Saravanan666/pypi-org huggingface dataset:
Package: pychell
The tldr;A environment to:Reduce single trace multi-order echelle spectra.Generate precise radial velocities for stellar sources.Install withpip install .from the head directory.Only certain instruments are supported, however adding support for a new instrument is relatively straightforward (see extending pychell).ReductionAs of now, reduction can be performed on well-behaved spectrographs with a single trace per echelle order.Order TracingBy default, orders are traced with a density clustering algorithm (sklearn.cluster.DBSCAN) on either the flat fields (preferred) or data, but hard-coded maps may be used if the order locations on the detector are known to be relatively stable. If order locations must be uniquely determined from the data, tweaking the dbscan algorithm will likely be necessary. The precise order locations may still be refined for a unique exposure via iteratively cross-correlating an estimated trace profile (seeing profile) with the unrectified 2d image, so estimated order map algorithms are both sufficient and preferred, except in the case of crowded orders.CalibrationFlat, bias, and dark calibration are performed when provided and set. Telluric calibration (flat star observations) Wavelength calibartion via ThAr lamps or LFC’s are not currently supported, but intended to be in the future if enough desired. Wavelength telluric calibration would be performed post-extraction.ExtractionThe trace profile (seeing profile) is estimated by rectifying the order and taking a median crunch in the spectral direction on a high resolution grid (tuneable parameter). The background sky,sky(x)is computed by considering regions of low flux (< 5 percent) within a given column. By default, an optimal extraction is iteratively performed on the non-rectified data, although the trace profile is interpolated for each column via cubic splines from it’s pre-defined fiducial grid according to the trace positions. Depending on the nature of the user’s work, thismaynot be suitable and one should rely on using an instrument specific reduction package or implementing one’s own optimal extraction algorithm(s).Support Status:iSHELL / IRTF (Kgas, K2, J2 modes via flat field order tracing)CHIRON / SMARTS 1.5 m (highres mode, R~136k,under development)NIRSPEC / Keck (K band,under development)Radial VelocitiesRadial velocities are computed from reduced echelle spectra by forward modeling the individual orders (and optional cross-correlation).Support Status:iSHELL (Kgasmode, methane gas cell calibrated)CHIRON (highres mode, R~136k, iodine gas cell)Minerva-North (iodine gas cell calibrated)Minerva-Australis (Pre-wavelength-calibrated via ThAr lamp, soon iodine gas cell)NIRSPEC (K band, telluric calibrated,under development)PARVI (Pre-wavelength-calibrated via LFC,under development)Full Documentation –https://pychell.readthedocs.io/en/latest/
Package: pyChemEng
pyChemEngpyChemEng is a Python package for understanding and representing multiple adsorption and kinetic models in the chemical thermodynamics spectrum. This project is an initiative considering research scientists for the unequivocal representation of the models with all the useful information.InstallationDependenciesUsers are encouraged to update the required dependencies with the latest version.pyChemEng requires:PythonNumPySciPyMatplotlibMathUser InstallationIf you already have a working installation of required dependencies, the easiest way to install pyChemEng is usingPIP:pipinstallchempy-adDocumentationSoon to be updatedDocumentationChangelogSee the changelog for a history of notable changes to pyChemEng.ExamplesThe variablesxandyare established initially as a list. Later, the function isotherm is called with all the parameters set toTruefor the graphs that could be produced with the function. The same procedure adheres to thekineticsfunction.The following is a precedent for implementing the package for adsorption isotherms models. All the available outputs are called for the current example.plot    -->  Varying adsorption (i.e., y) with concentration in the solution at equilibrium (i.e., x)     
  comp    -->  Predicted values vs experimetal values 
  res     -->  Residuals 
  std_res -->  Standardised residualsfrom adsorption import *

  x = [0.36, 1.26, 2.69, 3.95, 5.93, 8.98, 16.89, 26.23]
  y = [3.76, 6.15, 12.35, 14.49, 17.39, 21.96, 22.93, 23.17]

  isotherm(x,y,ad_model=langmuir,plot=True,comp=True,res=True,std_res=True)Currently available adsorption isotherm models in this package are:langmuir
  freundlich
  toth
  sips
  temkin
  dubininThe following is a precedent for implementing the package for adsorption kinetic models. All the available outputs are called for the current example.plot    -->  Varying adsorption (i.e., y) with time (i.e., x)     
  comp    -->  Predicted values vs experimetal values 
  res     -->  Residuals 
  std_res -->  Standardised residualsfrom kinetics import *

  x = [61.60,118.84,178.81,237.37,299.17,414.08,540.83]
  y = [7.77,10.02,11.45,13.09,13.91,14.32,14.32]
  
  kinetics(x,y,Kn_model=pfo,plot=True,comp=True,res=True,std_res=True)Currently available adsorption kinetic models in this package are:pfo
  pso
  elovich
  sips
  temkin
  dubininpfo --> Pseudo First Orderpso --> Pseudo Second OrderLicenseMITAuthors@Ashane Fernando@Brinthan Kanesalingam
Package: pychemkin
pychemkin is a Python 3 chemical kinetics library. The core functionality is to compute reaction rates of all species participating in a reaction system. New extended functionality now allows reaction rates and concentrations to be computed as a function of time and to be visualized, in order to facilitate understanding of the timescales upon which the system operates, the production yields of the system, and sensitivity to initial conditions.Further documentation is in ‘docs/pychemkin.pdf’ within the source file.
Package: pychemy
Helpers for handling chemical formulas in Python. Mostly adopted from work ofChristoph Gohlke. Added methods that are useful for mass spec
data analysis.Depends onOpen Babeland its Python bindings for handling InChI strings.DevelopmentDevelopment requires Docker and Make on your host system. Everything else, including Open Babel, is
taken care of inside the Docker containers.Spin up your container using the provideddocker-compose.ymlfile andMakefileby runningmake image. This creates an image with a correct git configuration for your user, which makes it
easy to release. All of the commands you should need to run are defined theMakefileas targets.
All of the targets except forimage, are meant to be run inside the Docker container, but can be
run from the host machine by having-extappended to them. For example, to run tests, you could
either callmake testfrom a shell inside the container, ormaketest-extfrom the host.This project supports both Python 2 and Python 3. To test Python 3, make run themake testandmaketest-extwith theTOXENVenvironment set topy3, e.g.:TOXENV=py3 ./make test-extAll pull requests are run through the Travis CI process specified in.travis.ymland must pass
all unit and doc tests in Python 2 and Python 3 before being accepted.DeploymentDeployment of tagged commits happens to PyPI automatically via Travis CI. To bump and deploy a new
version directly, you must have access to write to the master branch. Runmakebump/[foo]-ext,
where[foo]ismajor,minor, orpatch. Thengit push origin--tagsmaster. If
you do not have access to the master branch, do the same thing, but in a separate branch, and make
a pull request.
Package: pycheops
Python package for the analysis of light curves from theESA CHEOPS mission.Usepip install pycheopsto install.Seepycheops/examples/Notebooksfor examples.For discussion and announcements, please join thepycheops google groupSee pycheops/docs/PyCheops_Cookbook.pdf for a guide to using pycheops.See pycheops/examples/Notebooks for Jupyter notebook that illustrate the
features of pycheops.See Maxted et al. 2022MNRAS.514…77M for a full description of the
algorithms and assumptions used in pycheops.TroubleshootingSSLCertVerificationErrorTry$ pip uninstallpython-dace-client$ pip installdace-queryInstallation fails with “ModuleNotFoundError: No module named ‘pybind11’”Runpip install pybind11then try againStarProperties(dataset.target) produces “Segmentation fault: 11”You are running the wrong version of python, e.g., anaconda2 instead of anaconda3“TypeError: ‘str’ object is not callable” in animate framesInstall “pillow”, e.g., conda install pillow.“No matching distribution found for matplotlib>3,2 (from pycheops)”This error message may appear when upgrading to pycheops version 0.8.0 or
later.  Two solutions have been found …“conda update –all” then “pip install pycheops –upgrade”“pip install matplotlib –upgrade” then “pip install pycheops –upgrade”Installation fails with “ERROR: Could not build wheels for celerite2 which use PEP 517 and cannot be installed directly”This error message may appear when upgrading to pycheops version 0.9.1 or
later. The working solution is to install celerite2 prior to installing/
updating pycheops using:git clone--recursivehttps://github.com/dfm/celerite2.gitcd celerite2python-mpip installcelerite2==0.0.1rc1
Package: pycheops-ultra
Python package for the analysis of light curves from theESA CHEOPS mission.Usepip installpycheops-ultrato install.Seepycheops/examples/Notebooksfor examples.For discussion and announcements, please join thepycheops google groupSee pycheops/docs/PyCheops_Cookbook.pdf for a guide to using pycheops.See pycheops/examples/Notebooks for Jupyter notebook that illustrate the
features of pycheops.See Maxted et al. 2021 (arxiv:2111.08828) for a full description of the
algorithms and assumptions used in pycheops.TroubleshootingInstallation fails with “ModuleNotFoundError: No module named ‘pybind11’”Runpip install pybind11then try againStarProperties(dataset.target) produces “Segmentation fault: 11”You are running the wrong version of python, e.g., anaconda2 instead of anaconda3“TypeError: ‘str’ object is not callable” in animate framesInstall “pillow”, e.g., conda install pillow.“No matching distribution found for matplotlib>3,2 (from pycheops)”This error message may appear when upgrading to pycheops version 0.8.0 or
later.  Two solutions have been found …“conda update –all” then “pip install pycheops –upgrade”“pip install matplotlib –upgrade” then “pip install pycheops –upgrade”Installation fails with “ERROR: Could not build wheels for celerite2 which use PEP 517 and cannot be installed directly”This error message may appear when upgrading to pycheops version 0.9.1 or
later. The working solution is to install celerite2 prior to installing/
updating pycheops using:git clone--recursivehttps://github.com/dfm/celerite2.gitcd celerite2python-mpip installcelerite2==0.0.1rc1
Package: pycher
Requirements:alacrittyfzfConfig:
Write a python file (~/.config/pycher/config.py) that defines a dictionary (tag, command) named "commands"commands={"uh oh":"playerctl -p spotify open spotify:album:3PzrNuMGWGpp8WOfrmpkaU"}for i3 users, you might want to add this to your.i3/configfor_window [class="Pycher"] floating enable
for_window [class="Pycher"] resize set width 500
for_window [class="Pycher"] move position center
Package: py-chessboardjs
py-chessboardjsChess GUI using pywebview and chessboard.js.This is a part ofChess-Antproject.
That manual will be updated soon.Chess-Ant is currently too slow to function as a chess engine.  To make matters worse, there is a glitch in the call to chess-ant.  It is possible to experiment by loading pgn and having it solve the problem.InstallationPlease read thepywebviewandPyGObjectmanuals, and install dependent packages before proceeding.If you are Ubuntu user:sudoaptinstallpython3-venv
python3.11-mvenv~/.venv3.11source~/.venv3.11/bin/activate
whichpip
pipinstallpy-chessboardjs[gtk]If you want to install it on local repository:cdpy-chessboardjs
pipinstall.[gtk]QT user:pipinstallpy-chessboardjs[qt]CEF user:pipinstallpy-chessboardjs[cef]Install your favorite UCI engine:sudoaptinstallstockfishUsagepy-chessboardjs-gtkpy-chessboardjs-qtpy-chessboardjs-cef
Package: pychesscom
pychesscomAn asynchronous Python client for Chess.com's APIInstallationPython 3.7 or higher is requiredTo install stable version from PyPI (recommended):pipinstallpychesscomTo install development version:$gitclonehttps://github.com/vopani/pychesscom
$cdpychesscom
$python3-mpipinstall-rrequirements.txtUsageJupyter NotebookfrompychesscomimportChessComClientclient=ChessComClient()response=awaitclient.player.get_details('erik')print(response)Python ConsoleimportasynciofrompychesscomimportChessComClientasyncdefmain():client=ChessComClient()response=awaitclient.player.get_details('erik')print(response)loop=asyncio.get_event_loop()loop.run_until_complete(main())Documentationhttps://pychesscom.readthedocs.io/LicenseThis project is licensed under the Apache License 2.0. SeeLICENSEfor more information.CreditsChess.com API:https://www.chess.com/news/view/published-data-apiLichess Python client:https://github.com/amasend/lichess_python_SDK
Package: pychfs
PyCHFSPyCHFS is the Python bindings for CHFS (consistent hashing file system).RequirementsCHFSGetting StartedInstallation$ pip install pychfsCreate file systemYou can create CHFS bychfsctland setCHFS_SERVERenvironmental variable.$ eval `chfsctl start`
$ chlist # show started serversDevelopingBuilding locally$ python setup.py build_ext --inplaceVSCode devcontainerYou can use VSCode devcontainer to develop fsspec-chfs.
The setup steps are follows:Install Docker and Remote-Container extension.Press Ctrl+Shift+P in VSCode.selectRemote-Container: Open the folder in the ContainerTesting$ eval `chfsctl start` # start the server and set CHFS_SERVER
$ tox
Package: pyChiaLogProcessor
Chia Log ProcessorA library provide functionality process chia plot log.InstallationpipinstallpyChiaLogProcessororpip3installpyChiaLogProcessorDemofrompyChiaLogProcessorimportChiaLog,ChiaLogSummary# ChiaLoglog=ChiaLog("plot.log")print(log.find_start())print(log.find_id())print(log.find_phrase(1))print(log.find_phrase(2))print(log.find_phrase(3))print(log.find_phrase(4))print(log.find_plot_name())print(log.find_complete())#ChiaLogSummarysummary=ChiaLogSummary(log)print(summary.isCompleted())ifsummary.isCompleted():print(summary.completeDuration())print(summary.isPhrase1Done())ifsummary.isPhrase1Done():print(summary.phrase1Duration())print(summary.isPhrase2Done())ifsummary.isPhrase2Done():print(summary.phrase2Duration())print(summary.isPhrase3Done())ifsummary.isPhrase3Done():print(summary.phrase3Duration())print(summary.isPhrase4Done())ifsummary.isPhrase4Done():print(summary.phrase4Duration())
Package: pychievements
Pychievementsis a framework for creating and tracking achievements within a Python application.
It includes functions specifically for creating command line applications, though it is flexible
enough to be used for any application such as web applications.See theexamplesto get a good feel for what Pychievements offers. Documentation can be found RTD:http://pychievements.readthedocs.org/en/latest/Features:Create Achievements with any number of “goals” (based on levels) that can be reachedFlexible design makes it easy to customize the way levels are trackedEasy to add new achievements laterPluggable backend for storing tracked information in different formats to different locationsAchievements can be filtered by category or keywordsEasily specify “Icons” for individual goals within an achievement for dual states (achieved and
unachieved)ExampleA simple achievement.class MyAchievement(Achievement):
    name = "My Achievement"
    category = "achievements"
    keywords = ("my", "achievement")
    goals = (
        {"level": 10, "name": "Level 1", "icon": icons.star, "description": "Level One"},
        {"level": 20, "name": "Level 2", "icon": icons.star, "description": "Level Two"},
        {"level": 30, "name": "Level 3", "icon": icons.star, "description": "Level Three"},
    )Increment a level for a user.tracker.increment(user_id, MyAchievment)Re-evaluating a level for a user based on arguments (requires the evaluate function to be defined
or the Achievement).tracker.evaluate(user_id, MyAchievement, some, extra, args)Retrieve achievements.tracker.achievements()                # retrieves all registered achievements in the tracker
tracker.achieved(uid, achievement)    # all achieved goals by uid for achievement
tracker.unachieved(uid, achievement)  # all unachieved goals by uid for achievement
tracker.current(uid, achievement)     # goal currently being worked torwards by uidInstallationTo install pychievements, simply:$ pip install pychievementsLicensePychievements is licensed under the MIT License, see theLICENSE.ContributeIf you’d like to contribute, simply forkthe repository, commit your changes
to themasterbranch (or branch off of it), and send a pull request. Make
sure you add yourself toAUTHORS.RoadmapMore backendsMore iconsHistory0.1.0 (2014-08-31)Initial Release!
Package: pychimera
Use UCSF Chimera packages in any Python 2.7 interpreterWith PyChimera you can…Run scripts depending on chimerafrom CLIwithpychimera script.py.Enableimport chimerain interactive coding sessionsoutside UCSF Chimera,
including IPython and Jupyter Notebooks.Launch a standard UCSF Chimera instance, with the benefit of importing all
yourcondaorvirtualenvpackages withpychimera--gui.I hope it’s useful! Feedback is appreciated!DocumentationThe updated documentation is always available atreadthedocs.AcknowledgmentsLargely based on ideas byGreg Couch at chimera-users.CitationPyChimera is scientific software, funded by public research grants
(Spanish MINECO’s projectCTQ2014-54071-P, Generalitat de Catalunya’s
project2014SGR989and research grant2017FI_B2_00168, COST ActionCM1306).
If you make use of PyChimera in scientific publications, please cite it. It will help
measure the impact of our research and secure future funding!@article{pychimera2018,
  author ={Rodríguez-Guerra Pedregal, Jaime and Maréchal, Jean-Didier},
  title ={PyChimera: use UCSF Chimera modules in any Python 2.7 project},
  journal ={Bioinformatics},
  volume ={34},
  number ={10},
  pages ={1784-1785},
  year ={2018},
  doi ={10.1093/bioinformatics/bty021},
  URL ={http://dx.doi.org/10.1093/bioinformatics/bty021},
  eprint ={/oup/backfile/content_public/journal/bioinformatics/34/10/10.1093_bioinformatics_bty021/1/bty021.pdf}}
Package: py-chinese-pronounce
Python Chinese Pronounce文字轉注音、漢語發音注音、漢語發音轉文字尋找相似、相同發聲字/單詞資料來源：政府開放資料,超齊百萬字典檔,教育部國語辭典公眾授權網InstallFrom PyPIpipinstallpy-chinese-pronounceFrom Repopipinstall-Ugit+https://github.com/p208p2002/py-chinese-pronounce.gitUsagefrompy_chinese_pronounceimportWord2Pronounce,Pronounce2Wordw2p=Word2Pronounce()p2w=Pronounce2Word()Word2Pronounce字元轉注音w2p.to_chewin("我")# ㄨㄛˇ字元轉漢語發音w2p.to_han("我")# wo3句子轉注音w2p.sent_to_chewin("你來扮演這個角色")# ['ㄋㄧˇ', 'ㄌㄞˊ', 'ㄅㄢˋ', 'ㄧㄢˇ', 'ㄓㄜˋ', '˙ㄍㄜ', 'ㄐㄩㄝˊ', 'ㄙㄜˋ']句子轉漢語發音w2p.sent_to_han("你來扮演這個角色")# ['ni3', 'lai2', 'ban4', 'yan3', 'zhe4', 'ge5', 'jue2', 'se4']感謝 @Evanston0624 實作此功能發音相似度比較w2p.char_pronounce_similar(a,b)w2p.sent_pronounce_similar(sent_a,sent_b)# 數值區間落在[0-1]其他轉換Word2Pronounce._word2unicode(self, x)Word2Pronounce._uni2word(self,uni)Word2Pronounce._cns2word(self,cns)Word2Pronounce._uni2cns(self, uni)CNS:中文標準交換碼Pronounce2Word注音找字元p2w.chewin2word("ㄨㄛˇ")# ['䰀', '婑', '捰', '㦱', '我', '䂺']漢語發音找字元p2w.han2word("wo3")# ['䰀', '婑', '捰', '㦱', '我', '䂺']字元找同發音p2w.find_same("我")# ['䰀', '婑', '捰', '㦱', '䂺']字元找近似發音p2w.find_similar("我")# ['蠖', '臥', '䇶', '䂺', '䪝', '捾', '偓', '握', '捰', '卧', '雘', '㦱', '濣', '䠎', '楃', '沃', '渥', '䁊', '涴', '幄', '龌', '㓇', '矱', '斡', '㠛', '肟', '齷', '仴', '䰀', '婑', '喔', '腛', '䀑']相似發聲詞p2w.find_similar_vocab("汽車")# 去除聲調找相似# ['七尺', '棋車', '棋车', '气车', '氣車', '汽车', '騎車', '骑车']p2w.find_similar_vocab_level("清晨",level=1)# 發音編輯距離找相似# ['傾城', '清城', '清澄', '青城', '清淳', '清純', '清纯', '清醇', '清神', '青神', '星塵', ...相同發聲詞p2w.find_same_vocab("汽車")# ['气车', '氣車', '汽车']其他轉換Pronounce2Word._find_similar_han_pronounces(self,han,level=1)尋找相似發音han: 漢語發音level: 編輯距離（越大越寬鬆）
Package: pychipbuilder
Version: 1.0.0Author(s): Jorge L. RojaszGlue Inc, 883 N Shoreline Blvd, C200, Mountain View, CA 94043, USAContact:jorge@zglue.comReleased under zOH License v. 1.0 for distribution and open-source developmentDocumentationPyChipBuilder documentation is available fromhttps://zglue.com/oci/pychipbuilderOverviewPyChipBuilder is a Python implementation of zGlue’s ChipBuilder tool. For now, the only supported functions are bring-up, debug, and testing. The package does this by using FTDI’s D2XX drivers to communicate with zGlue’s Smart Fabric. The installation comes with a command line interface script calledsfabric. zGlue Inc is distributing this library in the Python Package Index (PyPI) under zGlue Open Hardware V1 license.Supported FTDI devices include:UART, GPIO and Multi-Protocol Synchronous Engine bridges:FT232H (single port, clock up to 30 MHz)FT2232H (dual port, clock up to 30 MHz)FT4232H (quad port, clock up to 30 MHz)FeaturesPyChipBuilder currently supports the following features:Programming routing files to registers and/or OTP memoryReading/Configuring SmartFabric’s integrated peripheralsSupported OSesWindowsMacOSLinuxWhat’s Included?PyChipBuilder source codeFTDI D2XX driverszGlue Open Hardware License V1 (zOH License V1)
Package: pychk
PychkPychk is a tool to help developers identify vulnerable dependencies being used in their project. It reads the project's dependencies from the specified file (which is usuallyrequirements.txt) and checks the entries in it against entries in a database of packages with known vulnerabilities associated with them.InstallationPychk can be easily installed with PIP with the following command:pip3installpychkUsagePychk reads the project dependencies from the file specified by the user (by default, Pychk will look for 'requirements.txt' in the current directory). It can also, optionally, write the output to a specified file in JSON format.Running Pychk with defaults:pychkRunning Pychk against a specific file:pychk[-p/--path<pathtofile>]Writing the output to a file:pychk[-o/--out-file<file>]Displaying help:pychk-h/--help
Package: pychkari
PychkariA Very Simple Python Dependency InjectorInstallationUse pip!pip install pychkariOverviewPychkari (pronounced peach kaa ree) is a simple dependency injector for Python. It is intended to be a no-fuss library that depends on as few packages as possible and just works.This example should get us started!# Class definitions for reference

class A:
    def __init__(self, 
                 depOne,                    # casing support
                 second_dep: "DepTwo",      # annotations support
                 const_dep=3):              # not a dependency
        self.dep1 = depOne
        self.dep2 = second_dep
        self.const_d = const_dep


class DepOne:
    def __init__(self):
        self.timestamp = datetime.now()


class DepTwo:
    def __init__(self):
        self.timestamp = datetime.now()


class B:
    def __init__(self, a, dep_one):
        self.a = a
        self.dep1 = dep_one


# registration

container = Container.instance()
container.register("MyAwesomeService", B)   # explicitly named registration
container.register_class(A)                 # service name "A" implicit
container.register_class(DepOne)            # service name "DepOne" implicit
container.register_class(DepTwo)            # service name "DepTwo" implicit

# instantiation

service = container.get("MyAwesomeService") # creates instance of "B" with dependencies injectedFeaturesLightweightPychkari is barely a few kilobytes. It doesn't have third party dependencies. Just drop it into your project and run with it.Zero CommitmentPychkari doesn't require you to change your code. As long as common Python conventions are followed, your existing code just works!No fancy annotations, no decorations, no commitments. You're free to mix and match with any other framework.This chart should tell you how dependencies are resolved:DependencyResolved Service Nameunderscore_caseUnderscoreCasecamelCaseCamelCasePascalCasePascalCasemy_Weird_caseMyWeirdCasemy_service:HttpClientHttpClientExtensibleWell, sort of! If the conventions don't satisfy your complex naming schemes, you can always make the names explicit by annotating the dependencies.For example,client:"MyService"will inject and instance ofMyServicein place ofclient.