Saravanan666/pypi-org huggingface dataset:
Package: entity-search
# Entity SearchThis is an entity search framework based on stationary distributions on random walks with diffusion kernel.## Development
You will need to include the configuration for this repo to your git local configuration.``
git config –local include.path ../.gitconfig
``
This step removes the result section in the json document for each execution in the notebooks.## Testing
In oder to execute the tests, you will need the development dataset. This dataset can be created from the following [repo](https://gitlab.insight-centre.org/ptorres/entity-search-development-data). Once the dataset is created, you need to registered in the configuration file. To simplify this step, just use the incorporated subcommnadconfigwhere you can defined the development data folder.For instance, if the dataset is installed in this directory/data/dev-es, then you need to execute config as follows:``
python setup.py config –config-datafolder=/data/dev-es
``This command will create a configuration file inetc/with the proper setup.Once the configuration file is created, you can execute the tests, as shown below:``
python setup.py test
``
Package: entity-selector-jupyter-widget
entity_selector_jupyter_widgetA Jupyter Widget library for selecting entities in textInstallationTo install run:$pipinstallentity_selector_jupyter_widget$jupyternbextensionenable--py--sys-prefixentity_selector_jupyter_widgetFor a development installation (requires npm),$gitclonehttps://gitlab.com/randomunrandom/entity_selector_jupyter_widget.git$cdentity_selector_jupyter_widget$./dev.sh### it a utilitary script which deactivates virtual environment, creates new and launches jupyter notebookA development version is available via test.pypiUsageSeeexample.pyfor example of hw to use this extension.JupyterLabDue to major differences between the Jupyter Notebook and JupyterLab, the extensions will not work in JupyterLab.LicenseIt's an open source prject under MIT license
Package: entitytocodepy-official
Entity to code py!!!WARNING!!! mathetc FUNCTIONS NOT AVAILIBLEVersion0.0.10.1.0StableyesNONew feats0.1.1SO FILE BUG DETECTION, AS OF NOW, MODULE WITH VERSIONS 0.1.0 - Latest is UNUSABLE0.1.0Binomials and Quadratic Calculations0.0.1First with add_one()Usage1) Installationpip install entitytocodepy_official==x.y.z
pip install entitytocodepy_official==0.1.02) Importing to your python scriptfrom entitytocodepy import main3) Using, here's a sample functionmain.add_one(1)Output2MethodsQuadratics (0.1.0 +)Importfrom entitytocodepy import mathetcUsemathetc.Roots(1,3,2)Factorials (0.1.0 +)Importfrom entitytocodepy import mathetcUsemathetc.Factorial(4)Binomials  (0.1.0 +)Note: This is for finding singular terms in an expansion of (1+x)^n, eg: find the term of x^5 in (1+x)^7, here R (2nd param) == 5 and N = 7Importfrom entitytocodepy import mathetcUsemathetc.Binomialterm(7,5)add_one (0.0.1 +)Importfrom entitytocodepy import mainUsemain.add_one(1)
Package: entium
entiumA command line tool to readentwine'soutput and convert it intoCesium 3DTiles.InstallationEntium supports python 2.7 or greater. To install run:pip install entiumEnsure there is an active installation of python on the machine!DevelopmentTo make modifications or install a development version run:git clone https://github.com/commaai/entium.git
cd entium
pip install .Command Usageusage: entium [-h] [-p [PRECISION]] [-c [CONFIG]] [--validate]
              {tileset,tile,both} entwine_dir output_dir

Convert the entwine hierarchy to a cesium tileset

positional arguments:
  {tileset,tile,both}
  entwine_dir           input folder for entwine
  output_dir            output folder for the cesium tilests

optional arguments:
  -h, --help            show this help message and exit
  -p [PRECISION], --precision [PRECISION]
                        precision in meters required to use quantized tiles
  -c [CONFIG], --config [CONFIG]
                        filepath to config file to use advanced features
  --validate            run post-process to validate point precisionConfigurationThe average user will not need a configuration file if the intent is to directly convert entwines output into cesium tiles. However, if the goal is to use more component types larger than a scalar many of the configuration options may be helpful. The config is a.jsonintended to exist within theentwine.json.Grouping AttributesGrouping works for bothbatch tableproperties andfeature tableproperties. To have grouped component stored as a feature table value instead of batch table use one of the specified names:RGBRGBARGB565NORMALNORMAL_OCT16PBATCH_IDSeepoint semanticsfor more information about these properties. Its also import to note that the order the items are defined in the array will define the order they exist in the component.Example 1RGBwill be recognized as a feature attribute and grouped whileposewill be recognized and grouped as a batch attribute."cesium": {
    "groups": {
        "rgb": [
            "r", 
            "g", 
            "b"
        ], 
        "pose": [
            "poseX", 
            "poseY", 
            "poseZ",
	        "poseW"
        ]
    }
}Example 2Single properties can also be renamed. In this example,coloris being renamed torgb565."cesium":{"groups":{"rgb565":"color"}}Batching Table Attributes (Experimental)Sometimes points have metadata attached to them but that value is representative of some kind of group. This has the potential to reduce data duplication and provide enhanced styling through cesium. Currently the design allows for some attributes to not be in a batch table and rather act as a batch attribute. The hope is sometime in the future both types of batch attributes can be used in unison.Example"cesium":{"batched":["id","version"]}TodoProperty groupingSupport batch propertiesSupport batch tablesAdd unit testingOnly update modified tilesParallelize conversion to 3DTiles
Package: entmoot
ENTMOOT(ENsembleTreeMOdelOptimizationTool) is a framework to perform Bayesian Optimization using tree-based surrogate models. Gradient-boosted tree models fromlightgbmare combined with a distance-based uncertainty
measure in a deterministic global optimization framework to optimize black-box functions. More
details on the method here:https://arxiv.org/abs/2003.04774.DocumentationThe docs can be found here:https://entmoot.readthedocs.io/How to reference ENTMOOTWhen using anyENTMOOTfor any publications please reference this software package as:@article{thebelt2021entmoot,
  title={ENTMOOT: A framework for optimization over ensemble tree models},
  author={Thebelt, Alexander and Kronqvist, Jan and Mistry, Miten and Lee, Robert M and Sudermann-Merx, Nathan and Misener, Ruth},
  journal={Computers \& Chemical Engineering},
  volume={151},
  pages={107343},
  year={2021},
  publisher={Elsevier}
}AuthorsAlexander Thebelt(ThebTron) - Imperial College LondonNathan Sudermann-Merx(spiralulam) - Cooperative State University MannheimToby Boyne(ThebTron) - Imperial College LondonLicenseThe ENTMOOT package is released under the BSD 3-Clause License. Please refer to theLICENSEfile for details.AcknowledgementsThe support of BASF SE, Lugwigshafen am Rhein is gratefully acknowledged.
Package: entomb
Keep your important files safe.Make them unable to be modified or deleted byanyuser, even root.Installation$ pip install entombHow it worksEntomb recursively sets/unsets the immutable attribute on all files on a path
using thechattrcommand.Files in.gitdirectories are ignored by default, but can be included.The immutable attribute is never set on directories, because this would stop
files being created in those directories. Entomb works on files only.At the moment Entomb only works on Linux.Entomb has no dependencies.Examples$ # Make all files in ~/photos immutable.
$ entomb ~/photos

$ # List all files in ~/photos which are not immutable.
$ entomb --list-mutable ~/photos

$ # Report on how many files in ~/photos are and aren't immutable.
$ entomb --report ~/photos

$ # Do a dry run on ~/photos, including any git files/directories.
$ entomb -d -g ~/photos

$ # Make all files in ~/photos mutable (i.e. unset the immutable
$ # attribute).
$ entomb -u ~/photosUsage$ entomb --help
usage: entomb [options] path

Manage file immutability.

positional arguments:
  path               the path to operate on

optional arguments:
  -h, --help         show this help message and exit
  -d, --dry-run      make no changes
  -g, --include-git  include .git directories (excluded by default)
  --list-immutable   list all immutable files
  --list-mutable     list all mutable files
  -r, --report       display a status report
  -u, --unset        unset immutability
  -v, --version      show program's version number and exitDevelopmentGet set up, preferably in a virtualenv:$ make init
$ make installLint the code:$ make lintRun the tests:$ make testCheck the test coverage:$ make coverageReleasingCheck out themainbranch.EnsureCHANGELOG.rstincludes everything to go in the release and is
committed.Ensure everything to go in the release is committed.Increment the version in__init__.py.Shift everything in theUnreleasedsection ofCHANGELOG.rstto a new
section named with the new version number and the current date.Ensure CI runs without warnings or errors:$ make ciMake and tag the release commit:$ make releaseBuild the package:$ make packagePublish the package to PyPI:$ make publishPush to the repo and clean up packaging artifacts:$ make push
$ make cleanCreate a GitHub release.Code styleOnly modules are imported. Classes, functions and variables are not imported
directly.A module’s functions are ordered alphabetically.A module’s private functions are placed alphabetically at the bottom of the
module.Docstrings follow theNumPy docstring guide.Strings are enclosed with double quotes.The last item of a multi-line dictionary or list has a trailing comma.ChangesFor what has changed in each version, seeCHANGELOG.rst.
Package: entoolkit
Epanet Toolkit Wrapper for Pythonhttps://www.epa.gov/water-research/epanetSourceAdapted fromhttps://github.com/USEPA/WNTRand othersInstall> pip install entoolkitLoad modulefromentoolkitimporttoolkitasetHelphttps://www.epa.gov/water-research/epanet
Package: entrainment-metrics
entrainment-metricsentrainment-metrics is all about being able to measure entrainment. Entrainment in spoken dialogue is commonly defined as a tendency of a speaker to adapt some properties of her speech to match her interlocutor’s. With this library you’ll be able to measure entrainment along one dimension: acoustic-prosodic (a/p) features.Checkoutthe docsand theGetting startedpage for a deeper dive into the library!InstallationTo use entrainment_metrics, first install it using pip:pipinstallentrainment_metricsSpeech feature extraction
If you'll be using praat for feature extraction it's also required the command-line tool ffmpeg to be installed on your system, which is available from most package managers:# on Ubuntu or Debiansudoaptupdate&&sudoaptinstallffmpeg# on Arch Linuxsudopacman-Sffmpeg# on MacOS using Homebrew (https://brew.sh/)brewinstallffmpeg# on Windows using Chocolatey (https://chocolatey.org/)chocoinstallffmpeg# on Windows using Scoop (https://scoop.sh/)scoopinstallffmpegAnd for installing praat on Ubuntu or Debian:sudoaptupdate&&sudoaptinstallpraat
Package: entrance
EnTranceEnTranceis a framework for writing
simple but robust web apps, with a particular set of characteristics. It uses a
Python 3.5+ asyncio backend, and an Elm frontend, communicating over a
websocket.This package provides the server-side functionality.Reading the documentation and sample applications inthe primary
repois recommended. Simple examples for
writing your own Python server-side feature are insample
2andsample
3.By default, the dependencies for this package cover the core functionality.
There is also a rich set of optional capability for interacting with routers,
that has a much more extended set of PyPi dependencies. If you want to use
this, then depend onentrance[with-router-features]rather than justentrance.
Package: entrancebar
这是什么?这是一个导入一次解耦方案, 封装了importlib并进行了底层的修改.这能干什么?fromentrancebar.ImportimportImportfromentrancebar.sourcesimportFromSitePackage,FromFileLocationos,Path,getframe,join,thisfile=Import(os="os::$this",Path="pathlib::Path",getframe="sys::_getframe",join="os::path/join",thisfile="testfile::$this")(os=FromSitePackage("os"),pathlib=FromSitePackage("pathlib"),sys=FromSitePackage("sys"),testfile=FromFileLocation("./test.py"))# 实际上, 导入不需要解构运算符, Import会直接把传入的具名实参导入到当前的globals()内.所以这么没用的玩意发出来干啥?给我自己用.
Package: entransit
Entransit enables Zope/Plone websites to transactionally push content
to a remote system where it can be processed using simple
transformation pipelines for delivering by your preferred web
server/web framework. Sample implementations of delivery frontends are
provided for Java, PHP, mod_python/vampire and Microsoft
.NET. Entransit builds on top of known existing libraries and
frameworks, including sqlobject, ZConfig, ZEO’s zrpc, rdflib and
xapian.
Package: entrezpy
Entrezpy READMESynopsis$ pip install entrezpy --user>>> import entrezpy.conduit
>>> w = entrezpy.conduit.Conduit('myemail')
>>> fetch_influenza = w.new_pipeline()
>>> sid = fetch_influenza.add_search({'db' : 'nucleotide', 'term' : 'H3N2 [organism] AND HA', 'rettype':'count', 'sort' : 'Date Released', 'mindate': 2000, 'maxdate':2019, 'datetype' : 'pdat'})
>>> fid = fetch_influenza.add_fetch({'retmax' : 10, 'retmode' : 'text', 'rettype': 'fasta'}, dependency=sid)
>>> w.run(fetch_influenza)Entrezpy is a dedicated Python library to interact withNCBIEntrez
databases[Entrez2016]via the E-Utilities ([Sayers2018],E-Utilities).
Entrezpy facilitates the implementation of queries to query or download data
from the Entrez databases, e.g. search for specific sequences or publications
or fetch your favorite genome. For more complex queriesentrezpyoffers the
classentrezpy.conduit.Conduitto run query pipelines or cache results.Licence and Copyrightentrezpyis licensed under theGNU Lesser General Public License v3
(LGPLv3)or later. Please seehttps://www.ncbi.nlm.nih.gov/home/about/policies/concerning the copyright of the material available through E-Utilities.Please cite the publication if you use entrezpy.InstallationEntrezpy requires at least Python 3.6 and the Standars Python Library.PyPiInstallentrezpyvia PyPi and check:$ pip install entrezpy --userIf you want to incude entrezpy as part of your pipeline, check the documentation
(https://entrezpy.readthedocs.io/en/master/setup/installation.html#append-to-sys-path)DocumentationEntrezpy is fully documented using Spinx
(http://www.sphinx-doc.org/en/stable/). The manual, usage examples and module
reference can be found here:http://entrezpy.readthedocs.io/entrezpyhas been published in Bioinformatics:https://doi.org/10.1093/bioinformatics/btz385References[Entrez2016]https://doi.org/10.1093/nar/gkw1071[Sayers2018]https://www.ncbi.nlm.nih.gov/books/NBK25497
Package: entries
entriesentriesis an open-sourceMicropubclient for the terminal (source code)Built as a client forentries.pubbut should work with any Micropub server.FeaturesZero configuration (everything is "guessed" from the URL)Create/update/delete articles directly within your favorite$EDITORCreated witharticlemicroformats2objects in mindSupport themp-slugextensionInstallThe easiest way to install (and use)entriesis to usepipx:$ pipx install entriesOr using pip (requires Python3+):$ pip install entriesUsageSee the help.$ entries -h

# Create a new article
$ entries create https://mywebsite.tld

# Update an article
$ entries update https://mywebsite.tld/posts/1

# Delete an article
$ entries delete https://mywebsite.tld/posts/1
Package: entropeer
entropeerSearches through files and directories for high entropy strings and secrets.Similar totrufflehogbut for local files rather than git repos.
Based also onentro.py.Installpip3installentropeerWhat it doesentropeer will dig secrets out of a file or a folder returning strings with high shannon entropy or secrets matching some rules.
This can be used to quickly pull secret keys out of a large collection of files like a local sourcecode repo.How toScan a single file:entropeer-f./filenameSearch all the files in the current directory:entropeerSearch all the files in a custom directory:entropeer-d/tmp/codeRecursively search all the files in the current directory and all of its subdirectories:entropeer-rBy defaultentropeerdoes Regex-based scan but you can change to Entropy-based scan with-Eor--entropyentropeer-Erd/tmp/codeComplete help dialogusage: entropeer [-h] [-f FILE | -d DIRECTORY] [-r] [-E | -X] [--rules RULES] [-M] [-v] [-t THREADS] [-V]

Search files for strings with high shannon entropy.

optional arguments:
  -h, --help            show this help message and exit
  -f FILE, --filename FILE
                        File to search.
  -d DIRECTORY, --directory DIRECTORY
                        Search all files in directory.
  -r, --recurse         Search directories recursively starting in the current directory. Use with -d
  -E, --entropy         Enable entropy checks
  -X, --regex           Enable high signal regex checks
  --rules RULES         Load external rules from json list file
  -M, --match-only      Print only the matching string
  -v, --verbose         Verbose output
  -t THREADS, --threads THREADS
                        Number of threads/processes to start
  -V, --version         show program's version number and exit
Package: entropica-qaoa
Entropica QAOAA modular package providing multiple features and workflow tools for the quantum approximate optimisation algorithm (QAOA), facilitating its use, prototyping, and testing. Includes several different parametrisations, integration with data science and graph analysis libraries such as Pandas and NetworkX, numerous utility functions, and convenient optimiser logging and analysis tools. Documentation contains extensive and didactic examples.Read more about EntropicaQAOA on ourblog.DocumentationThe documentation for EntropicaQAOA can be foundhere. Alternatively, it can be complied locally as follows:Install the Prerequisitespipinstallsphinxsphinx-rtd-themesphinx-autodoc-typehintsnbsphinxnbconvertCompile the documentationcddocs&&makehtmlThe compiled HTML version of the documentation is then found inentropica_qaoa/docs/build/html.InstallationWe assume that the user has already installed Rigetti's pyQuil package, as well as the Rigetti QVM and Quil Compiler. For instructions on how to do so, see Rigetti's documentationhere.You can install theEntropicaQAOApackage usingpip:pipinstallentropica-qaoaTo upgrade to the latest version:pipinstall--upgradeentropica-qaoaIf you want to run the Demo Notebooks, you will additionally need to installscikit-learnandscikit-optimize, which can be done as follows:pipinstallscikit-learn&&pipinstallscikit-optimizeAlternatively, you can clone directly from GitHub:gitclonehttps://github.com/entropicalabs/entropica_qaoa.gitTestingAll software tests are located inentropica_qaoa/tests/. To run them you will need to installpytest. To speed up the testing, we have tagged tests that require more computational time (~ 5 mins or so)  withrunslow, and the tests of the notebooks withnotebooks. The commands are as follows:pytestruns the default tests, and skips both the longer tests that need heavier simulations, as well as tests of the Notebooks in theexamplesdirectory.pytest --runslowruns the the tests that require longer time.pytest --notebooksruns the Notebook tests. To achieve this, the notebooks are
converted to python scripts, and then executed. Should any errors occur, this means that the line numbers given in the error
messages refer to the lines in<TheNotebook>.py, and not in<TheNotebook>.ipynb.pytest --allruns all of the above tests.QPU accessEntropicaQAOA provides full native support for Rigetti’s QVM and QPUs. For access to the QPUs, sign up online athttps://qcs.rigetti.com/, or reach out tosupport@rigetti.com.Contributing and feedbackIf you find any bugs or errors, have feature requests, or code you would like to contribute, feel free to open an issue or send us a pull request on GitHub .We are always interested to hear about projects built with EntropicaQAOA. If you have an application you’d like to tell us about, drop us an email atdevteam@entropicalabs.com.
Package: entropix
entropixGenerate count-based Distributional Semantic Models by sampling SVD singular vectors instead of using top components.Installpipinstallentropixor, after a git clone:python3setup.pyinstallUseSequential modeentropixsample\--model/abs/path/to/dense/numpy/model.npy\--vocab/abs/path/to/corresponding/model.vocab\--datasetdataset_to_optimize_on\# men, simlex or simverb--shuffle\--modeseq\--kfold-size.2\# size of kfold, between 0 and .5--metricpearson\# spr(spearman), pearson, rmse or both (spr+rmse)--num-threads5Limit modeentropixsample\--model/abs/path/to/dense/numpy/model.npy\--vocab/abs/path/to/corresponding/model.vocab\--datasetdataset_to_optimize_on\# men, simlex or simverb--modelimit\--metricpearson\--limit10# number of dimensions to sample
Package: entropy
entropythe natural state of entropy is to increase. we must fight against it. 💪entropy is a command line friend that helps you reduce entropy
in your lifeDemoInstallation1:PIP$pipinstallentropy2: From source$gitclonehttps://github.com/h4ck3rk3y/entropy
$cdentropy
$pythonsetup.pyinstallUsageTell entropy how today went$entropystatusaddAsk entropy at how today, yesterday, week, month, year and lfie have been going$entropystatusviewtoday
$entropystatusviewyesterday
$entropystatusviewweek
$entropystatusviewmonth
$entropystatusviewyear
$entropystatusviewlife
$entropystatusviewYYYY-mm-ddJournal with entropy every morning$entropyjournaladdAsk entropy to retrieve older journals for you$entropyjournalviewtoday
$entropyjournalviewyesterday
$entropyjournalviewYYYY-mm-ddIf you need help, don't be shy$entropy--helpContributingUse theGithubto file bugs or push new features.LicenseOpen sourced under theMIT License
Package: entropy-calculator
EntropyCalculate the information entropy of a string, file, etc.InstallationInstall withpipUsageCommand LineBasic usage:entropy [-h] [-f FILE | -t TEXT | -p] [-b BASE] [-s] [-m].Requires one of--shannon(to calculate the Shannon entropy)
or--metric(to calculate the Shannon entropy normalised by input size).Takes input fdom stdin by default, but can read from a file with--fileor from the--textargument.Entropy is calculated in base 2 by default, but this can be changed with--base.See--helpfor extended command line usage.ModuleImporting provides anEntropyclass which takes bytes and a base (both optional)
and exposes methodsshannonandmetricto calculate the Shannon and normalised entropy.
The data can be updated with theupdatemethod.
Package: entropy-gpu
# futhark-kullback-lieblerThe Kullback-Liebler divergence, Hellinger distance, and alpha-divergence in Futhark.## DocumentationDocumentation is available [here](https://vmchale.github.io/kullback-liebler/).## BenchmarksTo run the benchmarks:` make pipenv run python harness.py `and` futhark bench information.fut--backendopencl--runs=100`### ComparisonComputation | Array Size | Implementation | Time |———– | ———- | ————– | —- |Entropy | 10000000 | Futhark | 27.41 ms |Kullback-Liebler Divergence | 10000000 | Futhark | 19.61 ms |Entropy | 10000000 | Python + Futhark | 52.80 ms |Kullback-Liebler Divergence | 10000000 | Python + Futhark | 94.07 ms |Entropy | 10000000 | Python (SciPy) | 233.45 ms |Kullback-Liebler Divergence | 10000000 | Python (SciPy) | 340.83 ms |Entropy | 10000000 | J | 227.37 ms |