Saravanan666/pypi-org huggingface dataset:
Package: enso-nic
Ensō Python FrontendInstallTo install the frontend run in the current directory (frontend):python3-mpipinstall-e.To run the EnsōGen packet generator you also need to have thecapinfoscommand installed in the machine you want to use to generate packets.On Ubuntu (or other Debian-based system) run:sudoaptinstalltsharkFor other distributions refer to thewireshark docsto find the correct package name (eithertsharkorwireshark-cli).RunningFor usage information run:enso--helpEnable autocompletion (optional)For BashRun this:_ENSO_COMPLETE=bash_sourceenso>~/.enso-complete.bashThen add the following to your.bashrc:.~/.enso-complete.bashFor ZshRun this:_ENSO_COMPLETE=zsh_sourceenso>~/.enso-complete.zshThen add the following to your.zshrc:.~/.enso-complete.zshFor FishSave the script to~/.config/fish/completions/enso.fish:_ENSO_COMPLETE=fish_source enso>~/.config/fish/completions/enso.fish
Package: ens-permissions
Permissions audit packageThis project defines a simple library for obtaining smart contract permissions and building a graph.It's aimed at contracts usingOpenzeppelin's AccessControl module.There's a frontend for this athttps://ensuro.co/ens-permissions-frontend/DevelopmentThere's an app developed for Google Cloud Functions.To run the function locally you will need a virtualenv withfunctions-frameworkand the app requirements:python3 -m venv venv
. venv/bin/activate
pip install -r requirements.txt -r requirements.dev.txt
pip install -e .Running the function locallyRequires a few environment variables. See.env.sample.cp .env.sample .env

# Review .env vars
$EDITOR .env

# Run the function
functions_framework --debug --target=permissions_graphThen test it with:curl -o test.gv http://127.0.0.1:8080/?address=0x47E2aFB074487682Db5Db6c7e41B43f913026544

dot -Tsvg test.gv > test.svgDeploymentEditconfig/environment.ymlwith your config and then deploy with gcloud:gcloud functions deploy permissions_graph \
    --env-vars-file config/environment.yml \
    --runtime python39 --trigger-http --allow-unauthenticatedTODODeploy app from github actionsSplitens_permissionsinto its own pypi libraryAdd support forOwnablecontractsAddress bookAdd multisig intelligence (detect when a role member is a multisig and obtain its members)Timelock detection
Package: ensphere
# ensphereAdd XMP info for PanoSphere in the pictures.## RequirementsInstall `exempi` in advance.On MacOS,brew install exempi## Usageusage: ensphere [-h] [--version][--set UsePanoramaViewer=True,ProjectionType=equirectangular]filenames [filenames ...]Add XMP metadata for PanoSphere in the pictures. (version 0.3)positional arguments:filenames             File names (JPEG files only)optional arguments:-h, --help            show this help message and exit--version, -V         show program's version number and exit--set UsePanoramaViewer=True,ProjectionType=equirectangular, -s UsePanoramaViewer=True,ProjectionType=equirectangularSet metadata for PanSpheres. Available variables:['UsePanoramaViewer', 'CaptureSoftware','StitchingSoftware', 'ProjectionType','PoseHeadingDegrees', 'PosePitchDegrees','PoseRollDegrees', 'InitialViewHeadingDegrees','InitialViewPitchDegrees', 'InitialViewRollDegrees','InitialHorizontalFOVDegrees', 'FirstPhotoDate','LastPhotoDate', 'SourcePhotosCount','ExposureLockUsed', 'CroppedAreaImageWidthPixels','CroppedAreaImageHeightPixels', 'FullPanoWidthPixels','FullPanoHeightPixels', 'CroppedAreaLeftPixels','CroppedAreaTopPixels', 'InitialCameraDolly']## ExampleTo add the default metadata for a equirectangular JPEG image.ensphere equirectangular.jpgFor a cropped image, you also have to specify the original image size with `-s` option.ensphere -s 'FullPanoWidthPixels=8000,FullPanoHeightPixels=4000,CroppedAreaLeftPixels=0,CroppedAreaTopPixels=0' partial.jpg
Package: enstaller
The Enstaller (version 4) project is a package management and installation
tool for egg-based Python distributions.It supports python >= 2.6 and python >= 3.3, as well as pypi.InstallationThe preferred and easiest way to install enstaller on any platform is to
download thebootstrap.pyscript and then execute it with the python interpreter:$ python bootstrap.py
enstaller-4.7.5-1.egg                              [installing egg]
   4.34 MB [......................................................]If you already have an enstaller egg, you can use the bootstrap script
offline:$ python bootstrap.py enstaller-4.7.3-py2.7.egg
enstaller-4.7.3-1.egg                              [installing egg]
   4.31 MB [......................................................]or request a specific version:$ python bootstrap.py -l
4.6.5-1
4.7.5-1
4.7.6-1
$ python bootstrap.py --version 4.6.5-1
enstaller-4.6.5-1.egg                             [installing egg]
   766 KB [......................................................]Once Enstaller is installed, it can update itself.  Note that, as Enstaller is
the install tool for Canopy and EPD, those products already include enstaller.
The bootstrap script may be used to repair broken environments where enpkg is
not usable anymore.Installing a dev versionTo install a dev version, you should do as follows:# Build an egg compatible with Enthought format
$ python setup.py bdist_enegg

# Install it
$ python scripts/bootstrap.py dist/<produced_egg>Available featuresEnstaller consists of the sub-packages enstaller (package management tool) and
egginst (package (un)installation tool).enstallerenstaller is a management tool for egginst-based installs. The CLI, called
enpkg, calls out to egginst to do the actual installation. Enpkg is concerned
with resolving dependencies, managing user configuration and fetching eggs
reliably.egginstegginst is the underlying tool for installing and uninstalling eggs. It
installs modules and packages directly into site-packages, i.e.  no .egg
directories are created.
Package: enstaller4rc
This is a small library to read an existing.enstaller4rcfile without
importing enstaller.This supports.enstaller4rcfrom the 4.6.X series until 4.8.X. It only
supports parsing configuration, and purposedly does not support modifying it.Example:from enstaller4rc import Configuration

config = Configuration.from_file(".enstaller4rc")
print(config.auth)
print(config.use_pypi)
print(config.indexed_repositories)
Package: enstat
enstatDocumentation:enstat.readthedocs.ioHallmark featureenstatis a library to compute ensemble statisticswithout storing the entire ensemble in memory.
In particular, it allows you to compute:Ensemble averages (and their variance);Ensemble averages (and their variance) based on a certain binning of a quantity;Histograms of an ensemble.Below you find a quick-start.
For more information, see thedocumentation.Ensemble averageThe key feature is to store the sum of the first and second statistical moments and the number of samples.
This gives access to the mean (and variance) at all times, while you can keep adding samples.Suppose that we have 100 realisations, each with 1000 'items', and we want to know the ensemble average of each item:importenstatimportnumpyasnpensemble=enstat.static()forrealisationinrange(100):sample=np.random.random(1000)ensemble+=sampleprint(ensemble.mean())which will print a list of 1000 values, each around0.5.This is the equivalent ofimportnumpyasnpcontainer=np.empty((100,1000))forrealisationinrange(100):sample=np.random.random(1000)container[realisation,:]=sampleprint(np.mean(container,axis=0))The key difference is thatenstatonly requires you to have4 * Nvalues in memory for a sample of sizeN: the sample itself, the sums of the first and second moment, and the normalisation.
Instead the solution with the container uses much more memory.A nice feature is also that you can keep adding samples toensemble.
You can even store it and continue later.Ensemble histogramSame example, but now we want the histogram for predefined bins:importenstatimportnumpyasnpbin_edges=np.linspace(0,1,11)hist=enstat.histogram(bin_edges=bin_edges)forrealisationinrange(100):sample=np.random.random(1000)hist+=sampleprint(hist.p)which prints the probability density of each bin (so list of values around0.1for these bins).Thehistogramclass contains two additional nice features.It has several bin algorithms that NumPy does not have.It can be used for plotting with an ultra-sort interface, for example:importenstatimportnumpyasnpimportmatplotlib.pyplotaspltdata=np.random.random(1000)hist=enstat.histogram.from_data(data,bins=10,mode="log")fig,ax=plt.subplots()ax.plot(hist.x,hist.p)plt.show()You can even useax.plot(*hist.plot).Average per binSuppose you have some time series (t) with multiple observables (aandb); e.g.;importenstatimportnumpyasnpt=np.linspace(0,10,100)a=np.random.normal(loc=5,scale=0.1,size=t.size)b=np.random.normal(loc=1,scale=0.5,size=t.size)Now suppose that you want to compute the averagea,b, andtbased on a certain binning oft:bin_edges=np.linspace(0,12,12)binned=enstat.binned.from_data(t,a,b,names=["t","a","b"]mbin_edges=bin_edges)print(binned["a"].mean())InstallationUsing condacondainstall-cconda-forgeenstatUsing PyPipython-mpipinstallenstatDisclaimerThis library is free to use under theMIT license.
Any additions are very much appreciated.
As always, the code comes with no guarantee.
None of the developers can be held responsible for possible mistakes.
Package: enstelco
ENSTELCOEnergy-Strain-Elastic-Constants (ENSTELCO – rebranding desperately needed)ENSTELCO interfaces with the popularAtomic Simulation Environment (ASE)to provide a simple and versatile platform for calculating second-order elastic constants
(and ensuing mechanical properties) of materials. It implements the approaches described inthis work:J. Zhao, J. M. Winey and Y. M. Gupta, Phys. Rev. B, 2007, 75, 094105.FeaturesAutomatic detection of input structure lattice typePerforms relevant cell deformations depending on lattice symmetry (efficient!)Solves for 2nd-order elastic constants to generate elastic tensorsCalculates mechanical properties (bulk, Young's, and shear moduli) usingELATEPlot energy-strain curves to assess quality of elastic constant fitsCompatable with any ASE calculator!Simple CLI for performing deformations, evaluating stress tensor / mechanical properties, and plotting energy-strain behaviorInstallationClone the repository wherever you'd like on your system.$cd/wherever/you/want$clonehttps://github.com/tysours/ENSTELCO.gitChange to the ENSTELCO directory.$cdENSTELCOInstall using pip$pipinstall.And you're done!WARNINGENSTELCO has not been validated for all lattice types! Please report an issuehereor email me directly
(tsours@ucdavis.edu) if you find issues.
Package: enstools
Ensemble ToolsThis package provides core functionality to Python-based tools developed within
the framework ofWaves to Weather - Transregional Collaborative Research
Project (SFB/TRR165).Shared functionality includes:Clustering (enstools.clustering)Interpolation (enstools.interpolation)Reading and Writing data (enstools.io)Retrieval of open data (enstools.opendata)Post-processing (enstools.post)Scores (enstools.scores)Installationpipis the easiest way to installenstoolsalong with all dependencies.pip install enstoolsDocumentationExploreour documentation.Acknowledgment and licenseEnsemble Tools (enstools) is a collaborative development within
Waves to Weather (SFB/TRR165) coordinated by the subprojectZ2and funded by the
German Research Foundation (DFG).A full list of code contributors canCONTRIBUTORS.md.The code is released under anApache-2.0 licence.
Package: enstools-compression
enstools-compressionThis package extendsenstoolsto enablelossyandlosslesscompression
by usingHDF5 filters.Also, it contains a set of tools to help to find appropriate compression specifications.
It includes aCommand Line Interfaceto access its features from the command lineFor example:enstools-compressioncompress"input.nc"-o"output.nc"--compression"lossless"It has been developed under theWaves to Weather - Transregional Collaborative Research
Project (SFB/TRR165).Installationpipis the easiest way to installenstools-compressionalong with all dependencies.pip install enstools-compressionDocumentationExploreour documentation.Acknowledgment and licenseEnsemble Tools (enstools) is a collaborative development within
Waves to Weather (SFB/TRR165) coordinated by the subprojectZ2and funded by the
German Research Foundation (DFG).A full list of code contributors canCONTRIBUTORS.md.The code is released under anApache-2.0 licence.
Package: enstools-encoding
enstools-encodingLibrary to generate the encodings to write compressed files as easily as possible withxarrayusinghdf5 filters.Its only capability is to provide the encodings thatxarrayandh5pyneed in order to write files using filters.The package doesn't provide the filters, need to be installed separately.This package was originally insideEnsemble Toolsbut can be used standalone.It is also a building block forEnsemble Tools - Compression,
which includes a command line tool to make compression even easier, along with other useful tools to find proper compression parameters.It has been developed under theWaves to Weather - Transregional Collaborative Research
Project (SFB/TRR165).CompressorsAt the current stage it is possible to generate encodings for three compressors:BLOSCSZZFPInstallationpipis the easiest way to installenstools-encodingalong with all dependencies.pip install enstools-encodingDocumentationExploreour documentation.Acknowledgment and licenseEnsemble Tools (enstools) is a collaborative development within
Waves to Weather (SFB/TRR165) coordinated by the subprojectZ2and funded by the
German Research Foundation (DFG).A full list of code contributors canCONTRIBUTORS.md.The code is released under anApache-2.0 licence.
Package: ensureconda
ensurecondaInstallationPython-basedensureconda is distributed onPyPIas a universal
wheel and is available on Linux, macOS and Windows and supports
Python 3.6+ and PyPy.$pipinstallensurecondaGo basedAdditionally ensureconda is also available as a statically linked fully stand-alone
golang binary.  This is a full reimplementation with the same cli as the python versionThese can be downloaded fromreleasesAlternatively if you have go installed you can install from githubg using$goinstallgithub.com/conda-incubator/ensureconda@latest
$ensureconda--helpUsageEnsureconda is a cli tool that willFind a preexisting conda/mamba executableInstall one if nothing was found and installation is allowed.Return the path of the executable found/installed on stdoutensureconda --help
Usage: ensureconda [OPTIONS]

  Ensures that a conda/mamba is installed.

Options:
  --mamba / --no-mamba            search for mamba
  --micromamba / --no-micromamba  search for micromamba, install if not
                                  present

  --conda / --no-conda            search for conda
  --conda-exe / --no-conda-exe    search for conda.exe / conda-standalone,
                                  install if not present

  --no-install                    don't install conda/mamba if no version can
                                  be discovered

  --min-conda-version VERSIONNUMBER
                                  minimum version of conda to accept (defaults
                                  to 4.8.2)

  --min-mamba-version VERSIONNUMBER
                                  minimum version of mamba/micromamba to
                                  accept (defaults to 0.7.3)

  --help                          Show this message and exit.Licenseensureconda is distributed under the terms of theMIT License.
Package: ensure-import
ensure_importAuto install third part packages by pip into virtual environment when import error.Installpipinstallensure_importUsageSimple case that package name is module namefromensure_importimportEnsureImportas_EIwhile_ei:=_EI():with_ei:importuvicornfromfastapiimportFastAPIPackage name is difference from module namewhile_ei:=_EI():with_ei(dotenv='python-dotenv',odbc='pyodbc'):importnumpyasnpimportuvicornimportodbc# who's package name is `pyodbc`fromfastapiimportFastAPI# package name of dotenv is `python-dotenv`fromdotenvimportload_dotenvSupply module pathwhile_ei:=_EI('..'):with_ei:importgunicornimportuvicornThis is equal to:try:importgunicornimportuvicornexceptImportError:importsyssys.path.append('..')importgunicornimportuvicornSupport__file__while_ei:=_EI(__file__):with_ei:importlocal_module# is equal to:try:importlocal_moduleexceptImportError:importsysfrompathlibimportPathdirpath:str=Path(__file__).parent.as_posix()ifdirpathnotinsys.path:sys.path.append(dirpath)importlocal_module
Package: ensure-vpn
A Python function to make sure you are connected to your favorite VPN before running your script or function. It just raises an exception if you're not connected.Supported VPN providersCustom IPExpressVPN ("expressvpn")HideMyAss ("hidemyass")Hotspot Shield ("hotspotshield")IPVanish ("ipvanish")IVPN ("ivpn")Mullvad ("mullvad")NordVPN ("nordvpn")Private Internet Access ("privateinternetaccess")ProtonVPN ("protonvpn")Surfshark ("surfshark")VyprVPN ("vyprvpn")Add your own!Installationpip install ensure-vpnUsageImport the function and run it as the first thing in your script:fromensure_vpnimportensure_vpnensure_vpn("mullvad")# raises VPNNotConnectedException if you're not connected.# rest of your script goes hereYou can also use a custom IP or subnet:ensure_vpn("2.235.200.110")# or e.g. "2.235.200.0/24"You can also use the decorator to run the check every time before running a specific function. This is to make sure you don't run untrusted code if you lose your VPN connection after starting your program.Note that this can be resource intensive depending on how often you call your function so it may slow down your program considerably or get you rate-limited by the services used by this script.fromensure_vpnimportensure_vpn_decorator@ensure_vpn_decorator("nordvpn")defdo_stuff():# ...do_stuff()# VPN is checked every time you call do_stuff
Package: ensuro
ensuroPrototype and wrappers to work with Ensuro Smart ContractsThis package is for working with the Ensuro Protocol (https://github.com/ensuro/ensuro) from Python.It includes the prototype written in pure-python that can be used for simulation of Ensuro. Also includes
the wrappers that together with the compiled contracts can be used to deploy or use contracts deployed on the
blockchain.Getting information from Ensuro objectsPremiumsAccount.active_pure_premiums.surplus: balance of pure premiums of finalized policies..won_pure_premiums: accumulated pure premiums of expired/not defaulted policies..borrowed_active_pp: pure premiums of active policies that were used for payouts. Always<= active_pure_premiums` and always = 0 if won_pure_premiums != 0..pure_premiums: it’s a property that computes the total pure premiums asself.active_pure_premiums + self.won_pure_premiums - self.borrowed_active_ppEToken.total_supply(): total amount in dollars in the pool..scr: amount that it’s locked as solvency capital of active policies.scr_interest_rate: weighted average of the interest rate paid by the active policies.utilization_rate: percentage of utilization of the eToken. Property calculated asscr / total_supply.token_interest_rate: interest rate for the EToken, it’s calculated asscr_interest_rate * utilization_rate.funds_available: available capital (total_supply - scr).funds_available_to_lock: available capital that can be used as scr,(total_supply - scr) * max_utilization_rate.total_withdrawable(): amount that can be withdrawn, considering the liquidity_requirement..get_loan(): current debt of the pool with this eTokenRiskModule.active_exposure: total exposure currently allocated for this module.get_minimum_premium(payout, loss_prob, expiration): minimum premiumCopying files from Ensuro main repositoryInstructions to copy files from ensuro repository:for x in `find ../ensuro/artifacts/contracts/ -maxdepth 2 -name "*.json" -not -name "*.dbg.json" `; do
    cp $x src/ensuro/contracts/ ;
done
for x in `find ../ensuro/artifacts/contracts/interfaces/ -maxdepth 2 -name "*.json" -not -name "*.dbg.json" `; do
    cp $x src/ensuro/contracts/ ;
done
for x in ERC1967Proxy.json IERC20Metadata.json IERC20.json IERC721.json ; do
    cp `find ../ensuro/artifacts/@openzeppelin/ -name $x` src/ensuro/contracts/$x ;
done
cp ../ensuro/prototype/ensuro.py src/ensuro/prototype.py
cp ../ensuro/prototype/wrappers.py src/ensuro/wrappers.py
cp ../ensuro/prototype/utils.py src/ensuro/utils.pyNoteThis project has been set up using PyScaffold 4.1.1. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: ensuro-analytics
Ensuro Analytics PackageDescriptionEnsuro's analytics tool suite.Modulesensuro_analytics: This module contains the main analytics functions. It is further divided into two sub-modules:
-analytics/: This module functions to compute standard portfolio metrics (analytics/metrics), Ensuro's accessors for pandas dataframes (analytics/dataframe), and functions to compute portfolio reviews (analytics/review),
-download/: This file contains interfaces to download data from Ensuro's API and BigQuery,
-visual/: This file contains functions to setup plotting library according to Ensuro's layout and functions to visualize portfolio metrics.RequirementsSee the list of requirements inrequirements.txt/InstallationTo install the required packages, run the following command:pip install ensuro-analyticsUsageSee the packagenotebookfor the package instructions.ContributingThank you for your interest in Ensuro! Head over to ourContributing Guidelinesfor instructions on how to sign our Contributors Agreement and get started with
Ensuro!Please note we have aCode of Conduct, please follow it in all your interactions with the project.AuthorsLuca MungoLudovico NapoliLicenseThe repository and all contributions are licensed underAPACHE 2.0. Please review ourLICENSEfile.
Package: ensverif
Ensverif is a Python library that contains functions to assess the quality of ensemble forecasts or simulations. Those functions are:crpscrps_hersbach_decompositionlogscorerankhistreliabilityThose functions were initially coded in Matlab during my MSc and PhD degree. They were further improved over time, by my students (especially Rachel Bazile) and myself.If you don't want to use pip, the library is also available on Github, in Matlab and Python, herehttps://github.com/TheDropletsAuthors: Marie-Amélie Boucher, Rachel Bazile, Konstantin Ntokas and Alireza AmaniContact:marie-amelie.boucher@usherbrooke.caMain changes in that release:Place all the functions in one single module instead of one function per module, thus facilitating the call to each function and the use of this module:Replace np.mean by np.nanmean in the CRPS functionCorrect spelling mistakesCorrect convention mistakes (not perfect yet, but better)Improve documentation (more concise + adding an example on how to use the module)