Saravanan666/pypi-org huggingface dataset:
Package: energyplus-pet
EnergyPlus "PET"This project is a cross platform Python (Tk) GUI and tool kit for generating EnergyPlus inputs from component performance data.Code QualityCode is checked for style and with unit tests by GitHub Actions using nosetests to sniff out the tests.
Code coverage is exercised purely on Ubuntu and pushed to coveralls, availablehere.DocumentationDocs are built on ReadTheDocs and hostedhere.ReleasesWhen a release is tagged, a GitHub Action workflow will create a Python wheel and upload it to the PyPi server.To install into an existing Python environment, executepip install energyplus_pet
Package: energyplus-python-apps
EnergyPlus Python AppsThis is a meta-project which simply depends on all the EnergyPlus "Official" Python utilities.
This project itself does not create any new utilities, or contain any code.
This really just pins the dependencies at specific versions and ensures compatability in the installation dependencies.ReleasesI think long term, this "project" itself will be tagged with versions in sync with EnergyPlus itself.
To install all EnergyPlus Python utilities, simply execute, for example,pip install energyplus_python_apps==22.2.0.Current ReposThis project is currently linking to the following packages:PyPi Project NameProject DescriptionRepositoryenergyplus-launchA graphical interface and workflow manager for EnergyPlushttps://github.com/NREL/EP-Launchenergyplus-ruleset-modelA utility for generating ruleset model reports from EnergyPlus inputs and outputshttps://github.com/jasonglazer/createrulesetmodeldescriptionenergyplus-transition-toolsA lightweight interface for automatically transitioning EnergyPlus input fileshttps://github.com/Myoldmopar/EnergyPlusTransitionToolsenergyplus-petA library and graphical tool for generating input coefficients for EnergyPlus modelshttps://github.com/Myoldmopar/energypluspetenergyplus-idd-idf-utilitiesA lightweight library for processing and querying idd and idf fileshttps://github.com/Myoldmopar/py-idd-idfenergyplus-regressionsA tool for comparing results from two EnergyPlus builds, for E+ developer usagehttps://github.com/NREL/EnergyPlusRegressionToolenergyplus-api-helpersA set of helper classes and demos for interacting with the EnergyPlus APIhttps://github.com/Myoldmopar/EnergyPlusAPIHelperenergyplus-diff-analysisTools for plotting and comparing csv data from two separate EnergyPlus runshttps://github.com/mitchute/energyplus-diff-analysisUsageCover a few topics here:InstallationRunning the configuration script after installAccessing all the tools, including the dev toolsUpdating
Package: energyplus-regressions
EnergyPlus RegressionsOverviewThis library provides tools for performing regressions between EnergyPlus builds.
Developers often propose changes to EnergyPlus for:New feature developmentDefect repairRefactoring for structure or performanceWhen a developer proposes these changes, those code changes must be tested prior to accepting them into the main branch.
A continuous integration system runs the tests and provides results, but there can be a sometimes lengthy delay waiting on those results, depending on how busy the system is at that time.
This set of tools provides a way to run these regressions locally.UsageThis tool works on all three major platforms: Windows, Mac, and Ubuntu (LTS).
GitHub Actions test on multiple platforms, and it is regularly used on all three as well.To install the tool, simplypipinstall it into your Python environment (either system or virtual environment)
The project page on PyPi is:https://pypi.org/project/energyplus-regressions/.Download using Pip (pip install energyplus-regressions).Once installed into the Python install, there will be a binary available to run:energyplus_regression_runner.DevelopmentFor setting up a development environment to doworkon this tool, the steps are pretty minimal:Install Python, if neededClone this repository (git clone https://github.com/NREL/EnergyPlusRegressionTool)Install dependencies (pip3 install -r requirements.txt)DocumentationProgram documentation, including user guide and typical workflows, are available in the documentation.
This documentation is written using RST with Sphinx, and published onReadTheDocs.TestingExhaustive unit tests have been added to the "underneath the hood" code, like the functions that calculate diffs and run builds.
The unit tests are run onGithub Actions.
The GUI code is not unit tested, but tested routinely on all platforms.
Package: energyplus-transition-tools
EnergyPlusTransitionToolsA library of transition functionality.
Currently it is a super simple Python Tk-based IDFVersionUpdater tool.
Soon it will hopefully contain the actual transition mechanics.DocumentationProject is documented on ReadTheDocs.TestingProject is continually tested using GitHub ActionsReleasesProject is built into a wheel and pushed to pypi for each tag:https://pypi.org/project/ep-transition-tools/.
To install, it will bepip install energyplus-transition-tools.
This will (eventually) install 2 binaries into the python environment:energyplus_transition_guiandenergyplus_transition_cli.
Package: energypriceapi
EnergypriceAPIenergypriceapi is the official Python API wrapper for EnergypriceAPI.com. This allows you to quickly integrate our foreign exchange rate API and currency conversion API into your application. Checkhttps://energypriceapi.comdocumentation for more information.InstallationInstall the latest release with:pip install energypriceapiUsagefromenergypriceapi.clientimportClientapi_key='SET_YOUR_API_KEY_HERE'client=Client(api_key)DocumentationfetchSymbols()client.fetchSymbols()LinkfetchLive(base, currencies)base<string> Optional. Pass in a base currency, defaults to USD.currencies<List<string>> Optional. Pass in an list of currencies to return values for.client.fetchLive(base='USD',currencies=['BRENT','GASOLINE','NATURALGAS','WTI'])LinkfetchHistorical(date, base, currencies)date<string> Required. Pass in a string with formatYYYY-MM-DDbase<string> Optional. Pass in a base currency, defaults to USD.currencies<List<string>> Optional. Pass in an list of currencies to return values for.client.fetchHistorical(date='2021-04-05',base='USD',currencies=['BRENT','GASOLINE','NATURALGAS','WTI'])Linkconvert(from_currency, to_currency, amount, date)from_currency<string> Optional. Pass in a base currency, defaults to USD.to_currency<string> Required. Specify currency you would like to convert to.amount<number> Required. The amount to convert.date<string> Optional. Specify date to use historical midpoint value for conversion with formatYYYY-MM-DD. Otherwise, it will use live exchange rate date if value not passed in.client.convert(from_currency='USD',to_currency='EUR',amount=100,date='2021-04-05')Linktimeframe(start_date, end_date, base, currencies)start_date<string> Required. Specify the start date of your timeframe using the formatYYYY-MM-DD.end_date<string> Required. Specify the end date of your timeframe using the formatYYYY-MM-DD.base<string> Optional. Pass in a base currency, defaults to USD.currencies<List<string>> Optional. Pass in an list of currencies to return values for.client.timeframe(start_date='2021-04-05',end_date='2021-04-06',base='USD',currencies=['BRENT','GASOLINE','NATURALGAS','WTI'])Linkchange(start_date, end_date, base, currencies)start_date<string> Required. Specify the start date of your timeframe using the formatYYYY-MM-DD.end_date<string> Required. Specify the end date of your timeframe using the formatYYYY-MM-DD.base<string> Optional. Pass in a base currency, defaults to USD.currencies<List<string>> Optional. Pass in an list of currencies to return values for.client.change(start_date='2021-04-05',end_date='2021-04-06',base='USD',currencies=['BRENT','GASOLINE','NATURALGAS','WTI'])LinkOfficial documentationFAQHow do I get an API Key?Free API Keys are availablehere.I want more informationCheckout our FAQshere.SupportFor support, get in touch usingthis form.
Package: energypylinear
energy-py-linearDocumentation:energypylinear.adgefficiency.comA Python library for optimizing energy assets with mixed-integer linear programming:electric batteries,combined heat & power (CHP) generators,electric vehicle smart charging,heat pumps,renewable (wind & solar) generators.Assets & sites can be optimized to either maximize profit or minimize carbon emissions, or a user defined custom objective function.Energy balances are performed on electricity, high, and low temperature heat.SetupRequires Python 3.10+:$pipinstallenergypylinearQuick StartAsset APIThe asset API allows optimizing a single asset at once:importenergypylinearasepl#  2.0 MW, 4.0 MWh batteryasset=epl.Battery(power_mw=2,capacity_mwh=4,efficiency_pct=0.9,electricity_prices=[100.0,50,200,-100,0,200,100,-100],export_electricity_prices=40)simulation=asset.optimize()Site APIThe site API allows optimizing multiple assets together:importenergypylinearaseplassets=[#  2.0 MW, 4.0 MWh batteryepl.Battery(power_mw=2.0,capacity_mwh=4.0),#  30 MW open cycle generatorepl.CHP(electric_power_max_mw=100,electric_power_min_mw=30,electric_efficiency_pct=0.4),#  2 EV chargers & 4 charge eventsepl.EVs(chargers_power_mw=[100,100],charge_events_capacity_mwh=[50,100,30,40],charge_events=[[1,0,0,0,0],[0,1,1,1,0],[0,0,0,1,1],[0,1,0,0,0],],),#  natural gas boiler to generate high temperature heatepl.Boiler(),#  valve to generate low temperature heat from high temperature heatepl.Valve()]site=epl.Site(assets=assets,electricity_prices=[100,50,200,-100,0],high_temperature_load_mwh=[105,110,120,110,105],low_temperature_load_mwh=[105,110,120,110,105])simulation=site.optimize()DocumentationSee more asset types & use cases in the documentation.Test$maketest
Package: energyquantified
Energy Quantified Python ClientDocumentation|Python package|GitHub repositoryThe Python library forEnergy Quantified's
Time Series API. It allows you to access thousands of data series directly from
Energy Quantified's time series database. It integrates with the popularpandaslibrary for high-performance data
analysis and manipulation.Developed forPython 3.7+.fromdatetimeimportdate,timedeltafromenergyquantifiedimportEnergyQuantified# Initialize clienteq=EnergyQuantified(api_key='<insert api key here>')# Freetext search (filtering on attributes is also supported)curves=eq.metadata.curves(q='de wind production actual')# Load time series datacurve=curves[0]timeseries=eq.timeseries.load(curve,begin=date.today()-timedelta(days=10),end=date.today())# Convert to Pandas data framedf=timeseries.to_dataframe()Fulldocumentationavailable
at Read the Docs.FeaturesSimple authenticationMetadata cachingRate-limiting and automatic retries on network errorsFull-text search and keyword search for curves and powerplantsForecasts- and time series dataPeriod-based dataOHLC data with SRMC calculationsShows your subscription for each data seriesSupport for timezones, resolutions, aggregations and unit conversionsEasy-to-use filters for issue dates and forecast typesPush feed for live updates on data modificationsIntegrates with pandasNote:A user account with an API key is required to use this library.
Create an account onEnergy Quantified's
home page. Trial users get access to 30 days of history.InstallationInstall withpip:# Installpipinstallenergyquantified# Upgradepipinstall--upgradeenergyquantifiedDocumentationFind thedocumentationat
Read the Docs.LicenseThe Energy Quantified Python client is licensed under theApache License version 2.0.
Package: enetpy
enetpyPython wrapper for the ene't Marktpartner APISetupInstall the package:pipinstallenetpyMake sure to set the authentication token in your production environment.exportENET_AUTH_TOKEN='****'TestingThe API wrapper comes with a testing stub. To use it set your test environment to:exportENET_TESTING_STUB=1Usage>>>fromenetimportapi>>>api.get_gas_grid_operator_for_address(...zip_code='10115',...city='Berlin',...street='Brunnenstr.',...house_number='153'...)GasGridOperator(vnbg_nr=107851,title='NBB Netzgesellschaft Berlin-Brandenburg mbH & Co. KG',email='netzzugang@nbb-netzgesellschaft.de',contact_name='Herr Cornelius Baumann',phone_number='030/81876-1382',fax_number='',gas_type='H-Gas')
Package: enex2md
enex2mdEnex2md is a command-line utility to convert Evernote export files (*.enex) toGitHub Flavored Markdown.Python 3.6+ only!FeaturesIn addition to the note content itself, the note metadata is included in the resulting Markdown. The enex-bundle may contain one or more notes.Within the note content, the following features are supported:Strong and emphasis text styles.Ordered (i.e. numbered) and unordered listsTables created within Evernote are converted toGFM TablesTasks are converted toGFM Task list itemsImages and other attachmentsCode blocksSubsequent empty lines are compressed to one.The html in enex files issomewhat interesting, thus somemagic is used to massage the data to functioning feature-rich Markdown. The Magic Book used here has not yet been fully written, so there might be some unfortunate side effects. Should you find one,open an issue on GitHubwith a well written description anda test enex fileas an attachment.SeeChangelogfor more details.InstallationInstalling to a virtual environment is strongly recommended.To install, run:pip install -U enex2mdUsageTo use the CLI after installing, run the conversion with:enex2md [enex-file-to-process]The output is written toSTDOUTby default. If you want to write to disk instead, add a flag--diskto the command. This option will create a directory based on run time timestamp, and place individual files under that.Please note, that on STDOUT output option attachments (including images) are not processed!DevelopmentClone therepositoryto your local machine.I strongly recommend using a virtual environment for development.Install the requirements with:pip install -r requirements.txtFrom the root of the repository, you can run the app with:python -m enex2md.cli foo.enexAfter editing the content, try to install the package locally with:python setup.py installSee that everything works. You can uninstall the dev package withpip uninstall enex2md.
Package: enexlib
enexlibA python3 module for converting Evernote backup files (.enex) to plaintext.Usagepip install enexlibimport enexlib as nx

for title, note in nx.read_enex('<filename>.enex'):
	# do something

plain_text = nx.read_enex('<filename>.enex', text_only=True)
raw_content = nx.read_enex('<filename>.enex', raw_text=True)
all_combined = nx.read_enex('<filename>.enex', join_all=True)
Package: enflows
nflows-extended: An extension of nflowsThe originalnflowspackage is a comprehensive collection ofnormalizing flowsusingPyTorch.
Ournflows-extended, orenflows, is as the name suggests an extension.
The main focus lies in implementing more flow layers from the literature in one consistent framework.
In particular, we support conditional transformations based on hypernetworks.
In the original package, conditional networks were restricted to using a conditional base distribution. Inenflows, nearly every layer can be conditional :).The bijective layers we additionally provide includes but are not limited to Planar Flows, invertible ResNets/DenseNets, a variant of neural autoregressive flows, and a basic support of continuous normalizing flows (and FFJORD) basedtorchdiffeqpackage.Setting up the Environment.The.ymlfile for the environment is given inenv/conda_env.yml, and can be created from the base directory via:(base) $  conda env create --file env/conda_env.yml
(base) $  conda activate environment.yml
# This code should then work:
(enflows) $  python examples/conditional_toy_2d.pyIdeally, after a successfull install you should  be able to run and pass the unit tests with:(enflows) /lagrangian_flow_net$  pytestUsageAs the core is based onnflows, its usage is similar. To define a flow:fromenflowsimporttransforms,distributions,flows# Define an invertible transformation.transform=transforms.CompositeTransform([transforms.MaskedAffineAutoregressiveTransform(features=2,hidden_features=4),transforms.RandomPermutation(features=2)])# Define a base distribution.base_distribution=distributions.StandardNormal(shape=[2])# Combine into a flow.flow=flows.Flow(transform=transform,distribution=base_distribution)To evaluate log probabilities of inputs:log_prob=flow.log_prob(inputs)To sample from the flow:samples=flow.sample(num_samples)Additional examples of the workflow are provided inexamples folder.Changes and added features compared to nflowsThe core logic of the code for LFlows (i.e. thenflows/directory) is based on thenflows package.
Added Layers / Flwos:(Conditional)Sum-of-Sigmoid LayersCholesky Outer Product for flows on symmetric positive definite matricesLipschitz Constrained invertible DenseNetsIn particular, we provide three ways to condition these of these transformations without affecting the invertibility.Transformations for which the inverse is only known to exist, but not available:(Conditional) Planar Flow(Conditional) Sylvester FlowConditional Versions of existing non-conditional transformations from nflows. Can be found for imports atnflows.transforms.conditional.*:LU TransformOrthogonal Transforms based on parameterized Householder projectionsSVD based on the Orthogonal transformsShift TransformConditional Versions of existing auto-regressive Variations, i.e. getting rid of the autoregressive parts.ConditionalPiecewiseRationalQuadraticTransformConditionalUMNNTransform
Package: enfobench
Energy  Forecast Benchmark ToolkitEnergy Forecast Benchmark Toolkit is a Python project that aims to provide common tools to
benchmark forecast models.Documentation:https://attila-balint-kul.github.io/energy-forecast-benchmark-toolkit/Source code:https://github.com/attila-balint-kul/energy-forecast-benchmark-toolkitDataset:https://huggingface.co/datasets/attila-balint-kul/electricity-demandDashboardhttps://api.wandb.ai/links/attila-balint-kul/duaehj8cTable of ContentsInstallationUsageContributingLicenseInstallationUse the package manager pip to install foobar.pipinstallenfobenchUsageDownload the HuggingFace Dataset'attila-balint-kul/electricity-demand',
and download the files from the data folder to your computer.importpandasaspdfromenfobenchimportDatasetfromenfobench.datasetsimportElectricityDemandDatasetfromenfobench.evaluationimportcross_validate,evaluate_metricsfromenfobench.evaluation.metricsimportmean_bias_error,mean_absolute_error,root_mean_squared_error# Load the dataset from the folder that you downloaded the files to.ds=ElectricityDemandDataset("/path/to/the/dataset/folder/that/contains/all/subsets")# List all meter idsds.list_unique_ids()# Get one of the meter idsunique_id=ds.list_unique_ids()[0]# Get dataset for a specific meter idtarget,past_covariates,metadata=ds.get_data_by_unique_id(unique_id)# Create a datasetdataset=Dataset(target=target,past_covariates=past_covariates,future_covariates=None,metadata=metadata)# Import your model and instantiate itmodel=MyForecastModel()# Run cross validation on your modelcv_results=cross_validate(model,dataset,start_date=pd.Timestamp("2018-01-01"),end_date=pd.Timestamp("2018-01-31"),horizon=pd.Timedelta("24 hours"),step=pd.Timedelta("1 day"),)# Simply pass in the cross validation results and the metrics you want to evaluate.metrics=evaluate_metrics(cv_results,metrics={"MBE":mean_bias_error,"MAE":mean_absolute_error,"RMSE":root_mean_squared_error,},)To get started with some examples check out themodelsfolder and theexamplessection of the documentation.BenchmarkingOnce confident in your model, you can submit for evaluation.
The results of the benchmarks are openly accessiblehere.ContributingContributions and feedback are welcome! For major changes, please open an issue first to discuss
what you would like to change.If you'd like to contribute to the project, please follow these steps:Fork the repository.
Create a new branch for your feature or bug fix.
Make your changes and commit them.
Push your changes to your forked repository.
Submit a pull request describing your changes.LicenseBSD 2-Clause License
Package: enforce-ascii
Enforce ASCIIVERSION:0.2.1A Python package to find files containing non-ASCII encoded characters. If you
find any bugs, issues or anything, please use theissue trackeron GitHub -
issues and PRs are welcome ❤️InstallIt's onPyPiasenforce-ascii, you can install it withpip,pipx, etc.pipinstallenforce-asciiUsage$enforce-ascii--help
usage:enforce-ascii[-h][--version][--check]FILENAME[FILENAME...]Apre-commithook,thatrejectsfilescontainingnonASCIIcharacters.

positionalarguments:FILENAMEpathtothefilestocheck

optionalarguments:-h,--helpshowthishelpmessageandexit--versionshowprogram'sversionnumberandexit--checkreturncodeis`1`,whennon-ASCIIfilesarefoundExample$enforce-asciitests/files/*/*.txt
-tests/files/bad/special.txt(Windows-1252):there…
-tests/files/bad/umlaut.txt(utf-8):föur,käle,ÅirPre-CommitThis can be used as apre-commithook:-repo:https://github.com/brutus/enforce-asciirev:v0.2.1hooks:-id:enforce-ascii
Package: enforce-git-message
enforce-git-messageEnforcesconventional git commit messagesfor all new git repositories you create by runninggit init. For existing repositories, you can go to your source directory and simply run:enforce-git-messageExamples of valid commit messages:+ 61c8ca9 fix: navbar not responsive on mobile+ 479c48b test: prepared test cases for user authentication+ a992020 chore: moved to semantic versioning+ b818120 fix: button click even handler firing twice+ c6e9a97 fix: login page css+ dfdc715 feat(auth): added social login using twitterExamples of invalid commit messages resulting in an error message:- 61c8ca9 fix for navbar not responsive on mobile- 479c48b prepared test cases for user authentication- a992020 moved to semantic versioning- b818120 fixed button click even handler firing twice- c6e9a97 login page css fix- dfdc715 added social login auth feature using twitterInstallationpip install enforce-git-messageVerifyingGo to your source folder and try to commit with a non-conventional message like this and it should fail:> git commit -m "added a new feature for xyz"

COMMIT FAILED!

Please enter commit message in the conventional format and try to commit again. Examples:

+ 61c8ca9 fix: navbar not responsive on mobile
+ 479c48b test: prepared test cases for user authentication
+ a992020 chore: moved to semantic versioning
+ b818120 fix: button click even handler firing twice
+ c6e9a97 fix: login page css
+ dfdc715 feat(auth): added social login using twitterAfter that, try doing the commit with a valid message and it should work:> git commit -m "feat(test): added xyz"
>NotesRead this articleto fully understand how the enforcement actually works by using git hooks.AttributionTick Icon made bysmashiconsfromwww.flaticon.comis licensed byCC 3.0 BY