Saravanan666/pypi-org huggingface dataset:
Package: pmpsdb-client
pmpsdb_clientOverviewThis is a gui and cli application for managing the deployment and inspection of
PMPS database files on production PLCs at LCLS.
It provides tools to make deployment and verification of deployment seamless and easy.UsageOnce installed, this application can be invoked viapmpsdb. For example, here is
the current output ofpmpsdb --help:usage: pmpsdb [-h] [--version] [--verbose] [--export-dir EXPORT_DIR] {gui,list-files,upload-to,download-from,compare,reload} ...

PMPS database deployment helpers

positional arguments:
  {gui,list-files,upload-to,download-from,compare,reload}
    gui                 Open the pmpsdb gui.
    list-files          Show all files uploaded to a PLC.
    upload-to           Upload a database export file to a PLC.
    download-from       Download a database file previously exported to a PLC.
    compare             Compare files beteween the local exports and the PLC.
    reload              Force the PLC to re-read the database export while running.

optional arguments:
  -h, --help            show this help message and exit
  --version             Show version information and exit
  --verbose, -v         Show tracebacks and debug statements
  --export-dir EXPORT_DIR, -e EXPORT_DIR
                        The directory that contains database file exports.From a git clone, you can invoke the same script without needing to install the
package. This is done from the root directory here by callingpython -m pmpsdb --help, for example.This application will not work unless you have access to the LCLS controls networks.
It is designed to run on an endstation's operator consoles.The most common usage will be to open the gui from an operator console as simplypmpsdb gui.InstallationThis package can be installed using recent versions ofpipthat support
thepyproject.tomlformat.To install, you can choose one of the following:pip install pmpsdb_clientto install from pypiclone this repo, check out the desired tag, and run the following from the root directory:pip install .install directly from github via:pip install 'pmpsdb_client @ git+https://github.com/pcdshub/pmpsdb_client@v1.1.2'for example, to install version v1.1.2.PLC ConfigurationThe PLC must have the following configuration:ftp enabled, with either the default logins or anonymous uploads enabledfirewall TCP ports 20-21 allowedThese are both editable in the CX Configuration Tool.
Enabling the ftp server will require a PLC restart, updating the firewall will not.
Package: pmpy
PmPy - Project Management in pythonPmPy offers project management tools in python.Using PmpyGetting started quickly:pip install pmpyCommunityGithub DiscussionsContributing back to PmPyIf you run into an issue, please file a newissuefor us to discuss. If possible, follow up with a pull request.If you would like to add a feature, please reach out viaissue. A feature is most likely to be added if you build it!Changelog for PmPy1.0.14 - 2021-12-03[Added] Ability to cancel requests for resoruces. this allows modeling or between resource requests1.0.11 - 2021-11-28[Fixed] fixed namings of logs.[Added] added waiting time for resources.1.0.9 - 2021-11-28[Fixed] Fixed numbering of entities in the discrete event simulation  environment.[Fixed] Fixed spelling  of preemptive resources.1.0.8 - 2021-11-28[Fixed] fixed splelling of environment class.1.0.5 - 2021-11-28[CHANGED] changed importing disrtibution.1.0.5 - 2021-11-28[NEW] Added Docstrings for discrete event simulation.1.0.4 - 2021-11-24[CHANGE] Updated PmPy package structure. PmPy now has asimulationsubpackage that includesdiscrete_eventanddistributionsmodules.1.0.3 - 2021-11-20[FIX] Fixed PyPi project description1.0.2 - 2021-11-20[FIX] Fixedsetup.pyfile with an extra empty line at the end.[NEW] Updated information about the project (AUTHORS.mdandREADME.mdfiles).1.0.1 - 2021-11-12[FIX] Fixing typo1.0.0 - 2021-11-11[NEW] Deploying the first version of the PmPyAuthorsPmPy was originally created by Dr.Naimeh Sadeghiin 2021. After that, in October 2021,Pedram Elmihas joined the project.List of AuthorsNaimeh Sadeghi-naima.sadeghi@gmail.comPedram Elmi-pedram.elmi@gmail.com
Package: pm-pyro
PyMC3-like abstractions for pyro's stochastic function.
Define a model as a stochastic function in pyro.
Usepm_likewrapper to create a PyMC3-esqueModel.
Random variables are exposed to user as attributes ofModel.
pm-pyro provides abstractions for inference (NUTS : No-U-Turn Sampler), trace plots, posterior plot and posterior predictive plots.InstallInstall from pypipipinstallpm-pyroDeveloper setup# install requirementspipinstall-rrequirements-dev.txt# run testspython-mpytestpmpyro/tests.pyExampleBorrowed this example from aPyMC3 tutorial. Outcome variablesYis dependent on 2 featuresX_1andX_2. The notebook for this example is availablehereModel SpecificationWe design a simple Bayesian Linear Regression model.Stochastic FunctionThe model specification is implemented as a stochastic function.importpyro.distributionsasdistimportpyroimporttorchdefpyro_model(x1,x2,y):alpha=pyro.sample('alpha',dist.Normal(0,10))beta=pyro.sample('beta',pdist.Normal(torch.zeros(2,),torch.ones(2,)*10.))sigma=pyro.sample('sigma',dist.HalfNormal(1.))# Expected value of outcomemu=alpha+beta[0]*x1+beta[1]*x2# Likelihood (sampling distribution) of observationsreturnpyro.sample('y_obs',dist.Normal(mu,sigma),obs=y)Context-manager SyntaxThepm_likewrapper creates a PyMC3-esqueModel.
We can use the context manager syntax for running inference.pm.samplesamples from the model using the NUTS sampler.
The trace is a python dictionary which contains the samples.frompmpyroimportpm_likeimportpmpyroaspmwithpm_like(pyro_model,X1,X2,Y)asmodel:trace=pm.sample(1000)sample: 100%|██████████| 1300/1300 [00:16, 80.42it/s, step size=7.49e-01, acc. prob=0.911]TraceplotWe can visualize the samples usingtraceplot.
Select random variables by passing them as a list viavar_names = [ 'alpha' ... ]argument.pm.traceplot(trace)Plot PosteriorVisualize posterior of random variables usingplot_posterior.pm.plot_posterior(trace,var_names=['beta'])Posterior Predictive SamplesWe can sample from the posterior by runningplot_posterior_predictiveorsample_posterior_predictivewith the same
function signatures as the stochastic functiondef pyro_model(x1, x2, y), replacing observed variableYwithNone.ppc=pm.plot_posterior_predictive(X1,X2,None,trace=trace,model=model,samples=60,alpha=0.08,obs={'y_obs':Y})Trace SummaryThe summary of random variables is available as a pandas array.pm.summary()LicenseThis project is licensed under the GPL v3 License - see theLICENSE.mdfile for details
Package: pmr2.captcha
IntroductionThis package adds the support of captchas on spam-prone forms on a given
Plone instance.  Currently this package extends the user registration
form from the package plone.app.users with the support for captcha views
following the format found incollective.captcha.  A compatible
package must be installed before the captcha will function.InstallationThis package requires and is for Plone 4 or later.  Plone 3.x does not
depend onplone.app.usersfor the user registration form.  To add the
support for captchas on the form for those versions of Plone are done
using the ZMI using the TTW editor on the registration template and
script files.Installing with buildoutYou can install pmr2.cpatcha usingbuildoutby adding an entry for
this package in both eggs and zcml sections.Example:[buildout]
...

[instance]
...

eggs =
    ...
    pmr2.captcha

zcml =
    ...
    pmr2.captchaOnce the site is rebuilt, start the instance and install this product
using the Add-ons panel in Site Setup.Changelog0.2 - 2012-11-07Have zcml depend on plone.browserlayer as this package uses it.Correcting dependencies and updated tests to be more resilient.0.1 - 2012-10-30Initial release
Package: pmr2.client
ContentsIntroductionContributorsChangelog0.2 - 2021-10-050.1 - 2014-09-01IntroductionA requests and requests-oauthlib based PMR client.ContributorsTommy Yu, AuthorChangelog0.2 - 2021-10-05Various Python 3 compatibility fixes.0.1 - 2014-09-01Initial release.
Package: pmr2.wfctrl
IntroductionA basic and light-weight workspace manager for PMR.  While this package
will ultimately be usable as a general purpose library, for now features
are targeted for integration by the MAP client.Changelog0.6.0 - 2023-10-17Ensure that Dulwich methods return values match closer with the
cli command class counterpart.Cleaned up support code in testing to ensure the defaults are not
interfered with.0.5.0 - 2023-10-06Dulwich is now registered first.Have the registration by name show all available implementations.0.4.0 - 2023-06-16Updated Dulwich support to use official release.Ensure that the workspace workflows will return the error code for the
external tools and not just assume stderr output are errors.0.3 - 2016-09-08Support the use of a custom fork of Dulwich for Git access.0.2 - 2014-07-14Renaming to the standard top-level namespace for this group of
packages.0.1 - 2013-12-18Initial release.Core support for basic incremental workflow management using both git
and mercurial.
Package: pm.recipe.debianize
Debianize uses fpm (https://github.com/jordansissel/fpm) to create debian
packages from python source directories. The only thing it really adds, is
that debianize will also create packages for all depencencies that your
source package has (install_requires). Debianize will only create a debian
package from a pythonpackage. So youneeda setup.py.note!If you are not using buildout but still want to use debianize, use
https://gist.github.com/2929586, which is just a shell script that does
the same, but is configured with flagsUsage:[buildout]
parts =
    debianize

[debianize]
maintainer = somebody@example.com
follow_dependencies =
    someobscurepackage
    morestuff
    ivegotnodebianpackageyetIf you definefollow_dependenciesdebianize will only create packages for
things that match any of the regex patterns in that option. If omitfollow_dependencies, it will build debian packages for anything defined as
a dependency withinstall_requires. You can not use spaces in any of the
patterns! This is useful if some of the dependencies are allready available as
debian packages and others are not.The above definedfollow_dependencieswill be matched like this:$NAME =~ someobscurepackage|morestuff|ivegotnodebianpackageyetSo it will simply put a|symbol in between the patterns.UpstartIf you’ve got a folder namedupstartnext to your setup.py, this folder
will be packaged as well, with the same version number as your package, but
adding a.dpostfix to your package name.Last but not leastThis recipe does not install fpm for you.
instead usehttp://pypi.python.org/pypi/rubygemsrecipe/0.1.6:[rubygems]
recipe = rubygemsrecipe
gems = fpm
Package: pms
Currently under active development. It aims to be a generic solution for
logging events and creating aggregate reports.Installing Development Environmentgit clone git://github.com/philipcristiano/pms.gitcd pmsmake bootstrap && make deployDeploying PMSPMS can be deployed as a WSGI app. Once installed the app is available
aspms.app:appand can be run with gunicorn or a WSGI server of your
choice.ConfigurationPMS will look for a config file at/etc/pms/pms.confYou config file would look something like this:[mongodb]
host=33.33.33.10[aggregation][[host-level]]properties=host,level[[random]]properties=levelYou need to define a host for Mongo.The aggregration properties are to define the rollups.  The second level is the
name of the rollup and properties is a list of fields to rollup. PMS will
attempt to rollup any events added. The event will be rolled up if it has
properties matching the set defined for a rollup.Recording EventsEvents are recorded by posting a JSON document to/record. If you include
all fields for an aggregate rollup it will create/update the corresponding
record.Sample:curl -v -X POSThttp://localhost/record-H “Content-Type: application/json” -d ‘{“key”: “value”}’
Package: pms5003
PMS5003 Particulate SensorInstallingStable library from PyPi:Just runsudo pip install pms5003Latest/development library from GitHub:git clone https://github.com/pimoroni/pms5003-pythoncd pms5003-pythonsudo ./install.shRequirementsThe serial port on your Raspberry Pi must be enabled:# Disable serial terminal over /dev/ttyAMA0
sudo raspi-config nonint do_serial 1

# Enable serial port
raspi-config nonint set_config_var enable_uart 1 /boot/config.txtAnd additionally be using a full UART (versus the default miniUART):Add the linedtoverlay=pi3-miniuart-btto your/boot/config.txtThis will switch Bluetooth over to miniUART, seehttps://www.raspberrypi.org/documentation/configuration/uart.mdfor more details.1.0.0Repackage to hatch/pyproject.tomlPort to gpiod/gpiodevice (away from RPi.GPIO)0.0.5BugFix: Read start-of-frame a byte at a time to avoid misalignment issues, potential fix for #2, #3 and #4Enhancement: Clarified error message when length packet cannot be readEnhancement: Clarified error message when start of frame cannot be readEnhancement: Added new error message where raw data length is less than expected (frame length)0.0.4Packaging improvements/bugfix from boilerplate0.0.3Added pyserial dependency0.0.2Added reset function0.0.1Initial Release
Package: pms5003-micropython
PMS5003 MicroPythonMicroPython library for the Plantower PMS5003 Particulate Matter Sensor.InstallingEither downloadpms5003/__init__.pyand save it to your device as "pms5003.py", or use Thonny's "Tools -> Manage Packages" to find and install "pms5003-micropython" from PyPi.
Package: pms7003
A minimalistic python interface for PMS7003 sensorThe code reads PM values from serial port. Tested on Raspberry Pi, but it should work on any machine with Python and serial port.Device description:https://aqicn.org/sensor/pms5003-7003/SetupTo install the driver, simply do:pip3installpms7003Usage examplefrompms7003importPms7003Sensor,PmsSensorExceptionif__name__=='__main__':sensor=Pms7003Sensor('/dev/serial0')whileTrue:try:print(sensor.read())exceptPmsSensorException:print('Connection problem')sensor.close()The read function has an option of returning values as a dict or OrderedDict.sensor.read(ordered=True)Usage example with threading:importtimefrompms7003importPms7003Threadif__name__=="__main__":withPms7003Thread("/dev/serial0")assensor:whileTrue:print(sensor.measurements)# We're free to do computation in main threada=2**32time.sleep(1)
Package: pMTnet-Omni-Document
pMTnet Omni: pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding :microscope:PackageDocumentationCode CoveragepMTnet OmnipMTnet Omni DocumentpMTnet Omniis a deep learning algorithm for affinity prediction based on TCR Va, Vb, CDR3a, CDR3b sequences, peptide sequence, and MHC allele types. The predictions can be made for human and mouse alleles, and for both CD8 T cells/MHC class I and CD4 T cells/MHC class II.Please refer to our paper for more details:pMTnet Omni paper link hereWe host the online tool onDBAI, where you can find all the members of the pMTnet
family, includingpMTnet V1.We have also built a detailedonline documentationwhere we guide you step-by-step on how to format your data so it can be accpted by our algorithm.NOTE:This is the documentation for the data curation supporting tool forpMTnet Omni. Use this BEFORE you upload your dataset toDBAI.Model OverviewDependenciesnumpy==1.22.4pandas==1.5.2tqdm==4.64.1torch==1.13.1fair-esm==2.0.0Enviroment Setupcondaenvcreate-fpMTnet_Omni_Document_env.ymlInstallationcondaactivatepMTnet_Omni_Document
pipinstallpMTnet_Omni_DocumentQuick Start GuidePrepare your dataset so that it looks somewhat like the following:Along with the main program, we also published 5 datasets under the./validation_datafolder. Feel free
to use those datasets to check if you TCR namings, Amino Acid sequences, and MHC namings conform with our
standard.NOTE:When both TCR names (resp. MHC) and the
TCR sequences (resp. MHC sequences) are provided, we
willdisregard the sequences. If the names can NOT be
found in our reference database, the record WILL be
dropped.NOTE:On the other hand, if the names are NOT provided, we will use the sequences with minimal curation.Say your dataset is under./df.csv. In your terminal, runcondaactivatepMTnet_Omni_Document

python-mpMTnet_Omni_Document--file_path./df.csv--output_folder_path./Go toour websiteand upload your data including the.jsonfile.An example output would look like this:For a more in-depth explanation on input format, check out ouronline documentation.CITATIONWe have uploaded our article to bioRxiv. To cite@article {Han2023.12.01.569599,
	author = {Yi Han and Yuqiu Yang and Yanhua Tian and Farjana J. Fattah and Mitchell S. von Itzstein and Minying Zhang and Xiongbin Kang and Donghan M. Yang and Jialiang Liu and Yaming Xue and Chaoying Liang and Indu Raman and Chengsong Zhu and Olivia Xiao and Yifei Hu and Jonathan E. Dowell and Jade Homsi and Sawsan Rashdan and Shengjie Yang and Mary E. Gwin and David Hsiehchen and Yvonne Gloria-McCutchen and Ke Pan and Fangjiang Wu and Don Gibbons and Xinlei Wang and Cassian Yee and Junzhou Huang and Alexandre Reuben and Chao Cheng and Jianjun Zhang and David E. Gerber and Tao Wang},
	title = {pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding},
	elocation-id = {2023.12.01.569599},
	year = {2023},
	doi = {10.1101/2023.12.01.569599},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2023/12/12/2023.12.01.569599},
	eprint = {https://www.biorxiv.org/content/early/2023/12/12/2023.12.01.569599.full.pdf},
	journal = {bioRxiv}
}
Package: pmtutils
pmtutilsPyMetaTome utilities. When working with thePyMetaTome pipeline,
there is a need for portable helper tools that can easily be added to custom
containers.These utilities are currently targeted at key processes in PyMetaTome:Database creation and similarity matrix construction.YAML metadata file operations.Read sampling prior to bioinformatic analyses.Webhook handling.pmtutils requiresSamtoolsandClustalW2for database creation.Python dependencies:Biopythonruamel_yamlNumPyflask
Package: pmtw
InstallationPMTW requires Python 3.7+. The recommended way to install PMTW is viapippipinstallpmtwFor instructions on installing Python and pip see "The Hitchhiker's Guide to Python"Installation GuidesQuickstartAToolboxinstance is the recommended way of interacting with Toolbox through
PMTW. AToolboxinstance provides access to both Toolbox's settings and
usernotes throughtoolbox.settingsandtoolbox.usernotesrespectively.AToolboxinstance is instantiated with a prawSubredditinstance.
For Example:importprawimportpmtwreddit=praw.Reddit(client_id="my client id",client_secret="my client secret",password="my password",user_agent="my user agent",username="my username",)toolbox=pmtw.Toolbox(reddit.subreddit("my moderated subreddit"))Once you have atoolboxinstance, you can interact with usernotes throughtoolbox.usernotesand settings throughtoolbox.settingsDocumentationPMTW's documentation is located athttps://pmtw.readthedocs.io/LicensePMTW's source is provided under theSimplified BSD LicenseCopyright ©, 2021, 2022 adhesiveCheese
Package: pmutt
ThePythonMultiscaleThermochemistryToolbox
(pMuTT) is a Python library for Thermochemistry developed by the
Vlachos Research Group at the University of Delaware. This code was
originally developed to convertab-initiodata from DFT to observable
thermodynamic properties such as heat capacity, enthalpy, entropy, and
Gibbs energy. These properties can be fit to empirical equations and
written to different formats.DocumentationSee ourdocumentation pagefor examples, equations used, and docstrings.DevelopersJonathan Lym, Ph.D. (jonathanalym@gmail.com)Gerhard Wittreich, Ph.D, P.E. (wittregr@udel.edu)DependenciesPython3Atomic Simulation Environment: Used for I/O operations and to
calculate some thermodynamic propertiesNumpy: Used for vector and matrix operationsPandas: Used to import data from Excel filesxlrd: Used by Pandas to import Excel filesSciPy: Used for fitting heat capacities and generating smooth curves for
reaction coordinate diagramMatplotlib: Used for plotting thermodynamic datapyGal: Similar to Matplotlib. Used for plotting interactive graphsPyMongo: Used to read/write to databasesdnspython: Used to connect to databasesNetworkX: Used to plot reaction networksMore Itertools: Used for writing ranges for OpenMKM output.PyYAML: Used to write YAML input files for OpenMKM.Getting StartedInstall using pip (see documentation for more thorough instructions):pip install pmuttLook atexamples using the codeRun theunit tests.LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details.PublicationsJ. Lym, G.R. Wittreich and D.G. Vlachos, A Python Multiscale Thermochemistry
Toolbox (pMuTT) for thermochemical and kinetic parameter estimation, Computer
Physics Communications (2019) 106864,https://doi.org/10.1016/j.cpc.2019.106864.ContributingIf you have a suggestion or find a bug, please post to ourIssues pagewith
theortag respectively.Finally, if you would like to add to the body of code, please:fork the development branchmake the desired changeswrite the appropriate unit testssubmit apull request.QuestionsIf you are having issues, please post to ourIssues pagewith theortag. We will do our best to assist.FundingThis material is based upon work supported by the Department of Energy’s Office
of Energy Efficient and Renewable Energy’s Advanced Manufacturing Office under
Award Number DE-EE0007888-9.5.Special ThanksDr. Jeffrey Frey (pip and conda compatibility)Jaynell Keely (Logo design)
Package: pmuxy
pmuxyA simple tmux windows manager.Install$ pip3 install pmuxyCommandsList all configurations under ~/.pmuxy:$ pmuxy listOpen a configuration:$ pmuxy open <configuration>pmuxy will look for the first session on the tmux server and open a new window in there.ConfigurationConfiguration files are simple yaml files and they should be stored under ~/.pmuxyExample file:window_name:test-windowattach:falseshell_commands:-export TEST_VAR=test#Sets a new variabletmux_commands:-select-pane -P bg=colour236#The first pane will have the background color 236window_name- The new window name.attach- Boolean to tell pmuxy whether we want to attach to the new window or no, defaults totrue.shell_commands- These commands will be launched on the shell within the first pane of the new window.tmux_commands- These commands will be launched on the new tmux window.
Package: pm-vtt2txt
pm-vtt2txtTable of ContentsInstallationLicenseInstallationpipx install pm-vtt2txtThen run:vtt2txt my_file.vttThis package is installed under thepmpackage namespace.
To use it in Python code import like this:frompm.vtt2txtimportvtt_to_textPublishhatch buildhatch publishLicensepm-vtt2txtis distributed under the terms of theMITlicense.
Package: pmw3901
PMW3901 / PAA5100JE 2-Dimensional Optical Flow SensorInstallingStable library from PyPi:Just runsudo pip install pmw3901Latest/development library from GitHub:git clone https://github.com/pimoroni/pmw3901-pythoncd pmw3901-pythonsudo ./install.shUsageThe PAA5100JE has a slightly different init routine to the PMW3901, you should use the class provided to ensure it's set up correctly:from pmw3901 import PAA5100And for the PMW3901, continue using the old class:from pmw3901 import PMW3901The examplemotion.pydemonstrates setting up either sensor, and accepts a--boardargument to specify which you'd like to use.Changelog0.1.0Add init support for PAA5100JEAdd frame capture support0.0.1Initial Release
Package: pmworker
Papermerge Workerpmwroker's main job is OCR processing. It extracts text from pdf, tiff, jpeg and png.
For full project description please seePapermerge ProjectRequirementspython >= 3.6pmworker.wrapper uses subprocess.run method, method added in python 3.5.
Also argument of subprocess.run(encoding='utf-8') is used. This argument
was added python 3.6DependenciesDepends on celery, tesseract, imagemagick.Usage:export CELERY_CONFIG_MODULE='pmwroker.config'
celery -A pmworker.celery worker -l infoRun TestsRun all tests:python3 run.pyRun specific test file:python3 run.py -p test_endpointWhich is same as:python3 run.py -p test_endpoint.py