Saravanan666/pypi-org huggingface dataset:
Package: pyDive
pyDive======Distributed Interactive Visualization and Exploration of large datasets.## What is pyDive?Use pyDive to work with homogeneous, n-dimensional arrays that are too big to fit into your local machine's memory.pyDive provides containers whose elements are distributed across a cluster or stored ina large hdf5/adios-file if the cluster is still too small. All computation and data-access is then done in parallel by the cluster nodes in the background.If you feel like working with [numpy](http://www.numpy.org) arrays pyDive has reached the goal!pyDive is developed and maintained by the **[Junior Group Computational Radiation Physics](http://www.hzdr.de/db/Cms?pNid=132&pOid=30354)**at the [Institute for Radiation Physics](http://www.hzdr.de/db/Cms?pNid=132)at [HZDR](http://www.hzdr.de/).**Features:**- Since all cluster management is given to [IPython.parallel](http://ipython.org/ipython-doc/dev/parallel/) you can take yourexisting profiles for pyDive. No further cluster configuration needed.- Save bandwidth by slicing an array in parallel on disk first before loading it into main memory!- GPU-cluster array available thanks to [pycuda](http://mathema.tician.de/software/pycuda/) with additional support for non-contiguous memory.- As all of pyDive's distributed array types are auto-generated from local arrays like numpy, hdf5, pycuda, etc...you can easily make your own local array classes distributed too.## Dive in!```pythonimport pyDivepyDive.init(profile='mpi')h5field = pyDive.h5.open("myData.h5", "myDataset", distaxes=(0,1))ones = pyDive.ones_like(h5field)# Distribute file i/o and computation across the clusterh5field[::10,:] = h5field[::10,:].load() + 5.0 * ones[::10,:]```## DocumentationIn our [Online Documentation](http://ComputationalRadiationPhysics.github.io/pyDive/), [pdf](http://ComputationalRadiationPhysics.github.io/pyDive/pyDive.pdf) you can finddetailed information on all interfaces as well as some [Tutorials](http://computationalradiationphysics.github.io/pyDive/tutorial.html)and a [Quickstart](http://computationalradiationphysics.github.io/pyDive/start.html).## Software LicensepyDive is licensed under the **GPLv3+ and LGPLv3+** (it is *dual licensed*).Licences can be found in [GPL](COPYING) or [LGPL](COPYING.LESSER), respectively.
Package: pydiverse
pydiverseA collection of nicely interoperable libraries for data pipeline orchestration allowing for both SQL target and in-memory operation.InstallationYou can install the package in development mode using:gitclonehttps://github.com/pydiverse/pydiverse.gitcdpydiverse# create and activate a fresh environment named pydiverse# see environment.yml for detailsmambaenvcreate
condaactivatepydiverse

pre-commitinstall
pipinstall--no-build-isolation-e.
Package: pydiverse.pipetest
pydiverse.pipetestAn adaption layer for pydiverse.pipedag that simplyfies execution of pipedag steps as unit tests with cache invalidation awareness.InstallationYou can install the package in development mode using:gitclonehttps://github.com/pydiverse/pydiverse.pipetest.gitcdpydiverse.pipetest# create and activate a fresh environment named pydiverse.pipetest# see environment.yml for detailsmambaenvcreate
condaactivatepydiverse.pipetest

pre-commitinstall
pipinstall--no-build-isolation-e.
Package: pydiverse-transform
pydiverse.transformPipe based dataframe manipulation library that can also transform data on SQL databasesThis is an early stage version 0.x which lacks documentation. Please contacthttps://github.com/orgs/pydiverse/teams/code-ownersif you like to become an early adopter
or to contribute early stage usage examples.Usagepydiverse.transform can either be installed via pypi withpip install pydiverse-transformor via conda-forge
withconda install pydiverse-transform -c conda-forge.ExampleThis code illustrates how to use pydiverse.transform with pandas and SQL:frompydiverse.transformimportTablefrompydiverse.transform.lazyimportSQLTableImplfrompydiverse.transform.eagerimportPandasTableImplfrompydiverse.transform.core.verbsimport*importpandasaspdimportsqlalchemyassadefmain():dfA=pd.DataFrame({"x":[1],"y":[2],})dfB=pd.DataFrame({"a":[2,1,0,1],"x":[1,1,2,2],})input1=Table(PandasTableImpl("dfA",dfA))input2=Table(PandasTableImpl("dfB",dfB))transform=(input1>>left_join(input2>>select(),input1.x==input2.x)>>mutate(x5=input1.x*5,a=input2.a))out1=transform>>collect()print("\nPandas based result:")print(out1)engine=sa.create_engine("sqlite:///:memory:")dfA.to_sql("dfA",engine,index=False,if_exists="replace")dfB.to_sql("dfB",engine,index=False,if_exists="replace")input1=Table(SQLTableImpl(engine,"dfA"))input2=Table(SQLTableImpl(engine,"dfB"))transform=(input1>>left_join(input2>>select(),input1.x==input2.x)>>mutate(x5=input1.x*5,a=input2.a))out2=transform>>collect()print("\nSQL query:")print(transform>>build_query())print("\nSQL based result:")print(out2)out1=out1.sort_values("a").reset_index(drop=True)out2=out2.sort_values("a").reset_index(drop=True)assertlen(out1.compare(out2))==0if__name__=="__main__":main()
Package: py-django-health
Health CheckersFordjangoapplications.Installationpip install py-django-healthUsageInsettings.pyfile:INSTALLED_APPS=['health',...]Using checkersBy default, you have 3 checkers set (which are the built-in defined by the library):health.checkers.disk.DiskHealthCheckerhealth.checkers.database.DatabaseHealthCheckerhealth.checkers.cache.CacheHealthCheckerIf you want to replace them or use another, just modify this property in thesettings.pyfile:HEALTH_CHECKERS={'health.checkers.disk.DiskHealthChecker',...}Remember:HEALTH_CHECKERSis a set of strings.Exposing endpointsHealth Checkers has endpoints to be consumed by others apps or metrics counters.To enabled them, just add them into yoururls.pyfile:url('',include('health.urls'))Configuring the applicationHEALTH_CHECKERS_ENABLEDTypeRequiredDefaultboolNoTrueEnable or disabled all health checker features.HEALTH_CHECKERSTypeRequiredDefaultsetNo{'health.checkers.disk.DiskHealthChecker', 'health.checkers.database.DatabaseHealthChecker', 'health.checkers.cache.CacheHealthChecker'}Set of checkers to executeHEALTH_CHECKERS_CONTEXT_PATHTypeRequiredDefaultstrNo/infraContext path for exposed endpointAdding custom checkersYou can create and add custom checkers.Just follow these steps:Create a class inheriting the classHealthCheckerfrom the packagehealth.checkers.base.fromhealth.checkers.baseimportHealthCheckerclassMyCustomChecker(HealthChecker):_token:strOverride (if needed) thesetupmethod.importtypingfromhealth.checkers.baseimportHealthCheckerclassMyCustomChecker(HealthChecker):_token:strdefsetup(self)->typing.NoReturn:self._token='<token>'Override thecheckmethod.importtypingfromhealth.checkers.baseimportHealthCheckerfromhealth.modelsimportComponentHealthclassMyCustomChecker(HealthChecker):_token:strdefsetup(self)->typing.NoReturn:self._token='<token>'defcheck(self)->typing.List[ComponentHealth]:# Logic goes herereturn[]Add the custom checker into theHEALTH_CHECKERSset into thesettings.pyfile.HEALTH_CHECKERS={'health.checkers.disk.DiskHealthChecker','health.checkers.database.DatabaseHealthChecker','my_package.checkers.MyCustomChecker'}You can also create a newComponentHealth.fromhealth.modelsimportComponentHealthclassMyCustomHealthComponent(ComponentHealth):token:strConsuming endpointGet Health informationRetrieve health information from the application.MethodPathGET/{HEALTH_CHECKERS_CONTEXT_PATH}/healthResponse (JSON):{"status":"ALIVE","components":[{"type":"DISK","status":"ALIVE","total":50699194432,"free":5131434234,"used":4551234}]}
Package: pydjinni
PyDjinniAn opinionated Djinni reimplementation written in Python.FeaturesEasy Interface DefinitionThe PyDjinni IDL allows to define the interface between the host language and C++ with a clean and intuitive syntax.Flexible ConfigurationThe output can be configured comfortably with a YAML configuration file.ModularPydjinni is modular. Additional capabilities can be added through custom plugins.ToolchainThe tool provides additional utilities to help build, package and distribute cross-platform libraries.Python APIThe tools functionality can also be used from a Python API, in order to seamlessly embed into custom build processes.InstallationPyPi (recommended)pipinstallpydjinniGitTo get access to yet unreleased changes, PyDjinni can be installed directly from a git repository:gitclonegit@github.com:pydjinni/pydjinni.git
pipinstall-epydjinniorpipinstallgit+https://git@github.com/pydjinni/pydjinni.git@main
Package: pydk
Apythondeveloperkit with the prerequisite python nuts-and-bolts functions to make things clean and easy.Install withpippipinstallpydkInstall the development verison:gitclonehttps://github.com/mvinyard/python-developer-kit.git;cd./python-developer-kit
pipinstall-e.Questions or suggestions? Open anissueor send an email toMichael Vinyard.
Package: pydkit
PydKitExtended kit and tools for pydantic, to enjoy pydantic even more!Github repository:https://github.com/manimozaffar/pydkit/ContributionI'll work on this library in few months, i don't have free time right now, but feel free to contribute.
I'll check and test the PRs myself!FeaturesUse case of CSV read/write would be:Saving to a CSV filefrompydkitimportcsvfrompydanticimportBaseModelclassUser(BaseModel):id:intname:strusers=[User(id=1,name="Alice"),User(id=2,name="Abbas"),]csv.save("file.csv",users)Reading from a CSV filefrompydkit.csvimportread,StrNonefrompydanticimportBaseModelclassUser(BaseModel):id:intname:strlast_name:None|StrNone=None# StrNone helps you with reading from csv files!users=csv.read("file.csv",User)# now user is list of UserType extensions -> Auto converter typesfrompydkit.timezonesimportUTCTimeclassUser(BaseModel):created_at:UTCTimenow if you use this model, it'll convert timezones to UTC automatically in your FastAPI application
or else, you can also write custom validationfrompydkit.timezonesimportUTCBiggerThanNowclassUser(BaseModel):future_ts:UTCBiggerThanNow
Package: pydkron
pydkron - Python Library for Interacting withDkronRequirementsrequestsInstallingPippip install pydkronManuallygit clone https://github.com/oldmantaiter/pydkron
cd pydkron
python setup.py installUsage Examplesfrompydkron.clientimportDkronClient# Return all the configured jobsclient=DkronClient(hosts=["dkron01:8080","dkron02:8080"])forjobinclient.jobs():print"%sruns%s"%(job.name,job.schedule)# Get a job by namejob=client.get_job("job1")# Delete a jobjob.delete()# Create a new jobdata={"name":"job2","schedule":"@every 2m","command":"run stuff","owner":"Tait Clarridge","tags":{"role":"dkron:1",}}job=client.create_job(data)# Update a job (change the schedule)job.schedule="@every 2m"job.save()# Run a jobjob.run()# List executionsprintjob.executions()
Package: pydl
DescriptionThis package consists ofPythonreplacements for functions that are part of
theIDL®built-in library or part of astronomicalIDL®libraries.
The emphasis is on reproducing results of the astronomical library functions.
Only the bare minimum ofIDL®built-in functions are implemented to support this.There are four astronomical libraries targeted:idlutils: a general suite of tools heavily used bySDSS.Goddard utilities: TheIDL®Astronomy User’s Libary, maintained by Wayne Landsman and distributed withidlutils.idlspec2d: tools for working withSDSS,BOSSandeBOSSspectroscopic data.photoop: tools for working withSDSSimaging data.This package affiliated with theastropyproject and is registered withPyPI.Full DocumentationPlease visitPyDL on Read the DocsHistoryThis package was initially developed on theSDSS-IIIsvn repository.  It was
moved to the newGitHubrepository on 2013-03-06.  The present location of
the repository ishttp://github.com/weaverba137/pydl.LicensePyDL is free software licensed under a 3-clause BSD-style license. For details see
theLICENSE.rstfile.LegalIDL is a registered trademark ofNV5 Geospatial.
Package: pydl-dl
# PydlA Library to download music from Youtube , Soundcloud and many others sites. It is still in devolopment.## InstallationMake sure you’re usingpython3and havepipinstalled and enabled. On the command line, simply run:pip install pydl_dl## UsagePydl can be invoked from the commandline after installation.pydl <song_url>to download individual files.## Manual installation of the projectClone the repositorygit clone https://github.com/ayanbag/pydl.gitNavigate into the project directorycd pydlInstall pydl and its dependenciespython setup.py install
Package: pydle-game
Pydle GameWordle in the terminal!Terminal based wordle game written in pythonHow to InstallPydle can be installed from pypi using pip:pipinstallpydle_gameHow to runDuring installation, a script will be added to your path. To run, just runpydlePydle can also be run without installing as a package by cloningthis repositorygitclonehttps://gitlab.com/psalminen/pydlecdpydlepython-mpydleRequirementsappdatarichtermplotlib (only needed to show attempts bar graph at end)
Package: pydlfmt
Python data list FormatterTake a list of data elements and transform them into a spreadsheet or PDF document.Elements can be a dictionary, dataclass or any objectYou specify which columns are to be included in the outputColumns can have custom formattingExample - Assuming thedatavariable has been loaded prior to this snippet.df=DataFormatter(data=data)df.columns=[Column("id"),Column("name"),Column("sex"),Column("age"),Column("year"),Column("team"),Column("sport"),]df.to_pdf(filename=os.path.join("../examples","output","sample.pdf"))df.to_excel(filename=os.path.join("../examples","output","sample.xlsx"),format_table=True,)Create a DataFormatter instanceDefine the columns that you want included in your outputColumnnamesare used to match up with the input data column or element namesCall to_pdf or to_excel to build the outputColumnsYou have a lot of control over how the column is displayed. TheColumndataclass object has the following attributes.name - the name of the column. If your data list is a list of dictionaries, then this should match one of the keys of the dictionary.  It must exist in all rows in the list. If you data list is a list of dataclasses or objects, then this much match one of the attributes of the object that exists in all rows of the list.heading - the column heading. Will default to the column name if none provided.font_size - default 10 - for PDF, this is override the font size of the cell.bold - default False - for PDF only, should this column be boldjustify - default LEFT - LEFT, CENTER or RIGHTscalar - for Excel only - a scalar function that should be run on this column. Valid values are SUM, AVGdecimal_positions - number of decimal positions to round tocurrency - should we display the $excel_formula - an Excel formula that you want run to produce the output for this columndatatype - defualt str - what is the datatype for this columnwrap - default False - wrap text to fit in the columnwidth - the default width to use for this columnmax_width - the maximum width - use to ensure longer fields get the width they needparagraph_style - PDF only - pass in custom reportlab paragraph_style to be applied to all the cells in a columninclude_commas - default False - should we include commas when printing numbers
Package: pydll
PyDll for Windows这是一个自用的包，仅为个人爱好，有兴趣的也可以下载点击此处转到主页例子import func
func.dir(".\\")该语句将返回一个包含文件数量，大小，单位，和完整的路径的列表函数及其用法函数名用途返回值ari计算等差数列listfib计算斐波那契数列listchase_distance求追及问题路程intchase_time求追及问题时间intmeet_distance求相遇问题路程intmeet_time求相遇问题时间intmkfod创建文件或文件夹boolmkzip压缩文件strunzip解压文件strcopy复制和拷贝文件及文件夹booldirs遍历文件夹并打印所有文件listrename批量重命名文件intget_module_dir查询模块位置strdeletefile删除指定后缀名的文件boolmusicfile播放指定的音乐None常量Now_Time当前的时间
具体实现：Now_Time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))Data当前用户的具体信息
具体实现：Data = {"System" : platform.platform(),
        "UserName": getpass.getuser(),
        "NowTime": Now_Time,
        "File": sys.argv[0],
        "Args": sys.argv[1:]}Filelist \ d_filelist存储文件路径所有异常SameErrorInputErrorSystemVersionErrorNotFoundError
Package: pydlmafs
pydlmafs: math done the wrong wayThis package contains three modules:calc: Calculus with python functionslinalg: bad Linear Algebra implementationprobstat: Probability and StatisticskindaNote that the code in here was written without any concern about efficiency and has no use in real applicationsInstallationpipinstallpydlmafs#Testingimportpydlmafgs.calcasclcimportpydlmafgs.linalgasldlimportpydlmafgs.probstataspstdeff(x):return2*(x**2)+2print(clc.uni_deriv(f,5))vec=ldl.Tensor([6,-5,2])eye=ldl.Identity(3)(eye@vec).show()ldl.EWise('func',vec,func=pst.sigmoid)vec.show()
Package: pydlmeta
PyDLmetaFeatures: identify the model type belong to which deep learning framework
and extract the meta data if possible. The meta data includes input/output
name and shape of the model.The supported formats are:Tensorflow: frozen model *.pb, *.h5, SavedModel directory, *.tflitePytorch: *.pt, TorchScriptONNX: *.onnxCaffe model directory: *.caffemodel/ *.prototxtOpenvino IR directory: *.xml/ *.binInstallationCreate a Python 3.8 environment and activate it.git clone --depth 1 -b develop --recursive https://github.com/skymizer/pydlmeta.git
(cd pydlmeta && python3 -m pip install -e .)Usage— Retrieve the metadata of the modelfrom pydlmeta.meta import retrieve_model_metadata
res = retrieve_model_metadata("/path/to/your/model")Identify model formatfrom pydlmeta.identifier.model import identify
model_format = identify(model_path)
Package: pydload
PyDloadInstallationpip install pydloadDocumentation athttp://bpraneeth.com/docs/pydload/Usage# As a python moduleimportpydloadpydload.dload('url')# As a cli toolpydloadurl_to_downloadOptional Paramsmax_time - default:30sec. If download takes more than max_time stop download.timeout - default:10sec. Requests timeout. Max time allowed for establishing connection.
Package: pydlpack
pydlpackPyDLPack is a Python library for exchanging data between different
array libraries using [DLPack: Open In Memory Tensor
Structure]((https://github.com/dmlc/dlpack/). The provider library
does not need to implement the DLPack support, it will be sufficent if
the provider library implements one of the following protocols:dmlc/dlpackArray Interface Protocol, version 3CUDA Array Interface, version 3Python Buffer protocolArray Interface Protocol, version 2support can be provided on request.Currently, the package is tested with the following consumersNumPy,numpy.from_dlpackPyTorch,torch.from_dlpackCuPy,cupy.from_dlpackJax,jax.numpy.from_dlpackTensorflow,tf.experimental.dlpack.from_dlpackcuDF,cudf.from_dlpackusing the following provider objects with devices:Numpyndarray, CPUTorchTensor, CPU and CUDACuPyndarray, CUDANumbaDeviceNDArray, CUDAJaxArray, CPU and CUDATensorflowTensor, CPUPythonbytes, CPUPythonbytearray, CPUPythonarray, CPUNumpymemmap, CPUPythonmmap, CPUInstallpydlpack in conda-forgecondainstallpydlpackpydlpack in PyPi:pipinstallpydlpackBasic usage>>>fromdlpackimportasdlpack>>>importtorch>>>dl=asdlpack(b"Hello!")>>>torch.from_dlpack(dl)tensor([72,101,108,108,111,33],dtype=torch.uint8)that is, the Python packagedlpackprovides a functionasdlpackthat input can be any object that implements one of the above
mentioned protocols and it will return a light-weightDLPackObjectinstance which implements the DLPack protocol methods__dlpack__(stream=None)and__dlpack_device__(). ThisDLPackObjectinstance can be used as an argument to aconsumer.from_dlpack(obj)function of any DLPack-compatible consumer
library (a partial list of such libraries is listed above). For
example:>>>fromdlpackimportasdlpack>>>importnumba.cuda>>>importnumpy>>>arr=numba.cuda.to_device(numpy.array([[1,2],[3,4]]))>>>arr<numba.cuda.cudadrv.devicearray.DeviceNDArrayobjectat0x7fbed9c548b0>>>>dl=asdlpack(arr)>>>importtorch>>>torch.from_dlpack(dl)tensor([[1,2],[3,4]],device='cuda:0')>>>importjax>>>jax.numpy.from_dlpack(dl)Array([[1,2],[3,4]],dtype=int32)>>>importcupy>>>cupy.from_dlpack(dl)array([[1,2],[3,4]])that is, theDLPackObjectinstance can be efficiently used for
exchanging the CUDA buffer created using Numbato_devicefunctionality with different consumer objects such astorch.Tensor,jax.Array, andcupy.ndarraywhile all these array objects share the
same CUDA memory.TestingIt is a non-trivial task to install all dlpack-compatible libraries
into the same environment. Therefore,dlpacktests are included thedlpackpackage so that one can importdlpackand run the tests on
DLPack-compatible objects that are available in a particular
environment. For example:>>>importdlpack.tests>>>dlpack.tests.run()
Package: pydlr
libdlr: Imaginary time calculations using the Discrete Lehmann Representation (DLR)Authors:Jason KayeandHugo UR Strand(2021)libdlris a library providing Fortran subroutines to build and work with the
discrete Lehmann representation for single particle imaginary time Green's functions,
as well as the stand-alone Python modulepydlrimplementing the same functionality.For more information see thedocumentation.CitationIf this library helps you to create software or publications, please let
us know, and citeour repository:https://github.com/jasonkaye/libdlr"libdlr: Efficient imaginary time calculations using the discrete Lehmann representation", Jason Kaye and Hugo U.R. Strand, arXiv:2110.06765"Discrete Lehmann representation of imaginary time Green's functions", Jason Kaye, Kun Chen, and Olivier Parcollet, arXiv:2107.13094ContactPlease emailjkaye@flatironinstitute.orgwith any and all questions.Licenselibdlris licensed under the Apache License, Version 2.0, for more information see theLICENSEfile.
Package: pydlt
PyDLTA pyre-python library to handle AUTOSAR DLT protocol, which is based on
AUTOSAR Specification of Diagnostic Log and Trace V1.2.0 R4.0 Rev3, Section 7.7 Protocol Specification.Quick StartWrite messages to DLT filefrompydltimport(ArgumentString,DltFileWriter,DltMessage,MessageLogInfo,MessageType,StorageHeader,)# Create DLT messagemsg1=DltMessage.create_verbose_message([ArgumentString("hello, pydlt!")],MessageType.DLT_TYPE_LOG,MessageLogInfo.DLT_LOG_INFO,"App","Ctx",message_counter=0,str_header=StorageHeader(0,0,"Ecu"),)print(msg1)# > 1970/01/01 00:00:00.000000 0 Ecu App Ctx log info verbose 1 hello, pydlt!msg2=DltMessage.create_non_verbose_message(0,b"\x01\x02\x03",message_counter=1,str_header=StorageHeader(0,0,"Ecu"),)print(msg2)# > 1970/01/01 00:00:00.000000 1 Ecu non-verbose [0] 010203# Write DLT messages to filewithDltFileWriter("<path to DLT file>")aswriter:writer.write_messages([msg1,msg2])Read messages from DLT filefrompydltimportDltFileReader# Read DLT messages from fileformsginDltFileReader("<path to DLT file>"):# Print overview of each DLT messageprint(msg)LimitationThe following format of Type Info in a Payload has not been supported.TYPE_LENGTH_128BITTYPE_ARRAYVARIABLE_INFOFIXED_POINTTRACE_INFOTYPE_STRUCT
Package: pydlutils
pydlutilsAboutpython utils library for deep learning, computer vision, LLM, slam or other python related job.Module instructionbasic
place the basic module, type definition, utility function.third party
place modules which come from other repositories.torch
place torch related high level api.Installationpip install pydlutilsBuild and Upload# build
python setup.py sdist bdist_wheel
# upload
twine upload dist/*Publications@software{pydlutils,
  author = {zebincai},
  title = {pydlutils},
  url = {\url{https://github.com/zebincai/pydlutils}},
}Licensepydlutils has a Apache license, as found in theLICENSEfile.