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pilyso-io\n\nThe Python Image anaLYsis SOftware - IO library. This is a standalone version of the IO library originally bundled with\nthe mycelium analysis software [*mycelyso*](https://github.com/modsim/mycelyso).\n\n**Warning**: The library is currently intended for internal use only, and subject to change without further notice!\nUse at your own risk!\n\nIt is plugin-based, and can currently read the following formats using third-party libraries, in a unified manner:\n\n- TIFF\n- OME-TIFF\n- Zeiss' CZI\n- Nikon ND2\n- NDIP, an experimental pilyso-io specific meta format for non-destructive image processing\n\nMajor focus was spent on allowing access to multidimensional data – multi position data as well – and accompanying metadata, in a unified manner.\n\n\n# Example\n```python\nfrom pilyso_io.imagestack import ImageStack, Dimensions\nfrom pilyso_io.imagestack.readers import *  # loads all plugins\n\nims = ImageStack('file.nd2')\n\nims = ims.view(Dimensions.PositionXY)\n\nprint(ims.size)\n\nfor position in range(ims.size[Dimensions.PositionXY]):\n    print(ims[position])  # pixel data\n    print(ims.meta[position].calibration)  # calibration\n```\n\n# NDIP\n\nBioimage data is often very large, and copying around data is space and time-consuming. However, quite often a\nlot of processing steps have to be performed before the data can be meaningfully analyzed.\n\nTo this extend, NDIP is a JSON-based format, which contains individual processing steps, such as rotations or shifts, \nallowing one of the base formats to transparently be mapped into an altered form for the consuming software.\n\nThe current format is experimental and subject to change without notice!\n\n# OMERO\n\nSee [github.com/modsim/pilyso-io-omero](https://github.com/modsim/pilyso-io-omero) for GPL licensed code for accessing OMERO servers.\n\n# License\n\nBSD\n\n# Citation\n \n If you use `pilyso-io`, please cite [*mycelyso*](https://github.com/modsim/mycelyso):\n\n```\nSachs CC, Koepff J, Wiechert W, Grünberger A, Nöh K (2019)\nmycelyso – high-throughput analysis of Streptomyces mycelium live cell imaging data\nBMC Bioinformatics, volume 20, 452, doi: 10.1186/s12859-019-3004-1\n```\n\nAvailable on the *BMC Bioinformatics* homepage at [DOI: 10.1186/s12859-019-3004-1](https://dx.doi.org/10.1186/s12859-019-3004-1).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmodsim%2Fpilyso-io","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmodsim%2Fpilyso-io","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmodsim%2Fpilyso-io/lists"}