{"name":"napari-phasors","display_name":"Napari Phasors","visibility":"public","icon":"","categories":["Annotation","Segmentation","Acquisition"],"schema_version":"0.2.1","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"napari-phasors.get_reader","title":"Open data with Napari Phasors","python_name":"napari_phasors._reader:napari_get_reader","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-phasors.convallaria_FLIM_sample_data","title":"Load convallaria FLIM sample data","python_name":"napari_phasors._sample_data:convallaria_FLIM_sample_data","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-phasors.embryo_FLIM_sample_data","title":"Load FLUTE's FLIM Embryo sample data","python_name":"napari_phasors._sample_data:embryo_FLIM_sample_data","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-phasors.paramecium_HSI_sample_data","title":"Load Paramecium Hyperspectral Image sample data","python_name":"napari_phasors._sample_data:paramecium_HSI_sample_data","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-phasors.plot_phasor","title":"Plot Phasor","python_name":"napari_phasors:PlotterWidget","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-phasors.write_ome_tiff","title":"Write OME-TIFF","python_name":"napari_phasors:write_ome_tiff","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-phasors.PhasorTransform","title":"Phasor Custom Import","python_name":"napari_phasors:PhasorTransform","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-phasors.WriterWidget","title":"Export Phasor","python_name":"napari_phasors:WriterWidget","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":[{"command":"napari-phasors.get_reader","filename_patterns":["*.fbd","*.ptu","*.lsm","*ome.tif","*.tif","*.sdt"],"accepts_directories":false}],"writers":[{"command":"napari-phasors.write_ome_tiff","layer_types":["image*","labels*"],"filename_extensions":[],"display_name":"Export Phasor to OME-TIFF"}],"widgets":[{"command":"napari-phasors.PhasorTransform","display_name":"Phasor Custom Import","autogenerate":false},{"command":"napari-phasors.plot_phasor","display_name":"Phasor Plot","autogenerate":false},{"command":"napari-phasors.WriterWidget","display_name":"Export Phasor","autogenerate":false}],"sample_data":[{"command":"napari-phasors.convallaria_FLIM_sample_data","key":"unique_id.1","display_name":"Convallaria FLIM"},{"command":"napari-phasors.embryo_FLIM_sample_data","key":"unique_id.2","display_name":"FLUTE's FLIM Embryo sample data FLIM"},{"command":"napari-phasors.paramecium_HSI_sample_data","key":"unique_id.3","display_name":"Paramecium Hyperspectral Image"}],"themes":null,"menus":{"napari/file/io_utilities":[{"when":null,"group":null,"submenu":"io_phasors_submenu"}],"napari/layers/visualize":[{"when":null,"group":null,"submenu":"phasor_plot_submenu"}],"napari/layers/data":[{"when":null,"group":null,"submenu":"io_phasors_submenu"}],"phasor_plot_submenu":[{"command":"napari-phasors.plot_phasor","when":null,"group":null,"alt":null}],"io_phasors_submenu":[{"command":"napari-phasors.PhasorTransform","when":null,"group":null,"alt":null},{"command":"napari-phasors.WriterWidget","when":null,"group":null,"alt":null}]},"submenus":[{"id":"phasor_plot_submenu","label":"Phasors","icon":null},{"id":"io_phasors_submenu","label":"Phasors","icon":null}],"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.4","name":"napari-phasors","version":"0.4.0","dynamic":["license-file"],"platform":null,"supported_platform":null,"summary":"A simple plugin to use phasor analysis","description":"# napari-phasors\n\n[![License BSD-3](https://img.shields.io/pypi/l/napari-phasors.svg?color=green)](https://github.com/napari-phasors/napari-phasors/raw/main/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/napari-phasors.svg?color=green)](https://pypi.org/project/napari-phasors)\n[![Python Version](https://img.shields.io/pypi/pyversions/napari-phasors.svg?color=green)](https://python.org)\n[![tests](https://github.com/napari-phasors/napari-phasors/workflows/tests/badge.svg)](https://github.com/napari-phasors/napari-phasors/actions)\n[![codecov](https://codecov.io/gh/napari-phasors/napari-phasors/branch/main/graph/badge.svg)](https://codecov.io/gh/napari-phasors/napari-phasors)\n[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-phasors)](https://napari-hub.org/plugins/napari-phasors)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14647626.svg)](https://doi.org/10.5281/zenodo.14647626)\n\nA comprehensive plugin for phasor analysis in napari. Based on the \n[phasorpy](https://www.phasorpy.org/) library.\n\n[Jump to Intallation](#installation)\n\n----------------------------------\n\n## Usage\n\nnapari-phasors is a comprehensive plugin that provides a complete workflow \nfor phasor analysis in napari. It includes widgets for reading various FLIM \nand hyperspectral file formats, performing phasor analysis on multiple layers \nsimultaneously, calibration, component analysis, FRET analysis, filtering, \nphasor selections (manual, circular cursor, and automatic clustering), and \nexporting results.\n\n### Sample Data\n\nTwo sample datasets for FLIM are provided, along with their corresponding \ncalibration images. Additionally, a paramecium image is included as sample \ndata for hyperspectral analysis.\n\n![sample_data](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/samples.gif)\n\n### Phasor Analysis\n\n#### Plot FLIM Data\n\nFLIM phasor data can be plotted as a 2D histogram or scatter plot in the\n\"Phasor Plot\" widget. The colormap, the number of bins and the scale of the \ncolors can be customized.\n\n![phasors_flim](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/flim%20plot.gif)\n\n#### Plot Hyperspectral Data\n\nHyperspectral phasor data can also be plotted as a 2D histogram or scatter \nplot and visualized in the full universal circle. The \n'Universal Semi-Circle/Full Polar Plot' in the \"Plot Settings\" tab must be\nunchecked.\n\n![phasors_hyperspectral](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/hsi%20plot.gif)\n\n### Multiple-Layer Selection and Simultaneous Analysis\n\nMultiple image layers containing phasor data can be selected simultaneously \nfrom the layer dropdown in the \"Phasor Plot\" widget. All layers can be \nselected or deselected at once using the \"All\" and \"None\" controls. When more \nthan one layer is selected, their phasor coordinates are merged and displayed \ntogether in the phasor plot, enabling direct comparison and joint analysis. A \nprimary layer can be designated from the same dropdown to drive plot settings \nand analysis parameters (such as calibration, frequency, filter settings, and \ncomponent locations) for all selected layers. All analyses are then applied to \nevery selected layer at once.\n\n![multiple_layers](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/multiple%20layers.gif)\n\n### Phasor Calibration\n\nFLIM images can be calibrated using a reference image acquired under the same \nexperimental parameters in the \"Calibration\" tab of the \"Phasor Plot\" widget. \nThis reference image should consist of a homogeneous solution of a fluorophore\nwith a known fluorescence lifetime and the laser frequency used in the \nexperiment. This ensures accuracy and consistency in lifetime measurements.\n\n![calibration](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/calibration.gif)\n\n### Filtering and Thresholding\n\nApply various filters and thresholds to your phasor data to enhance analysis \nquality in the \"Filter\" tab on the \"Phasor Plot\" widget. You can \nfilter phasor coordinates using the median or wavelet filter.\n\n![filter_threshold](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/filter%20threshold.gif)\n\n### Mask\n\nYou can create a mask using either a shapes layer or a labels layer in napari. Once the\nmask is created, select it from the mask combobox in the \"Phasor Plot\" widget. Only the\npixels inside the selected mask will be plotted in the phasor space and included in subsequent\nanalyses. This allows you to focus your analysis on specific regions of interest within your data.\n\n![mask](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/mask.gif)\n\n### Copy Settings and Analysis\n\nYou can quickly copy plot settings and analysis parameters, such as calibration, frequency, filter\nsettings, and component locations, from another layer or from an OME-TIF file previously exported\nwith the napari-phasors plugin. This feature streamlines the workflow by allowing you to reuse\nestablished configurations, ensuring consistency and saving time when analyzing multiple datasets.\n\n![copy_settings](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/copy%20settings.gif)\n\n### Phasor Selections\n\nThe \"Selection\" tab of the \"Phasor Plot\" widget offers three modes for \nidentifying regions of interest in the phasor plot and mapping the \ncorresponding pixels back to the intensity image.\n\n#### Circular Cursor Selection\n\nDefine one or more circular cursors on the phasor plot to select regions of \ninterest. Each cursor can be positioned and resized interactively by dragging \nit on the plot, or by entering coordinates directly in the table. A separate \nlabels layer is created for each cursor, color-coded for easy identification. \nStatistics for each cursor region are displayed in the table.\n\n![circular_cursors](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/circular%20cursors.gif)\n\n#### Automatic Clustering\n\nAutomatically segment the phasor plot into clusters using Gaussian Mixture \nModels (GMM). The number of clusters can be specified, and the resulting \nclusters are displayed as ellipses on the phasor plot. Each cluster is \nassigned a color that can be customized, and the corresponding pixels are\nhighlighted in a labels layer. Cluster statistics are shown in the table.\n\n![automatic_clustering](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/automatic%20clustering.gif)\n\n#### Manual Selection\n\nDraw free-form selections directly on the phasor plot using different shape \ntools available at the top of the plot. The selection ID can be managed from \nthe \"Selection\" tab, allowing multiple independent selections to be stored \nand toggled. NOTE: Manual selections are not stored when exporting in OME-TIF format.\n\n![selections](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/selections.gif)\n\n### Component Analysis\n\nPerform multi-component analysis to identify and separate different \nfluorescent species in your sample. This feature allows you to decompose \ncomplex phasor distributions into individual components with distinct \nlifetimes. Multi-component analysis can be done in the \"Components\" tab of the\n\"Phasor Plot\" widget, either by projection to a line between components (only two\ncomponent analysis), or by component fitting 'n' number of components based on the\navailable harmonics. For more than three component analysis, the location of the\ncomponents in higher harmonics must be provided.\n\n![components](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/components.gif)\n\n### Apparent or Normal Lifetime Analysis\n\nA FLIM image can be colormapped according to the phase or modulation apparent \nlifetime, as well as the normal lifetime in the \"Lifetime\" tab of the \"Phasor \nPlot\" widget. A histogram is also created for visualization of the distribution\nof apparent lifetimes of the FLIM image.\n\n![lifetimes](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/lifetime.gif)\n\n### FRET Analysis\n\nAnalyze Förster Resonance Energy Transfer (FRET) trajectories and efficiencies\nin the \"FRET\" tab of the \"Phasor Plot\" widget. The lifetime of the donor and the location of the\nbackground in the phasor plot can be obtained automatically from another layer.\n\n![fret](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/fret.gif)\n\n\n### Phasor Custom Import\n\nSupported file formats (`.tif`, `.ptu`, `.sdt`, `.fbd`, `.lsm`, `.ome.tif`) \ncan be imported and converted to phasor space using the \"Phasor Custom Import\" widget.\nDepending on the file format, you can specify additional options such as harmonics, channels, and frames.\nThe signal can then be visualized according to the selected parameters for each file.\n\n![custom_import](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/import.gif)\n\n### Data Export\n\nThe average intensity image and phasor coordinates can be exported as OME-TIF files, which are compatible\nwith both napari-phasors and PhasorPy. Alternatively, you can export the phasor coordinates and selections\nas a CSV file using the \"Export Phasor\" widget. Analysis results—such as lifetime, FRET efficiency, and \ncomponent fractions—can also be exported to CSV. Additionally, the colormapped image layer can be exported\nwith or without its associated colorbar.\n\n![export_phasors](https://github.com/napari-phasors/napari-phasors-data/raw/main/gifs/export.gif)\n\n## Installation\n\nYou can install `napari-phasors` via [pip]. Follow these steps from a \nterminal.\n\nWe recommend using `miniforge` whenever possible. Click \n[here](https://conda-forge.org/download/) to choose the right download option for your OS.\n**If you do not use `miniforge`, but rather Anaconda or Miniconda, replace \nthe `mamba` term whenever you see it below with `conda`.**\n\nCreate a conda environment with napari by typing :\n\n    mamba create -y -n napari-phasors-env napari pyqt python=3.12\n    \nActivate the environment :\n\n    mamba activate napari-phasors-env\n\nInstall `napari-phasors` via [pip] :\n\n    pip install napari-phasors\n\nAlternatively, install latest development version with :\n\n    pip install git+https://github.com/napari-phasors/napari-phasors.git\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 [BSD-3] license,\n\"napari-phasors\" is free and open source software.\n\nPlease cite doi: [https://doi.org/10.5281/zenodo.14647626](https://doi.org/10.5281/zenodo.14647626) \nif napari-phasors contributes to a project that leads to a publication.\n\n## Issues\n\nIf you encounter any problems, please [file an issue] along with a detailed \ndescription.\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/napari-phasors/napari-phasors/issues\n\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/\n","description_content_type":"text/markdown","keywords":null,"home_page":null,"download_url":null,"author":null,"author_email":"Bruno Pannunzio <bpannunzio@pasteur.edu.uy>, Marcelo Leomil Zoccoler <marzoccoler@gmail.com>, Bruno Schuty <schutyb@schutyb.com>, Leonel Malacrida <lmalacrida@pasteur.edu.uy>","maintainer":null,"maintainer_email":null,"license":"Copyright (c) 2024, Marcelo L. Zoccoler, Bruno Pannunzio, Bruno Schuty\nand Leonel Malacrida. All rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n  list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n  this list of conditions and the following disclaimer in the documentation\n  and/or other materials provided with the distribution.\n\n* Neither the name of copyright holder nor the names of its\n  contributors may be used to endorse or promote products derived from\n  this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n","classifier":["Development Status :: 2 - Pre-Alpha","Framework :: napari","Intended Audience :: Developers","License :: OSI Approved :: BSD License","Operating System :: OS Independent","Programming Language :: Python","Programming Language :: Python :: 3","Programming Language :: Python :: 3 :: Only","Programming Language :: Python :: 3.12","Programming Language :: Python :: 3.13","Topic :: Scientific/Engineering :: Image Processing","Topic :: Scientific/Engineering :: Visualization"],"requires_dist":["phasorpy>=0.8","qtpy","scikit-image","biaplotter>=0.4.2","fbdfile","sdtfile","ptufile","tifffile","pandas","pyqt5","pawflim","tox; extra == \"testing\"","pytest; extra == \"testing\"","pytest-cov; extra == \"testing\"","pytest-qt; extra == \"testing\"","napari; extra == \"testing\"","qtpy; extra == \"testing\"","scikit-image; extra == \"testing\"","biaplotter>=0.4.2; extra == \"testing\"","PyQt5; extra == \"testing\"","pandas; extra == \"testing\"","black; extra == \"testing\"","isort; extra == \"testing\"","phasorpy>=0.8; extra == \"testing\"","tifffile; extra == \"testing\"","fbdfile; extra == \"testing\"","sdtfile; extra == \"testing\"","ptufile; extra == \"testing\"","pawflim; extra == \"testing\""],"requires_python":">=3.12","requires_external":null,"project_url":["Homepage, https://github.com/napari-phasors/napari-phasors","Bug Tracker, https://github.com/napari-phasors/napari-phasors/issues","Documentation, https://github.com/napari-phasors/napari-phasors#README.md","Source Code, https://github.com/napari-phasors/napari-phasors","User Support, https://github.com/napari-phasors/napari-phasors/issues"],"provides_extra":["testing"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}