Package: napari-ISM
napari-ISMThis plugin is built upon the python packageBrightEyes-ISM. Napari-ISM enables the simulation, loading, and analysis of ISM datasets.
More in detail, it performs:Loading and compression of .h5 files generated by theMCS software.Simulation of a realistic dataset of tubulin filaments.Simulation of realistic ISM Point Spread Functions.Summing over the detector array dimensionAdaptive Pixel ReassignmentMulti-image deconvolutionFocus-ISMInstallationYou can installnapari-ISMviaPyPI:pip install napari-ISMor by usingnapari hub.It requires the following Python packagesnumpy
scipy
h5py
PyQt5
brighteyes-ism>=1.2.0DocumentationTo generate a simulated dataset, go toFile > Open Sample > ISM dataset.To acces the plugin list, go toPlugins > Napari-ISM.To open a .h5 file, go toFile > Open.
You can then sum over the dimensions that are not needed, using the commandintegrateDims.
The default axes are 0 (repetition), 1 (axial position), and 4 (time).Note that all the analysis commands expect an input with sizeX x Y X Ch.To see the result of summing over the SPAD dimensionsCh, use the plugin commandSum. Then, pressRun.To see the result of Adaptive Pixel Reassignment, use the plugin commandAPR_stack.
Select as reference image (ref) the central one. Select an upsampling factor (usf),
which corresponds to the sub-pixel precision of the shift-vector estimation. Then, pressRun.To generate the PSFs, use the plugin commandPSFs. Select an image layer (img layer),
it will be used to determine the number of pixels and the pixel size.
Then, select the detector pixel size (pxsize) and pixel pitch (pxpitch) in microns.
Select the magnification of the system (M). Select the excitation (exWl) and emission wavelength (emWl) in nanometers.
Then, pressRun.To see the result of multi-image deconvolution, use the plugin commandDeconvolution.
Select an image layer (img layer) containing the ISM dataset to deconvolve and another image layer (psf layer) containing the PSFs, either simulated or experimental.
Then, pressRun.To use Focus-ISM, first select a region on the input dataset using ashapeslayer.
Select a rectangle containing mainly in-focus emitters. It will be used as a calibration.
Then, use the plugin commandFocus-ISM. Select an image layer (img layer) containing the ISM dataset and a shape layer (shape layer) defining the calibration region.
Select a lower bound for the standard deviation of the out-of-focus curve (sigma B bound) in units of standard deviations of the in-focus term. We suggest to never select a value below 2.
Select a threshold (threshold) in units of photon counts. Scan coordinates with less photons than the threshold will be skipped in the analysis and classified as background. Then, pressRun.To use FRC, prepare the dataset to be in the shapexyt.
Select the theshodling method (method) and smoothing method (smoothing) among those available.
Then, pressCalculate.ContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theGNU LGPL v3.0license,
"napari-ISM" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.