Saravanan666/pypi-org huggingface dataset:
Package: compdfkit-api-python
ComPDFKit API in PythonComPDFKitAPI provides a variety of Python API tools that allow you to create an efficient document processing workflow in a single API call. Try our various APIs for free — no credit card required.RequirementsProgramming Environment: Python 3.8 and higher.InstallationYou can install the library via pip. Run the following command:pipinstallcompdfkit-api-pythonCreate API ClientYou can use yourpublicKeyandsecretKeyto complete the authentication. You need tosign inyour ComPDFKit API account to get yourpublicKeyandsecretKeyat thedashboard. If you are new to ComPDFKit, click here tosign upfor a free trial.Project public Key : You can find the public key inManagement Panel.Project secret Key : You can find the secret Key inManagement Panel.# Create a clientclient=CPDFClient(public_key,secret_key)Create TaskA task ID is automatically generated for you based on the type of PDF tool you choose. You can provide the callback notification URL. After the task processing is completed, we will notify you of the task result through the callback interface. You can perform other operations according to the request result, such as checking the status of the task, uploading files, starting the task, or downloading the result file.# Create a clientclient=CPDFClient(public_key,secret_key)# Create a task# Create an example of a PDF TO WORD taskcreate_task_result=client.create_task(CPDFConversionEnum.PDF_TO_WORD)# Get a task idtask_id=create_task_result.task_idUpload FilesUpload the original file and bind the file to the task ID. The field parameter is used to pass the JSON string to set the processing parameters for the file. Each file will generate automatically a unique filekey. Please note that a maximum of five files can be uploaded for a task ID and no files can be uploaded for that task after it has started.# Create a clientclient=CPDFClient(public_key,secret_key)# Create a task# Create an example of a PDF TO WORD taskcreate_task_result=client.create_task(CPDFConversionEnum.PDF_TO_WORD)# Get a task idtask_id=create_task_result.task_id# Upload filesclient.upload_file(convert_file,task_id)Execute the taskAfter the file upload is completed, call this interface with the task ID to process the files.# Create a clientclient=CPDFClient(public_key,secret_key)# Create a task# Create an example of a PDF TO WORD taskcreate_task_result=client.create_task(CPDFConversionEnum.PDF_TO_WORD)# Get a task idtask_id=create_task_result.task_id# Upload filesclient.upload_file(convert_file,task_id)# execute Taskclient.execute_task(task_id)Get Task InfoRequest task status and file-related meta data based on the task ID.# Create a clientclient=CPDFClient(public_key,secret_key)# Create a task# Create an example of a PDF TO WORD taskcreate_task_result=client.create_task(CPDFConversionEnum.PDF_TO_WORD)# Get a task idtask_id=create_task_result.task_id# Upload filesclient.upload_file(convert_file,task_id)# execute Taskclient.execute_task(task_id)# Query TaskInfotask_info=client.get_task_info(task_id)ResourcesComPDFKit API Documentation
Package: compecon
CompEconA Python version of Miranda and Fackler's CompEcon toolboxThe aim of this toolbox is to provide a Python toolbox to replicate the funcionality ofMiranda and Fackler's CompEcon toolbox, which was written to accompany theirComputational Economics and Financeand is coded in Matlab.The source for this project is available in Github.A major difference in this implementation is that much of the code is object-oriented, providing classes to represent:Interpolation bases: Chebyshev, Spline, and LinearDynamic programming models: with discrete and/or continuous state and action variablesNonlinear problemsOptimization problemsSome other differences are:The solution of dynamic models is returned as apandas dataframe, as opposed to a collection of vectors and matrices.Some additional functionality is included, most notably forSmolyak interpolation.Basis objects are callable, so they can be used to interpolation function by "calling" the basis.The toolbox also replicates some of the demos and examples fromMiranda and Fackler's textbook. The examples can be found in the .\textbook directory, while the demos are in the directories .\demos (for .py files) and .\notebooks (for Jupyter notebooks).At this time the documentation is incomplete, so the best way to get going with this toolbox is by exploring the notebooks.
Package: compendium
CompendiumOverviewCompendium is a layered configuration management tool. It has the capability
to manage configuration files writen in JSON, TOML, XML and YAML. Settings
from these configuration files can then be managed easily with the help of
dpath.Documentationhttps://kuwv.github.io/python-compendium/Installpip install compendiumManage a configuration file>>>importos>>>fromcompendiumimportConfigFile>>>basedir=os.path.join(os.getcwd(),'tests')>>>filepath=os.path.join(basedir,'config.toml')>>>cfg=ConfigFile(filepath)>>>settings=cfg.load()Simplelookupfortitle>>>settings['/title']'TOML Example'Queryvalueswithinlist>>>settings.values('/servers/**/ip')['10.0.0.1','10.0.0.2']CheckthecurrentserverIPaddress>>>settings['/database/server']'192.168.1.1'UpdatetheserverIPaddress>>>settings['/database/server']='192.168.1.2'>>>settings['/database/server']'192.168.1.2'Checkthedatabasemaxconnections>>>settings['/database/connection_max']5000Deletethemaxconnections>>>delsettings['/database/connection_max']Checkthatthemaxconnectionshavebeenremoved>>>settings.get('/database/connection_max')Manage multiple layered configurationsTheConfigManageris a layered dictionary mapping. It allows multiple
configurations to be loaded from various files. Settings from each file
is overlapped in order so that the first setting found will be used.>>>importos>>>fromcompendiumimportConfigManagerReferenceconfigfilesfromexamples>>>basedir=os.path.join(os.getcwd(),'examples','config_manager')>>>config1=os.path.join(basedir,'config1.toml')>>>config2=os.path.join(basedir,'config2.toml')Retrievesettingsfromconfigfiles>>>cfg=ConfigManager(name='app',filepaths=[config1,config2])Getusingdpath>>>cfg.get('/default/foo2')'bar2'Lookupwithmulti-query>>>cfg.lookup('/example/settings/foo','/default/foo')'baz'Manage nested configurations>>>importos>>>fromanytreeimportRenderTree>>>fromcompendium.config_managerimportConfigManager,TreeConfigManager>>>basedir=os.path.join(os.getcwd(),'examples','tree')>>>cfg=TreeConfigManager(...name='fruit',...basedir=basedir,...filename='node.toml',...load_root=True,...load_children=True,...)>>>cfg.defaults=={}True>>>'succulant'incfg['/fruit/children']True>>>succulant=cfg.get_config('/fruit/succulant')>>>succulant.name'succulant'Manage configurations using Hierarchy File System (HFS)TBDDevelopmentpoetry shell
poetry install
python -m doctest README.md
Package: compenv
compenvTrack information about the execution environment of computations.InstallpipinstallcompenvUsageDecorate the auto-populated table on which you want to track the execution environments:fromcompenvimportrecord_environmentfromdatajointimportComputed,schemaschema=schema("my_schema")@record_environment(schema)classMyAutoPopulatedTable(Computed):...After populating the table you can view the generated records in the records table:MyAutoPopulatedTable.records()
Package: compETAG
Compute ETags and MD5sThe tool provides methods to calculate ETags for Objects on AWS S3, and also md5s, on local objects. It also allows comparing ETags or md5s to check the integrity of files, both locally and on AWS S3, given a list of files and their calculated ETags.Installationpip install compETAGUsageThe following example will compute ETags for local files with the same chunk size used by AWS on S3:compute_etags -i <file1> <file2> .... -m etag -s 8MB -o out.txtChunk size can be set to any desired value:compute_etags -i <file1> <file2> .... -m etag -s 1GBWith mode fixed atmd5, the resulting calculation will be md5 hashes:compute_etags -i <file1> <file2> .... -m md5To retrieve ETags from objects on AWS S3, setting mode tos3uriwill redirect the tool to AWS S3, provided a bucket/s, key/s, and pattern/s (optional):compute_etags -m s3uri -b <bucket1> <bucket2> .. -k <key1> <key2> .. -p <pattern1> <pattern2> ..To check the integrity of local files or AWS S3 objects, the following will calculate or retrieve ETags or md5s on the list of files provided in a text file, and compare the results to the provided hashings. Each row in the input text file represents a pair of ETag/md5 hash and a file name.Checking ETags of local files:compute_etags -c <input.txt> -m etagsChecking md5s of local files:compute_etags -c <input.txt> -m md5Checking ETags of AWS S3 objects:compute_etags -c <input.txt> -m s3uri -b <bucket1> <bucket2> .. -k <key1> <key2> ..
Package: competencias
Python CompetenciasImplementação em Python de biblioteca para trabalhar com Competencias no estilo YYYYMM.Retorna a lista de competêncais dada uma faixa de competências, a competência atual, a competência passada e a competência futura. Como atual entende-se o timestamp do now(). Cada competência tem os atributos as_int, as_decimal, as_date, as_datetime, as_time e as_string, conforme documentado abaixo.Como usarpipinstallcompetenciasfromcompetenciasimportCompetencia# para a competência atualCompetencias.current# para a próxima competênciaCompetencias.current.next# para a competência anteriorCompetencias.current.previous# para a competência 202301Competencias.get_instance(date(2023,1,25))# para a competência 2022/12, partindo da competência 2023/01Competencias.get_instance(date(2023,1,25)).previous# para as competências entre 2022/01 e 2023/11forcinCompetencias.range(date(2022,1,1),date(2023,11,2)):print(c)# para o ano da competênciaCompetencias.get_instance(date(2024,2,25)).date.year==2024Competencias.get_instance(date(2024,2,25)).year==2024# para o mês da competênciaCompetencias.get_instance(date(2024,2,25)).date.month==2Competencias.get_instance(date(2024,2,25)).month==2# para como uma inteiro 202301Competencia.get_instance(date(2023,1,30)).as_int==202301# para como uma float 2023.01Competencia.get_instance(date(2023,1,30)).as_float==2023.01# para como uma float (2023, 1)Competencia.get_instance(date(2023,1,30)).as_tuple==(2023,1)# para o primeito dia da competência 2023/12Competencia.get_instance(date(2023,12,25)).first_date==date(2023,12,1)# para o último dia da competência 2023/12Competencia.get_instance(date(2023,12,25)).last_date==date(2023,12,31)# para o primeiro carimbo de tempo da competência 2023/12Competencia.get_instance(date(2023,12,25)).first_datetime==datetime(2023,12,1,0,0,0)# para o último carimbo de tempo da competência 2023/12Competencia.get_instance(date(2023,12,25)).last_datetime==datetime(2023,12,31,23,59,59)# para o primeiro carimbo de tempo da competência 2023/12Competencia.get_instance(date(2023,12,25)).first_timestamp==1701399600.0# para o último carimbo de tempo da competência 2023/12Competencia.get_instance(date(2023,12,25)).last_timestamp==1704077999.0# Para validar datas mínimas, todas linhasclassCompetenciaComMinimo(Competencia):MIN_DATE=date(2023,11,1)# Agora todas as linhas abaixo vão lançar um exceptionCompetenciaComMinimo.get_instance(date(2023,12,1))CompetenciaComMinimo.get_instance(datetime(2023,12,1,23,59,59))CompetenciaComMinimo.get_instance(1800530519)CompetenciaComMinimo.get_instance(1800530519.0)classCompetenciaComMinimo(Competencia):MIN_DATE=date(2023,11,1)# Agora todas as linhas abaixo vão lançar um exceptionCompetenciaComMinimo.get_instance(date(2023,12,1))CompetenciaComMinimo.get_instance(datetime(2023,12,1,23,59,59))CompetenciaComMinimo.get_instance(1800530519)CompetenciaComMinimo.get_instance(1800530519.0)
Package: competitions
# CompetitionsCreate a machine learning competition for your organization, friends or the world!## Getting StartedThere are two types of competitions you can create:generic: a competition where you provide the data and the participants provide the predictions as a CSV file. all the test data is always available to the participants.script: a competition where you provide the data and the participants provide the code that generates the predictions. test data can be hidden from the participants.## CostsCreating a competition is free. However, you will need to pay for the compute resources used to run the competition. The cost of the compute resources depends the type of competition you create.generic: generic competitions are free to run. you can, however, upgrade the compute to cpu-basic to speed up the metric calculation and reduce the waiting time for the participants.script: script competitions require a compute resource to run the participant’s code. you can choose between a variety of cpu and gpu instances (T4, A10g and even A100). the cost of the compute resource is charged per hour.For information on the cost of the compute resources, please see the [pricing page](https://huggingface.co/docs/hub/spaces-overview#hardware-resources).## VisibilityYou can choose to make your competition public or private. Public competitions are visible to everyone and anyone can participate. Private competitions are only visible to the people you invite!## How to create a competition?Please read the [docs](https://huggingface.co/docs/competitions) to learn how to create a competition.
Package: competitions-cup
The goal of this package is to provide high-quality models of knockout cup
competitions.Currently, this package models standard single- and double-elimination brackets
for team counts that are powers of two (4, 8, 16, etc.) and stepladder-style
brackets for any number of teams.Changes in v0.5New interface to cup classesChanges within the v0.5 seriesv0.5.1: Merged several modules and renamed what was leftChanges in v0.4New “standard” single-elimination cup brackets for any number of teamsChanges in v0.3Double-elimination brackets for powers of two (4, 8, 16, etc.)Changes in v0.2Stepladder cup brackets for any number of teamsChanges in v0.1Single-elimination brackets for powers of two (4, 8, 16, etc.)
Package: competitions-match
The goal of this package is to provide match support for the competitions series
of packages.This package will include simple default match simulators to support basic usage
of this system with competitions-cup and competitions-league, among others.Changes in v0.2New namespacing for base classesRemoved setuptools entry points (use classes directly)Changes in v0.2Generic competitor fieldsWalkover detection for two-competitor matchesModified class hierarchy
Package: competitions-scheduler
The goal of this package is to provide high-quality, randomized schedule
generators for competitions, particularly leagues. The schedulers generate
lists of rounds for competitions when provided a list of teams or a team count.Right now, this library supports schedule generation for pure round-robin
competitions. While special wrappers are provided for round-robin leagues with
between 1 and 4 meetings between teams, competitions with more meetings
are possible.There will be a v0.3, but I don’t know what should be in it. Should I clone
scheduling formulas from real sports leagues? Do you want knockout cups, playoffs,
or more predictable round-robin scheduling? I won’t know unless you tell me.
Open feature requests on this project’s GitHub repo and tell me what you want to
see in version 0.3!Changes in v0.2Standard round-robin schedulers for leagues with odd numbers of meetings.Later changes in the v0.2 seriesv0.2.1 added tests for higher-level round-robin schedules.v0.2.2 and v0.2.3 added additional tests.v0.2.4 rewrote the odd-numbered matrix generation and even-numbered match
generation tests.v0.2.5 rewrote the odd-numbered match generation test.Changes in v0.1Even-numbered standard round-robin schedulers.
Package: competitive-verifier
competitive-verifierThe library is inspired byonline-judge-tools/verification-helper.Getting Started/日本語Reference/日本語DESIGN(日本語)Get startedUse in GitHub ActionsSeeGitHub Pages.日本語Use in localInstall(local)Needs Python 3.9 or greater.pipinstallcompetitive-verifierOrpipinstallgit+https://github.com/competitive-verifier/competitive-verifier.git@latestMigrate from verification-helperRun this script.competitive-verifiermigrateDevelopment for contributorspipinstall-Upoetry
poetryinstall# testpoetryrunpytest# formatpoetryrunpoeformat
Package: compfile
compfileCommon interfaces for manipulating compressed files (lzma, gzip etc)RationaleSometimes, we need to deal with different compressed files. There are
several packages/modules for compressed file manipulation, e.g.,gzipmodule for "*.gz" files,lzmamodule for "*.lzma" and
"*.xz" files, etc. If we want to support different types of
compressed file in our project, probably we need to do the following:iffnmatch.fnmatch(fname,"*.gz"):f=gzip.open(fname,'rb')# do something with feliffnmatch.fnmatch(fname,"*.bz2'):f=bz2.open(fname,'rb')# do something with felse:# other stuffsThe problems of the above approch are:We need to repeat the compression type inference logic everywhere we
want to support different compression types.Different compression type manipulation modules may have different
API convention.compfileis designed to solve the above problems. It abstracts the logic of compressed file manipulations and provides a single high level interface for users.InstallationInstall from PyPIpipinstallcompfileInstall from Anacondacondainstall-cliyugongcompfileInstall from GitHubpipinstallgit+https://github.com/gongliyu/compfile.git@masterSimple exampleUsingcompfileis pretty simple. Just construct acompfile.CompFileobject or callcompfile.openwithcompfile.open(fname,'r')asf:# do something with fThe object returned is a file object, so we can do ordinary file
processing with it.LicenseThecompfilepackage is released under theMIT LicenseDocumentationhttps://compfile.readthedocs.io
Package: compflow
compflowThe compflow library contains functions to convert back and forth between Mach
number and other non-dimensional groups in compressible flows. By using a
NumPy--Fortran interface, the code is vectorised and lightning-fast, yielding a
speed-up of up to two orders of magnitude.Full documentationis available online.FeaturesEvaluation of ten non-dimensional flow quantities as explicit functions of
Mach number;Iteration with Newton's method to invert explicit relations and solve for
Mach number;Creation and caching of lookup tables to speed up inversions;Fortran-accelerated, fully-vectorised in both directions.Basic usagecompflow is available on the Python Package Index, so installation is as simple as,python3-mpipinstallcompflowNote:as the library uses Fortran subroutines behind the scenes, you will
need a working Fortran compiler for the installation to complete successfully.We can now start doing some calculations. First, an explicit evaluation of
stagnation pressure ratio given a Mach number,>>>importcompflow>>>ga=1.4>>>compflow.Po_P_from_Ma(0.3,ga)1.0644302861529382Second, an inversion of flow function where
iterative solution for Mach number is required,>>>compflow.Ma_from_mcpTo_APo(0.8,ga)0.39659360325173604The names and symbols of non-dimensional quantities are fairly
self-explanatory, but a full list is given in theNomenclature.
All functions and the equations used for the calculations are documented in theAPI.Numpy arrays are also accepted as inputs,>>>importnumpy>>>Ma1=numpy.array([0.,0.5,1.,2.])>>>compflow.To_T_from_Ma(Ma1,ga)array([1.,1.05,1.2,1.8])>>>Ma2=numpy.array([[0.1,0.2],[0.3,0.4],[0.5,0.6]])>>>compflow.To_T_from_Ma(Ma2,ga)array([[1.002,1.008],[1.018,1.032],[1.05,1.072]])When solving for Mach number at a given normalised mass flow, it is assumed
that we are on the subsonic branch of the curve unless a flag is specified.
Where no solution is possible, i.e. if the flow would choke,NaNis
returned,>>>capacity=[0.6,2.]>>>compflow.Ma_from_mcpTo_APo(capacity,ga)array([0.28442265,nan])>>>compflow.Ma_from_mcpTo_APo(capacity,ga,sup=True)array([2.27028708,nan])TODOSort out packaging so that NumPy gets installed automatically (distutils due to be deprecated?).James Brind
Mar 2022
Package: compile
CompileCompiling Python source code into bytecode in production environmentHow To Usepython-mcompile-h# helppython-mcompile.-b-f# Compile current directory编译将 python 源代码编译成供生产环境使用的机器字节码如何使用python-mcompile-h# 帮助python-mcompile.-b-f# 编译当前目录
Package: compiled
compiledCompiled variants of the Python standard library.What is this project exactly?Pure Python modules in the standard library can be a speed bottleneck sometimes,
this package aims to provide "compiled" variants of the pure Python standard
library modules, which are somewhere between2-4x fasterthan the builtin ones.Installationpipinstallcompiled✨ This will install thepycompileCLI script as well.UsageSay your programasd.pylooks like this:importtomllibfromdifflibimportget_close_matches# ... rest of the codeYou can use the bundledpycompilescript to turn those imports into the
"compiled" variants:$pycompileasd.py✨Rewroteasd.pywithcompiledimports.$catasd.pyimportcompiled.tomllibastomllibfromcompiled.difflibimportget_close_matches# ... rest of the codeWith a real world program usingtomllibanddifflib, we get the following
difference in speed:ProgramPure Python timecompiledtimeSpeedup %hashes.py1.907 seconds1.028 seconds85.5% fasterLocal Development / TestingCreate and activate a virtual environment.Runpip install mypy, as mypy[c] is the only dependency.Scripts to test, build and package standard libraries are present inbuild.py:$./build.pytesttomllib.............----------------------------------------------------------------------Ran 13 tests in 0.006sOK$./build.pymypytomllibSuccess: no issues found in 4 source files$./build.pypackage[...]✨Built ./build/dist/compiled-0.2.1-cp311-cp311-macosx_13_0_arm64.whlRunpytest compiled_teststo run tests.
Package: compileMD
compileMDBrief introductionMarkdown files are a popular format for technical project documentation, but as the amount of docu grows, the files can become long and difficult to manage. The solution to this problem is to break down the markdown file into smaller, more manageable seperate.mdfiles and then compile them into a single file i.e.,README.md. Hence,compileMDtries to solve this problem.compileMD compile markdown files into the single README.md.Installation$pip3install--usercompileMDHow to usecompileMD?For simplicity, create the following folders and files:$mkdirtesting[testing]$mkdirmd_docs[ md_docs]$ vi 00_intro.md

# Project Title
A brief description of what this project does and who it's for.[ md_docs]$ vi 01_installation.md

## Installation
Install my-project with npm ...Now, run compileMD:[ testing]$ compileMD
[info] Compiling 00_intro.md into ./README.md
[info] Compiling 01_installation.md into ./README.mdtestingtree looks like this:.
├── md_docs
│   ├── 00_intro.md
│   ├── 01_installation.md
└── README.mdRelatedHere are some related links for this repo:https://pypi.org/project/compileMDArticle > How to publish a PIP package?Original/first articleContributingContributions are always welcome! Please contacthello@amaharjan.de.LicenseMIT
Package: compile-minifier
Compile-minifierCompile-minifier is a tool to minify and compile a structure code python.Package version0.1.9RequirementsPython 3.5+Installation & Usagepip installYou can install using:pipinstallcompile-minifierOr you can install directly from sources :pipinstallgit+https://github.com/bimdata/compile-minifier.git#masterGet startedThe package is used exclusively on the command line:compileminifyrunThis command minify the code withpython-minifierand compile all .py in .pyc recursively.By default, no python file modifications are executed in the root folder.DeploymentThe deployment on pypi is automated by circle when a tag is pushed on master.For manual deployment, you can run the following command :pipinstall.[ci]python-mbuild&&twineuploaddist/*HelpCommand line interfaces are generated withFireFor display help:compileminify--help