Saravanan666/pypi-org huggingface dataset:
Package: elemeno-mlops-client
A set of glue code and utilities to make using elemeno AI platform a smooth experienceFree software: Apache Software License 2.0Installationpip install elemeno-ai-sdkYou can also install the in-development version with:pip install https://gitlab.com/elemeno-ai/python-elemeno-ai-sdk/-/archive/master/python-elemeno-ai-sdk-master.zipDocumentationelemeno-ai-sdk.readthedocs.ioDevelopment> If you’re using Mac, first install make withbrew install makeTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxVersioningBumping version is as simple as runningpython -m bumpversion –new-version 0.0.5 patchChangelog0.0.1 (2021-10-16)First release on PyPI.
Package: elemental
Elemental makes Selenium automation faster and easier.Adds common use-casesCommon use-cases missing from Selenium are built into Elemental.Get an input
by its label or placeholder? Can do.Get a button by its text or type?Sure.Get an element’s parent?Yep.Get the fourth element in a list?No problem.Automatic waitingElemental has built-in automatic waiting so that your automation is less flaky.
It has a sensible default which can be overriden when necessary.The full power of SeleniumFor complex operations which require the full power of the Selenium, Elemental
gives you easy access to 100% of Selenium’s API.Terse, clean APIWrite less code than you would if you were using Selenium directly. The
Elemental API is terse and internally consistent while still being explicit and
unambiguous.The power of Elementalimportelemental# Set up.browser=elemental.Browser()# Search PyPI for Elemental.browser.visit("https://www.pypi.org")browser.get_input(placeholder="Search projects").fill("elemental")browser.get_button(type="submit").click()# Click the first search result.browser.get_element(partial_text="elemental").click()# Confirm that Elemental's PyPI page was found.assertbrowser.title=="elemental · PyPI"# Tear down.browser.quit()Installation$ pip install elementalDevelopmentGet set up, preferably in a virtualenv:$ make init
$ make installLint the code:$ make lintRun the tests:$ make testBuild the docs:$ cd docs
$ make htmlReleasingCheck 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 inelemental/__init__.pyanddocs/source/conf.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: elemental-analysis-tools
Documentation:https://elemental_analysis_tools.readthedocs.io/Instruction for a basic development setup:Libraries instalation:virtualenv -p python3 .virtualenv
source virtualenv/bin/activate
pip3 install -r requirements.txtExample of how to run tests, given you are at root directory:python3 tests/test_elementarDensity.py
python3 tests/test_responseFactor.py
python3 tests/test_winqxas.py
python3 tests/test_blankCorrection.py
python3 tests/test_micromatter.py
python3 tests/test_shimadzu.py
python3 tests/test_fitResponseFactor.pyPublishing a new version of the package inpip:python3 setup.py sdist bdist_wheel
pip3 install twine
twine upload dist/*
Package: elemental-cms
elemental CMSElemental is a Flask and MongoDB web CMS intended for developers.Our main goal is to allow developers to create and maintain web portals or web applications using their preferred programming IDE like VS Code, PyCharm, Visual Studio, etc.The main interaction with the tool takes place through its CLI, a self documented command line tool called "elemental-cms" which helps us perform deployment tasks directly from the terminal.It relies on MongoDB to store the metadata, pages' content, snippets' content, dependencies information, and user session data.SetupTo install the tool, we can issue the following command:pipinstallelemental-cmsThis will install all the dependencies required for elemental-cms to work, like pymongo, flask, flask-babel, etc.Running elemental-cms command for the first timeAfter setup, we can start using the tool like this:DocumentationMore details about the tool can be foundhereon the official documentation portal.
Package: elementals
elementalsPython package with basic utilities for CTF scripts (works well for exploit PoCs too).Installpip install elementalsRead The Docshttps://elementals.readthedocs.io/Githubhttps://github.com/eyalitki/elementalsBriefTheelementalspython package is a utility package with handy tools for CTF scripts and PoC-style scripts. What was first implemented for personal use in small research scripts was now upgraded to be used by security researchers as a lightweight substitute to the famous pwntools package.The features included inelementalsare:Logger:Basic (logging based) logger - configured and ready to usePrompter:Metasploit based stdout wrapper for the loggerProgressBar:User-friendly graphical progress barStatusBar:User-friendly graphical status barcreateAnchor:Creates a time-stamped output directory for all script outputshexDump:Stylized hex dump for binary blobs / stringsUsed byTheelementalspackage is used in most of my scripts and also in my public Github projects:Scout Debugger-https://github.com/CheckPointSW/ScoutKarta (IDA Plugin)-https://github.com/CheckPointSW/KartaReferencesTwitter:@EyalItkinE-mail: eyal dot itkin at gmail dot com
Package: element-array-ephys
DataJoint Element for Extracellular ElectrophysiologyDataJoint Element for extracellular array electrophysiology that processes data
acquired with a polytrode probe
(e.g.Neuropixels, Neuralynx) using theSpikeGLXorOpenEphysacquisition software andMATLAB-based Kilosortorpython-based
Kilosortspike sorting software. DataJoint
Elements collectively standardize and automate data collection and analysis for
neuroscience experiments. Each Element is a modular pipeline for data storage and
processing with corresponding database tables that can be combined with other Elements
to assemble a fully functional pipeline.Experiment flowchartData Pipeline DiagramGetting StartedInstall from PyPIpipinstallelement-array-ephysInteractive tutorial on GitHub CodespacesDocumentationSupportIf you need help getting started or run into any errors, please contact our team by email atsupport@datajoint.com.
Package: elementary-data
dbt native data observability for analytics & data engineersMonitor your data quality, operation and performance directly from your dbt project.⭐️ Star the repo⭐Demo »|Docs »|Slack »What is Elementary?Elementary is an open-source data observability solution, built for dbt users. Setup in minutes, gain immediate visibility, detect data issues, send actionable alerts, and understand impact and root cause.Quick startStep 1 -Install Elementary dbt packageStep 2 -Install Elementary CLIFeaturesData observability report- Generate a data observability report, host it or share with your team.Anomaly detection dbt tests- Collect data quality metrics and detect anomalies, as native dbt tests.Test results- Enriched with details for fast triage of issues.Models performance- Visibility of execution times, easy detection of degradation and bottlenecks.Data lineage- Enriched with test results, easy to navigate and filter.dbt artifacts uploader- Save metadata and run results as part of your dbt runs.Slack alerts- Actionable alerts, including custom channels and tagging of owners and subscribers.JoinSlackto learn more on Elementary.Our full documentation isavailable here.How it works?Elementarydbt packagecreates tables of metadata and test results in your data warehouse, as part of your dbt runs. The CLI tool reads the data from these tables, and is used to generate the UI and alerts.Community & SupportFor additional information and help, you can use one of these channels:Slack(Live chat with the team, support, discussions, etc.)GitHub issues(Bug reports, feature requests)Twitter(Updates on new releases and stuff)Integrationsdbt core (>=1.0.0)dbt cloudData warehouses:SnowflakeBigQueryRedshiftDatabricks SQLPostgresOperations:SlackGitHub ActionsAmazon S3Google Cloud StorageAsk us for integrations onSlackor as aGitHub issue.ContributionsThank you :orange_heart: Whether it’s a bug fix, new feature, or additional documentation - we greatly appreciate contributions!Check out thecontributions guideandopen issues.Elementary contributors: ✨
Package: element-coder
element_coderEncode chemical elements numerically and decode numerical representations of elements.💪 Getting Startedfromelement_coderimportencode,decodedecode(encode('Si','mod_pettifor'),'mod_pettifor')>'Si'Command Line InterfaceTheelement_coder.encodeandelement_coder.decodecommand line tools are automatically installed. They can
be used from the shell with the--helpflag to show help:$element_coder.encodeH102$element_coder.decode102Halso works for vector-valued encodings$element_coder.decode0000000000000000011000000000000000010000000000100000000000000000001000000000010000000001000--propertycgcnn
H🚀 InstallationThe most recent release can be installed fromPyPIwith:$pipinstallelement_coderThe most recent code and data can be installed directly from GitHub with:$pipinstallgit+https://github.com/kjappelbaum/element-coder.gitTo install in development mode, use the following:$gitclonegit+https://github.com/kjappelbaum/element-coder.git
$cdelement-coder
$pipinstall-e.BackgroundFor some applications (of ML in chemistry) elements must be numerically encoded. There are many libraries that do that. For most applications, evenpymatgencan get the job done:frompymatgen.coreimportElementdefencode_element(element:Element,property:str):returngetattr(element,property)However, this code has some issues, wherefore there are many other libraries that attempt to solve this issue includingmendeleev,elementy,EIMD. However,none of these libraries supported all the properties I was interested innone of these libraries supported decoding of descriptors into Elements.👐 ContributingContributions, whether filing an issue, making a pull request, or forking, are appreciated. SeeCONTRIBUTING.rstfor more information on getting involved.👋 Attribution⚖️ LicenseThe code in this package is licensed under the MIT License.🍪 CookiecutterThis package was created with@audreyfeldroy'scookiecutterpackage using@cthoyt'scookiecutter-snekpacktemplate.🛠️ For DevelopersSee developer instrutionsThe final section of the README is for if you want to get involved by making a code contribution.❓ TestingAfter cloning the repository and installingtoxwithpip install tox, the unit tests in thetests/folder can be
run reproducibly with:$toxAdditionally, these tests are automatically re-run with each commit in aGitHub Action.📦 Making a ReleaseAfter installing the package in development mode and installingtoxwithpip install tox, the commands for making a new release are contained within thefinishenvironment
intox.ini. Run the following from the shell:$tox-efinishThis script does the following:Uses BumpVersion to switch the version number in thesetup.cfgandsrc/element_coder/version.pyto not have the-devsuffixPackages the code in both a tar archive and a wheelUploads to PyPI usingtwine. Be sure to have a.pypircfile configured to avoid the need for manual input at this
stepPush to GitHub. You'll need to make a release going with the commit where the version was bumped.Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can
usetox -e bumpversion minorafter.
Package: element-deeplabcut
DataJoint Element for Pose Estimation with DeepLabCutDataJoint Element for markerless pose estimation withDeepLabCut.  DataJoint Elements collectively standardize
and automate data collection and analysis for neuroscience experiments.  Each Element is
a modular pipeline for data storage and processing with corresponding database
tables that can be combined with other Elements to assemble a fully functional pipeline.Experiment FlowchartData Pipeline DiagramGetting StartedInstall from PyPIpipinstallelement-deeplabcutInteractive tutorialDocumentationSupportIf you need help getting started or run into any errors, please open a GitHub Issue
or contact our team by email atsupport@datajoint.com.
Package: element-electrode-localization
DataJoint Element - Electrode LocalizationDataJoint Element for localizing Neuropixels electrodes within the Allen brain atlas -Allen Mouse Common Coordinate Framework (CCF). DataJoint
Elements collectively standardize and automate data collection and analysis for
neuroscience experiments. Each Element is a modular pipeline for data storage and
processing with corresponding database tables that can be combined with other Elements
to assemble a fully functional pipeline.Experiment FlowchartData PipelineGetting StartedInstall from PyPIpipinstallelement-electrode-localizationDocumentation & Tutorials
Package: element-event
DataJoint Element - Experimental trialselement-eventfeatures a DataJoint pipeline design for event, trial, and block management.element-eventis not a complete workflow by itself, but rather a modular design of tables and dependencies.element-eventcan be flexibly attached to any DataJoint workflow.See theElement Event documentationfor the background information and development timeline.For more information on the DataJoint Elements project, please visithttps://elements.datajoint.org.  This work is supported by the National Institutes of Health.Element architectureIn diagram below,BehaviorRecordingtable starts immediately downstream fromSession. Recordings can be segmented into both trials, which are assumed to have
duration, and events, which may be instantaneous. Researchers may find one or both  appropriate for their particular paradigm. A set of trials can be further organized into
blocks, representing a larger span of time. We provide anexample workflowwith apipeline scriptthat models combining this Element with the correspondingElement-Session.Trial & Event SchemasInstallationInstallelement-eventpip install element-eventUpgradeelement-eventpreviously installed withpippip install --upgrade element-eventUsageElement activationTo activate theelement-event, one need to provide:Schema names for the event or trial moduleUpstream Session table: A set of keys identifying a recording session (seeElement-Session).Utility functions. Seeexample definitions hereFor more detail, check the docstring of theelement-event:fromelement_eventimportevent,trialhelp(event.activate)help(trial.activate)Element usageSee theworkflow-calcium-imaging,workflow-array-ephys, andworkflow-miniscoperepositories for example usages ofelement-event.CitationIf your work uses DataJoint and DataJoint Elements, please cite the respective Research Resource Identifiers (RRIDs) and manuscripts.DataJoint for Python or MATLABYatsenko D, Reimer J, Ecker AS, Walker EY, Sinz F, Berens P, Hoenselaar A, Cotton RJ, Siapas AS, Tolias AS. DataJoint: managing big scientific data using MATLAB or Python. bioRxiv. 2015 Jan 1:031658. doi:https://doi.org/10.1101/031658DataJoint (RRID:SCR_014543) - DataJoint for<Select Python or MATLAB>(version<Enter version number>)DataJoint ElementsYatsenko D, Nguyen T, Shen S, Gunalan K, Turner CA, Guzman R, Sasaki M, Sitonic D, Reimer J, Walker EY, Tolias AS. DataJoint Elements: Data Workflows for Neurophysiology. bioRxiv. 2021 Jan 1. doi:https://doi.org/10.1101/2021.03.30.437358DataJoint Elements (RRID:SCR_021894) - Element Event (version<Enter version number>)
Package: elementflow
# elementflow[![Build Status](https://travis-ci.org/isagalaev/elementflow.png)](https://travis-ci.org/isagalaev/elementflow)Elementflow is a Python library for generating XML as a stream. Some existingXML producing libraries (like ElementTree, lxml) build a whole XML tree inmemory and then serialize it. It might be inefficient for moderately large XMLpayloads (think of a content-oriented Web service producing lots of XML dataoutput). Python's built-in xml.sax.saxutils.XMLGenerator is very low-level andrequires closing elements by hand.Also, most XML libraries, to be honest, suck when dealing with namespaces.## UsageBasic XML generation:```pythonimport elementflowfile = open('text.xml', 'w') # can be any  object with .write() methodwith elementflow.xml(file, u'root') as xml:xml.element(u'item', attrs={u'key': u'value'}, text=u'text')with xml.container(u'container', attrs={u'key': u'value'}):xml.text(u'text')xml.element(u'subelement', text=u'subelement text')```Using `with` is required to properly close container elements. The libraryexpects unicode strings on input and produces utf-8 encoded output (you *may*omit those "u"s for pure ASCII strings if you want to, Python will convertthem automatically).XML with namespaces:```pythonwith elementflow.xml(file, 'root', namespaces={'': 'urn:n', 'n1': 'urn:n1'}) as xml:xml.element('item')with xml.container('container', namespaces={'n2': 'urn:n2'):xml.element('n1:subelement')xml.element('n2:subelement')```Elements with namespaces are defined using prefixes. You can define namespacesat the root level and for any container. The library will check for namespaceprefixes that wasn't defined beforehand and will raise ValueError in that case.Pretty-printing is also supported:```pythonwith elementflow.xml(file, 'root', indent=True):# ...```In some cases it's more convenient to have such XML producer as a Pythoniterator. This is easily done by wrapping XML generation code into a generatorfunction:```pythondef g():xml = elementflow.xml(elementflow.Queue(), 'root')with xml:for item in collection:xml.element(...)yield xml.file.pop()yield xml.file.pop()````elementflow.Queue()` is a temporary buffer that accepts data from an XMLgenerator and is cleared upon calling .pop() on it. You also might want toyield data from the iterator only when this buffer reaches a certain size:```pythonif len(xml.file) > BUFSIZE:yield xml.file.pop()```
Package: element-interface
Elements Interface for external analysis packagesDataJoint Element for interoperability with other software. DataJoint Elements
collectively standardize and automate data collection and analysis for neuroscience
experiments. Typical Elements are modular pipelines for data storage and processing with
corresponding database tables that can be combined with other Elements to assemble a
fully functional pipeline. Element Interface is home to a number of utilities that make
this possible.Installation and usage instructions can be found at theElement documentation.
Package: elementMass
elementMass is a compact Python library for calculating atomic weights of chemical compounds as well as cation and oxygen amounts in simple oxides.DocumentationCode documentation is available atelementMass.readthedocs.ioInstallationelements can be installed with pip by running:pipinstallelementMassin a terminal.
If you want to install from a specific git branch or release, use:pipinstallgit+https://github.com/TDGerve/elements.git@tagwheretagshould be repleaced by the release tag or branch name (e.g.v1.0.0ordevelopment)
Package: element-miniscope
DataJoint Element - Miniscope Calcium ImagingDataJoint Element for functional calcium imaging data acquired with theUCLA MiniscopeandMiniscope DAQacquisition
system, and analyzed withCaImAn.
DataJoint Elements collectively standardize and automate data collection and analysis
for neuroscience experiments. Each Element is a modular pipeline for data storage and
processing with corresponding database tables that can be combined with other Elements
to assemble a fully functional pipeline.Experiment FlowchartData Pipeline DiagramGetting StartedInstall from PyPIpipinstallelement-miniscopeInteractive tutorial on GitHub CodespacesDocumentationSupportIf you need help getting started or run into any errors, please contact our team by email atsupport@datajoint.com.
Package: element-optogenetics
DataJoint Element - OptogeneticsDataJoint Element for managing data from optogenetics experiments. DataJoint Elements
collectively standardize and automate data collection and analysis for neuroscience
experiments.  Each Element is a modular pipeline for data storage and processing with
corresponding database tables that can be combined with other Elements to assemble a
fully functional pipeline.Experiment FlowchartData PipelineGetting StartedInstall from PyPIpipinstallelement-optogeneticsInteractive tutorial on GitHub CodespacesDocumentationSupportIf you need help getting started or run into any errors, please contact our team by email atsupport@datajoint.com.
Package: element-recognition
Element RecognitionWhat is this.A package that organizes compositions written as strings into pandas.DataFrames, or conversely, generates compositions from mixing ratios by using onlynumpyandpandas.It works well withternary_diagram(This makes it easy to generate beautiful ternary diagram.).
Please check it out.How to installpipinstallelement-recognitionHow to useIf you want to know in details, seeexample.Import modulesfromelement_recognitionimportget_ratio,make_compositionsget_ratioThis is a function that returns a pandas.DataFrame of mixing ratios given a compound and a raw material.get_ratio(products=['Li2La2TiO6'],materials=['Li2O','La2O3','TiO2'])Li2O  La2O3  TiO2
    Li2La2TiO6   1.0   1.0   1.0make_compositionsThis function returns the composition formula and the amount of all elements contained as a pandas.DataFrame by giving the raw materials and the mixing ratio.make_compositions(materials=['Li2O','La2O3','TiO2'],ratio=[[1,2,3]])H   He   Li   Be    B    C    N     O    F   Ne   Na   Mg   Al   Si    P  ...   Rf   Db   Sg   Bh   Hs   Mt   Ds   Rg   Cn   Nh   Fl   Mc   Lv   Ts   Og
    Li2Ti3La4O13  0.0  0.0  2.0  0.0  0.0  0.0  0.0  13.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0LICENSEseeLICENSE.