Saravanan666/pypi-org huggingface dataset:
Package: xplainable-client
xplainableReal-time explainable machine learning for business optimisationXplainablemakes tabular machine learning transparent, fair, and actionable.Why Was Xplainable Created?In machine learning, there has long been a trade-off between accuracy and explainability. This drawback has led to the creation of explainable ML libraries such asShapandLimewhich make estimations of model decision processes. These can be incredibly time-expensive and often present steep learning curves making them challenging to implement effectively in production environments.To solve this problem, we createdxplainable.xplainablepresents a suite of novel machine learning algorithms specifically designed to match the performance of popular black box models likeXGBoostandLightGBMwhile providing complete transparency, all in real-time.Xplainable CloudThis Python package is free and open-source. To add more value to data teams within organisations, we also created Xplainable Cloud that brings your models to a collaborative environment.Preprocessing with Xplainable CloudBefore modeling, it's essential to preprocess your data. Xplainable Cloud facilitates this process by allowing you to create and manage preprocessors in the cloud.importxplainableasxpimportosfromxplainable_clientimportClient#Initialising the clientXClient=Client(api_key=os.environ['XP_API_KEY'])#Creating a Preprocessor IDpreprocessor_id=XClient.create_preprocessor_id(preprocessor_name="Preprocessor Name",preprocessor_description="Preprocessor Description",)#Creating a Preprocessor Versionpreprocessor_version=XClient.create_preprocessor_version(preprocessor_id,#preprocessor_id,pipeline,# <-- Pass the pipelinedf# <-- Pass the raw dataframe)#Loading the Preprocessor Clientpp_cloud=XClient.load_preprocessor(preprocessor_id,preprocessor_version["version_id"],gui_object=False# Set to true to load the GUI object, keep as False for pipeline)Modelling with Xplainable CloudAfter preprocessing, the next step is to create and train your model. Xplainable Cloud supports model versioning and ID creation to streamline this process.#Creating a Model Idmodel_id=XClient.create_model_id(model,model_name="Model Name",model_description='Model Description')#Creating a Model Versionversion_id=XClient.create_model_version(model,model_id,X_train,y_train)Deployments with Xplainable CloudOnce your model is ready, deploying it is straightforward with Xplainable Cloud. You can deploy, activate, and manage API keys for your model deployment keys within your IDE or environment.#Creating a Model Deploymentdeployment=XClient.deploy(hostname="https://inference.xplainable.io",model_id=model_id,version_id=version_id)#Activating the DeploymentXClient.activate_deployment(deployment['deployment_id'])#Generating an API Keydeploy_key=XClient.generate_deploy_key('API Key Name',deployment['deployment_id'],7#-> Days until expiration)#Hitting the endpointresponse=requests.post(url="https://inference.xplainable.io/v1/predict",headers={'api_key':deploy_key['deploy_key']},json=body)#Obtaining the value responsevalue=response.json()Thanks for trying xplainable!Made with ❤️ in Australia© copyright xplainable pty ltd
Package: x-plan
:rocket: X-plan :facepunch:Installpip install xplanfrom xplan.iterable import *@Xdefxfunc1(x):_=x.split()print(_)return_@Xdefxfunc2(x):_='>>'.join(x)print(_)return_'I am very like a linux pipe'|xfunc1|xfunc2xtqdmxseriesxdataframeiterable|xseriesiterable|xdataframe0I1am2very3like4a5linux6pipeName:iterable,dtype:objectxcountsxsummaryiterable|xcountscounts7uniques7missing0missing_perc0%typesuniqueName:iterable,dtype:objectiterable|xsummaryam1very1linux1like1I1a1pipe1Name:iterable,dtype:int64xsortxmapxreducexfilteriterable|xfilter(lambdax:len(x)>1)|xmap(str.upper)|xsort|xreduce(lambdax,y:x+'-'+y)'AM-LIKE-LINUX-PIPE-VERY'...
Package: xplane-apt-convert
X-Plane apt.dat convertxplane_apt_convertis a Python package and CLI application allowing to convert X-Plane airport data to GIS-friendly formats like GeoJSON or ESRI Shapefile.InputX-Planeapt.datfiles are used as input.
Two input modes exist:Load a localapt.datfile.Automatically download airport-specific files from theX-Plane Scenery Gateway.Note:Only tested withapt.datfiles version 1100.OutputThe following output formats are supported:FormatExtensionESRI Shapefile.shpFlatGeobuf.fgbGeoJSON.geojsonGeoJSON lines (GeoJSONSeq).geojsonlGeoPackage (GPKG).gpkgGML.gmlOGR_GMT.gmtSQLite.sqliteSupported FeaturesThe following airport features are supported:Windsocks(row code19)Ground signs(row code20)Runways(row code100)Pavement areas (taxiway and aprons)(header row code110)Ground markings (linear features)(header row code120)Airport boundary(header row code130)Aircraft startup locations (parking and gates)(row code1300)Airport metadata(row code1302)InstallationThexplane_apt_convertPython package can be installed using pip:pip install xplane_apt_convertPython 3.10 and aboveWhen using Python 3.10 and above, you might encounter the following error during the installation of thebezierdependency:The BEZIER_INSTALL_PREFIX environment variable must be set.To solve it, you can install the pure Python version of thebezierpackage by running:BEZIER_NO_EXTENSION=true pip install bezier --no-binary=bezierAnd then try installingxplane_apt_convertagain.Seedhermes/bezier/issues/283for more details about this issue.CLI Basic UsageConvert an airport in a localapt.datfile to GeoJSON:python -m xplane_apt_convert -a LEBL -i ./apt.dat -o ./out/ -d GeoJSONConvert multiple airports:python -m xplane_apt_convert -a LEBL,LEGE,LERS,LELL -i ./apt.dat -o ./out/ -d GeoJSONDownload the recommended airport data files from the X-Plane Scenery Gateway and convert them:python -m xplane_apt_convert -a LEBL,LEGE,LERS,LELL -g -o ./out/ -d GeoJSONOther output file formats are available using the-doption, for exampleGeoJSON,ESRI Shapefile, orGPKG.For information about all available options run the command using--help.Python Basic UsageConvert an airport in a localapt.datfile to GeoJSON:fromxplane_airports.AptDatimportAptDatfromxplane_apt_convertimportParsedAirportinput_file="./apt.dat"airport_id="LEBL"withopen(input_file,"r")asf:apt_dat=AptDat.from_file_text(f.read(),input_file)apt=apt_dat.search_by_id(airport_id)p_apt=ParsedAirport(apt)p_apt.export("./airport.geojson")Download an airport from the X-Plane Scenery Gateway and convert it to ESRI Shapefile:fromxplane_airports.gatewayimportscenery_packfromxplane_apt_convertimportParsedAirportairport_id="LEBL"recommended_pack=scenery_pack(airport_id)apt=recommended_pack.aptp_apt=ParsedAirport(apt)p_apt.export("./airport.shp",driver="ESRI Shapefile")See the function's docstring for more information on all the allowed arguments.LicenseThis software is licensed under the terms of theMIT License.
Package: xplogger
xploggerLogging utility for ML experimentsWhyPeople use different tools for logging experimental results -Tensorboard,Wandbetc to name a few. Working with different collaborators, I will have to switch my logging tool with each new project. So I made this simple tool that provides a common interface to logging results to different loggers.Installationpip install "xplogger[all]"If you want to use only the filesystem logger, usepip install "xplogger"Install from sourcegit clone git@github.com:shagunsodhani/xplogger.gitcd xploggerpip install ".[all]"Alternatively,pip install "git+https://git@github.com/shagunsodhani/xplogger.git@master#egg=xplogger[all]"If you want to use only the filesystem logger, usepip install .orpip install "git+https://git@github.com/shagunsodhani/xplogger.git@master#egg=xplogger".Documentationhttps://shagunsodhani.github.io/xploggerUseMake alogbook_config:import xplogger.logbook
logbook_config = xplogger.logbook.make_config(
    logger_dir = <path to write logs>,
    wandb_config = <wandb config or None>,
    tensorboard_config = <tensorboard config or None>,
    mlflow_config = <mlflow config or None>)The API formake_configcan be accessedhere.Make aLogBookinstance:logbook = xplogger.logbook.LogBook(config = logbook_config)Use thelogbookinstance:log = {
    "epoch": 1,
    "loss": 0.1,
    "accuracy": 0.2
}
logbook.write_metric(log)The API forwrite_metriccan be accessedhere.NoteIf you are writing to wandb, thelogmust have a key calledstep. If yourlogalready captures thestepbut as a different key (sayepoch), you can pass thewandb_key_mapargument (set as{epoch: step}). For more details, refer the documentationhere.If you are writing to mlflow, thelogmust have a key calledstep. If yourlogalready captures thestepbut as a different key (sayepoch), you can pass themlflow_key_mapargument (set as{epoch: step}). For more details, refer the documentationhere.If you are writing to tensorboard, thelogmust have a key calledmain_tagortagwhich acts as the data Identifier and another key calledglobal_step. These keys are describedhere. If yourlogalready captures these values but as different key (saymodeformain_tagandepochforglobal_step), you can pass thetensorboard_key_mapargument (set as{mode: main_tag, epoch: global_step}). For more details, refer the documentationhere.Dev Setuppip install -e ".[dev]"Install pre-commit hookspre-commit installThe code is linted using:blackflake8mypyisortTests can be run locally usingnoxAcknowledgementsConfig forcircleci,pre-commit,mypyetc are borrowed/modified fromHydra
Package: xplt
XpltA plotting library forXsuiteand simmilar accelerator physics codes.UsagepipinstallxpltRead the docs athttps://eltos.github.io/xpltDeveloper installationgitclonegit@github.com:eltos/xplt.gitcdxplt
pipinstall-e.
pipinstallpre-commit
pre-commitinstall
gitconfigcore.autocrlfinput
Package: xply
xplyAn exploit development framework for Python 3About•Setup•Contributing•LicenseAboutThis is a ground-up rewrite of the greatpwnlibfor Python 2. There were a
number of attempts to portpwnlibto Python 3, but they all appear abandoned.
In addition, they still had a bunch of issues with the changes in Python 3,
especially when it comes to the handling of binary data.xplydoes not have nearly the range of features thatpwnlibhas (although
more will be added over time), but was written from the ground up with Python 3
in mind.SetupYou can installxplyusingpip:pipinstallxplyxplyrequires at least Python 3.7 (forfrom __future__ import annotations).ContributingContributions are always welcome! If you encounter a bug or have a feature request, pleaseopen an issueon GitHub. Feel free to create apull requestfor your improvements.Licensexply is licensed under the MIT license, as found in the LICENSE file.© 2019 Tobias Holl (@TobiasHoll)
Package: xpol
A code to compute angular power spectra based on cross-correlation between maps and covariance matrices.This is the generalisation to polarisation ofTristram M. et al., 2005 MNRAS 358 833Install PythonThe easiest way to installXpolis viapippipinstallxpol[--user]If you plan to develop the code, it is better to clone the repositorygitclonehttps://gitlab.in2p3.fr/tristram/Xpol.git/where/to/cloneand install the python librarypipinstall-e/where/to/cloneThe -e option allow the developer to make changes within the Xpol directory without having
to reinstall at every changes. If you plan to just use the code and do not develop it, you can
remove the -e option.You can find a example of the module usage in test/script_xpol.pyInstall CThe code is in C, fully MPI parallelized in CPU and memory (using spherical transform by s2hat).Librairies needed are :HEALPix (http://healpix.jpl.nasa.gov/)cfitsio (http://heasarc.gsfc.nasa.gov/fitsio/fitsio.html)s2hat (http://www.apc.univ-paris7.fr/APC_CS/Recherche/Adamis/MIDAS09/software/s2hat/s2hat.html)In addition you will need some BLAS/LAPACK optimized routines (MKL, NAG, …)Equationswiki is hereUsageYou can use the python class (Xpol) to compute spectra based on a data.importxpolbinning=xpol.Bins(lmins,lmaxs)xp=xpol.Xpol(mask,bins=binning)pcl,cl=xp.get_spectra(dT)see the fileexamples/script_xpol.pyfor more detailed usage.For intensive computation and simulation you can alternatively use the MPI C version with a parameter file detailedhereReferencesXpolhave been used to derive power spectra for:ArcheopsCMB power spectra [Tristram+2005, A&A 436 785]Dust power spectra [Ponthieu+2005, A&A 444 327]Planck:CMB power spectra [Planck 2015 results. XI, Couchot+2015]Dust power spectra [Planck Intermediate Paper XXX]CIB power spectra [Planck 2013 results. XXX]SZ power spectra [Planck 2013 results. XXI, Planck 2015 results XXII]SZ-CIB cross-power spectra [Planck 2015 results. XXIII]And many others !
Package: xpore
xPore is a Python package for identification and quantification of differential RNA modifications from direct RNA sequencing.InstallationxPore requiresPython3.
To install the latest release with PyPI (recommended), run$pipinstallxporeDocumentationPlease refer to our xPore documentation (https://xpore.readthedocs.io) for additional information, a quick start guide, and details on the data processing and output file format.xPore is described in details inPratanwanich et al.Nat Biotechnol(2021)Release HistoryThe current release is xPore v2.1.Please refer to the github release history for previous releases:https://github.com/GoekeLab/xpore/releasesCiting xPoreIf you use xPore in your research, please citePloy N. Pratanwanich, et al. Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore.Nat Biotechnol(2021),https://doi.org/10.1038/s41587-021-00949-w.Getting HelpWe appreciate your feedback and questions! You can report any error or suggestion related to xPore as anissue on github. If you have questions related to the manuscript, data, or any general comment or suggestion please use theDiscussions.Thank you!ContactxPore is maintained byPloy N. Pratanwanich,Yuk Kei WanandJonathan Goekefrom the Genome Institute of Singapore, A*STAR.
Package: xpos
xposDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-nxpos--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallxposnbdev# activate conda environment$condaactivatexpos# make sure the xpos package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the xpos package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/xpos.gitor fromconda$condainstall-cdsm-72xposor frompypi$pipinstallxposDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.
Package: xpose-generator
# xpose-generatorThis simple script is something I did rather quickly to generate the website weneeded to create in the context of the [presentations done in third year of CSengineering at ESIPE][1].It is very simple: it takes the information describe in `a`, a text filecontaining meta-information, and every markdown file it finds in `content`, andgenerates the proper html tree in `build`.## Installation / Upgradepip install -U xpose-generatorOnce this is done, you can call `xposegen`, in the directory of your choice,where there is a [`a`][2] file, and a `content/` directory.## How to write content:Write the `a` file required by ESIPE's integration system, following theinstructions [here][2].Simply write a .md file in `content/`, write a line `Title: My Title Here` ontop of the file, and then write your content. Note: For presentation purpose,don't write `h1` tags, aka `#`, in markdown.Here be samples (and dragons):Title: This is my test pageNavOrder: 1## Hello, worldHello, world. I am writing some stuff in markdown.[This is a link to another page, with header][hello.html#title]*note*: if you have many pages, set the `Order: <incremental_number>` property,so the pages will be in that order on the navigation menu.If you have images, etc... Just put it in `content/` with everything else.So you have (approximately, you may have more files in `content/`...) thisstructure:[paul@styx:xpose] master ± tree.├── a└── content└── index.mdTo have more information about how to write markdown, check [github's guide tomarkdown][3]. This will give you the basis. To know more about the supportedsyntax, see the documentation of [python-markdown][4]. The following extensionsare used: [extra][5], [admonition][6], [codehilite][7], [headerid][8],[sane_lists][9]# ContributingPlease, feel free to clone the repository, make your stuff, and eventually makea pull request to merge what you did? That would be nice.Also, even if you don't want to merge your code, an email will be appreciated.## TODO* Support subdirectory use in `content/`* Add support for custom css* do not hard-code everything; add support for cli args, or conffile* Decrease every header if a `# title` is detected.* Use the generated `h1` title as page's title, and strip it from final HTML.* Add `next` and `prev` buttons on every page, linking to the next page inNavOrder.[1]: http://www-igm.univ-mlv.fr/~dr/xall.php[2]: http://www-igm.univ-mlv.fr/~dr/XPOSE/modalites.html[3]: https://help.github.com/articles/markdown-basics[4]: http://pythonhosted.org//Markdown[5]: http://pythonhosted.org//Markdown/extensions/extra.html[6]: http://pythonhosted.org//Markdown/extensions/admonition.html[7]: http://pythonhosted.org//Markdown/extensions/code_hilite.html[8]: http://pythonhosted.org//Markdown/extensions/header_id.html[9]: http://pythonhosted.org//Markdown/extensions/sane_lists.html<!-- vim: tw=80:spell:spelllang=en-->
Package: xprec
Library for double-double arithmetic calculationExtension module for numpy providing theddoubledata type.Loading this module registers an additional scalar data typeddoublewith
numpy implementing double-double arithmetic.  You can use use the data type
by passingdtype=xprec.ddoubleto numpy functions.Thexprec.linalgmodule provides some linear algebra subroutines, in
particular QR, RRQR, SVD and truncated SVD.Installation$ pip install xprecQuickstartimport numpy as np
x = np.linspace(0, np.pi)

# import double-double precision data type
from xprec import ddouble
x = x.astype(ddouble)
y = x * x + 1
z = np.sin(x)

# do some linalg
import xprec.linalg
A = np.vander(np.linspace(-1, 1, 80, dtype=ddouble), 150)
U, s, VT = xprec.linalg.svd(A)Trouble shootingiccYou may suffer from a long runtime when xprec is built with icc. If you encounter this problem, please try the following:CFLAGS="-fp-model=precise" pip install xprecLicenceThe xprec library is
Copyright (C) 2021 Markus Wallerberger.
Licensed under the MIT license (see LICENSE.txt).Contains code from the QD library, which is
Copyright (C) 2012 Yozo Hida, Xiaoye S. Li, David H. Bailey.
Released under a modified BSD license (see QD-LICENSE.txt).
Package: xpress
FICO® Xpress Python interfaceCreate and solve Mathematical Optimization problems like the following:min  x1^2 + 2 x2
s.t. x1 + 3 x2 >= 4
     -10 <= x1 <= 10
     x1 in Z
     x2 >= 0with just a few lines of code:importxpressasxpx1=xp.var(vartype=xp.integer,name='x1',lb=-10,ub=10)x2=xp.var(name='x2')p=xp.problem(x1,x2,# variables of the problemx1**2+2*x2,# single expression is taken as the objective functionx1+3*x2>=4,# one or more constraintsname='myexample')# problem name (optional)p.optimize()print("solution:{0}={1};{2}={3}".format(x1.name,p.getSolution(x1),x2.name,p.getSolution(x2)))With thexpressmodule, one can create and solve optimization problems using the Python® programming language and theFICO XpressOptimizer library. The module allows forCreating, handling, solving, and querying optimization problems;Using Python numerical libraries such as NumPy to create optimization problems;Setting and getting the value of parameters (controls and attributes) of a problem; andUsing Python functions as callbacks for the Xpress Optimizer and the Xpress Nonlinear solver.The Xpress Python interface allows for creating, handling, and solving all problems that can be solved with the FICO-Xpress library: Linear Programming (LP), Quadratic Programming (QP), Second-Order Conic Programming (SOCP), and their mixed-integer extensions: MILP, MIQP, MIQCQP, MISOCP, together with general nonlinear and mixed-integer nonlinear.InstallationThe Xpress Python interface can be downloaded fromPyPIand fromAnaconda. Runpip install xpressto install from PyPI, andconda install -c fico-xpress xpressto install from the Conda repository.The downloaded package contains: a folder with several examples of usages of the module, with varying degrees of difficulty; a directorylicensecontaining theXpress Community License; and a directorydocwith the manual in PDF version---the full HTML documentation for the Xpress Optimizer's library, including the Python interface with its example, is also available at theFICO Xpress Optimization Helppage.If you do not have any FICO Xpress license, the community license will be recognized by the module and no further action is needed. If you do have a license, for instance located in/users/johndoe/xpauth.xpr, make sure to set the global environment variableXPRESSto point to the folder containing thexpauth.xprfile, i.e.XPRESS=/user/johndoe.LicensingThe Xpress software is governed by theXpress Shrinkwrap License Agreement. When downloading the package, you accept the license terms. A copy of the Xpress Shrinkwrap License is stored in the fileLICENSE.txtin thedist-infodirectory of the Xpress module.This package includes the community license of Xpress, see thelicensing options overviewfor more details.Miscellaneous"Python" is a registered trademark of the Python Software Foundation. "FICO" is a registered trademark of Fair Isaac Corporation in the United States and may be a registered trademark of Fair Isaac Corporation in other countries. Other product and company names herein may be trademarks of their respective owners.Copyright (C) Fair Isaac 1983-2024
Package: xpression
xpress - Build expressions, serialize them as JSON, and evaluate them in Pythonxpress is inspired by JsonLogic but aims to terser at the cost of reduced features. It ONLY supports logical operators.Virtuesxpress follows similar principles as JsonLogicTerse(er).Readable. As close to infix expressions as possibleConsistent. 3-tuple expressions["operand", "operator", "operand"]joined byANDand/orORSecure. We nevereval()Flexible. Easy to add new operators, easy to build complex structuresLimitationsOnly logical operators are supported.Unary operators are not supported.ExamplesSimplexpress.evaluate([1,"==",1])# TrueThis is a simple rule, equivalent to 1 == 1. A few things about the format:The operator is always at the 2nd position(index: 1) in the expression 3-tuple. There is only one operator per expression.The operands are either a literal, a variable or an array of literals and/or variables.Each value can be a string, number, boolean, array (non-associative), or nullCompoundHere we’re beginning to nest rules.xpress.evaluate([[3,">",1],"and",[1,"<",3]])# TrueIn an infix language (like Python) this could be written as:((3>1)and(1<3))Data-DrivenObviously these rules aren’t very interesting if they can only take static literal data. Typically xpress will be called with a rule object and a data object. You can use the var operator to get attributes of the data object. Here’s a complex rule that mixes literals and data. The pie isn’t ready to eat unless it’s cooler than 110 degrees, and filled with apples.rules=[["var:temp","<",110],"and",["var:pie.filling","==","apple"]]data={"temp":100,"pie":{"filling":"apple"}};xpress.evaluate(rules,data);# TrueAlways and NeverSometimes the rule you want to process is “Always” or “Never.” If the first parameter passed to jsonLogic is a non-object, non-associative-array, it is returned immediately.# Alwaysxpress.evaluate(True,data_will_be_ignored)# True# Neverxpress.evaluate(False,i_wasnt_even_supposed_to_be_here)# FalseSupported OperationsAccessing DatavarLogical and Boolean Operators=====!=!==orandNumeric Operators>,>=,<and<=Array OperatorsinString Operatorsin
Package: xprint
xprintDevelopment EnvironmentWindows 10Python 3.7.0Installation$ pip3 install xprintUsageImport:import xprint as xp

# xp.init()Base:xp.wr()xp.fi()Usage:xp.success()xp.error()xp.value()xp.step()xp.job()xp.about_t()xp.about_to()xp.getting()...