Light microscopy and adaptive optics, my uncensored and biased opinions

If you are new to adaptive optics (AO) like me, the selection of right hardware can be daunting. Starting with a  wavefront sensor - they range in price, resolution, and many options which are not obvious. By practical trial and error I learned something about resolution, which wasn't obvious to me a year ago. The Shack-Hartmann wavefront sensor (WFS) is essentially a camera with a lenslet array instead of an objective.  There are sensors with 15x15 lenses, 30x30 and higher. Naively, you might think "the more the better" - we are digital age kids used to get high-res for cheap. However, there is a catch. High-res sensor, say, 30x30 lenslets, divides your photon count by 900 per spot. Roughly speaking, when you image a fluorescent bead (or another point source) by a camera with "normal lens" (not a lenslet array), and your peak intensity is 2000, this makes a very nice, high SNR bead image. However, is you switch to Fourier (pupil) plane and image the wave...

For my DIY microscope I had a task - generate a train of digital pulses which simulate camera trigger, so that other devices (galvo and laser) are synched. I wanted to do it in Python , so that it seamlessly integrates in my data acquisition and analysis Jupyter notebook. After some quick search I found a PyDAQmx library which seemed mature and had good examples to begin with. Installation was smooth: download, unzip, open Anaconda prompt, python setup.py install After only 30 min fiddling, I was able to solve my problem in just a few lines of code: Holy crap, it just works, out of the box. Oscilloscope shows nice digital pulses every 100 ms, each 1 ms long. The code is much shorter and cleaner than would be in C, C#, or LabView. PyDAQmx appears to be a full-power wrapper around native NI DAQmx drivers (yes, they need to be installed), so presumably it can do all that can be done in C or even LabView (this statement needs to be tested). One can use PyDAQmx to control ga...