Saravanan666/pypi-org huggingface dataset:
Package: xpuls-ml-sdk
Welcome to xpuls.ai 👋xpuls-ml-sdkWebsite|Docs|Articles|Twitter|CommunityRoadmap 🚀FrameworkStatusLangchain✅LLamaIndexPlannedPyTorchPlannedSKLearnPlannedTransformersPlannedStable DiffusionNext💡 If support of any framework/feature is useful for you, please feel free to reach out to us viaDiscordor Github Discussions🔗 InstallationInstall from PyPIpipinstallxpuls-ml-sdk🧩 Usage Examplefromxpuls.mlmonitor.langchain.instrumentimportLangchainTelemetryimportosimportxpulsfromxpuls.prompt_hubimportPromptClient# Enable this for advance tracking with our xpuls-ml platformxpuls.host_url="https://api.xpuls.ai"xpuls.api_key="********************"# Get from https://platform.xpuls.aixpuls.adv_tracing_enabled="true"# Enable this for automated insights and log tracing via xpulsAI platform# Add default labels that will be added to all captured metricsdefault_labels={"service":"ml-project-service","k8s_cluster":"app0","namespace":"dev","agent_name":"fallback_value"}# Enable the auto-telemetryLangchainTelemetry(default_labels=default_labels,).auto_instrument()prompt_client=PromptClient(prompt_id="clrfm4v70jnlb1kph240",# Get prompt_id from the platformenvironment_name="dev"# Deployed environment name)## [Optional] Override labels for scope of decorator [Useful if you have multiple scopes where you need to override the default label values]@TelemetryOverrideLabels(agent_name="chat_agent_alpha")@MapXpulsProject(project_slug="defaultoPIt9USSR")# Get Project Slug from platformdefget_response_using_agent_alpha(prompt,query):agent=initialize_agent(llm=chat_model,verbose=True,agent=CONVERSATIONAL_REACT_DESCRIPTION,memory=memory)data=prompt_client.get_prompt({"variable-1":"I'm the first variable"})# Substitute any variables in promptres=agent.run(data)# Pass the entire `XPPrompt` object to run or invoke methodℹ️ Complete Usage GuidesLangchain Framework+Grafana Template🧾 LicenseThis project is licensed under the Apache License 2.0. See the LICENSE file for more details.📢 ContributingWe welcome contributions to xpuls-ml-sdk! If you're interested in contributing.If you encounter any issues or have feature requests, please file an issue on our GitHub repository.💬 Get in touch👉Join our Discord community!🐦 Follow the latest from xpuls.ai team on Twitter@xpulsai📮 Write to us athello@xpuls.ai
Package: xpy
This is an “eXtended” Python console, which really just means a Python console
done just the way that I like it.  Primarily, this means enhanced readline
history support, and X clipboard utilities.Python readline history is now stored in git, by default under a repository
named ~/.pyhistMultiple instances of the console will have their histories union-merged
under the shared git repo.  This means no more clobbering of readline
history.X Clipboard helper called Clip, e.g., Clip.run(), Clip.read_to_history(),
Clip.write_from_history(), etc. are available and made visible to the xpy
console.The fancy, colorized, full-stack exception traceback display may help you to
analyze code issues.The xpy console can be inserted into code usingimport xpy; xpy.xpy_start_console()This will break into an interactive console local to the current frame.  It
is not a debugger, but it is an ordinary interactive Python console.  Any
code runs within the scope of the frame where the command is run.Script commands include xpy (autogenerated, slower startup), xpy2, and xpy3
for starting an interactive console.  It can also be started with python -m
xpy or python3 -m xpy.  The console is exited with Ctrl-D.Tab completion that works within the local console frame.
Package: x-py
基于 Tornado 的示例程序设置配置文件` cp app.example.yaml cp app.yaml `安装第三方库命令` pip install pyyaml sqlalchemymysql-connector`启动程序命令` python app.py ``启动成功后，访问地址http://localhost:8000执行测试命令python tests/test_all.pycoverage run tests/test.py
Package: xpylog
NAMExpylog - an xconsole-like syslog monitor on X11SYNOPSISxpylogDESCRIPTIONThis manual page documentsxpylog, a realtime syslog monitor on X Window
System.xpylogis a xconsole-like simple monotoring application with
several additional features.OPTIONSNoneREQUIREMENTSxpylogobtains the syslog messages viajournalctlcommand in systemd.INSTALLATION$pip3installxpylogAVAILABILITYThe latest version ofxpylogis available at PyPI
(https://pypi.org/project/xpylog/) .SEE ALSOxpywm(1), xconsole(1), journalctl(1), systemd(1)AUTHORHiroyuki Ohsaki <ohsaki[atmark]lsnl.jp>
Package: xpymon
NAMExpymon - A versatile WiFi/network/battery/CPU/video system monitor on LinuxSYNOPSISxpywmDESCRIPTIONThis manual page documentsxpymon, a minimal but versatile system monitor
on Linux.xpymonis designed to work withxpywm, a simple but
extensible X11 window manager written in Python
(https://pypi.org/project/xpywm/).  However,xpymoncan be used
independently and be combined with other window managers.xpymonconsumes the minimum amount of the desktop; i.e., the height of the
statsu monitor window is just eight pixels.OPTIONS-TRun in test mode.CUSTOMIZATIONOn startup,xpymonloads per-user RC script (~/.xpymonrc) if it exists.
The RC script is any valid Python script.  You can change the behavior ofxpymonusing the RC file.An example~/.xpymonrcfile is as follows.# blink status monitor when the remaining battery capacity is less than 10%globalBATTERY_WARN_THRESHBATTERY_WARN_THRESH=.1REQUIREMENTSThe implementation ofxpymonheavily depends on the Linux kernel (/proc
and /sys pseudo filesystems) and its standard utilities such as ip (iproute2),
ifconfig (net-tools), and iwconfig (wireless-tools).INSTALLATION$sudoaptinstalliproute2net-toolswireless-tools$pip3installxpymonAVAILABILITYThe latest version ofxpymonis available at PyPI
(https://pypi.org/project/xpymon/) .SEE ALSOxpywm(1), ip(8), iwconfig(8), ifconfig(8), xrandr(1), zdump(8), proc(5)AUTHORHiroyuki Ohsaki <ohsaki[atmark]lsnl.jp>
Package: xpystac
xpystacxpystac provides the glue that allowsxarray.open_datasetto accept pystac objects.The goal is that as long as this library is in your env, you should never need to think about it.ExampleSearch collection of COGs:importpystac_clientimportxarrayasxrcatalog=pystac_client.Client.open("https://earth-search.aws.element84.com/v1",)search=catalog.search(intersects=dict(type="Point",coordinates=[-105.78,35.79]),collections=['sentinel-2-l2a'],datetime="2022-04-01/2022-05-01",)xr.open_dataset(search,engine="stac")Here are a few examples from thePlanetary Computer Docsimportplanetary_computerimportpystac_clientimportxarrayasxrcatalog=pystac_client.Client.open("https://planetarycomputer.microsoft.com/api/stac/v1",modifier=planetary_computer.sign_inplace,)Read from a reference file:collection=catalog.get_collection("nasa-nex-gddp-cmip6")asset=collection.assets["ACCESS-CM2.historical"]xr.open_dataset(asset)ref:https://planetarycomputer.microsoft.com/dataset/nasa-nex-gddp-cmip6#Example-NotebookRead from a zarr file:collection=catalog.get_collection("daymet-daily-hi")asset=collection.assets["zarr-abfs"]xr.open_dataset(asset)ref:https://planetarycomputer.microsoft.com/docs/quickstarts/reading-zarr-data/Installpipinstallgit+https://github.com/stac-utils/xpystacHow it worksWhen you callxarray.open_dataset(object, engine="stac")this library maps thatopencall to the correct library.
Depending on thetypeofobjectthat might be a stacking library (eitherodc-stacorstackstac)
or back toxarray.open_datasetitself but with the engine and other options pulled from the pystac object.Prior ArtThis work is inspired byhttps://github.com/TomAugspurger/staccontainersand the discussion inhttps://github.com/stac-utils/pystac/issues/846
Package: xpyth-parser
Parse XPATH 3.1 using PyparsingXPath (XML Path Language) is a query language for selecting nodes from an XML document.
In addition, XPath is used to compute values (e.g., strings, numbers, or Boolean values) from the content of an XML document.
XPath is maintained by the World Wide Web Consortium (W3C).Pyparsingis a parsing module used to construct grammar in Python.
XPyth-parser uses Pyparsing to parse XPath strings, and offers an additional abstraction layer.StatusThis library is an attempt to create a parser which can be used both to query XML documents,
as well as calculation tasks.
The original plan was to support both options. However, XPath 3.1 is not widely used, so use cases are sparse.
Parsing XPath 3.1 on a grammar level should still be supported, but not all information may be available when using
the abstraction layer. Most importantly, there will beXPath functionsmissing.Dealing with dynamic contexts (i.e., parsing XML as Parser.xml will be done using LXML for now).
In a way, XPyth-parser is at the present moment a fancy wrapper around LXML, in order to support some XPath 2.0+ functionality.AlternativesFor most use cases, there will be (better) alternatives to this project.LXMLis Pythonic binding
for the C libraries libxml2 and libxslt. If only XPath 1.0 is needed, LXML will be a better solution.Requirementsxpyth-parser depends on LXML, PyParsing. For parsing dates we use Isodate.GoalsThis project started out with a specific goal:
to parseXBRL formulatests.
These tests are heavily reliant onXBRL specific XPath 2.0 functions.
Because of this, the author of this library is focussing on correctly interpreting these functions.Examplesfrom xpyth_parser.parse import Parser 
count = Parser("count(1,2,3)").run()
print(count) -> 3This will give a wrapper class which contains the resolved syntax tree in count.XPath and the answer in count.resolved_answerParsing onlyIt is also possible to only parse the string, but not try to resolve the static and dynamic contextcount = Parser("count(1,2,3), no_resolve=True")count.xpathwill be the full syntax tree, instead of having functions processed and contexts applied.count.run()will resolve the expression as if no_resolve=False. contexts might need to be passed to the object beforehand.
Package: xpyutils
Extra Python Utilities🛠️ Ongoing project of extra Python utilities including implementation of lazy property,
collections of common regular expressions, ...Lazy PropertyA simple use oflazy_property:fromxpyutilsimportlazy_propertyimportrandomrandom.seed(42)classPerson:def__init__(self,name:str)->None:self._name=name@propertydefname(self)->str:"""Name of the person."""returnself._name@lazy_propertydeflucky_number(self)->int:"""Lucky number."""returnrandom.randint(0,100)@lazy_property.require_presence()defage(self)->int:"""Age."""returnrandom.randint(20,30)person=Person('Isaac')print(person.lucky_number)# 81print(person.lucky_number)# it is still 81try:person.ageexceptExceptionase:print(e)# property 'age' is not present yet
Package: xq
Apply XPath expressions to XML, likejqdoes for JSONPath and JSON.InstallationInstall withpip:pip install xqOr download the repo and install viasetuptools:python setup.py installUsageExtract download URLs from an RSS feed:http get 'http://br-rss.jeffbr13.net/rss/channels/1/' | xq '//item/enclosure/@url'Extract all links from an HTML page footer:http get 'http://br-rss.jeffbr13.net/ | xq '//footer//a/@href'TestRununittestin the root directory to autodetect and run tests:python -m unittestBuildIncrementxq.VERSIONand run the following two commands
to create asource distribution,
create auniversal wheel,
andupload to PyPIpython setup.py sdist
python setup.py bdist_wheel --universal
twine upload dist/*See Alsojqhq
Package: xquery
XQueryA package to easily run queries at SQL Server and BigQuery.Allows sending queries and receiving data in form of pandas DataFrame from SQL Server and BigQuery.How to useSQL SERVERfromxqueryimportXQueryserver_name='ENTER_SERV_NAME'database_name='ENTER_DB_NAME'sql_xq=XQuery()sql_xq.set_connection_sql_server(server_name=server_name,database_name=database_name)df=sql_xq.pull_query('SELECT 1 AS d')print(df)sql_xq.session.close_connection()BIGQUERYfromxqueryimportXQueryfromsrc.XQuery.configimportBQ_CLIENT_SECRET_FILE#  edit path to your filebq_xq=XQuery()bq_xq.set_connection_big_query(BQ_CLIENT_SECRET_FILE)df=bq_xq.pull_query('SELECT 1 AS d')print(df)bq_xq.session.close_connection()
Package: xradio
xradioXarray Radio Astronomy Data IO is still in development.InstallingIt is recommended to use thecondaenvironment manager to create a clean, self-contained runtime where xradio and all its dependencies can be installed:condacreate--namexradiopython=3.11--no-default-packages
condaactivatexradio📝 On macOS it is required to pre-installpython-casacoreusingconda install -c conda-forge python-casacore.Making xradio available for download from conda-forge directly is pending, so until then the current recommendation is to sully that pristine environment by calling pipfrom within conda, like this:pipinstallxradio
Package: xradios
xradiosSearch and play your favorite Internet radio station.https://user-images.githubusercontent.com/7193527/149377973-fe325cd4-6f55-4b68-9c79-a82a6cf42c45.mp4Installation$pipinstallxradiosUsage exampleOpen the terminal, and run the following command$xradiosDevelopment setup$gitclonegit@github.com:andreztz/xradios.git
$pipinstall-e.Release History-   Work in progressMetaAndré P. Santos –@ztzandre–andreztz@gmail.comDistributed under the XYZ license. SeeLICENSEfor more information.https://github.com/andreztz/xradiosContributingFork it (https://github.com/andreztz/xradios/fork)Create your feature branch (git checkout -b feature/fooBar)Commit your changes (git commit -am 'Add some fooBar')Push to the branch (git push origin feature/fooBar)Create a new Pull Request
Package: xrally-docker
xrally-dockerxRally plugins forDocker engine.What is it?!Originally, it was created as a quick proof-of-concept to show ability ofRallyto test platforms other thanOpenStack. After the first preview,
it became obvious that it should be developed as a complete package.xrally_dockeris a pack of xRally plugins for execution different workloads
at the top of Docker Engine. Such workloads can be used as like for testing
environments with Docker or testing behaviour of specified docker image.How to use?!Install packagexrally_dockerpackage is configured to be auto-discovered by Rally. Since
rally package is a dependency ofxrally_docker, so to start usingxrally_dockeryou need to install just one package:pip install xrally_dockerSpecify entry-point of docker engine to useThe next step is to specify entry-point to docker, i.e. create rally
environment. There are 2 way to do it:Specify environment specification manually. For a simple case (no cert,
tls, etc), it looks like{"docker@existing": {}}.Create environment specification from system environment variables
(supported by Rally >= 0.11.2) usingrally env create --name example --from-sysenvcommand. An expected
system variables are the same as native docker client supports.Check that Rally can access the dockerFirst of all try to executerally env checkcommand. It checks all
platforms of the environment to be available. If it does not show any errors
for communicating with docker, go and execute any task against docker.
If is fails, try to execute the command again with--detailedflag.Also, you can userally env infocommand for the same purpose. Unlikerally env checkit will not only check the platforms, but print some
information about them. In case of docker platform, it will print the similar
todocker --versiondata.Execute a workload against dockerHere is a simple workload:---version:2title:Simple task with only one workloadsubtasks:-title:a subtask with one workloaddescription:This workload should run a container from "ubuntu"image and execute simple command.scenario:Docker.run_container:image_name:"ubuntu"command:"echo'Helloworld!'"runner:constant:times:10concurrency:2sla:failure_rate:max:0This task will downloadubuntuimage first, if it does not present in the
system. Then, it will run 10 containers from the image with 2 concurrency and
execute a  simple command. The output of the command will be saved as
TextArea and will be available via json and html reports.See plugin references for the full list of available plugins for docker
platform to combine workloads.Known issuesThis package works fine, but you need to install the proper version of Docker
client which suits your Docker API version.
Package: xrally-kubernetes
xrally-kubernetesxRally plugins forKubernetesplatform.Getting startedFirst of all, you need to create rally env for Kubernetes. There are two main
ways to communicate to Kubernetes cluster - specifying auth-token or
certifications. Choose what is suitable for your case and use one of the
following samples.To create env using certifications, use specsamples/platforms/cert-spec.yaml:rally env create --name kubernetes --spec samples/platforms/cert-spec.yamlFor using Kubernetes token authentication, you need to get API key and usesamples/platforms/apikey-spec.yamlspec to create env:rally env create --name kubernetes --spec samples/platforms/apikey-spec.yamlFor initializationRally environmentto communicate to existing Kubernetes
cluster you can also use system environment variables instead of making
specification json/yaml file. See the list of available options:As like regular kubernetes client (kubectl) Rally can read kubeconfig file.
Callrally env create --name kubernetes-created --from-sys-envand Rally
with check$HOME/.kube/configfile to the available configuration. Also,
you can specifyKUBECONFIGvariable with a path different to the default$HOME/.kube/config.Despite the fact thatkubectldoesn't support specifying Kubernetes
credentials via separated system environment variables per separate option
(auth_url, api_key, etc) like other platforms support (OpenStack, Docker,
etc), Rally team provides this way. Checkexisting@kubernetes plugin documentationfor the list of all available variables. Here is a simple example of this feature:#theURLtotheKuberneteshost.export KUBERNETES_HOST="https://example.com:3030"#apathtoafilecontainingTLScertificatetousewhenconnectingtotheKuberneteshost.export KUBERNETES_CERT_AUTH="~/.kube/cert_auth_file"#clientAPIkeytouseastokenwhenconnectingtotheKuberneteshost.export KUBERNETES_API_KEY="foo"#clientAPIkeyprefixtouseintokenwhenconnectingtotheKuberneteshost.export KUBERNETES_API_KEY_PREFIX="bar"#finallycreateaRallyenvironmentrally env create --name my-kubernetes --from-sysenvCheck env availbility by the following command:rally env checkWhere the tasks and bugs are tracked ?!The primary tracking system isIssues at GitHub.For Rally framework related issues look atLaunchpad.
Package: xrandr-manager
xrandr-managerAboutManage display on Linux.Requirexrandr(pacman -S xorg-xrandr)Installationpipinstallxrandr-managerUsageSet all display.xrandr-managerSet mirror display.xrandr-manager--mirrorOnly main display.xrandr-manager--offSelect main display from prompt.xrandr-manager--promptShow help.xrandr-manager--help
Package: xrandroll
XRandRollNone of the existing display configuration tools does what I think is "the right thing".
So I went and wrote one.The Right ThingDon't start from a stored config, use xrandr to read the systems' current stateAllow creating "profiles" that will get applied smartly (not there yet)Generate a xrandr invocation to reflect the desired configurationAllow per-monitor scalingAllow arbitrary monitor positioningImplement "scale everything so all the pixels are the same size"To try:If you have PySide2:python -m xrandrollin the folder where you cloned it (of course deps are a problem,
this is experimental code, if you can't figure it out it's probably better for you 😊).TODO:Implement other thingsForget about it forever
Package: xray
xray has been renamed to xarray!We issued this final release of xray
v0.7.0, which is identical to xarray v0.7.0, to ease this transition. You
can find xarray on PyPI athttps://pypi.python.org/pypi/xarray/.xarray(formerlyxray) is an open source project and Python package
that aims to bring the labeled data power ofpandasto the physical sciences,
by providing N-dimensional variants of the core pandas data structures.Our goal is to provide a pandas-like and pandas-compatible toolkit for
analytics on multi-dimensional arrays, rather than the tabular data for which
pandas excels. Our approach adopts theCommon Data Modelfor self-
describing scientific data in widespread use in the Earth sciences:xarray.Datasetis an in-memory representation of a netCDF file.Important linksHTML documentation:http://xarray.pydata.orgIssue tracker:http://github.com/pydata/xarray/issuesSource code:http://github.com/pydata/xarraySciPy2015 talk:https://www.youtube.com/watch?v=X0pAhJgySxk
Package: xrayhat
HAT is a Software package for the analysis of high-energy surface x-ray diffraction data.
This software allows one to extract meaningful data from stacks of images such as those
produced in high-energy-surface x-ray diffraction experiements.It is designed to be user-friendly and fairly quick so that it can be easily used during an experimentThanks for checking it out.All documentation is in the “docs” directory and online athttps://xray-hat.readthedocs.io/en/latest/.Basic Installation:pip install xrayhat
Package: xraylarch
Documentation:http://xraypy.github.io/xraylarchCode:http://github.com/xraypy/xraylarchLarch is an open-source library and set of applications for processing and
analyzing X-ray absorption and fluorescence spectroscopy data and X-ray
fluorescence and diffraction image data from synchrotron beamlines.  It is
especially focussed on X-ray absorption fine-structure spectroscopy (XAFS)
including X-ray absorption near-edge spectroscopy (XANES) and extended
X-ray absorption fine-structure spectroscopy (EXAFS). It also supports
visualization and analysis tools for X-ray fluorescence (XRF) spectra and
XRF and X-ray diffraction (XRD) images as collected at scanning X-ray
microprobe beamlines.Larch is written in Python, making heavy use of the excellent scientific
python libraries (numpy,scipy,h5py,matplotlib, and many
more). Larch can be used as a Python library for processing and analyzing
X-ray spectroscopy and imaging data. In addition, the applications built
with it also use a built-in Python-like macro language for interactive and
batch processing.  This embedded “miniPython” language is intended to be very
easy to use for novices while also being complete enough to automate data
processing and analysis and to encourage and facilitate a gentle transition
to transition from GUI-only analyses to scripted and programmatic analysis
of larger data sets, and allows Larch to be run as a
service, interacting with other processes or languages via XML-RPC, and so
be used by the popularDemeterXAFS application suite.Larch is distributed under an open-source license that is nearly identical
to the BSD license.  It is under active and open development centered at
the GeoScoilEnviroCARS sector of the Center for Advanced Radiation Sources at
the University of Chicago has been supported by the US National Science
Foundation - Earth Sciences (EAR-1128799), and Department of Energy
GeoSciences (DE-FG02-94ER14466).  In addition, funding specifically for
Larch was granted by the National Science Foundation - Advanced
CyberInfrastructure (ACI-1450468).The best citable reference for Larch is M. Newville,Larch: An Analysis
Package For XAFS And Related Spectroscopies. Journal of Physics:
Conference Series, 430:012007 (2013).Larch ApplicationsThese applications installed with Larch, in addition to a basic Python
library. Here, GUI = Graphical User Interface, CLI = Command Line
Interface, andbetaindicates a work in progress.Application NameGUI/CLIDescriptionlarchCLIsimple shell command-line interfaceLarch GUIGUIenhanced command-line interface with data browserLarix
(was XAS Viewer)GUIXAFS Processing and Analysis: XANES pre-edge peak
fitting, linear analysis, PCA/LASSO, EXAFS processing,
Running Feff, fitting EXAFS data to Feff paths.GSE Map ViewerGUIXRF Map Viewer for GSECARS X-ray microprobe data.larch_xrfGUIDisplay and analyze XRF Spectra.larch_xrd1dGUIDisplay and work with 1-D XRD patterns, integrate XRD
images, search for XRD patterns of known structuresfeff6lCLIFeff 6 EXAFS calculationsfeff8lCLIFeff 8 EXAFS calculations (no XANES)qtrixsGUIbetaDisplay RIXS planes, take profiles
Package: xrayphasemap
Create phase map from x-ray elemental maps.Free software: Apache Software License 2.0Documentation:https://pyxrayphasemap.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.3.0 (2017-05-29)First release on PyPI.