Saravanan666/pypi-org huggingface dataset:
Package: scipion-em-flexutils
Scipion flexutils pluginThis plugin contains a series of methods for the visualization and manipulation of flexibility data (specially designed for continuous heterogeneity).Plugin InstallationThe plugin can be installed in production mode with the following command:scipion installp -p scipion-em-flexutilsOr in devel mode by executing the commands:git clone https://github.com/scipion-em/scipion-em-flexutils.git
scipion installp -p ~/scipion-em-flexutils --develThe plugin needs PyQt5 to be installed in the device. The following commands can be used to install PyQt5 in different distributions:Ubuntu:
sudo apt-get install python3-pyqt5

CentOS:
yum install qt5-qtbase-develThee following libraries are also needed by PyQt.Ubuntu:
sudo apt-get install libxcb-xinerama0
sudo apt install libxcb-image0The viewers associated to this plugin require OpenGL 3.2 or later and Mesa 11.2 or later.The installation of the Plugin also requires Conda to be installed in the system. In addition, Scipion needs to know where Conda has been installed. This can be done by adding to yourscipion.conffile the following variable:CONDA_ACTIVATION_CMD = eval "$(/path/to/your/conda/installation/condabin/conda shell.bash hook)"Additionally, the optional componentOpen3Dcan be installed to add extra functionalities during the network training phase. In order to install this package, the following requirements must be satisfied:CUDA must be installed in your system and properly added to thePATHandLD_LIBRARY_PATHvariablesYou should check the following dependencies are installed in your system:sudo apt install xorg-dev libxcb-shm0 libglu1-mesa-dev python3-dev clang libc++-dev libc++abi-dev libsdl2-dev ninja-build libxi-dev libtbb-dev libosmesa6-dev libudev-dev autoconf libtoolIf the previous requirements are not met,Open3Dinstallation will be just skipped.TutorialsFlexibility Hub tutorials are available in theScipion FlexHub Wikiand inYoutube.
Package: scipion-em-fsc3d
This plugin provides a wrapper for3DFSCprogram developed at Salk Institute and NYSBC.InstallationYou will need to use 3.0+ version of Scipion to be able to run these protocols. To install the plugin, you have two options:Stable versionscipion installp -p scipion-em-fsc3dDeveloper’s versiondownload repositorygit clone -b devel https://github.com/scipion-em/scipion-em-fsc3d.gitinstallscipion installp -p /path/to/scipion-em-fsc3d --develFSC3D software will be installed automatically with the plugin but you can also use an existing installation by providingFSC3D_HOME(see below).Important:you need to have conda (miniconda3 or anaconda3) pre-installed to use this program.Configuration variablesCONDA_ACTIVATION_CMD: If undefined, it will rely on conda command being in the
PATH (not recommended), which can lead to execution problems mixing scipion
python with conda ones. One example of this could can be seen bellow but
depending on your conda version and shell you will need something different:
CONDA_ACTIVATION_CMD = eval “$(/extra/miniconda3/bin/conda shell.bash hook)”FSC3D_HOME(default = software/em/fsc3D-3.0):
Path where the 3DFSC is installed.FSC3D_ENV_ACTIVATION(default = conda activate fsc3D-3.0):
Command to activate the 3DFSC environment.VerifyingTo check the installation, simply run the following Scipion test:scipion test fsc3d.tests.test_protocols_3dfsc.Test3DFSCSupported versions3.0 (modified for Scipion, seehttps://github.com/azazellochg/fsc3D)Protocolsresolution estimationReferencesYong Zi Tan, Philip R Baldwin, Joseph H Davis, James R Williamson, Clinton S Potter, Bridget Carragher & Dmitry Lyumkis. Addressing preferred specimen orientation in single-particle cryo-EM through tilting. Nature Methods volume 14, pages 793–796 (2017).
Package: scipion-em-gautomatch
This plugin provides a wrapper forGautomatch picker.InstallationYou will need to use 3.0+ version of Scipion to be able to run these protocols. To install the plugin, you have two options:Stable versionscipion installp -p scipion-em-gautomatchDeveloper’s versiondownload repositorygit clone -b devel https://github.com/scipion-em/scipion-em-gautomatch.gitinstallscipion installp -p /path/to/scipion-em-gautomatch --develGautomatch binaries will be installed automatically with the plugin, but you can also link an existing installation.
Default installation path assumed issoftware/em/gautomatch-0.56, if you want to change it, setGAUTOMATCH_HOMEinscipion.conffile to the folder where the Gautomatch is installed. Depending on your CUDA version and GPU card compute capability you might want to change the default binary fromGautomatch_v0.56_sm30-75_cu10.1to a different one by explicitly settingGAUTOMATCHvariable. If you need to use CUDA different from the one used during Scipion installation (defined by CUDA_LIB), you can addGAUTOMATCH_CUDA_LIBvariable to the config file. Various binaries can be downloaded from the official Gautomatch website.To check the installation, simply run the following Scipion test:scipion test gautomatch.tests.test_protocols_gautomatch.TestGautomatchAutomaticPickingSupported versions0.53 and 0.56Protocolsauto-pickingReferencesKai Zhang. Unpublished.
Package: scipion-em-gctf
This plugin provides a wrapper forGctfprogram.InstallationYou will need to use 3.0+ version of Scipion to be able to run these protocols. To install the plugin, you have two options:Stable versionscipion installp -p scipion-em-gctfDeveloper’s versiondownload repositorygit clone -b devel https://github.com/scipion-em/scipion-em-gctf.gitinstallscipion installp -p /path/to/scipion-em-gctf --develGctf binaries will be installed automatically with the plugin, but you can also link an existing installation. Default installation path assumed issoftware/em/gctf-1.18, if you want to change it, setGCTF_HOMEinscipion.conffile to the folder where the Gctf is installed. Depending on your CUDA version and GPU card compute capability you might want to change the default binary fromGctf_v1.18_sm30-75_cu10.1to a different one by explicitly settingGCTFvariable.If you need to use CUDA different from the one used during Scipion installation (defined byCUDA_LIB), you can addGCTF_CUDA_LIBvariable to the config file. Various binaries can be downloaded from the official Gctf website.To check the installation, simply run one of the following Scipion tests:scipion test gctf.tests.test_protocols_gctf.TestGctfRefine
scipion test gctf.tests.test_protocols_gctf.TestGctf
scipion tests gctf.tests.test_protocols_gctf_ts.TestGctfTsSupported versions1.06 and 1.18.1.18 is a special version designed for VPP data, it does not support local/movie CTF refinement or validation options.Protocolsctf estimationctf refinementtilt-series gctfReferencesZhang K. (2016). Gctf: Real-time CTF determination and correction. JSB, 193: 1-12.
Package: scipion-em-grigoriefflab
Grigoriefflab scipion pluginPlugin to use Grigorieff Lab (not CisTEM) programs within the Scipion framework.SetupInstall this plugin:scipion installp -p scipion-em-grigoriefflabAlternatively, in devel mode:scipion installp -p local/path/to/scipion-em-grigoriefflab --develTestsscipion test grigoriefflab.tests.test_protocols_grigoriefflab_movies
  scipion test grigoriefflab.tests.test_protocols_grigoriefflab_movies.TestUnblur
  scipion test grigoriefflab.tests.test_protocols_grigoriefflab_movies.TestSummovie
scipion test grigoriefflab.tests.test_protocols_grigoriefflab_magdist
  scipion test grigoriefflab.tests.test_protocols_grigoriefflab_magdist.TestMagDist
scipion test grigoriefflab.tests.test_protocols_grigoriefflab
  scipion test grigoriefflab.tests.test_protocols_grigoriefflab.TestImportParticles
  scipion test grigoriefflab.tests.test_protocols_grigoriefflab.TestFrealignRefine
  scipion test grigoriefflab.tests.test_protocols_grigoriefflab.TestFrealignClassify
  scipion test grigoriefflab.tests.test_protocols_grigoriefflab.TestCtftilt
  scipion test grigoriefflab.tests.test_protocols_grigoriefflab.TestCtffind4
Package: scipion-em-imagic
This plugin includes two protocols to provide wrappers around Multivariate Statistical Analysis (MSA) module ofIMAGICsoftware suite. IMAGIC is a licensed software, not distributed with Scipion and has to be installed by user.InstallationYou will need to use 3.0+ version of Scipion to be able to run these protocols. To install the plugin, you have two options:Stable versionscipion installp -p scipion-em-imagicDeveloper’s versiondownload repositorygit clone -b devel https://github.com/scipion-em/scipion-em-imagic.gitinstallscipion installp -p /path/to/scipion-em-imagic --develAlso, you need a working IMAGIC installation. Default installation path assumed issoftware/em/imagic-190830, if you want to change it, setIMAGIC_HOMEinscipion.conffile to the folder where the IMAGIC is installed (it is the same asIMAGIC_ROOTvariable in your shell environment). If you want to use MPI-based parallel job execution, make sure you haveopenmpidirectory inside IMAGIC installation folder.
To check the installation, simply run the following Scipion test:scipion tests tests.em.workflows.test_workflow_imagicMSA.TestImagicWorkflowSupported versions190830Since with every version of IMAGIC software user interaction with IMAGIC programs is changed, we came up with a way to provide multiple version support. Inimagic/scriptsdirectory you have a folder for each corresponding version, that contains batch scripts similar to those used by IMAGIC. This way one can create a similar script specific to a certain version. At the moment version 190830 (Aug 2019) is supported. If you experience any problems or need help with adapting scripts for your IMAGIC version, do not hesitate tocreate an issue on Github. Besides editing scripts directory you would need to add version number to_supportedVersionslist in fileimagic/__init__.pyand edit IMAGIC_HOME variable inscipion.conf.Protocolsimagic - msaimagic - msa-classifyReferencesM van Heel and W Keegstra (1981). IMAGIC: A fast, flexible and friendly image analysis software system. Ultramicroscopy 7: 113-130.M van Heel, G Harauz, EV Orlova, R Schmidt and M Schatz (1996). A new generation of the IMAGIC image processing system. J. Struct. Biol. 116: 17-24.M van Heel, R Portugal, A Rohou, C Linnemayr, C Bebeacua, R Schmidt, T Grant and M Schatz (2012). Four-Dimensional Cryo Electron Microscopy at Quasi Atomic Resolution: “IMAGIC 4D”. International Tables for Crystallography, vol. F, ch. 19.9: 624-628.M van Heel (1984). Multivariate statistical classification of noisy images (randomly oriented biological macromolecules). Ultramicroscopy 13(1-2): 165-183.Lisa Borland and Marin van Heel (1990). Classification of image data in conjugate representation spaces. Journal of the Optical Society of America A 7(4): 601-610.
Package: scipion-em-isonet
IsoNet pluginThis plugin allows to use IsoNet programs within the Scipion frameworkIsoNetstand for ISOtropic reconstruction of Electron Tomography.
It train deep neural networks to reconstruct meaningful contents in the missing wedge for electron tomography
increase signal-to-noise ratio using the information learned from the original tomogram. The software requires
tomograms as input. Observing at about 30A resolutions, the IsoNet generated tomograms are largely isotropic.You will need to use3.0.0version of Scipion to run these protocols.ProtocolsIsotropic Reconstruction: Isotropic Reconstruction of Electron Tomograms with Deep LearningLatest plugin versionv3.0.0new:  First plugin versionInstalling the pluginIn order to install the plugin follow these instructions:Install the pluginscipion installp -p scipion-em-isonetor through theplugin managerby launching Scipion and followingConfiguration>>PluginsTo install in development modeClone or download the plugin repositorygit clone https://github.com/scipion-em/scipion-em-isonet.gitInstall the plugin in developer mode.scipion installp -p local/path/to/scipion-em-isonet --develBuildbot statusStatus devel version:Status production version:IsoNet ReferencesThe publication associated with IsoNet can be foundhere:
Package: scipion-em-kiharalab
Kihara Lab pluginThis is aScipionplugin that offers differentKihara Lab tools.
These tools will make it possible to carry out different functions for working with electron density maps.
The code involving these functionalities can be found athttps://github.com/kiharalab.Therefore, this plugin allows to use programs from the DAQ software suite
within the Scipion framework.Install this pluginYou will need to useScipion3to run these protocols.DAQ, Emap2sec, Emap2sec+, and MainMast are installed automatically by scipion.Note:Emap2sec+ needs an Nvidia GPU to run.Install the stable versionThrough the plugin manager GUI by launching Scipion and followingOthers>>Plugin Managerorscipion3 installp -p scipion-em-kiharalabDeveloper’s versionDownload repository:git clone https://github.com/scipion-em/scipion-em-kiharalab.gitInstall:scipion3 installp -p path_to_scipion-em-kiharalab --develProtocolsscipion-em-kiharalab contains the following protocols:DAQ model validation: Executes the DAQ software to validate a structure modelEmap2sec: Identifies protein secondary structuressegment map: performs the segmentation of maps into different regions by using mainmast softwarePackages & enviromentsPackages installed by this plugin can be located in/path/to/scipion/software/em/.The following packages will be created:daq-versionemap2sec-versionmainMast-versionWhereversionis the current version of that specific package.Also, the following conda enviroments will be created:daq-versionemap2sec-versionemap2secPlus-versionAs of today, Scipion does not automatically uninstall the conda enviroments created in the installation process when uninstalling a plugin, so keep this list in mind if you want to clean up some disk space if you need to uninstall scipion-em-kiharalab.ChangelogAll the recent version changes can be foundhere.
Package: scipion-em-localrec
Localrec Scipion pluginCryogenic electron microscopy (cryoEM) can yield near-atomic resolution structures of highly ordered macromolecular complexes. Often however some subunits bind in a flexible manner, have different symmetry from the rest of the complex, or are present in sub-stoichiometric amounts, limiting the attainable resolution. We have developed a general method for the localized three-dimensional reconstruction of such subunits earlier (Ilca et al 2015 Nature Commun).In this project we implement Localized Reconstruction as a plugin (Locarec) for Scipion.If Localized Reconstruction is useful in your work, please cite:Ilca SL, Kotecha A, Sun X, Poranen MM, Stuart DI & Huiskonen JT (2015). Localized reconstruction of subunits from electron cryomicroscopy images of macromolecular complexes. Nat Commun 6, 8843. doi:10.1038/ncomms9843Abrishami V, Ilca SL, Gomez-Blanco J, Rissanen I, de la Rosa-Trevín JM, Reddy VS, Carazo JM, Huiskonen JT (2020). Localized reconstruction in Scipion expedites the analysis of symmetry mismatches in cryo-EM data. Prog. Biophys. Mol. Biol. doi:10.1016/j.pbiomolbio.2020.05.004.Install this pluginYou will need to use3.0.0version of Scipion or later to run these protocols.Stable versionEither use the following commandscipion3 installp -p scipion-em-localrecorInstall plugin through the plugin manager GUI by launching Scipion and followingConfiguration>>PluginsDeveloper’s versionDownload repository:git clone https://github.com/scipion-em/scipion-em-localrec.gitInstall:scipion3 installp -p path_to_scipion-em-localrec --develTest the plugin:scipion3 test localrec.tests.test_protocol_localized_reconstructionProtocolsThe following protocols have been implemented so farlocalrec - define subparticles: Determines the coordinates, orientations and defocus values of the sub-particles.localrec - filter subparticles: Excludes unwanted sub-particles based on alignment information.localrec - extract subparticles: Extracts sub-particle images from each particle image.localrec - set origin to subvolume: Fits a sub-volume back to it’s original volume (larger volume).localrec - particles subset by subparticles: makes a subset of particles for which there is at least one sub-particle.localrec - stitch subvolumes: Assembles a full volume from the sub-volumes generated by localized reconstruction.Testsscipion test localrec.tests.test_protocol_localized_reconstructionBuildbot statusStatus devel version:Status production version:AcknowledgementsThe authors acknowledge funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (649053).
Package: scipion-em-locscale
This plugin provides a wrapper aroundLocScaleprogram for local sharpening of cryo-EM density maps.InstallationYou will need to use 3.0+ version of Scipion to be able to run these protocols. To install the plugin, you have two options:Stable versionscipion installp -p scipion-em-locscaleDeveloper’s versiondownload repositorygit clone -b devel https://github.com/scipion-em/scipion-em-locscale.gitinstallscipion installp -p path/to/scipion-em-locscale --develLocScale software will be installed automatically with the plugin but you can also use an existing installation by providingLOCSCALE_ENV_ACTIVATION(see below).Important:you need to have conda (miniconda3 or anaconda3) pre-installed to use this program.Configuration variablesCONDA_ACTIVATION_CMD: If undefined, it will rely on conda command being in the
PATH (not recommended), which can lead to execution problems mixing scipion
python with conda ones. One example of this could can be seen below but
depending on your conda version and shell you will need something different:
CONDA_ACTIVATION_CMD = eval “$(/extra/miniconda3/bin/conda shell.bash hook)”LOCSCALE_ENV_ACTIVATION(default = conda activate locscale-2.1):
Command to activate the LocScale environment.VerifyingTo check the installation, simply run the following Scipion test:scipion test locscale.tests.test_protocol_locscale.TestProtLocscaleSupported versions2.1Protocolslocal sharpeningReferencesA.J. Jakobi, M. Wilmanns and C. Sachse, Model-based local density sharpening of cryo-EM maps, eLife 6: e27131 (2017).A.Bharadwaj and A.J. Jakobi, Electron scattering properties and their use in cryo-EM map sharpening, Faraday Discussions D2FD00078D (2022)
Package: scipion-em-modelangelo
This plugin provide a wrapper forModel angelowhich allowsde novoatomic modellingInstallationStable versionscipion3 installp -p scipion-em-modelangeloDeveloper’s versiondownload repositorygit clone https://github.com/scipion-em/scipion-em-modelangelo.gitinstallscipion3 installp -p path_to_scipion-em-modelangelo --develModelangelo will be installed automatically with the plugin using a Conda environment.Configuration variablesThere are some variables related to the model-angelo installation. If you have installed
model-angelo within Scipion, you may defineMODEL_ANGELO_ENV_ACTIVATIONfor specifying
how to activate the environment. This variable with be used together with the general
conda activation to generate the final model-angelo command. For example:MODEL_ANGELO_ENV_ACTIVATION = conda activate model_angeloIf this variable is not defined, a default value will be provided that will work if the
latest version is installed.If model-angelo is installed already outside Scipion, one could defineMODEL_ANGELO_ACTIVATION.
This variable will provide an activation (or load) command that can be anything and the Scipion
conda activate will not be prepended. For example (loading model-angelo as a module):MODEL_ANGELO_ACTIVATION = module load model-angelo/mainIf you need to use CUDA different from the one used during Scipion installation (defined byCUDA_LIB), you can addMODEL_ANGELO_CUDA_LIBvariable to the config file.ProtocolsModel builderTestsscipion3 tests modelangelo.tests.tests_model_angelo.TestModelAngel
Package: scipion-em-motioncorr
This plugin allows to use motioncor3 program within the Scipion framework.Motioncor3 is a GPU-accelerated program for correction of electron beam-induced sample motion. It is developed byShawn Zheng.IMPORTANT!If you have imported movies with a gain file inDM4format, you need toflip the gain reference upside-downin the motioncor protocol! (see details)When importing EER movies, you should specify dose per single EER frame during import step.When importing EER tilt-series movies, you should specify dose per tilt during import step. It is not relevant for motion-correction (no dose-weighting is done here), but will be used later on.If you are processing EER movies and providing *.gain reference file camera defects will be automatically extracted from the gain file header and converted to Motioncor format. This step is omitted if you provide a defects file yourself.InstallationYou will need to use 3.0+ version of Scipion to be able to run these protocols. To install the plugin, you have two options:Stable versionscipion installp -p scipion-em-motioncorrDeveloper’s versiondownload repositorygit clone -b devel https://github.com/scipion-em/scipion-em-motioncorr.gitinstallscipion installp -p /path/to/scipion-em-motioncorr --develImportant:Starting from plugin v3.15, the config variables have been renamed. See:scipion3 config -p motioncorrMotioncor binaries will be installed automatically with the plugin, but you can also link an existing installation.Default installation path assumed issoftware/em/motioncor3-1.0.1, if you want to change it, setMOTIONCOR_HOMEinscipion.conffile to the folder where the Motioncor3 is installed.Depending on your CUDA version this plugin will guess the right default binary fromMotionCor3_1.0.1_CudaXY_10-26-2023(X is for cuda major version, Y for the minor). You can always set a different one by explicitly settingMOTIONCOR_BINvariable.If you need to use CUDA different from the one used during Scipion installation (defined by CUDA_LIB), you can addMOTIONCOR_CUDA_LIBvariable to the config file. Various binaries can be downloaded from the official UCSF website.For an automatically updated installation of motioncor binaries, do not define neitherMOTIONCOR_HOMEnorMOTIONCOR_BIN.To check the installation, simply run the following Scipion tests:scipion test motioncorr.tests.test_protocols_motioncor.TestMotioncorAlignMovies
scipion test motioncorr.tests.test_protocols_tomo.TestMotioncorTiltSeriesAlignMoviesLicensingMotioncor3 is provided under BSD-3-Clause licenseSupported versionsmotioncor3 1.0.1
motioncor2 1.6.4Protocolsmovie alignmentalign tilt-series moviesReferencesShawn Q Zheng, Eugene Palovcak, Jean-Paul Armache, Kliment A Verba, Yifan Cheng & David A Agard. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nature Methods volume 14, pages 331–332 (2017).
Package: scipion-em-novactf
This plugin provides wrappers for several programs ofNovaCTFsoftware suite.InstallationYou will need to use 3.0+ version of Scipion to be able to run these protocols. To install the plugin, you have two options:Stable versionscipion installp -p scipion-em-novactfDeveloper’s versiondownload repositorygit clone -b devel https://github.com/scipion-em/scipion-em-novactf.gitinstallscipion installp -p /path/to/scipion-em-novactf --develNovaCTF binaries will be downloaded and installed automatically with the plugin, but you can also link an existing installation. Default installation path assumed issoftware/em/novactf-master, if you want to change it, setNOVACTF_HOMEinscipion.conffile to the folder where the NovaCTF is installed.To check the installation, simply run the test below:scipion3 tests novactf.tests.test_protocols_novactf.TestNovaCtfReconstructionWorkflowSupported versionsmaster (8 Nov 2018)Protocolscompute defocus array3D CTF correction and reconstructionReferencesBeata Turoňová and Florian K.M. Schur and William Wan and John A.G. Briggs. Efficient 3D-CTF correction for cryo-electron tomography using NovaCTF improves subtomogram averaging resolution to 3.4Å. Journal of Structural Biology 199 (3), p. 187-195, 2017. doi:https://doi.org/10.1016/j.jsb.2017.07.007