Saravanan666/pypi-org huggingface dataset:
Package: pymetamap
pymetamap=========Python wrapper around `MetaMap <http://metamap.nlm.nih.gov/>`_.This will take a list of sentences and extract concepts using MetaMapthen return them in the form of a list of Concept objects.**Note:** This code does **not** work with Windows because of my use of NamedTemporaryFile in SubprocessBackend.py.How to Install--------------First, install MetaMap by using the following instructions: https://metamap.nlm.nih.gov/Installation.shtmlNext, pymetamap can be installed using the following command:>>> python setup.py installExample Usage-------------To start you must create a MetaMap instance from the pymetamap package.::>>> from pymetamap import MetaMap>>> mm = MetaMap.get_instance('/opt/public_mm/bin/metamap12')You must supply the metamap binary to ``get_instance()`` in order toextract concepts. Depending on where you installed MetaMap and depending on the version you are using, you will need to change the ``/opt/public_mm/bin/metamap12`` to the correct location. For example, if you installed the 2016 version of MetaMap, then the binary will be called ``metamap16``.To extract concepts from a sentence use the ``extract_concepts()``method. This method taks a list of sentences as input and will returna list of Concept objects.::>>> sents = ['Heart Attack', 'John had a huge heart attack']>>> concepts,error = mm.extract_concepts(sents,[1,2])>>> for concept in concepts:...     print conceptConcept(index='1', mm='MM', score='14.64', preferred_name='Myocardial Infarction', cui='C0027051', semtypes='[dsyn]', trigger='["Heart attack"-tx-1-"Heart Attack"]', location='TX', pos_info='1:12', tree_codes='C14.280.647.500;C14.907.585.500')Concept(index='2', mm='MM', score='13.22', preferred_name='Myocardial Infarction', cui='C0027051', semtypes='[dsyn]', trigger='["Heart attack"-tx-1-"heart attack"]', location='TX', pos_info='17:12', tree_codes='C14.280.647.500;C14.907.585.500')This example shows two seperate concepts extracted via MetaMap from twodifferent sentences (sentence 1 and sentence 2).More Information----------------Licensed under `Apache 2.0 <http://www.apache.org/licenses/LICENSE-2.0>`_.Written by Anthony RiosSpecial thanks to `joaopalotti <https://github.com/joaopalotti>`_ and others for their contributions.webpage: `https://github.com/AnthonyMRios/pymetamap <https://github.com/AnthonyMRios/pymetamap>`_
Package: pyMetaModel
pyMetaModelmigrateshttps://wiki.bitplan.com/index.php/MetaModel#tab=Modelto pythonIntroductionpyMetaModel is a python library for a Knowledge Graph MetaModelIt started with the ProfiWikiSemanticMediaWiki MetaModelin
2015 consisting ofContextTopicTopicLinkPropertyActionSMWTypeDocumentationWikiAuthorsWolfgang Fahl
Package: pymetamodels
Pymetamodels package for materials, systems and component metamodelingThe pymetamodels package combines machine learning (ML) metamodeling and analysis tools for the virtual development of modeling systems within a common abstract framework implemented in an accessible and distributable Python package. The development of pymetamodels package is oriented to support ML applications in  material science, material informatics and the construction of materials, components and systems soft metamodels informed by hard physics-based modelling (continuum, mesosocopic, ... ) and experimental characterisations.Basic turtorials and advanced examples can be found in the tutorials sectionpymetamodels.readthedocs.io.The package has been build inITAINNOVA. And is distributed with permissive MIT license.Installing pymetamodelsTo install the latest stable version of pymetamodels via pip fromPyPItogether with all the dependencies, run the following command:pip install pymetamodelsFirst steps, basic turtorials an advanced examples can be found in the documentation tutorials sectionpymetamodels.readthedocs.io. To load and test installation try,import pymetamodels

    ### Load main object
    mita = pymetamodels.metamodel()

    ### Load main object (alternative)
    mita = pymetamodels.load()Installing pre-requisite softwarePymetamodels requires Python >3.7 or an above of releasePython.org.Pymetamodels requiresNumPy,SciPy,sklean,matplotlibandSALibinstalled on your computer.  Usingpip, these libraries can be installed with the following command:pip install numpy scipy scikit-learn matplotlib SALib Pillow xlrd xlwt xlutilsThe packages are normally included with most Python bundles, such as Anaconda. Generally, they are installed automatically when using pip to install pymetamodels.
Package: pymetasec
PyMetaPyMeta is a Python3 rewrite of the toolPowerMeta, created bydafthackin PowerShell. It uses specially crafted search queries to identify and download the following file types (pdf, xls, xlsx, csv, doc, docx, ppt, pptx) from a given domain using Google and Bing scraping.Once downloaded, metadata is extracted from these files using Phil Harvey'sexiftooland added to a.csvreport.  Alternatively, Pymeta can be pointed at a directory to extract metadata from files manually downloaded using the-dircommand line argument. See theUsage, orAll Optionssection for more information.Why?Metadata is a common place for penetration testers and red teamers to find: domains, user accounts, naming conventions, software/version numbers, and more!Still not convinced? Checkout -Hacking Organizations One Document at a Time With MetadataGetting StartedPrerequisitesExiftoolis required and can be installed with:Ubuntu/Kali-apt-get install exiftool -yMac OS-brew install exiftoolInstall:git clone https://github.com/m8sec/pymeta
cd pymeta
python3 setup.py installUsageSearch Google and Bing for files within example.com and extract metadata to a csv report:pymeta -d example.comExtract metadata from files within the given directory and create csv report:pymeta -dir Downloads/All OptionsTarget Options:
  -d DOMAIN             Target domain
  -dir FILE_DIR         Pre-existing directory of files

Search Options:
  -s {google,bing,all}  Search engine(s) to scrape (Default: all)
  -m MAX_RESULTS        Max results per file type, per search engine (Default: 50)
  -j JITTER             Seconds between search requests (Default: 2)

Output Options:
  -o OUTPUT_DIR         Path to store PyMeta's download folder (Default: ./)
  -f FILENAME           Custom report path/name.csv (Optional)
  --debug               Show links as they are collected during scrapingCreditBeau Bullock(@dafthack)-https://github.com/dafthack/PowerMetaPhil Harvey -https://exiftool.org/
Package: pymetasnap
pymetasnappymetasnap is a command-line tool that enables you to extract metadata from the Python Package Index (PyPI). It allows you to retrieve essential information about Python packages hosted on PyPI, including package names, versions, licenses, project URLs, and more.By leveraging the PyPI API, pymetasnap automates the process of gathering package metadata, making it convenient for developers, researchers, and anyone interested in exploring package information in a structured manner.FeaturesRetrieve metadata for Python packages from PyPI.Extract package names, versions, licenses, and other relevant information.Fetch project URLs and version-specific URLs for detailed package exploration.Store the extracted metadata in CSV or Excel format for further analysis.InstallationYou can install pymetasnap using pip:pipinstallpymetasnapUsageDetached modeTo extract metadata for Python packages from PyPI, use the following command:pymetasnapextract--source-path<path_of_the_txt_file>--output<output_path>--format<input_format>Replace the following placeholders in the command:<path_of_the_txt_file>: Names of the packages to retrieve metadata for (separated by spaces).<output_path>: Path to store the extracted metadata file.<input_format>: Format of the input requirements file (pip_list or pip_freeze).Interactive modeAdditionally, an interactive mode is available, allowing you to provide the required values through user prompts as follows:OutputThe tool generates a file containing the extracted metadata for the specified packages in the provided output format (CSV or Excel). The output file includes columns for package name, version, license, repository URL, project URL, and version-specific URL. This information can be used for various purposes, such as dependency analysis, license compliance, or package research.ContributingContributions to pymetasnap are welcome! If you encounter any issues or have suggestions for improvements, please open an issue on the project's GitHub repository.When contributing, please ensure that you follow the existing coding style, write tests for new features, and make sure the tests pass before submitting a pull request.Licensepymetasnap is licensed under the MIT License. See the LICENSE file for more details.AcknowledgmentsThe pymetasnap tool builds upon the PyPI API to provide a convenient way to access package metadata. We would like to express our gratitude to the PyPI maintainers and the Python community for their continuous efforts in maintaining and improving the Python Package Index.ContactFor any inquiries or feedback, please contact the project maintainer atcristian.o.rincon.b@gmail.com
Package: pymetastore
pymetastore 🐝 🐍pymetastoreis a Python client for Hive Metastore.FeaturesPython-friendly interface for Hive MetastoreComprehensive support for metadata operationsFully compatible with the Hive metastore service over Thrift protocolInstallationInstall pymetastore with pip:pipinstallpymetastoreQuick StartHere's a taste of usingpymetastoreto connect to Hive Metastore and interact with metadata:frompymetastore.metastoreimportHMSwithHMS.create(host="localhost",port=9083)ashms:databases=hms.list_databases()database=hms.get_database(name="test_db")tables=hms.list_tables(database_name=database.name)table=hms.get_table(database_name=database.name,table_name=tables[0],)partitions=hms.list_partitions(database_name=database.name,table_name=table.name,)partition=hms.get_partition(database_name=database.name,table_name=table.name,partition_name="partition=1",)
Package: pymetawear
PyMetaWear is a community developed Python SDK started byHenrik Blidh. MbientLab does not provide support for this SDK.Python package for connecting to and usingMbientLab’s MetaWearboards.PyMetaWear can, from version 0.11.0, be used from both Windows and Linux. This is thanks to that themetawearpackagepackage is now depending on a
new BLE library calledPyWarbleinstead ofgattlib.
See installation instructions for more details about how to make it build on Windows.Capabilities and LimitationsPyMetaWear was previously a wrapper around theMetaWear C++ API,
providing a more Pythonic interface. In version 0.9.0 it instead becomes
a wrapper aroundMetaWear’s official Python SDK,
doing the very same thing. The official SDK handles the actual board
connections and communication while PyMetaWear aims to remove the low level
feeling of interacting with the MetaWear board.Installation$pipinstallpymetawearSince version 0.11.0, the installation requirements forpymetawearhas changed. Seedocumentationfor details on requirements for Linux and Windows respectively.DocumentationAvailable in theGithub pagesof this repository.Basic UsageThe MetaWear client can be used in two ways: either as Pythonic
convenience class for handling a MetaWear board or as
a simple communication client governed by thelibmetawearC++ library.Creating a client, and thus also setting up a Bluetooth connection to the
MetaWear board, is equal for both the two usage profiles:frompymetawear.clientimportMetaWearClientc=MetaWearClient('DD:3A:7D:4D:56:F0')ExampleBlinking with the LED lights can be done like this with the
convenience methods:pattern=c.led.load_preset_pattern('blink',repeat_count=10)c.led.write_pattern(pattern,'g')c.led.play()or like this using the rawlibmetawearshared library:fromctypesimportbyreffrompymetawearimportlibmetawearfrommbientlab.metawear.cbindingsimportLedColor,LedPresetpattern=Led.Pattern(repeat_count=10)libmetawear.mbl_mw_led_load_preset_pattern(byref(pattern),LedPreset.BLINK)libmetawear.mbl_mw_led_write_pattern(c.board,byref(pattern),LedColor.GREEN)libmetawear.mbl_mw_led_play(c.board)Bluetooth Low Energy ScanningActual addresses to your MetaWear board can be found by scanning with the includeddiscover_devicesmethod:frompymetawear.discoverimportdiscover_devicesout=discover_devices()print(out)[(u'DD:3A:7D:4D:56:F0',u'MetaWear'),(u'FF:50:35:82:3B:5A',u'MetaWear')]See the examples folder for more examples on how to use thelibmetawearlibrary with this client.ModulesAll functionality of the MetaWear C++ API is able to be used using the
PyMetaWear client, and some of the modules have had convenience methods
added to simplify the use of them. Below is a list of modules which
have had their convenience methods written and one of modules that are
awaiting such focus.Completed ModulesUnimplemented ModulesAccelerometerGPIOGyroscopeNeoPixelHapticColor DetectorSwitchHumidityLEDiBeaconBarometerI2CMagnetometerTemperatureSettingsAmbient Light
Package: pymeteobridgedata
Python Wrapper for Meteobridge DataloggerThis module communicates with aMeteobridge Dataloggerusingtheir Template Script.The module is primarily written for the purpose of being used in Home Assistant for the Custom Integration calledmeteobridgebut might be used for other purposes also.Installpymeteobridgedatais avaible onPyPi:pipinstallpymeteobridgedataUsageThis library is primarily designed to be used in an async context.The main interface for the library is thepymeteobridgedata.MeteobridgeApiClient. This interface takes 6 options:username: (required) The username to login to your Meteobridge device. Default thismeteobridge.password: (required) The password for your meteobridge device.ip_address: (required) IP Address of the Meteobridge device.units: (optional) Valid options here aremetricorimperial. Metebridge devices always deliver data in metric units, so conversion will only take place if if metric is not selected. Default value ismetricextra_sensors: (optional) Number of extra sensors attached to the Meteobridge Logger (Default is 0, max is 7)homeassistant: (optional) Valid options areTrueorFalse. If set to True, there will be some unit types that will not be converted, as Home Assistant will take care of that. Default value isTrueExample Python scriptimportasyncioimportloggingimporttimefrompymeteobridgedataimportMeteobridgeApiClient,Invalid,NotAuthorized,BadRequestfrompymeteobridgedata.dataimportDataLoggerDescription,ObservationDescription_LOGGER=logging.getLogger(__name__)asyncdefmain()->None:logging.basicConfig(level=logging.DEBUG)start=time.time()meteobridge=MeteobridgeApiClient(USERNAME,PASSWORD,IP_ADDRESS,homeassistant=False,units="imperial",extra_sensors=0)try:awaitmeteobridge.initialize()exceptInvalidaserr:_LOGGER.debug(err)exceptNotAuthorizedaserr:_LOGGER.debug(err)exceptBadRequestaserr:_LOGGER.debug(err)data:DataLoggerDescription=meteobridge.device_dataifdataisnotNone:forfieldindata.__dataclass_fields__:value=getattr(data,field)print(field,"-",value)data:ObservationDescription=awaitmeteobridge.update_observations()ifdataisnotNone:forfieldindata.__dataclass_fields__:value=getattr(data,field)print(field,"-",value)end=time.time()awaitmeteobridge.req.close()_LOGGER.info("Execution time:%sseconds",end-start)asyncio.run(main())
Package: pymetfrag
This libraries wrapsMetfrag. It needs pandas installed and works nicely with emzed, though
emzed is not mandatory.MetFragis a freely available software for the annotaion of high precision
tandem mass spectra of metabolites which is a first and critical step for the
identification of a molecule’s structure. Candidate molecules of different
databases are fragmented in silico and matched against mass to charge values. A
score calculated using the fragment peak matches gives hints to the quality of
the candidate spectrum assignment.
Package: pymeth
pymethA python package for basic algorithmsINSTALLATIONInstalling from source code on GitHubYou can clone this repository locally and install package from the source code.gitclonehttps://github.com/saurabhmehta1601/pymeth.gitcdpymeth
pythonsetup.pyinstallInstalling directly with pipYou can use pip to install pymeth withpip install pymethPublishing package to PyPIFirstly, check the package version and metadata in setup.py.Create the binary distribution wheel file for the package withpython setup.py bdist_wheelWe will be usingtwineto upload package to PyPI, so if you have not installed it already install it with command :pip install twine==4.0.2Now, we will use twine to upload package to PyPINOTE: In order to upload python package you will need an account on PyPI with 2Factor Authentication enabled.twine upload --repository-url https://upload.pypi.org/legacy/ -u __token__ -p YOUR_ACCESS_TOKEN dist/*
Package: pymetheus
=========
Pymetheus.. image::https://img.shields.io/pypi/v/pymetheus.svg:target:https://pypi.python.org/pypi/pymetheus.. image::https://img.shields.io/travis/vinid/pymetheus.svg:target:https://travis-ci.org/vinid/pymetheus.. image::https://readthedocs.org/projects/pymetheus/badge/?version=latest:target:https://pymetheus.readthedocs.io/en/latest/?badge=latest:alt: Documentation StatusPyMetheus: Deep Nets for Logical ReasoningFree software: GNU General Public License v3Documentation:https://pymetheus.readthedocs.io.FeaturesProvides an out of the box tool to learn (fuzz) first order logic with the use of an underlying vector spaceFeaturesCreate a Logic Deep Network.. code-block:: pythonimport pymetheus
import itertools
from pymetheus.pymetheus import LogicNet

ll = LogicNet()..Introduce Some Constants.. code-block:: pythonll.constant("Rome")
ll.constant("Milan")
ll.constant("Italy")..Introduce Some Predicates and Knowledge.. code-block:: pythonll.predicate("capital")
ll.predicate("country")

ll.knowledge("country(Milan,Italy)")
ll.knowledge("capital(Rome,Italy)")

ll.zeroing() # Initialize KB with all knowledge as false..Add quantified rule with data
.. code-block:: pythonrule = "forall ?a,?b: capital(?a,?b) -> country(?a,?b)"
ll.universal_rule(rule)
var = ["Italy", "Rome", "Milan"]
ll.variable("?a", var)
ll.variable("?b", var)
..Learn and Reason.. code-block:: pythonll.learn(epochs=1000, batch_size=25)


ll.reason("capital(Rome,Italy)", True)..CreditsThis package was created with Cookiecutter_ and theaudreyr/cookiecutter-pypackage_ project template... _Cookiecutter:https://github.com/audreyr/cookiecutter.. _audreyr/cookiecutter-pypackage:https://github.com/audreyr/cookiecutter-pypackage=======
History0.1.0 (2019-08-22)First release on PyPI.
Package: pymethylprocess
PyMethylProcesshttps://github.com/Christensen-Lab-Dartmouth/PyMethylProcessWiki:https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess/wikiHelp documentation:https://christensen-lab-dartmouth.github.io/PyMethylProcess/Alternatively, you can access the pdf: PyMethylProcess.pdfWhat is it:Preprocess 450k and 850k methylation IDAT files in parallel using Minfi, ENmix, and meffilConvenient and scalable implementationImputation and Feature SelectionPreparation for machine learning pipelinesWhy:Make DNAm accessible to python developers and more machine learning oriented researchersStreamlined analysis makes processing easyPyMethyProcessis now available in Bioinformatics:https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz594/5542385.Getting Started:Installation:pip install git+https://github.com/bodono/scs-python.git@bb45c69ce57b1fbb5ab23e02b30549a7e0b801e3git+https://github.com/jlevy44/hypopt.git@af59fbed732f5377cda73fdf42f3d4981c2be3cepip install pymethylprocess && pymethyl-install_r_dependencies (Note: May need to prefix pip install with MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ for Mac OS install)If incompatibilities with tensorboard: pip install --upgrade setuptoolsdocker pull joshualevy44/pymethylprocess:0.1.3Alternatively, run sh build_docker.sh to build the docker container, and then run sh run_docker.sh to run the docker container.Or see example scripts for usage.Example Usage Scripts (in github repo): Located in ./example_scripts/Help docs (in github repo):https://christensen-lab-dartmouth.github.io/PyMethylProcess/See GitHub wiki for more information on getting started, preprocessing quickly, and setting up and running machine learning analyses / classic methylation analyses (cell type deconvolution, age estimation).Running the CWL tool (temporary instructions until new docker upload):Clone this repository.sh docker_build.shThen execute the cwl/pymethylprocess.cwl tool using Toilhttps://toil.readthedocs.io/en/latest/or Rabix Composer or executorhttps://github.com/rabix/composer, amongst others.Try this dataset for quick testing: GSE109541Note: This CWL tool has limited functionality, if you would like to see additional functions automated (eg. age calculation, cell type deconvolution, running machine learning pipelines), please submit an issue, and we'll add new features.NOTE: There have been reported issues with installing PyMethylProcess on Mac OS Mojave (rpy2). If this is the issue, try the docker installation and please report an issue.CWL Workflow VisualizationBenchmark Results:Supplementary Figure Removed from Manuscript:Supplemental Figure 1:UMAP embeddings (colored) of: a) GSE87571 (age), b) GSE81961 (disease status), c) GSE69138 (subtype), d) GSE42861 (disease status), e) GSE112179 (brain disorder), f) GSE90496 (subclass), g) TCGA Pancancer (subtype)
Package: pymetis-mesh
IntroductionThis repository contains a simple Python wrapper ofMETIS_PartMeshDualandMETIS_PartMeshNodal, which can partition finite element unstructured meshes
either element-wisely or node-wisely, resp. The wrapper script is written in
Cython, and the C code has been already generated. Notice that regenerating the
C source code is pretty straightforward.Installation$pip3installpymetis_meshLicenseMIT LicenseCopyright (c) 2018 Qiao ChenMETISandParMETIShave their own licenses, please check the source directory.