A Napari plugin for analysing and simulating ISM images
This plugin is built upon the python package BrightEyes-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 the MCS software.
- Simulation of a realistic dataset of tubulin filaments.
- Simulation of realistic ISM Point Spread Functions.
- Summing over the detector array dimension
- Adaptive Pixel Reassignment
- Multi-image deconvolution
You can install
napari-ISM via PyPI:
pip install napari-ISM
or by using napari hub.
It requires the following Python packages
To generate a simulated dataset, go to
File > Open Sample > ISM dataset.
To acces the plugin list, go to
Plugins > Napari-ISM.
To open a .h5 file, go to
File > Open .
You can then sum over the dimensions that are not needed, using the command
The default axes are 0 (repetition), 1 (axial position), and 4 (time).
Note that all the analysis commands expect an input with size
X x Y X Ch.
To see the result of summing over the SPAD dimensions
Ch, use the plugin command
Sum. Then, press
To see the result of Adaptive Pixel Reassignment, use the plugin command
Select as reference image (
ref) the central one. Select an upsampling factor (
which corresponds to the sub-pixel precision of the shift-vector estimation. Then, press
To generate the PSFs, use the plugin command
PSFs. Select an image 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.
To see the result of multi-image deconvolution, use the plugin command
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.
To use Focus-ISM, first select a region on the input dataset using a
Select a rectangle containing mainly in-focus emitters. It will be used as a calibration.
Then, use the plugin command
Focus-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, press
To use FRC, prepare the dataset to be in the shape
Select the theshodling method (
method) and smoothing method (
smoothing) among those available.
Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.
Distributed under the terms of the GNU LGPL v3.0 license, "napari-ISM" is free and open source software
If you encounter any problems, please file an issue along with a detailed description.
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