Saravanan666/pypi-org huggingface dataset:
Package: pminterface
This is a simple Python library that is designed to read in the Paasmaker
configuration of the container, falling back to a custom configuration
file in development.You can read more about the Paasmaker PaaS athttp://paasmaker.org/.UsageIn the startup of your application create an interface object. Supply the
constructor with a list of locations to look for override configuration
files for development. You won’t need override configuration files if you
are using the development directory SCM in Paasmaker.importpminterfaceinterface=pminterface.PaasmakerInterface(['../my-project.yml'])interface.is_on_paasmaker()# Returns true if on Paasmaker.# Raises NameError if no such service exists.service=interface.get_service('named-service')# service now is a dict of the parameters. Typically this will# have the keys 'hostname', 'username', 'password', etc. Use this# to connect to revelant services.# Get other application metadata.application=interface.get_application_name()Override configuration files can be in either YAML or JSON format. If using
the YAML format, be sure to installpyyamlfirst. Ifpyyamlisn’t present,
only the JSON format is supported, and it will raise an exception when
trying to read YAML files.Example YAML configuration file:services:parameters:foo:barapplication:name:testversion:1workspace:Testworkspace_stub:testExample JSON configuration file:{"services":{"parameters":{"foo":"bar"}},"application":{"name":"test","version":1,"workspace":"Test","workspace_stub":"test"}}DevelopmentYou can run the unit tests with thetest.pyscript in the
root directory.The code is currently stored on BitBucket at:git@bitbucket.org:paasmaker/paasmaker-interface-python.gitFeel free to fork and submit pull requests.
Package: pmipy
xlwings is aBSD-licensedPython library that makes it easy to call
Python from Excel and vice versa:Scripting: Automate/interact with Excel from Python using a syntax that is close to VBA.Macros: Replace your messy VBA macros with clean and powerful Python code.UDFs: Write User Defined Functions (UDFs) in Python (Windows only).Numpy arraysandPandas Series/DataFramesare fully supported. xlwings-powered workbooks are easy to distribute and work
onWindowsandMac.LinksHomepage:http://xlwings.orgQuickstart:http://docs.xlwings.org/en/stable/quickstart.htmlDocumentation:http://docs.xlwings.orgSource Code:http://github.com/zoomeranalytics/xlwings
Package: pmkoalas
pmkoalas: A Process Mining Projectpmkoalasprovides data structures for process mining research in a well-organized pythonic style.Current FeaturesEvent log structuresImporting and exporting of logs to XES formatted XMLSeveral views/types of logSimplified logsThis type only considers sequences of process activities, and
nothing else.Complex logsThis type considers sequences of events. Where an event is a
mapping of data. This log type can always be reduced to the
simplified type.Generating logs quickly using delimited stringscurrently only supports simplified logsProcess model structuresPetri netsexporting to pnmlcreating dot files for a netgeneration of a net using fragments of desirable behaviourProcess discovery techniquesGenerating a directly follows language from a logThe Alpha miner (original variant)Development InformationTo install dependencies:py -m pip install -e .[dev]TestingTo run tests:py -m unittestThe values of the teamPostel's law"Be liberal in what you accept, and conservative in what you send"https://en.wikipedia.org/wiki/Jon_PostelBranches off main are meant to be either merged in a timely manner,
or show off a potential feature, or should be removed/released back
into the wild.Features need a use case or a user before they can be explored
in detail.It is better to delete code that is not used, than to keep it
around. You have written it once, you will write it better the
next time. Don't be afraid to let it go.
Package: pmk-probes
PMK Probespmk-probes is a Python package to control active oscilloscope probes byPMK.InstallationUse the package managerpipto install pmk-probes.pipinstallpmk-probesRequirementsPython 3.10 or higherpyserialUsagefrompmk_probes.probesimportBumbleBee2kVfrompmk_probes.power_suppliesimportPS03,Channelps=PS03('<YOUR_COM_PORT>')# replace with the COM port of your power supply or specify ip_address parameterbumblebee1=BumbleBee2kV(ps,Channel.CH1)# use the BumbleBee probe on channel 1print(bumblebee1.metadata.serial_number)# print the serial number of the probebumblebee1.attenuation=500# set the attenuation to 500xDocumentationThe documentation is available athttps://pmk-probes.readthedocs.io.LicenseBSD-3-Clause
Package: pmlab-lite
pmlab-liteA Process Mining scripting environment.
Containing the following functionalities among others:Petri Netmodelling, exploring and executionReading and working withEvent LogsExecuting state of the artProcess Miningtechniquessuch as theInductive MinerforProcess Model Discoveryor the A*-algorithm for computingAlignmentsinConformance CheckingInstallationInstall pmlab-lite from pypi using pip:$pip3installpmlab-liteTestingIn the test folder involved parties cancreate test filesandrun the test files.Create test-files in the test-directory following the naming convention:test_*.pypmlab-lite├──pmlab_lite│├──...│├──pn││└──...│└──__init__.py├──test│├──test_pn.py│├──...│└──test_*.py├──.gitignore├──LICENSE├──README.md├──distribute_to_pypi.sh└──setup.pyRun the test-file from the top-level-directory using following command:$python3-munittesttest.test_*CertificationXES certifiedby the IEEE Task Force on Process Mining.LicenseGNU General Public License 3.0
Package: pmlauncher
# pml
Python Minecraft Launcher Library - crossplatform, support forge, all version
this project is ported version from [CmlLib](https://github.com/AlphaBs/MinecraftLauncherLibrary)1.4.*
tested : windows 10 / ubuntu 18.04 LTS / macOS 10.14 MojaveContactsEmail :ksi123456ab@naver.comDiscord : ksi123456ab#3719License<a rel=”license” href=”http://creativecommons.org/licenses/by-nc/4.0/”><img alt=”Creative Commons License” style=”border-width:0” src=”https://i.creativecommons.org/l/by-nc/4.0/88x31.png” /></a><br />This work is licensed under a <a rel=”license” href=”http://creativecommons.org/licenses/by-nc/4.0/”>Creative Commons Attribution-NonCommercial 4.0 International License</a>.**NO COMMERCIAL**DependencyPython 3.6.8
requests 2.22Use venvHow To Useinstall :pip install pmlaunchercheck sample script
[main.py](https://github.com/AlphaBs/pml/blob/master/main.py)
Package: pmlayer
# Partially Monotone LayerPartially monotome layer (pmlayer) is a library for neural network models.
It provides several neural network layers to add monotonicity constraints on neural network models.
Current implementation is available only for PyTorch.Read the [documentation](https://ibm.github.io/pmlayer/) to get started, and feel free to contact us for any inquiry.# InstallationYou can installpmlayerby using thepipcommand:`pip install pmlayer`# CitationPlease consider citing this paper.
> H. Yanagisawa, K. Miyaguchi, and T. Katsuki, “Hierarchical Lattice Layer for Partially Monotone Regression,” NeurIPS 2022.
Package: pmll
Author:Kirill Pavlov <kirill.pavlov@phystech.edu>Version:Github:https://github.com/pavlov99/pmll.vimPmll is MIT Licensed machine learning library, it is written in Python. Most of existing libraries cover specific part of machine learning (svm solvers, neural networks) or collect range of algorithms, sometimes with visualization tools. Purpuse of pmll is to bring structure and make machine learning easy. It has module structure and it is easy to extend the library. Pmll ispired by R, MATLAB and orange.FeaturesEasy to use Data and Feature modelsInstallationTo installpmllas package, simply run:pip install pmllDocumentation:https://pmll.readthedocs.orgCompetitorsComing soonLaunchpadhttps://launchpad.net/pmll
Package: pml-pinn
Physics-informed neural networks packageWelcome to the PML repository for physics-informed neural networks. We will use this repository to disseminate our research in this exciting topic.InstallTo install the stable version just do:pip install pml-pinnDevelop modeTo install in develop mode, clone this repository and do a pip install:git clone https://github.com/PML-UCF/pinn.git
cd pinn
pip install -e .Citing this repositoryPlease, cite this repository using:@misc{2019_pinn,
    author    = {Felipe A. C. Viana and Renato G. Nascimento and Yigit Yucesan and Arinan Dourado},
    title     = {Physics-informed neural networks package},
    month     = Aug,
    year      = 2019,
    doi       = {10.5281/zenodo.3356877},
    version   = {0.0.3},
    publisher = {Zenodo},
    url       = {https://github.com/PML-UCF/pinn}
    }The corresponding reference entry should look like:F. A. C. Viana, R. G. Nascimento, Y. Yucesan, and A. Dourado, Physics-informed neural networks package, v0.0.3, Aug. 2019. doi:10.5281/zenodo.3356877, URLhttps://github.com/PML-UCF/pinn.PublicationsJournal papersA. Dourado and F. A. C. Viana, "Physics-informed neural networks for missing physics estimation in cumulative damage models: a case study in corrosion fatigue," ASME Journal of Computing and Information Science in Engineering, Online first, 2020. (DOI: 10.1115/1.4047173).Y. A. Yucesan and F. A. C. Viana, "A physics-informed neural network for wind turbine main bearing fatigue," International Journal of Prognostics and Health Management, Vol. 11 (1), 2020. (ISSN: 2153-2648).Conference papersA. Dourado and F. A. C. Viana, "Physics-informed neural networks for bias compensation in corrosion-fatigue," AIAA SciTech Forum, Orlando, USA, January 6-10, 2020, AIAA 2020-1149 (DOI: 10.2514/6.2020-1149).Y. A. Yucesan and F. A. C. Viana, "A hybrid model for main bearing fatigue prognosis based on physics and machine learning," AIAA SciTech Forum, Orlando, USA, January 6-10, 2020, AIAA 2020-1412 (DOI: 10.2514/6.2020-1412).A. Dourado and F. A. C. Viana, "Physics-Informed Neural Networks for Corrosion-Fatigue Prognosis," Proceedings of the Annual Conference of the PHM Society, Scottsdale,USA, September 21-26, 2019.Y. A. Yucesan and F. A. C. Viana, "Wind turbine main bearing fatigue life estimation with physics-informed neural networks," Proceedings of the Annual Conference of the PHM Society, Vol. 11 (1), Scottsdale, USA, September 21-26, 2019 (DOI:10.36001/phmconf.2019.v11i1.807).R.G. Nascimento and F. A. C. Viana, "Fleet prognosis with physics-informed recurrent neural networks," The 12th International Workshop on Structural Health Monitoring, Stanford, USA, September 10-12, 2019 (DOI:10.12783/shm2019/32301).
Package: pmm
### PyPi Mirror Manager[![command line demo](https://asciinema.org/a/3e67r6npdb4i7syuqdmxkctgm.png)](https://asciinema.org/a/3e67r6npdb4i7syuqdmxkctgm)#### Installationpip install pmm#### Usageusage: pmm [-h] [-m]Select PyPI index server used by pip.optional arguments:-h, --help     show this help message and exit-m, --mirrors  download list of PyPI mirrors and add them to selection#### ConfigurationYou can add package indexes to your `pip.conf` file. Example:[global]use-wheel = Trueindex-url = https://pypi.python.org/simpleindex-servers =pypipypi-testmy-devpi[pypi]index = pypi.python.org[pypi-test]index = testpypi.python.org/pypi[my-devpi]index = devpi.example.com/main/devinfo = Development team local package indexIf you have any indexes listed in the `index-servers` setting in the `globals`section, `pmm` will then only offer these indexes for selection, unless you usethe `-m` command line option.#### Credits* inspired by https://github.com/Pana/nrm* mirrors data from https://www.pypi-mirrors.org/* [pick](https://github.com/wong2/pick) for the interactive selection list
Package: pmm-cfg-gen
Plex-Meta-Manager-Config-GeneratorA python application to automatically generate Plex Meta Manager configuration files based on your plex libraries.Library types supported:MovieTVMusic (json format only)Installpipinstallpmm-cfg-genRunning from command lineUsage:usage:pmm-cfg-gen[-h][--plex.serverUrlPLEX.SERVERURL][--plex.tokenPLEX.TOKEN][--plex.lbraries[PLEX.LBRARIES...]][--output.pathOUTPUT.PATH][--output.overwriteOUTPUT.OVERWRITE][--theMovieDatabase.apiKeyTHEMOVIEDATABASE.APIKEY][--thePosterDatabase.enablePro][--pmm.deltaOnly][--logLevel{INFO,WARN,DEBUG,CRITICAL}]options:-h,--helpshowthishelpmessageandexit--plex.serverUrlPLEX.SERVERURLPlexServerfullyqualifedURL--plex.tokenPLEX.TOKENAuthenticationToken(notclaimtoken)fortheplexserver--plex.lbraries[PLEX.LBRARIES...]Commadelimitedlistoflibrariestoprocess--output.pathOUTPUT.PATHRootpathtostoregeneratedfiles(default:./data)--output.overwriteOUTPUT.OVERWRITEOverwriteexistingfiles(default:False)--theMovieDatabase.apiKeyTHEMOVIEDATABASE.APIKEYTheMovieDatabaseAPIKey--thePosterDatabase.enableProEnableProfeaturesforThePosterDatabase(requiresyoutobeabletologintothesite)--pmm.deltaOnlyOnlygeneratefilesforitemsthatdonotalreadyexistincurrentPMMconfigs--logLevel{INFO,WARN,DEBUG,CRITICAL}LoggingLevel(default:INFO)Configuration FileAll of the configuration can be stored in aconfig.yamlfile that uses the following format (with the exception of logLevel).config.yaml:plex:serverUrl:<plex server url>token:<plex token>libraries:-{name:"TVShows",path:"tv",pmm_path:"/pmm_config/tv"}-{name:"Movies",path:"movies"}plexMetaManager:cacheExistingFiles:truetheMovieDatabase:apiKey:<tmdb api key>output:path:<path to store generated output>Notes:It is possible to use ENV variables (standard bash syntax supported).If The Movia Database API Key is set, collection details are looked up realtimeExample:plex:serverUrl:${PLEX_SERVER:-https://plex:32400}token:${PLEX_TOKEN}
Package: pmmif
Predictive Modelling Metadata Interchange FormatPython support for dataset metadata for predictive modelling and data analysis.Thepmmifpackage provides two levels of support..pmm Metadata FilesClasses for reading and writing extended metadata to .pmm JSON files.Pandas DataFrame SupportClasses for associating Pandas DataFrames with additionaltypeandtagsannotation metadata, including support for persisting additional
metadata alongsidefeatherdatafiles.Documentationhttp://pmmif.readthedocs.io
Package: pmnb
产品经理常用工具pmnb（pm牛逼） import pmnb as pmjsq （计算器首字母）方法：from pmnb import jsqipr：提升量计算：输入：jsq.ipr(之前的值，新的值)，输出：提升的绝对值，相对值ABSample：AB样本量计算  ABSample(),直接执行，根据提示输入就有结果rank_wilson_score:威尔逊置信区间下界，jsq.rank_wilson_score(正样本数，总数)confidence：置信度计算，直接执行，输出置信区间，置信度feed（内容相关） 方法：from pmnb import feedxsd：相似度的首字母，计算标题相似度，输入文本1跟文本2，输出他们的相似度count_words:统计excel某个sheet里某一列的词频analyse(分析相关) 方法：from pmnb import analyselr_one_hot()：根据lr模型输出各个特征以及重要程度,字符用one-hot编码lr_factorize():根据lr模型输出各个特征以及重要程度,字符用序号编码Tree_one_hot()：根据决策树模型输出各个特征以及重要程度,字符用one-hot编码Tree_factorize():根据决策树模型输出各个特征以及重要程度,字符用序号编码xgb_one_hot()：根据xgb模型输出各个特征以及重要程度,字符用one-hot编码xgb_factorize():根据xgb模型输出各个特征以及重要程度,字符用序号编码growth（增长相关） 方法：from pmnb import growthDAU_predict:DAU预估，输入次日，7日，15日留存，日新增，天数，返回留存曲线跟第X天的DAU
Package: pmnlp
说明支持中文语句的模版匹配。可以理解为，简单版本的百度unit或者科大讯飞的AIUI对于，简单语句的自然语言理解也是很方便的。欢迎大家试用。对于不喜欢调用API，喜欢在自己代码中嵌入的人是蛮好用的。用例```
from pmnlp.word import build_sentence_word_dict
from pmnlp.sentence import SentenceTplTrie

# 初始化，用于记录用户配置的词槽，以及语句的模版匹配。
user_word_dict = {'num': ['一杯', '两杯'], 'coffee': [
    '拿铁', '拿铁咖啡'], 'common': [], 'phone': ['re\d+']}
sentent_intent_tpl = {
    'coffee': '[common:0-4][num][common:0-10][coffee]'
}

# 建立模型
sentence_word_dict = build_sentence_word_dict(word_dict=user_word_dict)
test_tree = SentenceTplTrie(word_dict=sentence_word_dict)
test_tree.build(sentence_tpl_dict=sentent_intent_tpl, common_key='common')


# 理解用户输入
_, intent, result = test_tree.sep('我要一杯热啊啊啊啊啊啊拿铁咖啡',common_key='common')
if isinstance(intent, str):
    print(intent, result)
else:
    print(intent, result)
```
Package: pmodhygro
pmodhygropmodhygro is python library to read PmodHYGRO that is relative humidity sensor with integrated temperature sensor on a Raspberry Pi-> Reference page of PmodHygro sensor :https://digilent.com/reference/pmod/pmodhygro/start?redirect=1InstallingInstall with pipUse pip to install from PyPI.Python 2:sudopipinstallpmodhygroPython 3:sudopip3installpmodhygroCompile and install from the repositoryDownload library for ZIP file from GitHub, unzipping the archive, and execute:Python 2:cdpmodhygro
sudopythonsetup.pyinstallPython 3:cdpmodhygro
sudopython3setup.pyinstallYou may also git clone the repository:git clonehttps://github.com/devpola/pmodhygro.gitUsagefrompmodhygroimportPmodHygro# Create i2c bussensor=PmodHygro()sensor.begin_i2c()# Read data from Pmod HYGROtemp=sensor.get_temperature()temp_f=sensor.get_temperature_f()hum=sensor.get_humidity()You have to run program with 'sudo' command for avoiding permission errorCheck list before using libraryCheck for I2CRaspbian:Using “Raspi-config” on Command Linesudoraspi-configEnable I2C InterfaceUbuntu(20.04):Open '/boot/firmware/syscfg.txt' fileCheck if there are contents below. If not, add it.dtparam=i2c_arm=on,dtparam=spi=onCheck the connectionCheck port sensor is connected using command below.ls/dev/*i2c*You can get/dev/i2c-0or/dev/i2c-1In case of Raspberry Pi B model, there are two i2c ports, 0 and 1.Remember whether the port number is 0 or 1Check device address is 0x40Installi2c-toolspackage for checking connected devices to i2c interfacesudoapt-getinstalli2c-toolsCheck device address at the port is 0x40 using command below.sudoi2cdetect-y0(or1)
Package: pmon
pmonA process monitor for linux that shows detailed RAM usage info.$ pmon 261071
vms       rss       pss       uss       shared    swap      rss (%)   pss (%)   uss (%)   cpu (%)
2.576G    1.122G    1.110G    1.107G    99.699M   0B        7.22      7.14      7.12      0.00
2.576G    1.122G    1.110G    1.107G    99.699M   0B        7.22      7.14      7.12      12.23
2.576G    1.122G    1.110G    1.107G    99.699M   0B        7.22      7.14      7.12      6.48RationaleMeasuring RAM usage on Linux can betricky.
Probably the most correct way to measure it is to useUSS, i.e:USS (Unique Set Size) is the memory which is unique to a process and which would be freed if the
process was terminated right now.Unfortunately, tools liketopandhtopdo not report this metric. Nevertheless,psutildoes collect it, so, we use it as the backend to monitor
RAM usage.Installpipx install pmonUsagepmon --help
Package: pmotif-lib
pmotif_libPerform motif detection, either with traditional frequency, or with positional metrics for each graphlet occurrence.This library implements each step of a (p)motif detection pipeline such asgraphlet detection in networksrandomization of networksfrequency comparison of graphlet occurrencespositional metric comparison of graphlet occurrences.SetupInstall this package:pip install pmotif-libThis library relies on thegtrieScannertool. Pleasedownload it, compile it, and add the executable to your path.Finally, this library loads environment variables. Create an.envfile::exportDATASET_DIRECTORY=/path/where/edgelists/are/locatedexportEXPERIMENT_OUT=/path/where/raw/graphlets/and/pmetric/should/goexportGTRIESCANNER_EXECUTABLE=/path/to/the/gtriescanner/executableexportWORKERS=1# Optional, controls the max. degree of parallelizationIn order to make these variablea available to the library, usesource .envbefore command line usage!
You can also use thepython-dotenvpackage to load the.envfile from python code:fromdotenvimportload_dotenvload_dotenv(".env")Usageshowcase/contains a number of examples:graphlet detectionp-graphlet detectionmotif detectionp-motif detectionAfter installingpmotif_lib, navigate intoshowcase/, runsource .showcase_env, and then runpython3 graphlet_detection(or one of the other examples).
This expects thegtrieScannerexecutable to be in your system's path undergtrieScanner!GlossaryInduced Subgraph: A graph created by cutting out a set of nodes from a graphG, retaining all edges between these nodesIsomorphic graphs: Graphs, that are structurally the same when ignoring node labelsIsomorphic Classes of Size k: A set of graphs with k nodes, so that every other graph with k nodes is isomorphic to one graph in the setk-Graphlet: An isomorphic class of size k, so that at least one induced subgraph in a graphGis isomorphic to that classGraphlet Occurrences: All induced sub-graphs in a graphGthat belong to a specific k-GraphletGraphlet Frequency: The number of graphlet occurrences of a specific k-graphlet in a graphGGraph Motif: A k-Graphlet, which has a graphlet frequency which is significantly higher than expected, usually tested against randomized graphs generated based onGp-Motif: A k-Graphlet, which has graphlet occurrences with a significant expression of a positional metric, when compared against randomized graphs generated based onG
Package: pmpd
pmpd [git puhmp-eed] is acommand line interface for git deploymentsthat release engineers can use to make life easier. It is heavily influenced by
Vincent Driessen’sbranching modeland uses a syntax similar togitflow.
Its goal is to simplify and streamline deployments of complex branching systems
by making commands and actions ashuman-friendlyas possible. It provides a
simplepmpdcommand that allows for merging and deploying branches to
any number of distinct servers that areconnected by a common git serverlikegitolite.Main FeaturesInstallationConfigurationUsageResourcesContributersMain FeaturesExpressive and intuitive syntaxMakes continuous integration easierRelease branches are pmpd outIdentify conflicting feature branches before release timeAutomated pmpd deployments similar to Google’s AppEngine approachMulti server environments supportedDistributed branching model for large group collaborationsInstallationThe lateststable versionof pmpd can always be installed or updated to
via pip:$ pip install --upgrade pmpdConfigurationAfter installation is complete pmpd puts.pmpd/config.jsoninto the root
of your repository. You may want to add the folder to .gitignore, but leaving it
there is also a good way to keep track of your build history. pmpd.conf is used
to relate servers to reference branches and to specify which feature branches
belong in a given release.You must fill out.pmpd/config.jsonso that it matches the flow of your own
deployments and you’ll probably update this file in some way every time a new
release is pmpd out.UsageInitialize:$ pmpdSynopsis:$ pmpd [flags] [METHOD] [BRANCH]See alsopmpd--helpExamplesBuild an alpha release based on the current state of production:$ pmpd build alphaResourcespmpd GitHubpmpd PyPIpmpd PyScapeContributersJared Hall,PyScape
Package: pmppm
PPMPPM The Python Project Manager·Report Bug·Request FeatureTable of ContentsGetting StartedInstallationUsageRoadmapContributingGetting StartedInstallationpipinstallpmppmUsagecdMyProjDir
ppminitRoadmapInitPackage InstallerContributingContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make aregreatly appreciated.If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull Request
Package: pm-process-video
pm-process-videoInstallationpipx install pm-process-videoThen run:process_video my_file.vttThis package is installed under thepmpackage namespace.
To use it in Python code import like this:frompm.process_videoimportvtt_to_textUsageRunning this:process_video "My video file.mkv"Will normalize the audio, transcribe using Open AI's whisper, and save the file, transcription, and text transcript in~/Documents/screencast/encoded.
You can customize the target directory with the--target-diroption.PublishTo try out the current implementation without building:hatch run process_video ...To build and publish:hatch buildhatch publishLicensepm-process-videois distributed under the terms of theMITlicense.