation, and graphical user interface (GUI) integration is still lacking.
# What is OpenWFS?
-OpenWFS is a Python package for performing and for simulating wavefront shaping experiments. It aims to accelerate
-wavefront shaping research by providing:
+OpenWFS is a Python package that is primarily designed for performing and for simulating wavefront shaping experiments. It aims to accelerate wavefront shaping research by providing:
-* **Hardware control**. Modular code for controlling spatial light modulators, cameras, and other hardware typically
- encountered in wavefront shaping experiments. Highlights include:
- > * **Spatial light modulator**. The :class:`.~SLM` object provides a versatile way to control spatial light
- modulators,
- allowing for software lookup tables, synchronization, texture warping, and multi-texture functionality accelerated
- by OpenGL.
- > * **Scanning microscope**. The :class:`.~ScanningMicroscope` object uses a National Instruments data acquisition
- card to
- control a laser-scanning microscope.
- > * **GenICam cameras**. The :class:`.~Camera` object uses the harvesters backend [[24](#id39)] to access any camera
- supporting
- the GenICam standard [[25](#id42)].
- > * **Automatic synchronization**. OpenWFS provides tools for automatic synchronization of actuators (e.g. an SLM) and
- detectors (e.g. a camera). The automatic synchronization makes it trivial to perform pipelined measurements that
- avoid the delay normally caused by the latency of the video card and SLM.
-* **Wavefront shaping algorithms**. A (growing) collection of wavefront shaping algorithms. OpenWFS abstracts the
- hardware control, synchronization, and signal processing so that the user can focus on the algorithm itself. As a
- result, most algorithms can be implemented cleanly without hardware-specific programming.
-* **Simulation**. OpenWFS provides an extensive framework for testing and simulating wavefront shaping algorithms,
- including the effect of measurement noise, stage drift, and user-defined aberrations. This allows for rapid
- prototyping and testing of new algorithms, without the need for physical hardware.
-* **Platform for exchange and joint collaboration**. OpenWFS can be used as a platform for sharing and exchanging
- wavefront shaping algorithms. The package is designed to be modular and easy to expand, and it is our hope that the
- community will contribute to the package by adding new algorithms, hardware control modules, and simulation tools.
- Python was specifically chosen for this purpose for its active community, high level of abstraction and the ease of
- sharing tools. Further expansion of the supported hardware is of high priority, especially wrapping c-based software
- support with tools like ctypes and the Micro-Manager based device adapters.
-* **Platform for simplifying use of wavefront shaping**. OpenWFS is compatible with the recently developed PyDevice [],
- and can therefore be controlled from Micro-Manager [[26](#id65)], a commonly used microscopy control platform.
-* **Automated troubleshooting**. OpenWFS provides tools for automated troubleshooting of wavefront shaping experiments.
- This includes tools for measuring the performance of wavefront shaping algorithms, and for identifying common problems
- such as incorrect SLM calibration, drift, measurement noise, and other experimental imperfections.
+* **Hardware control**. Modular code for controlling spatial light modulators, cameras, and other hardware typically encountered in wavefront shaping experiments. Highlights include:
+ > * **Spatial light modulator**. The `SLM` object provides a versatile way to control spatial light modulators, allowing for software lookup tables, synchronization, texture warping, and multi-texture functionality accelerated by OpenGL.
+ > * **Scanning microscope**. The `ScanningMicroscope` object uses a National Instruments data acquisition card to control a laser-scanning microscope.
+ > * **GenICam cameras**. The `Camera` object uses the `harvesters` backend [[25](#id43)] to access any camera supporting the GenICam standard [[26](#id46)].
+ > * **Automatic synchronization**. OpenWFS provides tools for automatic synchronization of actuators (e.g. an SLM) and detectors (e.g. a camera). The automatic synchronization makes it trivial to perform pipelined measurements that avoid the delay normally caused by the latency of the video card and SLM.
+* **Simulation**. The ability to simulate optical experiments is essential for the rapid development and debugging of wavefront shaping algorithms. OpenWFS provides an extensive framework for testing and simulating wavefront shaping algorithms, including the effect of measurement noise, stage drift, and user-defined aberrations. This allows for rapid prototyping and testing of new algorithms without the need for physical hardware.
+* **Wavefront shaping algorithms**. A growing collection of wavefront shaping algorithms. OpenWFS abstracts the hardware control, synchronization, and signal processing so that the user can focus on the algorithm itself. As a result, even advanced algorithms can be implemented in a few dozens of lines of code, and automatically work with any combination of hardware and simulation tools that OpenWFS supports.
+* **Platform for exchange and joint collaboration**. OpenWFS can be used as a platform for sharing and exchanging wavefront shaping algorithms. The package is designed to be modular and easy to expand, and it is our hope that the community will contribute to the package by adding new algorithms, hardware control modules, and simulation tools. Python was specifically chosen for this purpose for its active community, high level of abstraction and the ease of sharing tools.
+* **Micro-Manager compatibility**. Micro-Manager [[27](#id71)], a widely used open-source microscopy control platform. The devices in OpenWFS, such as GenICam camera’s, or the scanning microscope, as well as all algorithms, can be controlled from Micro-Manager using the recently developed [[28](#id83)] adapter that imports Python scripts into Micro-Manager
+* **Automated troubleshooting**. OpenWFS provides tools for automated troubleshooting of wavefront shaping experiments. This includes tools for measuring the performance of wavefront shaping algorithms, and for identifying common problems such as incorrect SLM calibration, drift, measurement noise, and other experimental imperfections.
# Getting started
-OpenWFS is available on the PyPI repository, and it can be installed with the command `pip install openwfs`. The latest
-documentation and the example code can be found on the [Read the Docs](https://openwfs.readthedocs.io/en/latest/)
-website [[27](#id66)]. To use OpenWFS, you need to have Python 3.9 or later installed. At the time of writing, OpenWFS
-is tested up to Python version 3.11 (not all dependencies were available for Python 3.12 yet). OpenWFS is developed and
-tested on Windows 11 and Manjaro Linux.
+To use OpenWFS, Python 3.9 or later is required. Since it is available on the PyPI repository, OpenWFS can be installed using `pip`:
-[Listing 3.1](#hello-wfs) shows an example of how to use OpenWFS to run a simple wavefront shaping experiment. This
-example illustrates several of the main concepts of OpenWFS. First, the code initializes objects to control a spatial
-light modulator (SLM) connected to a video port, and a camera that provides feedback to the wavefront shaping algorithm.
+```bash
+pip install openwfs[all]
+```
-
+This will also install the optional dependencies for OpenWFS:
+
+*opengl* For the OpenGL-accelerated SLM control, the `PyOpenGL` package is installed. In order for this package to work, an OpenGL-compatible graphics card and driver is required.
+
+*genicam* For the GenICam camera support, the `harvesters` package is installed, which, in turn, needs the `genicam` package. At the time of writing, this package is only available for Python versions up to 3.11. To use the GenICam camera support, you also need a compatible camera with driver installed.
+
+* nidaq\* For the scanning microscope, the `nidaqmx` package is installed, which requires a National Instruments data acquisition card with corresponding drivers to be installed on your system.
+If these dependencies cannot be installed on your system, the installation will fail. In this case, you can instead install OpenWFS without dependencies by omitting `[all]` in the installation command, and manually install only the required dependencies, e.g. `pip install openwfs[opengl]`.
+
+At the time of writing, OpenWFS is at version 0.1.0, and it is tested up to Python version 3.11 on Windows 11 and Manjaro and Ubuntu Linux distributions. Note that the latest versions of the package will be available on the PyPI repository, and the latest documentation and the example code can be found on the [Read the Docs](https://openwfs.readthedocs.io/en/latest/) website [[29](#id72)]. The source code can be found on [[30](#id45)].
+
+[Listing 3.1](#hello-wfs) shows an example of how to use OpenWFS to run a simple wavefront shaping experiment. This example illustrates several of the main concepts of OpenWFS. First, the code initializes objects to control a spatial light modulator (SLM) connected to a video port, and a camera that provides feedback to the wavefront shaping algorithm. It then runs a WFS algorithm to focus the light.
+
+This example uses the `StepwiseSequential` wavefront shaping algorithm [[31](#id62)]. The algorithm needs access to the SLM for controlling the wavefront. This feedback is obtained from a `SingleRoi` object, which takes images from the camera, and averages them over the specified circular region of interest. The algorithm returns the measured transmission matrix in the field results.t, which is used to compute the optimal phase pattern to compensate the aberrations. Finally, the code measures the intensity at the detector before and after applying the optimized phase pattern.
+
+
```python
"""
Hello wavefront shaping
@@ -117,223 +88,40 @@ after = feedback.read()
print(f"Intensity in the target increased from {before} to {after}")
```
-This example uses the StepwiseSequential wavefront shaping algorithm [[28](#id58)]. The algorithm needs access to the
-SLM for controlling the wavefront. This feedback is obtained from a `SingleRoi` object, which takes images from the
-camera, and averages them over the specified circular region of interest. The algorithm returns the measured
-transmission matrix in the field results.t, which is used to compute the optimal phase pattern to compensate the
-aberrations. Finally, the code measures the intensity at the detector before and after applying the optimized phase
-pattern.
-
-This code illustrates how OpenWFS separates the concerns of the hardware control (SLM and Camera), signal processing (
-SingleROIProcessor) and the algorithm itself (StepwiseSequential). A large variety of wavefront shaping experiments can
-be performed by using different types of feedback signals (such as optimizing multiple foci simultaneously using
-a `MultiRoiProcessor` object), using different algorithms, or different image sources, such as a `ScanningMicroscope`.
-Notably, these objects can be replaced by *mock* objects, that simulate the hardware and allow for rapid prototyping and
-testing of new algorithms without direct access to wavefront shaping hardware (see `section-simulations`).
-
-# Analysis and troubleshooting
-
-The principles of wavefront shaping are well established, and under close-to-ideal experimental conditions, it is
-possible to accurately predict the signal enhancement. In practice, however, there exist many practical issues that can
-negatively affect the outcome of the experiment. OpenWFS has built-in functions to analyze and troubleshoot the
-measurements from a wavefront shaping experiment.
-
-The `result` structure in [Listing 3.1](#hello-wfs), as returned by the wavefront shaping algorithm, was computed with
-the utility function `analyze_phase_stepping()`. This function extracts the transmission matrix from phase stepping
-measurements, and additionally computes a series of troubleshooting statistics in the form of a *fidelity*, which is a
-number that ranges from 0 (no sensible measurement possible) to 1 (perfect situation, optimal focus expected). These
-fidelities are:
-
-* `fidelity_noise`: The fidelity reduction due to noise in the measurements.
-* `fidelity_amplitude`: The fidelity reduction due to unequal illumination of the SLM.
-* `fidelity_calibration`: The fidelity reduction due to imperfect phase response of the SLM.
-
-If these fidelities are much lower than 1, this indicates a problem in the experiment, or a bug in the wavefront shaping
-experiment. For a comprehensive overview of the practical considerations in wavefront shaping and their effects on the
-fidelity, please see [[29](#id31)].
-
-Further troubleshooting can be performed with the `troubleshoot()` function, which estimates the following fidelities:
-
-* `fidelity_non_modulated`: The fidelity reduction due to non-modulated light., e.g. due to reflection from the front
- surface of the SLM.
-* `fidelity_decorrelation`: The fidelity reduction due to decorrelation of the field during the measurement.
-
-All fidelity estimations are combined to make an order of magnitude estimation of the expected
-enhancement. `troubleshoot()` returns a `WFSTroubleshootResult` object containing the outcome of the different tests and
-analyses, which can be printed to the console as a comprehensive troubleshooting report with the method `report()`.
-See `examples/troubleshooter_demo.py` for an example of how to use the automatic troubleshooter.
-
-Lastly, the `troubleshoot()` function computes several image frame metrics such as the *unbiased contrast to noise
-ratio* and *unbiased contrast enhancement*. These metrics are especially useful for scenarios where the contrast is
-expected to improve due to wavefront shaping, such as in multi-photon excitation fluorescence (multi-PEF) microscopy.
-Furthermore, `troubleshoot()` tests the image capturing repeatability and runs a stability test by capturing and
-comparing many frames over a longer period of time.
+This code illustrates how OpenWFS separates the concerns of the hardware control (`SLM` and `Camera`), signal processing (`SingleRoi`) and the algorithm itself (`StepwiseSequential`). A large variety of wavefront shaping experiments can be performed by using different types of feedback signals (such as optimizing multiple foci simultaneously using a `MultiRoi` object), using different algorithms, or different image sources, such as a `ScanningMicroscope`. Notably, these objects can be replaced by *mock* objects, that simulate the hardware and allow for rapid prototyping and testing of new algorithms without direct access to wavefront shaping hardware (see `section-simulations`).
# Acknowledgements
-We would like to thank Gerwin Osnabrugge, Bahareh Mastiani, Giulia Sereni, Siebe Meijer, Gijs Hannink, Merle van Gorsel,
-Michele Gintoli, Karina van Beek, Abhilash Thendiyammal, Lyuba Amitonova, and Tzu-Lun Wang for their contributions to
-earlier revisions of our wavefront shaping code. This work was supported by the European Research Council under the
-European Union’s Horizon 2020 Programme / ERC Grant Agreement n° [678919], and the Dutch Research Council (NWO) through
-Vidi grant number 14879.
-
-# Conflict of interest statement
-
-The authors declare no conflict of interest.
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+We would like to thank Gerwin Osnabrugge, Bahareh Mastiani, Giulia Sereni, Siebe Meijer, Gijs Hannink, Merle van Gorsel, Michele Gintoli, Karina van Beek, Abhilash Thendiyammal, Lyuba Amitonova, and Tzu-Lun Wang for their contributions to earlier revisions of our wavefront shaping code. This work was supported by the European Research Council under the European Union’s Horizon 2020 Programme / ERC Grant Agreement n° [678919], and the Dutch Research Council (NWO) through Vidi grant number 14879.
+
+* **[1]** Joel Kubby, Sylvain Gigan, and Meng Cui, editors. *Wavefront Shaping for Biomedical Imaging*. Advances in Microscopy and Microanalysis. Cambridge University Press, 2019. [doi:10.1017/9781316403938](https://doi.org/10.1017/9781316403938).
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diff --git a/docs/source/conf.py b/docs/source/conf.py
index f78cba6..55219b4 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -137,22 +137,11 @@
# Hide some classes that are not production ready yet
def skip(_app, _what, name, _obj, do_skip, _options):
- if name in ("Gain"):
+ if name in ("Gain",):
return True
return do_skip
-def visit_citation(self, node):
- """Patch-in function for markdown builder to support citations."""
- id = node["ids"][0]
- self.add(f'')
-
-
-def visit_label(_self, _node):
- """Patch-in function for markdown builder to support citations."""
- pass
-
-
def setup(app):
# register event handlers
app.connect("autodoc-skip-member", skip)
@@ -163,8 +152,7 @@ def setup(app):
# monkey-patch the MarkdownTranslator class to support citations
# TODO: this should be done in the markdown builder itself
cls = MarkdownBuilder.default_translator_class
- cls.visit_citation = visit_citation
- cls.visit_label = visit_label
+ cls.visit_citation = cls.visit_footnote
def source_read(app, docname, source):
diff --git a/pyproject.toml b/pyproject.toml
index 3809bf7..a2c4ad3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -73,7 +73,7 @@ sphinx_mdinclude = ">=0.5.0"
sphinx-rtd-theme = ">=2.0.0"
sphinx-autodoc-typehints = ">=2.2.0"
sphinxcontrib-bibtex = ">=2.6.0"
-sphinx-markdown-builder = ">=0.6.6"
+sphinx-markdown-builder = "=0.6.7"
sphinx-gallery = ">=0.15.0"