Saravanan666/pypi-org huggingface dataset:
Package: scip
SCIP: Scalable Cytometry Image ProcessingScalable Cytometry Image Processing (SCIP) is an open-source tool that implements
an image processing pipeline on top of Dask, a distributed computing framework written in Python.
SCIP performs normalization, image segmentation and masking, and feature extraction.Check thedocsfor
installation and usage instructions, and API documentation.DevelopmentGenerating documentationFor publishing online:cd docs
make clean
make htmlFor development with live reloading:cd docs
make livehtmlGenerate release changeloggit log v{previous version tag}..HEAD --oneline | xclip -sel clip
Package: sci-palettes
sci palettes for matplotlib/seabornInstallationpython3-mpipinstallsci-palettesUsageimportseabornassnsimportmatplotlib.pyplotaspltimportsci_palettesprint(sci_palettes.PALETTES.keys())sci_palettes.register_cmap()# register all palettessci_palettes.register_cmap('aaas')# register a special palette# methods for setting paletteplt.set_cmap('aaas')plt.style.use('aaas')sns.set_theme(palette='aaas')sns.set_palette('aaas')sns.scatterplot(...)# set palette when plottingsns.scatterplot(...,palette='aaas')Full examples inexamplesGallery展开查看AAASJAMANPGJCOLANCETInspired by the R Packageggsci
Package: sciparse
sciparse - Scientific Dataset parsingGetting StartedFeaturesCommon IssuesHow to UseAdding Additional Unit TestsAny time you want to add additional unit tests just add them to those in thetests/directory and prepend with the nametest. These will be
automatically found by pytest and run during local commits and remote builds.Writing the DocumentationThe documentation source is located indocs/sourceand is written in
restructured text (markdown is also available).Building the DocumentationSimply runmake htmlfrom thedocs/directory. This will compile the
files in thedocs/source/directory, and place them in the maindocs/directory where github pages can find them.Dependencies / Technologies UsedSphinxpytestGithub ActionsCodecovGithub PagesAcknowledgementsThanks to all the great people on stack overflow and github, for their
seemingly boundless tolerance to my and others' questions.
Package: scipdf-mirror
SciPDF ParserA Python parser for scientific PDF based onGROBID.InstallationUsepipto install from this Github repositorypipinstallgit+https://github.com/titipata/scipdf_parserNoteWe also need anen_core_web_smmodel for spacy, where you can runpython -m spacy download en_core_web_smto download itYou can change GROBID version inserve_grobid.shto test the parser on a new GROBID versionUsageRun the GROBID using the given bash script before parsing PDFbashserve_grobid.shThis script will download GROBID and run the service at default port 8070 (see morehere).
To parse a PDF provided inexample_datafolder or direct URL, use the following function:importscipdfarticle_dict=scipdf.parse_pdf_to_dict('example_data/futoma2017improved.pdf')# return dictionary# option to parse directly from URL to PDF, if as_list is set to True, output 'text' of parsed section will be in a list of paragraphs insteadarticle_dict=scipdf.parse_pdf_to_dict('https://www.biorxiv.org/content/biorxiv/early/2018/11/20/463760.full.pdf',as_list=False)# output example>>{'title':'Proceedings of Machine Learning for Healthcare','abstract':'...','sections':[{'heading':'...','text':'...'},{'heading':'...','text':'...'},...],'references':[{'title':'...','year':'...','journal':'...','author':'...'},...],'figures':[{'figure_label':'...','figure_type':'...','figure_id':'...','figure_caption':'...','figure_data':'...'},...],'doi':'...'}xml=scipdf.parse_pdf('example_data/futoma2017improved.pdf',soup=True)# option to parse full XML from GROBIDTo parse figures from PDF usingpdffigures2, you can runscipdf.parse_figures('example_data',output_folder='figures')# folder should contain only PDF filesYou can see example output figures infiguresfolder.
Package: scipdf-parser
SciPDF ParserA Python parser for scientific PDF based onGROBID.InstallationUsepipto install from this Github repositorypipinstallgit+https://github.com/titipata/scipdf_parserNoteWe also need anen_core_web_smmodel for spacy, where you can runpython -m spacy download en_core_web_smto download itYou can change GROBID version inserve_grobid.shto test the parser on a new GROBID versionUsageRun the GROBID using the given bash script before parsing PDFbashserve_grobid.shThis script will download GROBID and run the service at default port 8070 (see morehere).
To parse a PDF provided inexample_datafolder or direct URL, use the following function:importscipdfarticle_dict=scipdf.parse_pdf_to_dict('example_data/futoma2017improved.pdf')# return dictionary# option to parse directly from URL to PDF, if as_list is set to True, output 'text' of parsed section will be in a list of paragraphs insteadarticle_dict=scipdf.parse_pdf_to_dict('https://www.biorxiv.org/content/biorxiv/early/2018/11/20/463760.full.pdf',as_list=False)# output example>>{'title':'Proceedings of Machine Learning for Healthcare','abstract':'...','sections':[{'heading':'...','text':'...'},{'heading':'...','text':'...'},...],'references':[{'title':'...','year':'...','journal':'...','author':'...'},...],'figures':[{'figure_label':'...','figure_type':'...','figure_id':'...','figure_caption':'...','figure_data':'...'},...],'doi':'...'}xml=scipdf.parse_pdf('example_data/futoma2017improved.pdf',soup=True)# option to parse full XML from GROBIDTo parse figures from PDF usingpdffigures2, you can runscipdf.parse_figures('example_data',output_folder='figures')# folder should contain only PDF filesYou can see example output figures infiguresfolder.
Package: sciPENN
sciPENNsciPENN (singlecellimputationProteinEmbeddingNeuralNetwork) is a deep learning computational tool that is useful for analyses of CITE-seq data. sciPENN can be used to:Predict proteins in a query scRNA-seq dataset from a reference CITE-seq dataset.Integrate the scRNA-seq and CITE-seq data into a shared latent space.Combine multiple CITE-seq datasets with different protein panels by imputing missing proteins for each CITE-seq dataset.Transfer cell-type labels from a reference CITE-seq dataset to a query scRNA-seq dataset.ReproducibilityTo find code to reproduce the results we generated in that paper, please visit this separategithub repository, which provides all code (including that for other methods) necessary to reproduce our results.InstallationRecomended installation procedure is as follows.InstallAnacondaif you do not already have it.Create a conda environment, and then activate it as follows in terminal.$ conda create -n scipennenv
$ conda activate scipennenvInstall an appropriate version of python.$ conda install python==3.7Install nb_conda_kernels so that you can change python kernels in jupyter notebook.$ conda install nb_conda_kernelsFinally, install sciPENN.$ pip install sciPENNNow, to use sciPENN, always make sure you activate the environment in terminal first ("conda activate scipennenv"). And then run jupyter notebook. When you create a notebook to run sciPENN, make sure the active kernel is switched to "scipennenv"UsageAtutorial jupyter notebook, together with a dataset, is publicly downloadable.Software RequirementsPython >= 3.7torch >= 1.6.1scanpy >= 1.7.1pandas >= 1.1.5numpy >= 1.20.1scipy >= 1.6.1tqdm >= 4.59.0anndata >= 0.7.5numba <= 0.50.0
Package: sciphi-synthesizer
Synthesizer[ΨΦ]: A multi-purpose LLM framework 💡With Synthesizer, users can:Custom Data Creation: Generate datasets via LLMs that are tailored to your needs.Anthropic, OpenAI, vLLM, and HuggingFace.Retrieval-Augmented Generation (RAG) on Demand: Built-in RAG Provider Interface to anchor generated data to real-world sources.Turnkey integration with Agent Search API.Custom Data Creation: Generate datasets via LLMs that are tailored to your needs, for LLM training, RAG, and more.Fast Setuppipinstallsciphi-synthesizerUsing SynthesizerGenerate synthetic question-answer pairsexportSCIPHI_API_KEY=MY_SCIPHI_API_KEY
python-msynthesizer.scripts.data_augmenterrun--dataset="wiki_qa"tailaugmented_output/config_name_eq_answer_question__dataset_name_eq_wiki_qa.jsonl{"formatted_prompt":"... ### Question:\nwhat country did wine originate in\n\n### Input:\n1. URL: https://en.wikipedia.org/wiki/History%20of%20wine (Score: 0.85)\nTitle:History of wine....",{"completion":"Wine originated in the South Caucasus, which is now part of modern-day Armenia ..."Evaluate RAG pipeline performanceexportSCIPHI_API_KEY=MY_SCIPHI_API_KEY
python-msynthesizer.scripts.rag_harness--rag_provider="agent-search"--llm_provider_name="sciphi"--n_samples=25DocumentationFor more detailed information, tutorials, and API references, please visit the officialSynthesizer Documentation.Community & SupportEngage with our vibrant community onDiscord.For tailored inquiries or feedback, pleaseemail us.Developing with SynthesizerQuickly set up RAG augmented generation with your choice of provider, from OpenAI, Anhtropic, vLLM, and SciPhi:# Requires SCIPHI_API_KEY in envfromsynthesizer.coreimportLLMProviderName,RAGProviderNamefromsynthesizer.interfaceimportLLMInterfaceManager,RAGInterfaceManagerfromsynthesizer.llmimportGenerationConfig# RAG Provider Settingsrag_interface=RAGInterfaceManager.get_interface_from_args(RAGProviderName("agent-search"),limit_hierarchical_url_results=rag_limit_hierarchical_url_results,limit_final_pagerank_results=rag_limit_final_pagerank_results,)rag_context=rag_interface.get_rag_context(query)# LLM Provider Settingsllm_interface=LLMInterfaceManager.get_interface_from_args(LLMProviderName("openai"),)generation_config=GenerationConfig(model_name=llm_model_name,max_tokens_to_sample=llm_max_tokens_to_sample,temperature=llm_temperature,top_p=llm_top_p,# other generation params here ...)formatted_prompt=raw_prompt.format(rag_context=rag_context)completion=llm_interface.get_completion(formatted_prompt,generation_config)
Package: scipion-app
Scipionis an image processing framework to obtain 3D models of
macromolecular complexes using Electron Microscopy (3DEM). It integrates
several software packages and presents an unified interface for both biologists
and developers. Scipion allows to execute workflows combining different
software tools, while taking care of formats and conversions. Additionally,
all steps are tracked and can be reproduced later on.Want to learn more? [Go to our web site.](http://scipion.i2pc.es)
Package: scipion-ed
scipion-edis the base plugin defining the Domain for Electron Diffraction image processing.InstallationFor using scipion-ed, we need to have a working Python 3 environment (e.g via virtualenv or conda).
After that, we might easily install by:pipinstallscipion-edscipion-ed-dialsDevelopmentFor development, we probably want to download the source code and install from there. In that way
changes can be made and we can test it quickly. We also need a working Python 3 environment and we
recommend to create a development folder to download the source code.# Create a folder for the installationmkdirscipion-ed-devcdscipion-ed-dev# Then we can install scipion-ed by:gitclonegit@github.com:scipion-ed/scipion-ed.gitpython-mpipinstall-escipion-ed# Install in the environment as development# And also install the pluginsgitclonegit@github.com:scipion-ed/scipion-ed-dials.gitpython-mpipinstall-escipion-ed-dials# Install in the environment as developmentPublishing the package to PyPIIn order to make scipion-ed available to install withpip install scipion-ed,
we need to:pythoninstalltwinerestructuredtext-lintcdscipion-ed# It might be a good idea to check the README.rst before uploading:rst-lintREADME.rstpythonsetup.pysdisttwineuploaddist/scipion-ed-3.0.1.tar.gzRunning tests (TO BE UPDATED)cdscipion-edcdpwed/testspython-munittestdiscover# To visualize the test project you need to specify SCIPION_DOMAIN and SCIPION_VERSIONexportSCIPION_DOMAIN=scipion-ed/pwedexportSCIPION_VERSION=3.0.0pythonscipion-pyworkflow/pyworkflow/apps/pw_project.pyTestEdBaseProtocolsPython 3 environmentsFor development, we probably want to download the source code and install from there. In that way
changes can be made and we can test it quickly.We also need a working Python 3 environment and we recommend to create a development folder to download the source code.# Create a clean virtual environmentpython-mvirtualenv--python=python3envsourceenv/bin/activateIt is also possible to use a conda environment.# Create the environmentcondacreate-nsped-devpython=3.8pipcondaactivatesped-devTroubleshootingIf you get “error: command ‘x86_64-linux-gnu-gcc’ failed with exit status 1” you may need to install python3-dev:
sudo apt install python3-dev -yTkinter with Python3Tkinter with Conda
Package: scipion-em
pwempwemis a Python module of Scipion framework for image processing in Electron MicroscopyThe entire collection is licensed under the terms of the GNU Public License,
version 3 (GPLv3).DevelopmentTo installpwemfor development purposes, one can do:# Create a clean virtual environmentcondacreate-nscipionpython=3.8condaactivategitclonehttps://github.com/scipion-em/scipion-em.gitcdscipion-empipinstall-e.Running testscondaactivatescipioncdscipion-emexportSCIPION_DOMAIN="pwem"python-munittestdiscoverAPI documentationhttps://scipion-em.github.io/docs/release-3.0.0/api/pwem/pwem.html
Package: scipion-em-appion
This plugin allows to use Appion Dogpicker within the Scipion framework.SetupInstall this plugin:scipion installp -p scipion-em-appionORthrough the plugin manager GUI by launching Scipion and followingConfiguration>>PluginsAlternatively, in devel mode:scipion installp -p local/path/to/scipion-em-appion --develSupported versions0.2.1.1
Package: scipion-em-aretomo
This plugin provides a wrapper forAreTomoprogram.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-aretomoDeveloper’s versiondownload repositorygit clone -b devel https://github.com/scipion-em/scipion-em-aretomo.gitinstallscipion installp -p /path/to/scipion-em-aretomo --develAreTomo binaries will be installed automatically with the plugin, but you can also link an existing installation.Default installation path assumed issoftware/em/aretomo2-1.0.0, if you want to change it, setARETOMO_HOMEinscipion.conffile to the folder where the AreTomo is installed.Depending on your CUDA version this plugin will guess the right default binary fromAreTomo2_1.0.0_CudaXY(X is for cuda major version, Y for the minor). You can always set a different one by explicitly setting ARETOMO_BIN variable.If you need to use CUDA different from the one used during Scipion installation (defined by CUDA_LIB), you can addARETOMO_CUDA_LIBvariable to the config file. Various binaries can be downloaded from the official website.To check the installation, simply run the following Scipion test:scipion test aretomo.tests.test_protocols_aretomo.TestAreTomoLicensingAreTomo2 is available under BSD-3 licenseSupported versionsaretomo2: 1.0.0
aretomo: 1.3.4Protocolstilt-series align and reconstructDetailed manual can be found insoftware/em/aretomo2-1.0.0/bin/AreTomo2Manual_1.0.0_10-26-2023.pdfReferencesAreTomo: An integrated software package for automated marker-free, motion-corrected cryo-electron tomographic alignment and reconstruction. Shawn Zheng, Georg Wolff, Garrett Greenan, Zhen Chen, Frank G. A. Faas, Montserrat Bárcena, Abraham J. Koster, Yifan Cheng, David Agard. JSB vol. 6, 2022, 100068.
Package: scipion-em-atomstructutils
Atom_struct_utils scipion pluginUtilities for handling PDB/mmcif atomic structure files:Current plugin protocols:operate (option extract): extract a chain from an atom structure (pdb/cif file).operate (option add): perform union of several atomic structures.convert_sym: takes an atomStruct in any icosahedric symemtry and rotates it to another.Install this pluginREADThe “Binary file” section before installing this packageYou will need to use3.0.0version of Scipion to run these protocols.Stable versionscipion installp -p scipion-em-atom_struct_utilsORthrough the plugin manager GUI by launching Scipion and followingConfiguration>>PluginsDeveloper’s versionDownload repository:git clone https://github.com/scipion-em/scipion-em-atom_struct_utils.gitInstall:scipion installp -p path_to_scipion-em-atom_struct_utils --develBinary filesAtom_struct_utils plugin is a pure Python module but uses the program “maxit”
for conversion of pdb/cif files with atomic coordinates. If not available maxit will be
instaled. Prerequisites for maxit arebison,flex,cshandtcshsudo apt install bison  (command valid for ubuntu/debian and similar OS)
sudo apt install felx   (command valid for ubuntu/debian and similar OS)
sudo apt install csh   (command valid for ubuntu/debian and similar OS)TestsTo check the installation, simply run the following Scipion test:scipion test –grep TestOperate –runscipion test –grep TestConvertSymmetry –runExamplesSeeModel Building TutorialBuildbot statusStatus devel version:Status production version:
Package: scipion-em-atsas
This plugin provides a wrapper forATSAS- a program suite for small-angle scattering data analysis from biological macromolecules.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-atsasDeveloper’s versiondownload repositorygit clone -b devel https://github.com/scipion-em/scipion-em-atsas.gitinstallscipion installp -p /path/to/scipion-em-atsas --develImportant:ATSAS binaries is free for academic users, but you will have to register, download and install them yourself fromhttps://www.embl-hamburg.de/biosaxs/download.htmlConfiguration variablesATSAS_HOME: Installation path to ATSAS. Default issoftware/em/atsas-3.2.0Supported versions3.0.1, 3.2.0Protocolsconvert PDB to SAXS curveReferencesManalastas-Cantos, K., Konarev, P.V., Hajizadeh, N.R., Kikhney, A.G., Petoukhov, M.V., Molodenskiy, D.S., Panjkovich, A., Mertens, H.D.T., Gruzinov, A., Borges, C., Jeffries, C.M., Svergun, D.I., Franke, D. (2021). ATSAS 3.0: expanded functionality and new tools for small-angle scattering data analysis J. Appl. Cryst. 54, 343-355Svergun, D., Barberato, C., and Koch, M. H. J. (1995). CRYSOL - a Program to Evaluate X-ray Solution Scattering of Biological Macromolecules from Atomic Coordinates. Journal of Applied Crystallography, 28(6): 768–773.
Package: scipion-em-bsoft
This plugin allows to useBsoft(https://lsbr.niams.nih.gov/bsoft/) within the Scipion framework.SetupInstall this plugin:Using the command line:scipion installp -p scipion-em-bsoftor trough the plugin manager GUI by launching Scipion and followingConfiguration>>PluginsAlternatively, in devel mode:scipion installp -p local/path/to/scipion-em-bsoft --devel
Package: scipion-em-ccp4
CCP4 scipion pluginThis plugin allows to use CCP4 programs within the Scipion framework.You need to install the CCP4 suite before installing the plugin, see section “Binary Files” for details.CCP4, from Collaborative Computational Project Number 4, is a software suite that allows model building of macromolecule structures obtained by X-ray crystallography, and that has been extended to other techniques like cryo-EM (seeCCP4 home pagefor details).Programs from CCP4 included in the Scipion framework for model building:cootrefmacInstall this pluginYou will need to use3.0.0version of Scipion (o latter) to run these protocols. To install the plugin, you have two options:Stable versionscipion installp -p scipion-em-ccp4ORthrough the plugin manager GUI by launching Scipion and followingConfiguration>>PluginsDeveloper’s versionDownload repository:git clone https://github.com/scipion-em/scipion-em-ccp4.gitInstall:scipion installp -p path_to_scipion-em-ccp4 --develBinary filesCCP4 binaries willNOTbe installed automatically with the plugin. The independent installation of CCP4 software suite by the user is required before running the programs. Default installation path assumed is/usr/local/ccp4-7.0; this path or any other of your preference has to be set inCCP4_HOMEinscipion.conf. We recommend to install CCP4 version 7.0.056 or higher. (seehttp://www.ccp4.ac.uk/download/#os=linux)TestsTested with CCP4 versions: 7.0.056 and 7.0.066.To check the installation, simply run the following Scipion test:scipion test ccp4.tests.test_protocol_coot_refmacSupported versions of CCP47.0.056 or higher.Additional Instructionseehttps://github.com/scipion-em/scipion-em-ccp4/wikiProtocolscoot refinement: Molecular interactive graphics application used for flexible fitting, refinement, model completion, and validation of structures of macromolecules regarding electron density maps. See thedetailsofCootutilities.refmac: Automatic refinement program in Fourier space of macromolecule structures regarding electron density maps. See ` <http://www.ccp4.ac.uk/html/refmac5/description.html>`_ ofRefmacutilities.ExamplesSeeModel Building TutorialBuildbot statusStatus devel version:Status production version:Version in Pypi
Package: scipion-em-chimera
Chimera scipion pluginThis plugin allows to use chimeraX commands within the Scipion framework.Chimera  is a program for interactive visualization and analysis of molecular structures and related data. It is developed by the Resource for Biocomputing, Visualization, and Informatics (seeChimeraX home pagefor details).Install this pluginYou will need to use3.0.0version of Scipion to run these protocols. To install the plugin, you have two options:Stable versionscipion3 installp -p scipion-em-chimeraORthrough the plugin manager GUI by launching Scipion and followingConfiguration>>PluginsDeveloper’s versionDownload repository:git clone https://github.com/scipion-em/scipion-em-chimera.gitInstall:scipion3 installp -p path_to_scipion-em-chimera --develBinary filesChimera binaries could be installed automatically with the plugin after accepting ChimeraX licence terms,
but you can also link an existing installation. Default installation path assumed issoftware/em/chimerax-1.0,
if you want to change it, set *CHIMERA_HOMEinscipion.conffile to the folder where ChimeraX is installed
or link your chimerax folder tosoftware/em/chimerax-1.0.TestsTested with ChimeraX version: 1.0.To check the installation, simply run the following Scipion tests:scipion test chimera.tests.test_protocol_chimera_operatescipion test chimera.tests.test_protocol_chimera_fitscipion test chimera.tests.test_protocol_modeller_searchscipion test chimera.tests.test_protocol_contactscipion test chimera.tests.test_protocol_chimera_map_subtractionSupported versions of ChimeraX1.0, 1.1, 1.2 and 1.3Protocolsrigit fit: Fits an atomic structure (PDB/CIF) to a 3D map using a rigid fit algorithm.model from template: Models three-dimensional structures of proteins usingModeller.contacts: Identifies interatomicclashes and contactsbased on van der Waals radii.operate: Provides access to Chimera and allows to save the result in Scipion framework.restore: This protocol opens Chimera and restores the session previously saved with commandsscipionwriteorscipionss.map subtraction: Protocol to identify remnant densities in a density map by subtracting two maps or masking one of them.alphafold prediction: finds and retrieves existing models from the AlphaFold Database, runs new AlphaFold predictions using Google Colab, executes a local implementation of alphafold.ExamplesSeeModel Building TutorialFAQcheck the FAQ <https://github.com/scipion-em/scipion-em-chimera/blob/devel/FAQ.rst> for known problemsBuildbot statusStatus devel version:Chimera Extra commandsA set of comamnd to allow interaction between scipion and chimerax has been implemented.
They may be executed from ChimeraX command line:scipionwrite: saves model filescipionss: saves chimera sessionscipionrs: restores chimera sesionscipioncombine: combines two models [No longer needed since chimerax now implements combine]scipionshell: shows the density of a 3D Map on a spherical shell of the map at a given radiusscipionshellcrown: shows a shell of a 3D map between two radiiscipion: summary with all scipion related commands