{"name":"napari-ISM","display_name":"Napari-ISM","visibility":"public","icon":"","categories":[],"schema_version":"0.2.0","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"napari-ISM.get_reader","title":"Open data with Adaptive Pixel Reassignment","python_name":"napari_ism._reader:napari_get_reader","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-ISM.write_multiple","title":"Save multi-layer data with Adaptive Pixel Reassignment","python_name":"napari_ism._writer:write_multiple","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-ISM.write_single_image","title":"Save image data with Adaptive Pixel Reassignment","python_name":"napari_ism._writer:write_single_image","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-ISM.make_sample_data","title":"Load sample data from Adaptive Pixel Reassignment","python_name":"napari_ism._sample_data:make_sample_data","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-ISM.APR_stack","title":"Calculate adaptive pixel reassignment on a single dataset or a z-stack","python_name":"napari_ism._widget:APR_stack","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-ISM.Fingerprint","title":"Calculate the fingeprint of a dataset","python_name":"napari_ism._widget:Fingerprint","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-ISM.SumSPAD","title":"Generate an image by summing all the channels","python_name":"napari_ism._widget:SumSPAD","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-ISM.MultiImgDeconvolution","title":"Perform multi-image deconvolution","python_name":"napari_ism._widget:MultiImgDeconvolution","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-ISM.SimulatePSFs","title":"Simulate a dataset of PSFs","python_name":"napari_ism._widget:SimulatePSFs","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-ISM.integrateDims","title":"Sum the dataset along the specified dimensions","python_name":"napari_ism._widget:integrateDims","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-ISM.Focus_ISM","title":"Apply focus-ISM on a single dataset","python_name":"napari_ism._widget:Focus_ISM","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-ISM.FRC","title":"Calculate the FRC curve and resolution from a 3D image (x,y,t)","python_name":"napari_ism._widget:FRC","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":[{"command":"napari-ISM.get_reader","filename_patterns":["*.npy","*.h5"],"accepts_directories":false}],"writers":[{"command":"napari-ISM.write_multiple","layer_types":["image*","labels*"],"filename_extensions":[],"display_name":""},{"command":"napari-ISM.write_single_image","layer_types":["image"],"filename_extensions":[".npy",".h5"],"display_name":""}],"widgets":[{"command":"napari-ISM.APR_stack","display_name":"APR_stack","autogenerate":true},{"command":"napari-ISM.Fingerprint","display_name":"Fingerprint","autogenerate":true},{"command":"napari-ISM.SumSPAD","display_name":"Sum","autogenerate":true},{"command":"napari-ISM.MultiImgDeconvolution","display_name":"Deconvolution","autogenerate":true},{"command":"napari-ISM.SimulatePSFs","display_name":"PSFs","autogenerate":true},{"command":"napari-ISM.integrateDims","display_name":"integrateDims","autogenerate":true},{"command":"napari-ISM.Focus_ISM","display_name":"Focus_ISM","autogenerate":true},{"command":"napari-ISM.FRC","display_name":"FRC","autogenerate":false}],"sample_data":[{"command":"napari-ISM.make_sample_data","key":"unique_id.1","display_name":"ISM dataset"}],"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.1","name":"napari-ISM","version":"1.0.7","dynamic":null,"platform":null,"supported_platform":null,"summary":"A Napari plugin for analysing and simulating ISM images","description":"# napari-ISM\n\n[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-ISM)](https://napari-hub.org/plugins/napari-ISM)\n[![License](https://img.shields.io/pypi/l/napari-ISM.svg?color=green)](https://github.com/VicidominiLab/napari-ISM/raw/main/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/napari-ISM.svg?color=green)](https://pypi.org/project/napari-ISM)\n[![Python Version](https://img.shields.io/pypi/pyversions/napari-ISM.svg?color=green)](https://python.org)\n\n\nThis plugin is built upon the python package [BrightEyes-ISM]. Napari-ISM enables the simulation, loading, and analysis of ISM datasets.\nMore in detail, it performs:\n\n* Loading and compression of .h5 files generated by the [MCS software].\n* Simulation of a realistic dataset of tubulin filaments.\n* Simulation of realistic ISM Point Spread Functions.\n* Summing over the detector array dimension\n* Adaptive Pixel Reassignment\n* Multi-image deconvolution\n* Focus-ISM\n\n----------------------------------\n\n<!--\nDon't miss the full getting started guide to set up your new package:\nhttps://github.com/napari/cookiecutter-napari-plugin#getting-started\n\nand review the napari docs for plugin developers:\nhttps://napari.org/plugins/index.html\n-->\n\n## Installation\n\nYou can install `napari-ISM` via [PyPI]:\n\n    pip install napari-ISM\n    \nor by using [napari hub].\n\nIt requires the following Python packages\n\n    numpy\n    scipy\n    h5py\n    PyQt5\n    brighteyes-ism>=1.2.0\n\n## Documentation\n\nTo generate a simulated dataset, go to `File > Open Sample > ISM dataset`. \n\n![](https://github.com/VicidominiLab/napari-ISM/raw/main/docs/sample.png)\n\nTo acces the plugin list, go to `Plugins > Napari-ISM`.\n\n![](https://github.com/VicidominiLab/napari-ISM/raw/main/docs/plugin_list.png)\n\nTo open a .h5 file, go to `File > Open `.\nYou can then sum over the dimensions that are not needed, using the command `integrateDims`.\nThe default axes are 0 (repetition), 1 (axial position), and 4 (time).\n\n![](https://github.com/VicidominiLab/napari-ISM/raw/main/docs/file.png)\n\nNote that all the analysis commands expect an input with size `X x Y X Ch`.\n\nTo see the result of summing over the SPAD dimensions `Ch`, use the plugin command `Sum`. Then, press `Run`.\n\n![](https://github.com/VicidominiLab/napari-ISM/raw/main/docs/sum.png)\n\nTo see the result of Adaptive Pixel Reassignment, use the plugin command `APR_stack`.\nSelect as reference image (`ref`) the central one. Select an upsampling factor (`usf`), \nwhich corresponds to the sub-pixel precision of the shift-vector estimation. Then, press `Run`.\n\n![](https://github.com/VicidominiLab/napari-ISM/raw/main/docs/apr.png)\n\nTo generate the PSFs, use the plugin command `PSFs`. Select an image layer (`img layer`), \nit will be used to determine the number of pixels and the pixel size.\nThen, select the detector pixel size (`pxsize`) and pixel pitch (`pxpitch`) in microns.\nSelect the magnification of the system (`M`). Select the excitation (`exWl`) and emission wavelength (`emWl`) in nanometers.\nThen, press `Run`.\n\n![](https://github.com/VicidominiLab/napari-ISM/raw/main/docs/PSF.png)\n\nTo see the result of multi-image deconvolution, use the plugin command `Deconvolution`.\nSelect an image layer (`img layer`) containing the ISM dataset to deconvolve and another image layer (`psf layer`) containing the PSFs, either simulated or experimental.\nThen, press `Run`.\n\n![](https://github.com/VicidominiLab/napari-ISM/raw/main/docs/deconv.png)\n\nTo use Focus-ISM, first select a region on the input dataset using a `shapes` layer.\nSelect a rectangle containing mainly in-focus emitters. It will be used as a calibration.\nThen, 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.\nSelect 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.\nSelect 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 `Run`.\n\n![](https://github.com/VicidominiLab/napari-ISM/raw/main/docs/shapes.png)\n\nTo use FRC, prepare the dataset to be in the shape `xyt`.\nSelect the theshodling method (`method`) and smoothing method (`smoothing`) among those available.\nThen, press `Calculate`.\n\n![](https://github.com/VicidominiLab/napari-ISM/raw/main/docs/frc.png)\n\n## Contributing\n\nContributions are very welcome. Tests can be run with [tox], please ensure\nthe coverage at least stays the same before you submit a pull request.\n\n## License\n\nDistributed under the terms of the [GNU LGPL v3.0] license,\n\"napari-ISM\" is free and open source software\n\n## Issues\n\nIf you encounter any problems, please [file an issue] along with a detailed description.\n\n[napari]: https://github.com/napari/napari\n[Cookiecutter]: https://github.com/audreyr/cookiecutter\n[@napari]: https://github.com/napari\n[MIT]: http://opensource.org/licenses/MIT\n[BSD-3]: http://opensource.org/licenses/BSD-3-Clause\n[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt\n[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt\n[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0\n[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt\n[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin\n\n[file an issue]: https://github.com/VicidominiLab/napari-ISM/issues\n\n[napari hub]: https://www.napari-hub.org/plugins/napari-ISM\n[napari]: https://github.com/napari/napari\n[tox]: https://tox.readthedocs.io/en/latest/\n[pip]: https://pypi.org/project/pip/\n[PyPI]: https://pypi.org/project/napari-ISM/\n\n[BrightEyes-ISM]: https://github.com/VicidominiLab/BrightEyes-ISM\n[MCS software]: https://github.com/VicidominiLab/BrightEyes-MCS\n","description_content_type":"text/markdown","keywords":null,"home_page":"https://github.com/VicidominiLab/napari-ISM","download_url":null,"author":"Alessandro Zunino","author_email":"Alessandro Zunino <alessandro.zunino@iit.it>","maintainer":null,"maintainer_email":null,"license":null,"classifier":["Framework :: napari","Programming Language :: Python :: 3.7","Programming Language :: Python :: 3.8","Programming Language :: Python :: 3.9","Programming Language :: Python :: 3.10","Operating System :: OS Independent","License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)"],"requires_dist":["numpy","scipy","h5py","PyQt5","brighteyes-ism >=1.2.2","tox ; extra == 'testing'","pytest ; extra == 'testing'","pytest-cov ; extra == 'testing'","pytest-qt ; extra == 'testing'","napari ; extra == 'testing'","pyqt5 ; extra == 'testing'"],"requires_python":">=3.7","requires_external":null,"project_url":["Homepage, https://github.com/VicidominiLab/napari-ism","Documentation, https://brighteyes-ism.readthedocs.io"],"provides_extra":["testing"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}