g : If the "Groups" module of your pipeline is turned on, you can use the grouping variables specified there to set which group this copy of CellProfiler should process - i.e., if your pipeline is set up to group by Well, you might use -g Well=A01 If you want to break your CellProfiler job up (to run in stages, or across many machines, or many terminal windows on the same machine), you may want to add a grouping component to your command. ![]() file-list : A list of files, one per line, that should be loaded by the Images module and then analyzed by CellProfiler. data-file : A CSV designed to work with the LoadData module (See module help for more details). Unless using a batch file, CellProfiler will additionally scan all the files in this folder and try to make them into image sets for your pipeline - great if you don't want to otherwise specify input sets with one of the methods below, but be warned it can be very slow if you point it at a huge directory! i : Specifies the Default Input Directory (if any modules in your pipeline are using files you've told it will be in the Default Input Directory). Your input component can be one of a few different things - or might be omitted entirely, if you're using a batch file. p : Your pipeline file (or batch file created with the CreateBatchFiles module) you want to execute See Adapting CellProfiler to a LIMS environment for more information. c -r : Flags you need to use to run CellProfiler headless. You can drag and drop the executable into your terminal window, which is typically easier than typing it out. If NOT, this needs to be the path to your existing CellProfiler executable, which for a PC will look something like C:\Users\UserName\ProgramFiles\CellProfiler\CellProfiler.exe , and for a Mac will look something like /Applications/CellProfiler/Contents/MacOS/cp . ComponentsĬellprofiler -c -r -p /path/to/pipeline/file.cppipe -o /path/where/the/output/goesĬellprofiler : If you've installed CellProfiler from source, rather than downloading it prebuilt from our website, just typing cellprofiler is how you'll call it from the terminal. If you're willing to script some ways to scatter the CellProfiler jobs, though, or don't mind running one job at a time, headless processing can be incredibly helpful!Ī general CellProfiler headless command will have a few common pieces, and a few that will change depending on your exact setup. It also has a major potential disadvantage in that while CellProfiler running graphically on your desktop will automatically use as many cores on your computer as you permit, CellProfiler running headlessly from the command line will only run one job at a time. Running CellProfiler from the command line has a major advantage - you don't need to spend computational power or memory creating the graphical user interface (GUI) that you're used to using in CellProfiler! ![]() Cross posted from our GitHub wiki - check there for any future updates! Big Data, new epistemologies and paradigm shifts. That said, several jupyter notebooks are provided to allow those who are interested to develop relevant coding skills through interactive exercises on your own time. ![]() The workshop is suitable for absolute beginners and NO CODING SKILLS are required. The workshop intersperses instruction with discussion and invites participants the chance to develop a research project idea through several steps. It offers attendees a chance to understand and discuss what computational social science is, what it is good for, how to become a computational social scientist and what are the basic steps to follow in and ideal computational social science research project. This free workshop, organised by the UK Data Service, is an introduction to computational social science. While to some degree social science research has always been marked by technological approaches, the field of computational social science involves the use of tools, data and methods that require a different skill set and mindset. ![]() The social sciences are no different, with many new forms of social data only available or practical through computational means (Kitchen, 2014). Scientific research and teaching is increasingly influenced by computational tools, methods and paradigms.
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