Package: aliby
ALIBY (Analyser of Live-cell Imaging for Budding Yeast)End-to-end processing of cell microscopy time-lapses. ALIBY automates segmentation, tracking, lineage predictions, post-processing and report production. It leverages the existing Python ecosystem and open-source scientific software available to produce seamless and standardised pipelines.Quickstart DocumentationInstallation ofVS StudioNative MacOS support for is under work, but you can use containers (e.g., Docker, Podman) in the meantime.To analyse local datapipinstallalibyAdd any of the optional flagsomeroandutils(e.g.,pip install aliby[omero, utils]).omeroprovides tools to connect with an OMERO server andutilsprovides visualisation, user interface and additional deep learning tools.See ourinstallation instructionsfor more details.CLIIf installed via poetry, you have access to a Command Line Interface (CLI)aliby-run--expt_idEXPT_PATH--distributed4--tpsNoneAnd to run Omero servers, the basic arguments are shown:aliby-run--expt_idXXX--hostSERVER.ADDRESS--userUSER--passwordPASSWORDThe output is a folder with the original logfiles and a set of hdf5 files, one with the results of each multidimensional inside.For more information, including available options, see the page onrunning the analysis pipelineUsing specific componentsAccess raw dataALIBY's tooling can also be used as an interface to OMERO servers, for example, to fetch a brightfield channel.fromaliby.io.omeroimportDataset,Imageserver_info={"host":"host_address","username":"user","password":"xxxxxx"}expt_id=XXXXtps=[0,1]# Subset of positions to get.withDataset(expt_id,**server_info)asconn:image_ids=conn.get_images()#To get the first positionwithImage(list(image_ids.values())[0],**server_info)asimage:dimg=image.dataimgs=dimg[tps,image.metadata["channels"].index("Brightfield"),2,...].compute()# tps timepoints, Brightfield channel, z=2, all x,yTiling the raw dataATilerobject performs trap registration. It may be built in different ways but the simplest one is using an image and a the default parameters set.fromaliby.tile.tilerimportTiler,TilerParameterswithImage(list(image_ids.values())[0],**server_info)asimage:tiler=Tiler.from_image(image,TilerParameters.default())tiler.run_tp(0)The initialisation should take a few seconds, as it needs to align the images
in time.It fetches the metadata from the Image object, and uses the TilerParameters values (all Processes in aliby depend on an associated Parameters class, which is in essence a dictionary turned into a class.)Get a timelapse for a given tile (remote connection)fpath="h5/location"tile_id=9trange=range(0,10)ncols=8riv=remoteImageViewer(fpath)trap_tps=[riv.tiler.get_tiles_timepoint(tile_id,t)fortintrange]# You can also access labelled trapsm_ts=riv.get_labelled_trap(tile_id=0,tps=[0])# And plot them directlyriv.plot_labelled_trap(trap_id=0,channels=[0,1,2,3],trange=range(10))Depending on the network speed can take several seconds at the moment.
For a speed-up: take fewer z-positions if you can.Get the tiles for a given time pointAlternatively, if you want to get all the traps at a given timepoint:timepoint=(4,6)tiler.get_tiles_timepoint(timepoint,channels=None,z=[0,1,2,3,4])ContributingSeeCONTRIBUTINGon how to help out or get involved.