Saravanan666/pypi-org huggingface dataset:
Package: pesfit
pesfitScalable distributed multicomponent lineshape fitting routines and benchmarks for spectral or spectrum-like dataRationaleLineshape fitting is a universal task in photoemission spectroscopy and can be a tedious endeavor due to the increasing amount of data measured in modern instruments (e.g. angle-resolved hemispherical analyzer, time-of-flight electron momentum microscope). It extracts physically meaningful quantities directly related to the materials' electronic properties, which can be reproduced from theory calculations. However, the efficiency bottleneck in lineshape fitting puts constraints on the potential new physical insights obtainable within a meaningful time frame. We set up here examples of multiband photoemission spectra along with existing domain knowledge in the field. The routines and benchmarks featured here offer an open-source data and algorithm platform for continuous algorithm development to improve the computational efficiency of the lineshape fitting task, which, in the meantime, also paves the way towards the automation of materials characterization using photoemission spectroscopy.Design and scopeThe fitting part of the package builds and improves on the existingModelandCompositeModelclasses in the widely-usedlmfitpackageto include multiband lineshapes (involving an arbitrary number of peaks) evaluated using map-reduce operations. Fitting of many intensity profiles (i.e. line spectra) can be carried out either in sequence or in parallel (built in usingdaskandmultiprocessing).The focus of the software and its benchmarks in on determining the momentum-dependent band positions in valence band photoemission dataat scale(e.g. 104-105spectra with each containing 10+ bands), which meets the needs for aglobal understandingof these complex data with reasonable accuracy (not necessarily accounting for all photoemission physics) to yield empirical structural information (i.e. band structure parameters). For conventional data analysis of core-level photoemission spectroscopy that often exhibits a complex background, please consult software packages such aslmfit,xps, andgxps.InstallationInstall from scratchpip install git+https://github.com/mpes-kit/pesfit.gitUpgrade or overwrite an existing installationpip install --upgrade git+https://github.com/mpes-kit/pesfit.gitInstall fromPyPIpip install pesfitData sourcePlease download the data frommpes-kit/pesarxiv. To run the examples and benchmarks, create a./datafolder in the cloned repository and copy the downloaded data into it.Benchmarks and examplesBesides source code, the package comes withexamplespresented in Jupyter notebooks andbenchmarksfor multiband dispersion fitting routines in scripts, using the data described above.DocumentationOnline documentation is providedhere.ReferencePlease cite the following paper if you want to refer to the software:R. Patrick Xian, R. Ernstorfer, Philipp M. Pelz, Scalable multicomponent spectral analysis for high-throughput data annotation, arXiv:2102.05604.
Package: pesi
PESI（English version README）从终端打包Python项目到远程Docker服务器使用环境（目前测试）LinuxPython 3.5 / 3.6Base on python packageclick == 7.1.1docker == 4.1.0(已经集成在lib中)requests == 2.23.0websocket == 0.2.1安装sudopipinstallpesi快速上手(example)注意: 确保你要远程的Docker服务器或者本地Docker已经打开remote api端口,取决于你在setup.cfg里面的配置(https://success.docker.com/article/how-do-i-enable-the-remote-api-for-dockerd)gitclonehttps://github.com/pesi1874/pesi.gitcdpsei/examplepesibuild项目文件结构要求|--project-name/|--deploy/|--Dockerfile|--requirements.txt|--run.sh|--project-name/|--main.py|--setup.cfg构建项目cd<pythonproject>pesibuildHelppesi--help
Package: pesic
PESIC - PetroElektroSbyt Integrated ClientУстановка$pip3install-UpesicИспользованиеfrompesic.apiimportPESClientimportasynciousername="88005553535"password="ЧемУкогоТоЗанимать"asyncdefmain():client=PESClient(username=username,password=password)# Получить список групп в личном кабинетеawaitclient.get_groups()# Получить список учетных записей для группы с ID 123456awaitclient.get_group_accounts(group_id=123456)# Получить детали для учетной записи с ID 654321awaitclient.get_account_details(account_id=654321)# Получить детали счетчиков для учетной записи с ID 654321awaitclient.get_meters_info(account_id=account)# Передать показания для однотарифного счетчикаdata=[{'scale_id':1,'scale_value':9136}]# Передать показания для двухтарифного счетчикаdata=[{'scale_id':2,'scale_value':9136},{'scale_id':3,'scale_value':2775}]awaitclient.set_meters_reading(account_id=654321,readings=data)# Закрыть соединениеawaitclient.close()loop=asyncio.new_event_loop()loop.run_until_complete(main())ОтладкаBash:exportPESIC_LOGLEVEL="DEBUG"Powershell:$env:PESIC_LOGLEVEL="DEBUG"ПредостережениеЛичный кабинет ПетроЭлектроСбыт поддерживает несколько типов учетных записей. Как классические, для передачи показаний потребления электроэнергии, так и учетные записи поставщиков холодной и горячей воды и, возможно, какие-то еще.Методset_meters_readingвыполняет несколько проверок:количество новых показаний в обновлении должно совпадать с текущим количеством счетчиков;текущий счетчик должен иметь все переданныеscale_id;текущий счетчик должен иметь показания меньше, чем те, что переданы вscale_valueдля данногоscale_idМетод set_meters_reading в данный поддеживает только обновление однотарифных и двухтарифных счетчиков электроэнергии. Обновление счетчиков холодной и горячей воды не тестировалось и в данный момент не поддерживается.
Package: pesky-defaults
pesky-defaults is a small library for querying platform, distribution,
and package defaults using a uniform interface.The following default keys are made accessible and their values set
according to the detected platform:PREFIXBIN_DIRSBIN_DIRLIB_DIRLIBEXEC_DIRSYSCONF_DIRLOCALSTATE_DIRRUN_DIRDATA_DIRA setuptools command is also provided (‘pesky_default’) so that project
defaults can be specified, potentially overriding platform or distribution
defaults.Basic UsageTo use pesky-defaults, simply instantiate a new Defaults object and invoke
the query methods ‘get’ or ‘getorelse’:from pesky.defaults import Defaults
defaults = Defaults()
print defaults.get('PREFIX')    # displays /usr on a linux platformThe Defaults class implements the collections.Mapping interface, so you can
use the familiar mapping methods as well:print defaults['LIB_DIR']       # displays /usr/lib on a linux platformProject DefaultsIf you specify a project name when instantiating the Defaults object, then
all defaults from the project metadata will be loaded as well:from pesky.defaults import Defaults
defaults = Defaults('my-project')   # the project name as given to setuptoolsThis is all well and good, but how do the project defaults get set in the first
place?  This is where the ‘pesky_default’ setuptools command comes into play.  By
invoking the ‘pesky_default’ setuptools command, the .egg-info file called
‘pesky_defaults.json’ is manipulated.  Once all project defaults are set as
desired, the ‘install’ setuptools command can be invoked, which will perform the
standard setuptools install routine and copy all egg-info files into place. For
example:$ cd my-project/
$ python ./setup.py pesky_default --command set --key foo --value bar
$ python ./setup.py installThis snippet enters the source tree of the fictional project ‘my-project’, adds
the project default ‘foo’, giving it the value ‘bar’, then installs the project
(with the updated project defaults).  The application code can then retrieve the
‘foo’ default:from pesky.defaults import Defaults
defaults = Defaults('my-project')   # the project name as given to setuptools
print defaults.get('foo')           # displays bar
Package: pesky-settings
pesky-settings is a library for managing application settings, merging data
from configuration files and command line arguments and converting to python
native data types.  Additionally, pesky-settings has an ‘action’ interface
which is useful for constructing hierarchical command sets.pesky-settings is built on top of the venerable getopt and ConfigParser
packages from the python standard library, so it should work on any python
you can find (as opposed to optparse or argparse packages, which were
somewhat late additions to python 2).
Package: pesn-sdk
Quantori Python SDK for PerkinElmer Signals NotebookCopyright (c) 2022 Quantori.pesn-sdkis a Python package that provides an interface between your Python application and PerkinElmer's external API of Signals Notebook.Pre-requisitesWe assume, that you have already contacted with PerkinElmer and have a working instance of Signals Notebook.Obtain an API keyVisit the pagehttps://<your signals notebook instance>/snconfig/settings/apikeyand generate an API KeyInstallationpipinstallpesn-sdkUsageImport and initialize the API instance with your Signals Notebook host and API-tokenfromsignals_notebook.apiimportSignalsNotebookApiSignalsNotebookApi.init('https://signalsnotebook.perkinelmer.cloud','<your api key>')Then you can perform CRUD operations with such entities as Notebook, Experiment, etc.ExamplesCreate a new Notebookfromsignals_notebook.entities.notebookimportNotebooknotebook=Notebook.create(name='Test creation by SDK',description='Created by me')change its fieldsnotebook.name='Changed name'notebook.save()finally delete itnotebook.delete()Also, you can retrieve all existing notebooksnotebooks=Notebook.get_list()or one specified by idfromsignals_notebook.entities.entity_storeimportEntityStorenotebook=EntityStore.get("journal:111a8a0d-2772-47b0-b5b8-2e4faf04119e")Deletion can be performed without retrieving the whole object if you know an idEntityStore.delete("journal:111a8a0d-2772-47b0-b5b8-2e4faf04119e")Jupyter Notebooks with examples see in examples folderAdditional informationExamples of usageAPI ReferenceDevelopers notesLicenseQuantori Python SDK for PerkinElmer Signals Notebook is released underApache License, Version 2.0
Package: pesos
pesospesos is a pure python implementation of the mesos framework api based uponcompactor.Usingpesos is intended to be a drop-in replacement for themesos.nativepackage.  While Python bindings are provided by the Mesos project, they
require libmesos which can be challenging to build and package.  pesos
requires no C extensions to run.To use:try:frompesos.executorimportPesosExecutorDriverasMesosExecutorDriverfrompesos.schedulerimportPesosSchedulerDriverasMesosSchedulerDriverexceptImportError:frommesos.nativeimportMesosExecutorDriver,MesosSchedulerDriverThen use the pesos-provided equivalents as you would the native Mesos versions.Testingpesos usestoxas a test harness.  To run tests,pip install toxand execute$tox
Package: pessimist
pessimistThe name "optimist" was already taken?Given your listed requirements, and how to run your tests, tries various
versions to ensure the minimums are accurate.Usagepython -m pessimist [-c 'make test'] [--fast] [--extend=name[,name...]] [--requirements=requirements*.txt] /path/to/repo-c-- command to run.  If you're using a src/ layout you can usecd src; python -m unittestor so.--fast-- only verify min and max versions--extend-- ignore specifiers entirely for the listed canonical names;
intended to let you go back past==and may be improved to do something more
like that in the future.  Also allows*as a name to mean all names that are
"variable"--requirements-- comma-separated globs which represented "fixed"
requirements.--verbose-- show logs as it's workingFixed and variableFixed requirements are fromrequirements*.txt.  If these match more than one
version, only the newest is kept.Variable requirements are from your setup.py/setup.cfg/etc that make it into
the metadata.  These are the ones we're interested in trying.If a name is in both sets, the variable logic is followed.StrategyTry newest versions of everything. Bail if this fails to pass.For each dep independently, try progressively older versions.Try oldest versions of all.  Bail if this fails to pass.I subscribe to the "requirements.txt should be concrete versions you want to
use in CI" school of thought; the constraints in setup.py/setup.cfg/pyproject.toml
should be>=the minimum version that works, and<the next major version
("compatible", in poetry terms).My goal in creating this is to have an automated check that we haven't broken
compatibility with an older version unintentionally.  You could have a simpler
version of this that doessed -e 's/>=/==/on your requirements files, but if
that fails, finding the new minimum is still a research projct that's automated
by this one.Licensepessimist is copyrightTim Hatch, and licensed under
the MIT license.  I am providing code in this repository to you under an open
source license.  This is my personal repository; the license you receive to
my code is from me and not from my employer. See theLICENSEfile for details.
Package: pestcontrol
Pest ControlA Python Unit Testing LibraryUsageTest Functions:assertEquals(actual, expected, msg) - Test if actual = expected, if test fails, print out includes msgassertTrue(actual, msg) - Test if actual = True, if test fails, print out includes msgassertFalse(actual, msg) - Test if actual = False, if test fails, print out includes msgHow toStart by importing the library. Assuming file is in same directory as the PestControl library directory:
for example for the test script "basic_test.py", use the following folder structure for this example:/
|- pestcontrol/
|	- pest_control.py
|- basic_test.pyimportpestcontrolThen make a class that extends PestCase to be the unit test class (class name can be anything, "BasicTestCase" is used here)classBasicTestCase(pestcontrol.PestCase):Then write one or more functions for the actual test. NOTE: "test" MUST be somewhere in the function name.
For example, add_test(), addTest(), addTesting(), add_Tester(), add_tester(), will all run, but add(), will not run.classBasicTestCase(PestCase):defadd_test(self):self.assertEquals(1+1,2,"simple add test")Now just add the main() funciton callif__name__=="__main__":BasicTestCase().main()That's it! Your unit test will run and print to the console the results. Note: any errors that occur from a call to an assert function, ie errors caused by the code being tested, will be caught and logged as a failed test (divide by zero error for example).Full Example:importpestcontrolclassBasicTestCase(pestcontrol.PestCase):defadd_test(self):self.assertEquals(1+1,2,"simple add test")if__name__=="__main__":BasicTestCase().main()
Package: pestifer
PestiferAutomatic NAMD System Input BuilderPestifer is a Python utility for generating atomistic inputs with appropriate topology and parameter files required for molecular dynamics simulations using NAMD.  It is intended as a fully automated system builder requiring as inputs only the PDB codes of biomolecular structures and a handful of customization parameters.  Pestifer primarily works as a front end for VMD'spsfgenutility.InstallationpipinstallpestiferOnce installed, the user has access to the mainpestifercommand.Release History1.3.5bugfix:renumbering of author resids in non-protein segments if user adds protein residues by insertion that may conflicttransferance of atomselect macros from YAML input to any VMD scriptinittclsubcommand makes this transfer; needs only to be run one time post-installation1.3.4new TcL procs for asymmetric unit generation from non-symmetric assembliespestifer_initTcL proc provided in docs for user VMD startup scriptscriptsubcommand removedsyntax ofwheretclsubcommand expanded1.3.3NAMDLogclass introduced for parsing NAMD2-generated log filesmdplottask for generating plots of various energy-like quantities vs timestep1.3.2allow for user-defined links in the config fileall example builds now have tests in the test suite1.3.1bug fixes for cleaving1.3.0Support for reading from already-build PSF/PDB systems1.2.9improved declashing and domain-swapping1.2.8graftsfor adding glycans1.2.7cleavetask andCleavageMod1.2.6ModManagerreplacesModContainer1.2.5insertionmod; corrected bug inbrottcl procedure1.2.3script subcommand handles local scripts1.2.2addedwheretclsubcommand1.2.1addedscriptsubcommand1.2.0split all namd subtasks out; now they are level-1 tasks1.1.3addedmanipulatetask1.1.2more control over production NAMD2 config generated by the package directive1.1.1position restraints control in minimization and relaxation1.1.0other_parametersfor any NAMD2 relaxation task1.0.9alternate coordinate files and Cfusions1.0.8chain-specific control over building in zero-occupancy residues at N and C termini1.0.7alphacrotation for folding a span of residues into an alpha helix1.0.6cif_residue_map_filegenerated to report mapping between CIF-residue numbering and author residue numbering1.0.5enhancements to packaging task1.0.4support for topogromacs added1.0.1Initial versionMetaCameron F. AbramsDistributed under the MIT license. SeeLICENSEfor more information.https://github.com/cameronabramsContributingFork it (https://github.com/cameronabrams/pestifer/fork)Create your feature branch (git checkout -b feature/fooBar)Commit your changes (git commit -am 'Add some fooBar')Push to the branch (git push origin feature/fooBar)Create a new Pull Request
Package: pesto-testing
Pesto🚧 Under Development 🚧A lightweight, fun-to-use Python testing framework made to use like the popular JS Jest library.Why?Coming back to software development after being a front-end engineer opened my eyes on how verbose and anti-user-friendly Python testing really is - in my opinion.I want to enjoy writing tests as much as I loved them with my front-end stack, so I decided to create a little testing framework to mimic that behavior, while still being a robust testing tool.Get StartedThe CLI is very similar to PyTest. Simply give the directory of the tests as the first argument (or./is used by default.)A drag and drop replacement for PyTestPesto looks for test files and functions with_testortest_in the name.pipinstallpesto
pesto<test-dir>DevelopmentI'm still a novice when it comes to testing, so the capabilities of this library will grow as I grow as a developerTodoGeneralAdd multiprocessing support to run tests in parallelCreate github actionAdd Poetryunittest / pytest like functionality:Mocking (integrate unittest.mock)
Package: pestotools.genshi
Functions to integrate Genshi templates with Pesto applications.CHANGELOGVersion 0.2.3Bugfix for docstring_as_string using TextTemplateMinor change to formfilled decorator, which now calculates form default
values after the wrapped function has returned.Version 0.2.2Added GenshiRender.docstring_as_stringAdded support for Genshi filters to be passed in to render methods
via thefilterargumentVersion 0.2.1Rewrote genshi_app function as a class for more flexibilityVersion 0.2No longer export render/render_docstring functions that can be configured
with global variables. Instead clients should create instances of GenshiRender
to access properly configured decorators.The @genshi decorator is now redundant and has been removed.Version 0.1.1Values returned from decorated functions that can’t be rendered are passed
through unchanged wherever possible.Version 0.1Initial release.
Package: pet
pi-email-transmission[PET]This program needs to set up two serivce. Email service and
Transmission’s Web ControllerIt will be make cool if you download any files on Raspberry-pi
through transmissionThis program’s all about feature depend on Email service and
Transmission.Simply say about this. It just connect Email service and transmission
web controllerMotivateI don’t wanna waste resources on my laptop and Smartphone. cause’
it’s totally be hot when running torrent.RecommandBasically, This program is designed for reducing laptop resources.
Therefore, I recommand to run this program onraspberry-pi.Use this program withDropboxorGoogleDriveto access your
downloaded media files.If you can use E-Mail notification service on your smartphone, It’s
good interaction.Install$gitclonehttps://github.com/drake-jin/pi-email-transmission$cdpi-email-transmission$pipinstall-e.ConfigurationSet up transmission web controller settingIf you use Dropbox or GoogleDrive. redirect directory to there.Must check you have account information and host, portCheck your e-mail account available IMAP and SMTP ProtocolThis application need to transport through IMAP, SMTP Protocol.Therefore, you must set up IMAP and SMTP protocol available on you
email account.Set uppi-email-transmission’s config.json file# config.json{"check_interval":10,"log_level":"DEBUG","email":{"imap":"imap.gmail.com:993","smtp":"smpt.gmail.com:465""folder":"pi-imap-transmission","user":"{email_id}@gmail.com","password":"{email_password}"},"transmission":{"user":"{trnsmsn_id}","password":"{trnsmsn_passwd}","host":"http://localhost:9091"}}Set up Envrironment variables$exportPET_HOME=/home/likemilk/workspace/pi-email-transmissionFinally, You can check correct setting about this program when you
execute this command$pythonsetup.pytest# Have to finish setting Configurations!Configuration ImagesEmail-NaverEmail-Naver ConfigurationEmail-GoogleEmail-Google ConfigurationTransmission-gtkTransmission ConfigurationHow is it work going?# *It's just psuedo code. not a real code*classService(Thread):while(True):time.sleep(config['interval_check'])# 1. read all of e-mails and check UNSEEN email# 2. get torrent file and add it on tranmission through pay load# 3. send email about complete or failed# 4. send seen flagtorrentList=email.get_torrent('(UNSEEN)')fortorrentintorrentList:if(transmission.add_torrent(torrent)):email.send('add_complete',torrent)else:email.send('add_fail',torrent)email.flag('(SEEN)',torrent.email_id)# 1. read all of Trnasmission download Queue.# 2. get Completed entry and delete the entry.# 3. send email about complete.torrentList=transmission.get_completed('Completed')fortorrentintorrentList:if(transmission.delete_torrent(torrent)):email.send('download_complete')else:email.send('delete_error')Contact Medydwls121200@gmail.comis my mailWelcome PR or be Contributor. I want to play coding with you.Really Welcome Feedback and Any Issues.
Package: petab
petab - a Python package for handling PEtab filesPEtabis a data format for specifying
parameter estimation problems in systems biology. This repository provides
thepetabPython package for reading, writing and validating PEtab files.DocumentationDocumentation of thepetabPython package is available athttps://libpetab-python.readthedocs.io/en/latest/.
Documentation of the PEtab format in general is available athttps://petab.readthedocs.io/en/latest/.InstallationThe PEtab library is available onpypiand the easiest way to install it is runningpip3 install petabIt will require Python>=3.9 to run. (We are following thenumpy Python support policy).Development versions of the PEtab library can be installed usingpip3 install https://github.com/PEtab-dev/libpetab-python/archive/develop.zip(replacedevelopby the branch or commit you would like to install).When setting up a new parameter estimation problem, the most useful tools will
be:ThePEtab validator,
which is automatically installed using Python
entrypoints to be available as a shell command from anywhere, calledpetablintpetab.create_parameter_dfto create the parameter table, once you have set up the model,
condition table, observable table and measurement tablepetab.create_combine_archiveto create aCOMBINE Archivefrom PEtab
filesExamplesExamples for PEtab Python library usage:ValidationVisualizationGetting helpIf you have any question or problems with this package, feel free to post them
at our GitHubissue tracker.ContributingContributions and feedback to this package are very welcome, see ourcontribution guide.