{"name":"napari-omaas","display_name":"napari OMAAS","visibility":"public","icon":"","categories":[],"schema_version":"0.2.0","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"napari-omaas.write_multiple","title":"Save multi-layer data with napari OMAAS","python_name":"napari_omaas._writer:write_multiple","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-omaas.write_single_image","title":"Save image data with napari OMAAS","python_name":"napari_omaas._writer:write_single_image","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-omaas.make_sample_data","title":"Load sample data from napari OMAAS","python_name":"napari_omaas._sample_data:make_sample_data","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-omaas.make_qwidget","title":"Optical Mapping Analysis","python_name":"napari_omaas._widget:OMAAS","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-omaas.make_magic_widget","title":"Make example magic widget","python_name":"napari_omaas._widget:example_magic_widget","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-omaas.make_func_widget","title":"Make example function widget","python_name":"napari_omaas._widget:example_function_widget","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":null,"writers":[{"command":"napari-omaas.write_multiple","layer_types":["image*","labels*"],"filename_extensions":[],"display_name":""},{"command":"napari-omaas.write_single_image","layer_types":["image"],"filename_extensions":[".npy"],"display_name":""}],"widgets":[{"command":"napari-omaas.make_qwidget","display_name":"Optical Mapping Analysis","autogenerate":false},{"command":"napari-omaas.make_magic_widget","display_name":"Example Magic Widget","autogenerate":false},{"command":"napari-omaas.make_func_widget","display_name":"Example Function Widget","autogenerate":true}],"sample_data":[{"command":"napari-omaas.make_sample_data","key":"heartsample","display_name":"napari OMAAS"}],"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.1","name":"napari-omaas","version":"0.1.3","dynamic":null,"platform":null,"supported_platform":null,"summary":"napari-OMAAS stands for Optical Mapping Acquisition and Analysis Software","description":"# napari-omaas\n\n[](https://github.com/rjlopez2/napari-omaas/raw/main/LICENSE)\n[](https://pypi.org/project/napari-omaas)\n[](https://python.org)\n[](https://github.com/rjlopez2/napari-omaas/actions)\n[](https://codecov.io/gh/rjlopez2/napari-omaas)\n[](https://napari-hub.org/plugins/napari-omaas)\n\n**napari-OMAAS stands for Optical Mapping Acquisition and Analysis Software for panoramic heart imaging**\n\nThis plugin intends to be an analysis and acquisition tool for optical mapping in potentiometric (Vm) or calcium (Ca2+) fluorescence signals obtained from panoramic imaging of intact hearts.\n\nThis plugin is in a very early developmental/experimental stage so expect very braking changes at anytime. At the momment supports reading images in .sif format from Andor Technologies powered by the [sif_parser] python module.\n\n## Usage\n\nThis plugin can read images generated with Andor Technologies cameras. It has been currently tested on Zyla cameras. Just drag and drop an image to the napari GUI, and the image will display. Alternatively, you can programmatically load/read the image within a notebook.\n \n import napari\n \n file = \"path/to/my/file/my_image.sif\"\n\n viewer = napari.Viewer()\n viewer.open(path=file, plugin=\"napari-omaas\", name = \"my_image\")\n\nto display the metadata use the standard call to the corresponding layer:\n\n viewer.layers['my_image'].metadata\n\nIn addition to opening this specific image format (.sif), it allows the users to perform some basic operations and visualization on images, such as normalization, temporal/spatial filters, motion tracking/compenstaion, plot profile, etc.\n## Examples\n\nThe following example ilustrate how to perform normalization (pixelwise) on a time serie image and plot its 2d profile along the t dimension withing the average data from the ROI selected.\n\n\n\n\nThe next example shows how to compute action potetnial duration in the same image stack.\n\n\n\n\n\n## Roadmap\n\nThis plugin is composed of two major components: **analysis** and **acquisition**.\n\nBellow is a list of some features this pluggin aims to do.\n\n### Analysis Features\n \n- [x] Read sif files from Andor Technologies.\n- [x] Display time profile of ROIs on image sequences.\n- [x] Normalize images.\n - [x] Perform peak analysis of action potential / Calcium traces.\n - [x] Add motion correction.\n - [x] APD analysis.\n - [ ] Create activation maps.\n - [ ] Segment images and align heart ROIs.\n- [x] Export results and analysis log.\n\n### Acquisition Features\n\n- [ ] Control Zyla camera for the acquisition of data\n - [ ] test using the PYME module\n- [ ] Real-time analysis(?)\n\n \n\n----------------------------------\n\nThis [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.\n\n\n\n## Installation\n\nYou can install `napari-omaas` via [pip]:\n\n pip install napari-omaas\n\n\n\nTo install the latest development version (recommended) :\n\n pip install git+https://github.com/rjlopez2/napari-omaas.git\n\n\n## Contributing\n\nContributions are very welcome. Run tests with [tox], ensuring\nthe coverage remains the same before you submit a pull request.\n\n## License\n\nDistributed under the terms of the [BSD-3] license,\n\"napari-omaas\" is free and open source software.\n\n## Issues\n\nIf you encounter any problems, please [file an issue] and 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/rjlopez2/napari-omaas/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[sif_parser]: https://pypi.org/project/sif-parser/\n","description_content_type":"text/markdown","keywords":null,"home_page":"https://github.com/rjlopez2/napari-omaas","download_url":null,"author":"Ruben Lopez","author_email":"rjlopez2@gmail.com","maintainer":null,"maintainer_email":null,"license":"BSD-3-Clause","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 :: 3.8","Programming Language :: Python :: 3.9","Programming Language :: Python :: 3.10","Topic :: Scientific/Engineering :: Image Processing"],"requires_dist":["numpy","magicgui","qtpy","pyqtgraph","napari-macrokit","napari-sif-reader","napari-matplotlib","napari-mat-file-reader","tox ; extra == 'testing'","pytest ; extra == 'testing'","pytest-cov ; extra == 'testing'","pytest-qt ; extra == 'testing'","napari ; extra == 'testing'","pyqt5 ; extra == 'testing'"],"requires_python":">=3.8","requires_external":null,"project_url":["Bug Tracker, https://github.com/rjlopez2/napari-omaas/issues","Documentation, https://github.com/rjlopez2/napari-omaas#README.md","Source Code, https://github.com/rjlopez2/napari-omaas","User Support, https://github.com/rjlopez2/napari-omaas/issues"],"provides_extra":["testing"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}