pymodaq_plugins_slmsuite

PyMoDAQ plugin giving access to the spatial light modulators (SLM) supported by slmsuite (Holoeye, Meadowlark, Santec, Hamamatsu, mirrored screens, ...). It is meant to be used with the BeamShaping extension of pymodaq_plugins_beam_shaping, but any Dashboard can use the actuators.

This branch targets PyMoDAQ 5.3.x (Python 3.10 to 3.13).

Authors

• Sebastien J. Weber (sebastien.weber@cemes.fr)

Instruments

Actuators

All the actuators have the same interface: the target is a 2D array of the shape of the SLM (or a single value spread over the SLM). It is displayed as a phase in radians, converted to grey levels by slmsuite (wrapping, look-up table, wavefront correction if one was loaded), or as raw grey levels if the Input as grey levels setting is checked or if the data carries the attribute as_grey_levels=True (as DataShaper of BeamShaping does). This is also the interface of the Holoeye plugin and of the mock Shaper of BeamShaping.

• Slmsuite: generic, choose the slmsuite class (SLM type) and give its constructor arguments as a Python dictionary (Arguments)
• SlmsuiteSimulated: simulated SLM of slmsuite, no hardware needed
• SlmsuiteMeadowlark: Meadowlark SLM (Windows only, Blink SDK)
• SlmsuiteHoloeye: Holoeye SLM (SLM Display SDK)

Another SLM supported by slmsuite with specific settings can be added by subclassing pymodaq_plugins_slmsuite.resources.daq_move_SlmsuiteBase.DAQ_Move_SlmsuiteBase.

The working wavelength (Wavelength (nm) setting, default in the configuration file of the plugin) is given to slmsuite to convert phases into grey levels.

Status

The simulated SLM of slmsuite is tested automatically (pytest). The Meadowlark and Holoeye actuators have been validated on real devices by the maintainer.

BeamShaping algorithms

The BeamShaping extension of pymodaq_plugins_beam_shaping computes the phase (or amplitude) mask to display on the SLM with an algorithm chosen in its interface. The algorithms are discovered at startup: the ones of pymodaq_plugins_beam_shaping itself (Gerchberg-Saxton, Projections, Direct, Diffractive, Minimize, TorchOptim, ...) and the ones that other installed packages declare in the pymodaq_plugins_beam_shaping.algorithms entry point. This plugin declares one of them, so that the slmsuite algorithms can be used next to the others, on the same input beam and target, and sent to any of the actuators above (or to the Holoeye or mock Shaper actuators).

Installation and use

• pip install pymodaq_plugins_slmsuite[beamshaping] installs pymodaq_plugins_beam_shaping together with this plugin. Without this extra, the actuators work but the algorithm is not available.
• In the Dashboard, name the SLM actuator Shaper (the extension sends the mask to the actuator of this name) and start the BeamShaping extension.
• Choose the algorithm in the algorithm settings of the extension: slmsuite Hologram appears in the list with the others. Switching from one algorithm to another keeps the same input beam and target, so the results can be compared directly (output plots, loss/stopping criteria of the extension).
• The extension reads the number of pixels, the pixel size and whether the SLM has an internal calibration from the Shaper actuator itself (the shaper_geometry and shaper_properties settings groups defined in pymodaq_plugins_utils), which these actuators fill when initialized.

slmsuite Hologram algorithm

It computes the phase with slmsuite.holography.algorithms.Hologram.optimize, in the Fourier plane of a lens (the same setup as the Gerchberg-Saxton algorithm of BeamShaping): the amplitude of the input beam is the nearfield amplitude and the target amplitude is the farfield one. It is iterative: each step runs Iterations per step iterations of the chosen Method and keeps the state of the optimization (weights, fixed farfield phase for WGS-Kim) from step to step. Changing the input beam, the target or the initial phase restarts the optimization.

Method	Reference	Specific settings
GS	Gerchberg-Saxton	none
WGS-Leonardo	https://doi.org/10.1364/OE.15.001913	Feedback exponent (0.8)
WGS-Kim	https://doi.org/10.1364/OL.44.003178	Feedback exponent (0.8), Fix phase at iteration (10)
WGS-Nogrette	https://doi.org/10.1103/PhysRevX.4.021034	Feedback factor (0.1)
WGS-Wu	https://doi.org/10.1364/OE.413723	Feedback exponent (0.5)
WGS-tanh	weights updated with a hyperbolic tangent	Feedback factor (0.2), Feedback exponent (0.5)

The values in parentheses are the defaults of slmsuite, restored when the method is changed. The weighted (WGS) methods give a more uniform intensity on a set of spots or regions than GS. The GPU is used if cupy is installed. The conjugate gradient method of slmsuite is not exposed yet. See the documentation of slmsuite (Hologram.optimize) for the details of the parameters.

Adding your own algorithm

Any package can add algorithms to the extension: write a subclass of pymodaq_plugins_beam_shaping.algorithms.AlgoBase decorated with @AlgorithmFactory.register_algorithm() (see pymodaq_plugins_slmsuite/algorithms/slmsuite_hologram.py for a complete example) and declare the module that holds it in the pymodaq_plugins_beam_shaping.algorithms entry point of its pyproject.toml. For this plugin the entry point is added in hatch_build.py.

Installation instructions

• PyMoDAQ 5.3.x, slmsuite >= 0.5.0 (installed with this plugin)
• The vendor SDK of your SLM has to be installed separately, see the slmsuite documentation for each manufacturer.
• Development: pip install -e ., then check_plugin and pytest (needs pytest-qt)