Saravanan666/pypi-org huggingface dataset:
Package: scipion-em-tomo3d
Scipion plugin for Tomo3DThis plugin allows to use some of the software packages from3demimageprocessingwithin the Scipion framework.The binaries for the plugin MUST be downloaded manually:InstallationThe plugin can be installed in user (stable) or developer (latest, may be unstable) mode:1. User (stable) version::scipion installp -p scipion-em-tomo3d2. Developer (latest, may be unstable) version::Clone the source code repository:git clone https://github.com/scipion-em/scipion-em-tomo3d.gitInstall:scipion3 installp -p local/path/to/scipion-em-tomo3d --develProtocolsThe integrated protocols are:tomo3d - denoise tomogramstomo3d - reconstruct tomogramsTestsThe installation can be checked out running some tests:scipion3 tests tomo3d.tests.test_protocol_reconstruct_tomogramscipion3 tests tomo3d.tests.test_protocol_denoise_tomogramReferencesFast tomographic reconstruction on multicore computers.J.I. Agulleiro, J.J. Fernandez. Bioinformatics 27:582-583, 2011.Tomo3D 2.0 – Exploitation of Advanced Vector eXtensions (AVX) for 3D reconstruction.J.I. Agulleiro, J.J. Fernandez. Journal of Structural Biology 189:147-152, 2015.TOMOBFLOW: feature-preserving noise filtering for electron tomography.J.J. Fernandez. BMC Bioinformatics 2009, 10:178.TomoEED: Fast Edge-Enhancing Denoising of Tomographic Volumes.J.J. Moreno, A. Martinez-Sanchez, J.A. Martinez, E.M. Garzon, J.J. Fernandez. Bioinformatics 34:3776-3778, 2018.Contact informationIf you experiment any problem, please contact us here:scipion-users@lists.sourceforge.net,scipion@cnb.csic.esor open anissue.We’ll be pleased to help.Scipion Team
Package: scipion-em-tomosegmemtv
Scipion plugin for TomoSegMemTV and MembraneAnnotatorScipion plugin for segmentation and annotation of membranes in tomograms (withTomoSegmenTVandmemb-annotator,
respectively)InstallationThe plugin can be installed in user (stable) or developer (latest, may be unstable) mode:1. User (stable) version::scipion3 installp -p scipion-em-tomosegmemtv2. Developer (latest, may be unstable) version::Clone the source code repository:git clone https://github.com/scipion-em/scipion-em-tomosegmemtv.gitInstall:scipion3 installp -p local/path/to/scipion-em-tomosegmemtv --develProtocolsThe integrated protocols are:tomosegmemtv - annotate segmented membranes: Manual annotation tool for segmented membranes.tomosegmemtv - resize segmented or annotated volume: Resize segmented volumes or annotated (TomoMasks).tomosegmemtv - tomogram segmentation: segment membranes in tomograms.TestsThe installation can be checked out running some tests (Important: test_resize_tomomask requires the pluginscipion-em-imodto be installed).scipion3 tests tomosegmemtv.tests.test_tomosegmemtvscipion3 tests tomosegmemtv.tests.test_resize_tomomaskTutorialA tutorial about how to use tomosegmemtv and the membrane annotator within Scipion can be foundhere.ReferencesRobust membrane detection based on tensor voting for electron tomography.A. Martinez-Sanchez, I. Garcia, S. Asano, V. Lucic, J.J. Fernandez.
Journal of Structural Biology  186:49-61, 2014.Contact informationIf you experiment any problem, please contact us here:scipion-users@lists.sourceforge.net,scipion@cnb.csic.esor open
anissue.We’ll be pleased to help.Scipion Team
Package: scipion-em-tomotwin
This plugin provides a wrapper forTomoTwinsoftware: Particle picking in Tomograms using triplet networks and metric learningInstallationYou 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-tomotwinDeveloper’s versiondownload repositorygit clone -b devel https://github.com/scipion-em/scipion-em-tomotwin.gitinstallscipion installp -p /path/to/scipion-em-tomotwin --develTomoTwin software will be installed automatically with the plugin but you can also use an existing installation by providingTOMOTWIN_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)”TOMOTWIN_ENV_ACTIVATION(default = conda activate tomotwin-0.8.0):
Command to activate the TomoTwin environment. Tomotwin uses cuda-11.8, so you might want to activate specific CUDA libs via e.g.TOMOTWIN_ENV_ACTIVATION = . /etc/profile.d/lmod.sh && module load cuda/11.8 && conda activate tomotwin-0.8.0TOMOTWIN_MODEL(default = software/em/tomotwin_model-092023/tomotwin_model_p120_092023_loss.pth):
Path to the pre-trained model.NAPARI_ENV_ACTIVATION(default = conda activate napari-0.4.17):
Command to activate the Napari viewer environment.VerifyingTo check the installation, simply run the following Scipion tests:scipion tests tomotwin.tests.test_protocols_tomotwin.TestTomoTwinRefBasedscipion tests tomotwin.tests.test_protocols_tomotwin.TestTomoTwinClusterBasedSupported versions0.6.1, 0.7.0, 0.8.0Protocolsclustering-based picking (step 1)clustering-based picking (step 2)create tomo masksreference-based pickingReferencesTomoTwin: Generalized 3D Localization of Macromolecules in Cryo-electron Tomograms with Structural Data Mining. Gavin Rice, Thorsten Wagner, Markus Stabrin, Stefan Raunser.https://www.biorxiv.org/content/10.1101/2022.06.24.497279v1
Package: scipion-em-tomoviz
Scipion tomoviz pluginThis plugin contains a series of methods for the visualization and manipulation of 3D data (specially desgined for tomography).Plugin InstallationThis plugin is currently under initial development and it is not ready for production yet.In the meantime, it can be used for development, base on Scipion v3.x with plugins.This tomography plugin can be enabled by cloning this repository and execute the command:git clone https://github.com/scipion-em/scipion-em-tomoviz.git
scipion installp -p ~/scipion-em-tomoviz --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.
Package: scipion-em-topaz
This plugin allows to use Topaz programs within the Scipion framework.
It can denoise, pre-process micrographs and pick particles within Scipion.Topazis a pipeline for particle detection in cryo-electron
microscopy images which uses convolutional neural networks trained from
positive and unlabeled examples.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 versionIt can be installed in user mode via Scipion plugin manager (Configuration>Plugins) or using the command line:scipion installp -p scipion-em-topazDeveloper’s versiondownload repositorygit clone -b devel https://github.com/scipion-em/scipion-em-topaz.gitinstallscipion installp -p /path/to/scipion-em-topaz --develTopaz software will be installed automatically with the plugin but you can also use an existing installation by providingTOPAZ_ENV_ACTIVATION(see below).Important:you need to have conda (miniconda3 or anaconda3) pre-installed to use this program.To check the installation you can run the plugin’s tests:scipion tests topaz.tests.test_protocol_topaz.TestTopazConfiguration 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)”TOPAZ_ENV_ACTIVATION(default = conda activate topaz-0.2.5):
Command to activate the Topaz environment.Supported versions0.2.3, 0.2.4, 0.2.5Protocolsimport training modelpickingtrainingReferencesBepler, T. et al. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. arXiv (2018).
Package: scipion-em-ucm
UCM MethodsA scipion plugin with electron-microscopy validation methods, based on maps
that expose local quantities of interest related to the given electron-density
volumes.The methods are developed by the Cryo-EM group at theUCM.InstallTo install it, you can just clone this repository and install it with the
standard scipion command to install plugins:gitclonehttps://gitlab.com/jordibc/scipion-em-ucm.git

scipion3installp-pscipion-em-ucm--develNext time you open scipion, there should be two new protocols (inProtocols SPA -> 3D -> Analysis -> Validation) calledlocal b-factorandlocal occupancy.
Package: scipion-em-warp
This plugin provides a wrapper for some of the programs of theWarpsoftware.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-warpDeveloper’s versiondownload repositorygit clone -b devel https://github.com/scipion-em/scipion-em-warp.gitinstallscipion installp -p /path/to/scipion-em-warp --develVerifyingTo check the installation, simply run one of the following Scipion tests:scipion3 tests warp.tests.test_protocols.TestDeconvolveMicsscipion3 tests warp.tests.test_protocols_tomo.TestDeconvolveTomoProtocolsdeconvolve micrographsdeconvolve tomogramsdeconvolve tilt-seriesReferencesDimitry Tegunov and Patrick Cramer. Real-time cryo-electron microscopy data preprocessing with Warp. Nature Methods 16(11), 2019, p. 1146-1152.
Package: scipion-em-xlmtools
This plugin provide wrappers around several programs ofXLM-Tool.stable:devel: |InstallationYou will need to use3.0version of Scipion to be able to run these protocols. To install the plugin, you have two options:Stable version (not avalaible yet)scipion installp -p scipion-em-xlmtoolsDeveloper’s versiondownload repositorygit clone https://github.com/scipion-em/scipion-em-xlmtools.gitinstallscipion installp -p path_to_scipion-em-xlmtools --develXML-Tools binaries will be installed automatically with the plugin.To check the installation, simply run one of the following Scipion tests:scipion3 tests xlmtools.tests.test_protocols_xlm.TestWLMA complete list of tests can also be seen by executingscipion test--show--grepxlmtoolsSupported versions1.0ProtocolsXLM Tools protocolReferencesSinnott et al., Combining Information from Crosslinks and Monolinks in the Modeling of Protein Structures, Sturcture (2020),https://doi.org/10/1016/j.str.2020.05.012
Package: scipion-em-xmipp
[![Build](https://github.com/I2PC/scipion-em-xmipp/actions/workflows/build.yml/badge.svg)](https://github.com/I2PC/scipion-em-xmipp/actions/workflows/build.yml)# scipion-em-xmippPlugin to use Xmipp programs within the Scipion framework.You can install it either, using the Scipion software following
the [Scipion’s installation guide](https://scipion-em.github.io/docs/release-3.0.0/docs/scipion-modes/how-to-install.html)
, (remember to install the scipion-em-xmipp plugin or remove the -noXmipp flag in the Scipion installation)**strongly recommended for non-developers** or cloning it on your own and
[linking it to Scipion](https://github.com/I2PC/xmipp#linking-standalone-version-to-scipion).
The latter is only for those who intended to develop the Xmipp plugin.Fordevelopers, we strongly recommend to follow the guide on
[how to install xmipp-bundle](https://github.com/i2pc/xmipp#xmipp-as-a-standalone-bundle-for-developers)
in order to get a complet Xmipp’s development ecosystem.
Package: scipion-em-xmipp2
Xmipp2 pluginThis plugin allows to use xmipp2 within the Scipion framework.SetupInstall this plugin:scipion installp -p scipion-em-xmipp2ORthrough the plugin manager GUI by launching Scipion and followingConfiguration>>PluginsAlternatively, in devel mode:scipion installp -p local/path/to/scipion-em-xmipp2 --devel
Package: scipion-em-xmipptomo
Scipion xmipptomo pluginThis is a xmipptomo plugin forscipionSetupInstall this plugin in devel mode:Using the command line:git clone git@github.com:I2PC/scipion-em-xmipptomo.git
cd scipion-em-xmipptomo
scipion installp -p local/path/to/scipion-em-xmipptomo --devel
Package: scipion-installer
Scipion installerScipion installeris a python module to install scipion 3 (already inalfa)
and not ready for production.This installer is python2-python3 compatible and is a very lightweight package.
It will create apython3virtual environment (conda or virtualenv) with scipion in it.The entire collection is licensed under the terms of the GNU Public License,
version 3 (GPLv3).InstallationIt is a 2 lines’ installation: First to install the installer, second to use the installer (to install scipion).* Python 3 *python3 -m pip install scipion-installer
python3 -m scipioninstaller where-to-install-scipion* Python2 *(You are going to need python3 anyway)python2 -m pip install scipion-installer
python2 -m scipioninstaller where-to-install-scipionAdvanced optionsusage: scipioninstaller [-h] [-conda] [-venv] [-dev] [-noXmipp] [-j J] [-dry]
                      [-httpsClone] [-noAsk] [-n N]
                      path

positional arguments:
  path         Location where you want scipion to be installed.

optional arguments:
  -h, --help   show this help message and exit
  -conda       Force conda as environment manager, otherwise will use conda
               anyway if found in the path, else: virtualenv.
  -venv        Force virtualenv as environment manager, otherwise will use
               conda if found in the path, otherwise: virtualenv.
  -dev         installs components in devel mode
  -noXmipp     Xmipp is installed in devel mode under xmipp-bundle dir by
               default. This flag skips the Xmipp installation.
  -j J         Number of processors, Xmipp may take a while...
  -dry         Just shows the commands without running them.
  -httpsClone  Only when -dev is active, makes git clones using https instead
               of ssh
  -noAsk       try to install scipion ignoring some control questions in that
               process. You must make sure to write the correct path where
               Scipion will be installed
  -n N         Name of the virtual environment. By default, if this parameter
               is not passed, the name will be .scipion3env
  -sciBranch SCIBRANCH  Name of the branch of scipion repos to clone when -dev
               is passed.
  -xmippBranch XMIPPBRANCH
               Name of the branch of xmipp repos to clone when -dev
               is passed.Bundle installationCheckout Jesper L. Karlsen’s script to make a full installation –>https://github.com/jelka71/scipion_auto_install.gitTroubleshootingProblemgit@github.com: Permission denied (publickey).
fatal: Could not read from remote repository.SolutionAdd-httpsCloneProblempip/pip3 is needed to get the installer.SolutionFor ubuntu/debian you might need root access to runsudo apt-get install python-pipNo root access?: You can trypip install –user scipion-installerto install it locallyProblemMissing dependencies: scipion needs python3-tkinter to work and an existing python3 installation.SolutionFor ubuntu/debian you might need root access to runsudo apt-get install python3-tkIfError: “pkg_resources.DistributionNotFound: The ‘zipp>=0.5’is raised when launching installscipion, try to install zipp bypip install zipp
Package: scipion-pyworkflow
pyworkflowpyworkflowis a simple workflow platform used in scientific applications. It has been initially developed
within the Scipion framework for image processing in Electron Microscopy.pyworkflowallows you to define a workingDomainby defining the following group of classes:Objects: input/outputs that will be generated by different programsProtocols: special type of Objects that have defined input parameters and will produce some outputViewers: Entities that provide graphical analysis of results.Wizards: Small GUIs that can be develop to help users to select given parameter values.DevelopmentTo installpyworkflowfor development purposes, one can do:# Create a clean virtual environmentcondacreate-nscipionpython=3.8condaactivategitclonehttps://github.com/scipion-em/scipion-pyworkflow.gitcdscipion-pyworkflowpipinstall-e.Running testscondaactivatescipioncdscipion-pyworkflowexportSCIPION_DOMAIN="pyworkflowtests/tests/mock_domain"python-munittestdiscover# Simple project GUI can be shown after running tests:pythonpyworkflow/apps/pw_project.pyTestProtocolOutputsAll test projects are located in ~/ScipionUserData/Tests/API documentationhttps://scipion-em.github.io/docs/release-3.0.0/api/pyworkflow/pyworkflow.html
Package: scipolate
SciPolate=========Scipolate offers a small helper class that can be used to perform2D interpolation tasks using scipy. It is meant to be used as a commoninterface to run and validate the task automated in the same way.Installation============Install Scipolate using pip:```bashpip install scipolate```Note====Scipolate was originally a part of a interpolation web-app used in one of mylectures. That means it was used in an API. Hence, the parameters are set inone single JSON-like dictionary, which is un-pythonic.For the same reason, the class does provide an output *Report* including theresult as a base64 encoded image. Nevertheless, the class can be used outsideof a web-application context. Mind that performance was not important duringdevelopment. In case you need a fast algorithm, use scipy directly, orsomething like the [interpolation](https://pypi.org/project/interpolation/)library.With the new version the Interpolation itself is outsourced into a class onits own. All the image processing and transformation used for the reportingtools in my web based applications, a class called `WebInterface` is implemented.Usage=====There are two main interfaces that can be used:* The *Interpolator* class, which is the core class performing theinterpolation.* The *WebInterface* class which is meant to be used in a API, as it takes thearguments as JSON and returns JSON along with base64 encoded images.Example-------An Example will follow.
Package: scipp
ScippSeehttps://scipp.github.io/for project context and how to use and install thescippPython module.Bug reports or feature requests should be submitted by opening anissue.
For general discussions, or questions about how to achieve something with Scipp, start a newdiscussion.DevelopmentIf you are interested in developing Scipp, please see thedeveloper getting started guide.LinksPerformance benchmarksCode coverageContributing OrganizationsThe following organizations have made major contributions to the development of scipp, and other developments in thescipp ecosystem:European Spallation Source ERIC, SwedenScience and Technology Facilities Council, UKSee individualrelease notesfor the individual contributors to each release.
Package: scippy
#Setting up a new RepositoryPronounced "skippy". Module for instrument communication via SCPI, implemented using pyvisa and pyserial (depending on the device)FeaturesCommunicate with devices over GPIB, RS232, or USB via SCPI protocolSeveral common instruments (Agilent 33210A, Keithley 2400 sourcemeter) already have dedicated classesCreate new instruments with the SCPIDevice classGetting StartedInstallationThis package can be installed directly with pip frompypi:pip install scippyAgilent ExampleThe following example sets the frequency, amplitude, and output state of an Agilent 33210A instrument, and verifies that the parameters match the ones you set.from scippy import Agilent

agilent = Agilent()
agilent.frequency = 2500
agilent.amplitude = 0.5
agilent.output_on = True
agilent.verify()Keithley ExampleThe following sets up a Keithley 2400 source meter, sets the voltage compliance, and sets a currentfrom scippy import Keithley, ureg

keithley = Keithley()
keithley.current = 0.05 # Units assumed to be in Amps
keithley.voltage_compliance = 25 * ureg.mV # Set compliance to 25mV
voltage, current = keithley.measure() # Sets the output on, measures the current and voltage
print(voltage)
print(current)Common Issues (Keithley)Some instruments, unfortunately, have much less reliable communication interfaces than others. In particular, the Keithley is extremely sensitive to not being closed.
Package: scipr
sciprSingle Cell Iterative Point set Registration (SCIPR)Free software: BSD licenseHomepage & Documentation:https://scipr.readthedocs.io.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-05-13)First release on PyPI.0.2.0 (2020-05-22)New logging capabilitiesscipr API calls will no longer result in any output to stdout under normal conditionsInstead, now using Python’s built-inloggingmodule to emit logging informationCan also track SCIPR model fitting metrics per iteration in TensorBoard (optional)Now compatible with AnnData objects for fitting and transforming0.2.1 (2020-05-26)Fix for bug that would cause MNN to break ifk=1Fix for mistake in SCIPR.transform_adata documentation about ReturnsMore tests for matching algorithms added
Package: sciproc
Sciproc in experimental stage provides tools to select, edit, convert
scientific (observed, model-generated) data. It needs Numpy. It’s very
experimental, as some functions aren’t tested or only tested in ‘idealised
cases’, so please be careful.  Please let me know if you would like to
contribute. Currently selection from 1D data by coordinates or certain timestep
and applying a function repeatedly on a multidimensional matrix is implemented.
However selecting, interpolating and editing procedures for multidimensional
data is planned in the near future.  You might want to use it if you have have
any observational data and you want to select a period, make a selection with a
certain timestep or make an interpolation. The aim is to make an addition to
the cdo climate data operators with python power (see also pynacolada). It
should be working with normal numpy data. However, if you want to process
netcdf-files, we recommend to use the pynacolada interface which acutally uses
sciproc. Typical usage
often looks like this:#!/usr/bin/env python

from numpy import *
from sciproc import *

# select data from a 1-D array:
data = array([1.0,2.0,4.0,2.5])
incoords = array([0.0,1.0,2.0,3.0])
print(datatimeco(data,coords = incoords,outcoords = array([1.0,2.0]))

a = array([[[1,3,2],[2,1,3],[4,1,3]],[[1,2,3],[4,1,2],[3,0,1]]])
print('copy')
print( multifunc(a,[False,False,True],lambda x: copyfunction(x)))
print('take only elements 2 and 3 from third dimension')
print(multifunc(a,[False,False,True],lambda x: secondandthirdelement(x)))
print('take only elements 2 and 3 from second dimension')
print(multifunc(a,[False,True,False],lambda x: secondandthirdelement(x)))
print('reduce dimension')A SectionA Sub-Section
Package: sciprs
sciprsscipybut really written in pure Rust. Checkoutsci-rsfor Rust implementations behind the Python interfaceWhy sciprs instead of scipyscipy is largely written in Python, Pyrex, C, and Fortran. scipy is great for research and servers. Rust is a cross platform language that provides both performance improvements and portability to scipy.sciprs acts as the wrapper around a library that is ready to translate directly into embedded, mobile, web, or server deployments without losing the agility of Python's powerful interactive scripting abilities.How to use sciprsChange you scipy import to sciprs. sciprs strives to provide the same functionality as scipy without compromising with a rich history of documentation and published research.fromscipy.signalimportsosfilt,butterbecomesfromsciprs.signalimportsosfilt,butterInstallationInstall the package from PyPi like usual,sciprs. The GitHub Actions will generate a compiled wheel for most major platforms each release.pip3installsciprsCompilation from sourcematurinprovides the build system hooks withpyo3as the intermediate between Python and Rustmaturinbuild