Saravanan666/pypi-org huggingface dataset:
Package: emgfit
Fitting of time-of-flight mass spectra with Hyper-EMG modelsFree software: 3-clause BSD licenseOnline documentation:https://RobbenRoll.github.io/emgfit.Source code:https://github.com/RobbenRoll/emgfitemgfitis a Python package for peak fitting of time-of-flight (TOF) mass
spectra with hyper-exponentially modified Gaussian (Hyper-EMG[1]) model
functions.emgfitis a wrapper around thelmfit[2]curve fitting package
and uses many of lmfit’s user-friendly high-level features. Experience withlmfitcan be helpful but is not an essential prerequisite for usingemgfitsince thelmfitfeatures stay largely ‘hidden under the hood’.emgfitis
designed to be user-friendly and offers automation features whenever reasonable
while also supporting a large amount of flexibility and control for the user.
Depending on the user’s preferences an entire spectrum can be rapidly analyzed
with only a few lines of code. Alternatively, various optional features are
available to aid the user in a more rigorous analysis. The model functions and
statistical methods provided by emgfit could be useful for analyses of
spectroscopic data from a variety of other fields.Amongst other features, theemgfittoolbox includes:Automatic and sensitive peak detectionAutomatic import of literature values from theAME2020[3](orAME2016[4]) mass databaseAutomatic selection of the best suited peak-shape modelFitting of low-statistics peaks with a binned maximum likelihood methodSimultaneous fitting of an entire spectrum with various peaksExport of all relevant fit results including fit statistics and plots to an
EXCEL output file for convenient post-processingemgfitis designed to be used within Jupyter Notebooks which have become a
standard tool in the data science community. The usage and capabilities ofemgfitare best explored by looking at the tutorial. The tutorial and more
details can be found in theonline documentation.References[1]Purushothaman, S., et al. “Hyper-EMG: A new probability distribution
function composed of Exponentially Modified Gaussian distributions to analyze
asymmetric peak shapes in high-resolution time-of-flight mass spectrometry.”
International Journal of Mass Spectrometry 421 (2017): 245-254.[2]Newville, M., et al. “LMFIT: Non-linear least-square minimization and
curve-fitting for Python.” Astrophysics Source Code Library (2016):
ascl-1606.[3]Wang, M., et al. “The AME2020 atomic mass evaluation (II). Tables, graphs
and references.” Chinese Physics C 45 (2021): 030003.[4]Wang, M., et al. “The AME2016 atomic mass evaluation (II). Tables, graphs
and references.” Chinese Physics C 41.3 (2017): 030003.
Package: emgraph
EmgraphEmgraphis a Python toolkit for graph embedding.Algorithms tableNumberAlgorithm1TransE2ComplEx3HolE4DistMult5ConvE6ConvKB7RandomBaselineInstallationInstall the latest version ofEmgraph:$ pip install emgraphDocumentationSoonSimple exampleEmbedding wordnet11 graph usingTransEmodel:fromsklearn.metricsimportbrier_score_loss,log_lossfromscipy.specialimportexpitfromemgraph.datasetsimportBaseDataset,DatasetTypefromemgraph.modelsimportTransEdeftrain_transe():X=BaseDataset.load_dataset(DatasetType.WN11)model=TransE(batches_count=64,seed=0,epochs=20,k=100,eta=20,optimizer='adam',optimizer_params={'lr':0.0001},loss='pairwise',verbose=True,large_graphs=False)model.fit(X['train'])scores=model.predict(X['test'])print("Scores: ",scores)print("Brier score loss:",brier_score_loss(X['test_labels'],expit(scores)))# Executing the functionif__name__=='__main__':train_transe()EvaluatingComplExmodel after training:importnumpyasnpfromemgraph.datasetsimportBaseDataset,DatasetTypefromemgraph.modelsimportComplExfromemgraph.evaluationimportevaluate_performancedefcomplex_performance():X=BaseDataset.load_dataset(DatasetType.WN18)model=ComplEx(batches_count=10,seed=0,epochs=20,k=150,eta=1,loss='nll',optimizer='adam')model.fit(np.concatenate((X['train'],X['valid'])))filter_triples=np.concatenate((X['train'],X['valid'],X['test']))ranks=evaluate_performance(X['test'][:5],model=model,filter_triples=filter_triples,corrupt_side='s+o',use_default_protocol=False)returnranks# Executing the functionif__name__=='__main__':ranks=complex_performance()print("ranks{}".format(ranks))Call for ContributionsTheEmgraphproject welcomes your expertise and enthusiasm!Ways to contribute toEmgraph:Writing codeReview pull requestsDevelop tutorials, presentations, and other educational materialsTranslate documentation and readme contentsIssuesIf you happened to encounter any issue in the codes, please report ithere. 
    A better way is to fork the repository onGithuband/or create a pull request.More examplesEmbedding wordnet11 graph usingDistMultmodel:fromsklearn.metricsimportbrier_score_loss,log_lossfromscipy.specialimportexpitfromemgraph.datasetsimportBaseDataset,DatasetTypefromemgraph.modelsimportDistMultdeftrain_dist_mult():X=BaseDataset.load_dataset(DatasetType.WN11)model=DistMult(batches_count=1,seed=555,epochs=20,k=10,loss='pairwise',loss_params={'margin':5})model.fit(X['train'])scores=model.predict(X['test'])print("Scores: ",scores)print("Brier score loss:",brier_score_loss(X['test_labels'],expit(scores)))# Executing the functionif__name__=='__main__':train_dist_mult()Future workModulate the functionsAdd more algorithmsRun on CUDA coresMake it faster using vectorization etc.Add more preprocessorsAdd dataset, graph, and dataframe manipulationsUnify and reconstruct the architecture and eliminate redundancyIf you found it helpful, please give us a:star:LicenseReleased under the BSD licenseCopyright © 2019-2022EmgraphDevelopersSoran Ghaderi(soran.gdr.cs@gmail.com)Taleb Zarhesh(taleb.zarhesh@gmail.com)
Package: emhub
Web application for monitoring EM resultsLaunching the development servercondacreate--name=emhubpython=3.8condaactivateemhubcd~/work/developmentgitclonegit@github.com:3dem/emhub.gitgitclonegit@github.com:3dem/emhub-testdata.gitcdemhubpipinstall-e.exportFLASK_APP=emhubexportFLASK_ENV=developmentexportEMHUB_TESTDATA=~/work/development/emhub-testdataexportEMHUB_INSTANCE=~/work/development/emhub/instance# Now launch the built-in Flask development server:flaskrun# or with gunicorn:gunicorn-kgevent--workers=2'emhub:create_app()'--bind0.0.0.0:8080To initialize the db:python -m emhub.dataRunning testspython -m unittest emhub.testsPublishing the package to PyPIIn order to make the emhub available to install withpip install emhub,
we need to:pythoninstalltwinerestructuredtext-lintcdemhub# It might be a good idea to check the README.rst before uploading:rst-lintREADME.rstpythonsetup.pysdisttwineuploaddist/emhub-0.0.1a3.tar.gzCreating a Docker imageA Dockerfile has been include to create Docker images.cdemhubdockerbuild.-temhubdockerrun--rm-p8080:8080--name=emhub-v$PWD/instance:/instanceUpgrading Database model with AlembicIf we modify the database models, then an update/migration is required.# Do changes in the model (data_models.py)alembicrevision--autogenerate# Review the generated scriptalembicupgradehead# or use first the --sql option to see the commands
Package: emi-calculator
EMI CalculatorThe EMI Calculator is a Python library that provides a convenient way to calculate the Equated Monthly Installment (EMI) for a loan. It helps borrowers determine the fixed monthly payment, considering the principal loan amount, interest rate, and tenure in months.InstallationYou can install the EMI Calculator library using pip:pipinstallemi_calculatorUsagefromemiimportemi_calculator
OR
importemi# Note* emi_calculator takes 3 arguments:#                1. loan amount#                2. interest rate#                3. tenure_month#  the emi will result in float format# you can use round function to round up what u want# Input loan detailsloan_amount=float(input('Enter loan amount: '))interest_rate=float(input('Enter interest rate: '))tenure_months=int(input('Enter tenure in months: '))# Calculate EMIcalculate_emi=emi_calculator(loan_amount,interest_rate,tenure_months)print(f"The Equated Monthly Installment (EMI) is: {calculate_emi:.2f}")ExampleLet's calculate the EMI for a loan of $10,000 with an annual interest rate of 5% and a tenure of 36 months:fromemiimportemi_calculatorloan_amount=10000interest_rate=5tenure_months=36emi=emi_calculator(loan_amount,interest_rate,tenure_months)print(f"The Equated Monthly Installment (EMI) is: {emi:.2f}")Output:TheEquatedMonthlyInstallment(EMI)is:304.17ContributingContributions are welcome! If you have any suggestions, improvements, or bug fixes, please create an issue or submit a pull request.LicenseThis project is licensed under the MIT License. See the LICENSE file for details.
Package: emigrate
A Python package for performing electrophoresis calculations.emigratecalculates equilibrium state and flux of ions under an
electric field in a pseudo-1D domain.Theemigratemodel uses techniques demonstrated byPeakmaster,Spresso, andSTEEP. Theemigratemodel takes into account pH, and will take into account
ionic strength, and temperature effects, including the most up-to-date
temperature model published in STEEP. Theemigrateobject classes
make these techniques directly accessible as a backend for simulations
written in python.InstallationOne-line install usingpip:pip install emigrateTutorialWant to useemigrate? Read thetutorial,
written with iPython Notebook.
Package: emildecoster
This is my first package!Installationpip install emildecosterYou can also install the in-development version with:pip install https://github.com/Emil-Decoster/python-emildecoster/archive/master.zipDocumentationhttps://python-emildecoster.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.0.2 (2020-02-15)First release on PyPI.
Package: emil-fixed-django-stubs
pep484 stubs for Django frameworkThis package contains type stubs and mypy plugin to provide more precise static types and type inference for Django framework. Django uses some Python "magic" that makes having precise types for some code patterns problematic. This is why we need to accompany the stubs with mypy plugins. The final goal is to be able to get precise types for most common patterns.Supports Python 3.6/3.7, and Django 2.1.x series.Could be run on earlier versions of Django, but expect some missing imports warnings.Installationpip install django-stubsTo make mypy aware of the plugin, you need to add[mypy]
plugins =
    mypy_django_plugin.mainin yourmypy.inifile.ConfigurationIn order to specify config file, setMYPY_DJANGO_CONFIGenvironment variable with path to the config file. Default is./mypy_django.iniConfig file format (.ini):[mypy_django_plugin]

# specify settings module to use for django.conf.settings, this setting
# could also be specified with DJANGO_SETTINGS_MODULE environment variable
# (it also takes priority over config file)
django_settings = mysettings.local

# if True, all unknown settings in django.conf.settings will fallback to Any,
# specify it if your settings are loaded dynamically to avoid false positives
ignore_missing_settings = True

# if True, unknown attributes on Model instances won't produce errors
ignore_missing_model_attributes = TrueTo get helpWe have Gitter herehttps://gitter.im/mypy-django/Lobby.If you think you have more generic typing issue, please refer tohttps://github.com/python/mypyand their Gitter.
Package: emin
emin makes static Web pages for presenting lots of imagey things: photos, PDFs,
graphs with thumbnails as well as links to the image/doc file proper.It’s primarily intended for making Web photo galleries for the sorts of people
who don’t want to install some PHP monstrosity just to put their photos
online. On the assumption that most people will want to tweak their gallery’s
appearance, the output is customisable using the Cheetah templating engine.Supported image formats are JPEG, PNG, GIF, TIFF, PDF and EPS, with the latter
two being converted to PNG for Web display.  Image resizing, renaming and
thumbnailing is supported, as is building a zip file to download the whole
set. Large image sets can be split over several pages.
Package: eminus
eminuseminus is a pythonic plane wave density functional theory (DFT) code with self-interaction correction (SIC) functionalities.
The goal is to create a simple code that is easy to read and easy to extend while using minimal dependencies.
It is built upon theDFT++pragmas proposed by Tomas Arias et al. that aim to let programming languages and theory coincide.
This can be shown by, e.g., solving the Poisson equation. In the operator notation of DFT++ the equation reads$$
\boldsymbol \phi = 4\pi\hat L^{-1}\hat O\hat J \boldsymbol n.
$$The corresponding Python code (implying that the operators have been implemented properly) readsphi=-4*np.pi*Linv(O(J(n)))InstallationThepackageand all necessary dependencies can be installed withpip install eminusMore information about installing eminus can be foundhere.DocumentationTo learn more about the features, usage, or implementation of eminus, take a look inside thedocumentation.CitationThe project has been published withZenodoand has an assigned DOI. The following BibTeX key can be used@Misc{Schulze2021,
   author    = {Wanja Timm Schulze and Kai Trepte and Sebastian Schwalbe},
   title     = {eminus},
   year      = {2021},
   month     = nov,
   doi       = {10.5281/zenodo.5720635},
   publisher = {Zenodo},
  }LicenseThis project is licensed under the Apache 2.0 License. See thelicense pagefor more details.Changelogv2.7.1 - Feb 09, 2024New featuresStabilized Fermi smearing!Updated docsRestyle many documentation pagesAdd a citation pageAdd an overview page with a workflow exampleAdd a smearing exampleMiscellaneousSmall performance improvementsTemperature unit conversion functionsTests for the smearing implementationUpdate Ruff rulesMisc coding style updatesv2.7.0 - Jan 19, 2024New featuresAdd k-points!Add k-point dependent calculationsAdd a k-points objectAdd a band structure, k-point, and Brillouin zone viewerAdd minimization functions for fixed HamiltoniansAdd a symmetry extra to symmetrize k-pointsUpdated docsAdd k-point examplesIncrease coverage precisionCoding styleActivate several Ruff rulesLint check notebooksRewrite operator handlingAdd a lot of new testsMiscellaneousAdd a contour line viewerPlot lattice vectors in the view_atoms functionAdd a NixOS CI testAdd a Nix lock fileUse Python 3.12 as the CI base imageMove Matplotlib to dev extrasUnpin the notebook versionSmall performance improvements, e.g, in Atoms object creationExperimentalSmearing functionalities
Package: emipy
Aboutemipy is a package for processing data of pollutant releases and pollutant transfers.
It's main target is to enable a fast output of individual data set requests and to provide visualisation options.The data are provided by theEuropean Environment AgencyandEurostat.DocumentationFor the documentation take a look overhere.ContributionWe are happy about ideas for new features or concrete implementations in emipy.
Just get in touch and we will hopefully work together on your subject ASAP.To contribute changes:Fork the project on GitLabCreate a feature branch to work in your forkCommit your changes to the feature branchPush the branch to GitLabCreate a new pull request from the feature branch on GitLabContactFor contact write a mail top.boettcher@fz-juelich.de
Package: emission
EmissionCalculatorLib=====================This python library uses formulas and factors from EU to calculate the emission(s) from personal cars, busses to trailers of various sizes give a start and stop point (see 'examples/simple_planner.py').You could also calculate the emission for a given vehicle.Pull requests are more than welcome! Please visit the `project on GitHub <https://github.com/NPRA/EmissionCalculatorLib>`_.Also checkout the `CHANGELOG.md <CHANGELOG.md>`_.Quickstart---------Show the "make help" and run the test cases.. highlight:: bash:linenothreshold: 5$ make helpUse target: test, install, uninstall, upload or clean.. highlight:: bash:linenothreshold: 5$ make testUse target: test, install, uninstall, upload or cleanExamples--------We have some examples (more to come) under the 'examples' directory. You can use them to view how a simple planning with some pre-defined emissions might look like.If you see some improvements, bugs or something, then please file a Github issue so we can continue to improve.Screencast(s)-------------To ease the learning curve a bit we've included a screecast to help you get started:.. image:: https://asciinema.org/a/150349.png:scale: 50 %:target: https://asciinema.org/a/150349Routing Service---------------We are using the Routing Service which serves NPRA's "vegkart": https://www.vegvesen.no/vegkart/vegkart/. The routing services is served from Triona (triona.se).
Package: emissionrpc
EmissionRPC, a (Yet Another) Python wrapper JSON-RPC toTransmissionBitTorrent client.See theRPC interface.Prerequisitespython 2.7 and 3.6How to install$pipinstallemissionrpcUsageimportemissionrpcemitter=emissionrpc.Client()# Get the torrent's listemitter.list_torrent()# Adding a torrent (the torrent is not started)emitter.add_torrent('http://urltorrent.torrent')# To start all the torrentsemitter.start()# To start a specific torrent (pass the id to the client)emitter.start(1)# To delete a torrent (pass the id to the client)emitter.delete(3)
Package: emissionsapi-worldmap-creator
This project is a small command line tool to create beautiful world map images with data from theEmissions API.Like this one:Emissions API itself gives easy access to satellite-based emission data.
For more information take a look at ourwebsite.InstallationInstall the command line tool using:%> pip install emissionsapi-worldmap-creatorExampleGenerate an image with the ozone data from the forth of June (the image above):emissionsapi-worldmap-creatorozone2020-06-04To show all available options, run:emissionsapi-worldmap-creator--help
Package: emit
Emit is a Python library for realtime data processing. It can distribute work
withCeleryorRQ,coordinate execution in other languages, and let you swing
from the trees of your graph with Tarzan-like precision.A taste:fromemitimportrouterrouter=Router()@router.node(['word'],entry_point=True)defparse_document(msg):forwordinmsg.document.strip().split(' '):yieldword@router.node(['word','count'],'parse_document')defcount_word(msg):returnmsg.word,redis.zincrby('word_counts',msg.word,1)importrandomdocument='the words in this document will be counted and emitted by count_word'.split(' ')router(document=' '.join(random.choice(document)foriinrange(20)))So how do you get it? On PyPI!pip install emitSome Links:Project DocumentationTravis()GithubApache
LicenseSupported Pythons:CPython 2.6CPython 2.7CPython 3.2CPython 3.3 (except for Celery, currently)PyPy 1.9Installing from source/development branch:Emit’s releases are fairly frequent, so you should be good to use a released
version. However, if you need something currently not in a version, try thedevelopbranch:pip install git+git://github.com/BrianHicks/emit.git@developJust do be warned that things will break. And be sure to checkTravisto see if the platform you want is
currently passing. It should be, but maybe not.
Package: emitime
emiTimeTheemiTimelibrary is designed to simplify the work with objects describing calendar time points and intervals between them.
All algorithms presented in the library are based on two entitiesMomentandInerval.
Each of them can be fired from anything that looks like a calendar time point or a time interval.Installationpip install emitimeOr from GitHubgit clone https://github.com/rilshok/emitime.git
cd emitime
pip install -e .UsageAMomentobject can be created by astr, datetime module objects such asdateanddatetime, or anotherMoment.
In turn,Intervalcan also be derived from astr,datetime.timeor anotherInterval.importdatetimeasdtfromemitimeimportMomentd1=Moment("1970-01-01")d2=Moment(dt.datetime(1970,1,1))d3=Moment(dt.date(1970,1,1))d4=Moment(d3)print(f"{d1=},{d2=},{d3=},{d4=}")print(f"{d1==d2==d3==d4= }")d1=1970-01-01, d2=1970-01-01, d3=1970-01-01, d4=1970-01-01
d1 == d2 == d3 == d4 = TrueimportdatetimeasdtfromemitimeimportIntervalt1=Interval("2d")t2=Interval("12:00")t3=Interval(dt.timedelta(hours=5))t4=Interval(t3)print(f"{t1=},{t2=},{t3=},{t4=}")print(f"{t1>t2>t3>=t4= }")t1=+2^00:00, t2=+12:00, t3=+05:00, t4=+05:00
t1 > t2 > t3 >= t4 = TrueMomentandIntervalcan participate in natural mathematical operations and be compared with each other.
Also, whenever possible, depending on the context,emiTimepromotes the type of one of the operands of the operation toMomentorIntervalimportdatetimeasdtfromemitimeimportInterval,Momentm1=Moment("1970-01-01")+"2d"m2=dt.timedelta(hours=5)+Moment("01.01.1970")m3="1970-01-01"+Interval("12:00")i1=Moment("2020-01-01")-"2019-01-01"i2="12:00"-Interval("1d")i3=dt.date(2021,1,1)-Moment("2020-01-01")print(f"{m1=},{m2=},{m3=}")print(f"{i1=},{i2=},{i3=}")print(f"{m1>m2=}")print(f"{m2<m3<=m1=}")print(f"{m3+i2==m1-i3+i1-i3+i1= }")m1=1970-01-03, m2=1970-01-01d05:00, m3=1970-01-01d12:00
i1=+365^00:00, i2=-12:00, i3=+366^00:00
m1 > m2=True
m2 < m3 <= m1 =True
m3+i2 == m1 - i3+i1 - i3+i1 = TrueMomentis based on the standarddatetime.datetimeandIntervalis based ondatetime.timedelta. Which can be accessed as follows.fromemitimeimportInterval,Momentm=Moment("1970-01-01")i=Interval("02:10")mdt=m.datetimeitd=i.timedeltaprint(f"{mdt= }")print(f"{itd= }")mdt = datetime.datetime(1970, 1, 1, 0, 0)
itd = datetime.timedelta(seconds=7800)Emit and enjoy.
Package: em-keyboard
em: the cli emoji keyboardEmoji your friends and colleagues from the comfort of your own
terminal.emis a nifty command-line utility for referencing emoji characters
by name. Provide the names of a few emoji, and those lucky chosen emojis
will be displayed in your terminal, then copied to your clipboard.
Automagically.Emoji can be also searched by both categories and aspects.Example UsageLet's serve some delicious cake:$emsparklesshortcakesparklesCopied! ✨ 🍰 ✨Let's skip the copying (for scripts):$em'chocolate bar'--no-copy🍫Let's find some emoji, by color:$em-syellow💛  yellow_heart🌼  blossom🌕  full_moon⭐  star🚸  children_crossing🟡  yellow_circle🟨  yellow_squareIf there's only a single search result, it's copied:$em-sukraineCopied! 🇺🇦  flag_ukraineInstallationAt this time,emrequires Python and pip:python3-mpipinstallem-keyboardThat's it!TestsIf you wanna develop, you might want to write and run tests:python3-mpipinstalltox
toxHave fun!✨🍰✨
Package: em-keyboard-py3
Emoji your friends and colleagues from the comfort of your own terminal.emis a nifty command-line utility for referencing emoji characters
by name. Provide the names of a few emoji, and those lucky chosen emojis
will be displayed in your terminal, then copied to your clipboard.
Automagically.Emoji can be also searched by both categories and aspects.Example UsageLet’s serve some delicious cake:$ em sparkles cake sparkles
Copied! ✨🍰✨Let’s skip the copying (for scripts):$ em 'chocolate bar' --no-copy
🍫Let’s find some emoji, by color:$ em -s red
🚗  car
🎴  flower_playing_cards
👹  japanese_ogre
👺  japanese_goblinInstallationAt this time,emrequires Python and pip:$ pip install em-keyboard-py3That’s it!Have fun!✨🍰✨Why This Repo and Not The Original?Python 3 support has been broken for almost 2 years.  It’s been fixed in the repo but for some reason a new version haven’t been released for it.  This is to fill the gap; shouldemget updated this will go away.