Saravanan666/pypi-org huggingface dataset:
Package: py-melissa-climate
This is an unofficial wrapper for melissa web api:http://seemelissa.com/enCoffee fundraiserInstallingpip install py-melissa-climatepython
>>> from melissa import Melissa
>>> m = Melissa(username="email_adress", password="password")
>>> m.fetch_devices()
{'********': {'user_id': 1, 'serial_number': '********', 'mac': '********', 'firmware_version': 'V1SHTHF', 'name': 'Melissa ********', 'type': 'melissa', 'room_id': None, 'created': '2016-07-06 18:59:46', 'id': 1, 'online': True, 'brand_id': 1, 'controller_log': {'temp': 25.4, 'created': '2018-01-06T10:12:16.249Z', 'raw_temperature': 28188, 'humidity': 18.5, 'raw_humidity': 12862}, '_links': {'self': {'href': '/v1/controllers'}}}}Running the testsWIPAnd coding style testsWe use flake8pip install flake8flake8 melissaBuilt WithrequestsVersioningWe useSemVerfor versioning. For the versions
available, see thetags on this
repository.AuthorsMagnus Knutas-Initial work-kennedysheadLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for detailsAcknowledgmentsMelissa dev team who made a great APIHome-assistant.io for the inspiration to write this
Package: pymelo
pymeloWrapper forDiscordPomeloAPIInstallPython 3 or higher is required.# Linux/OS X
$ python -m pip install -U pymelo

# Windows
> py -3 -m pip install -U pymeloExampleCheck if the username 'nemupy' is available.importpymelocheck=pymelo.Check("nemupy")ifcheck.status==2:print(f"The username '{check.username}' is available.")elifcheck.status==3:print(f"The username '{check.username}' is not available.")else:print(f"An error occurred while checking the username '{check.username}' availability.")# The username 'nemupy' is not available.ResponseusernameYour checked username.statusSeeDiscordPomeloAPI.attemptHow many attempts it took until a valid response was returned by discord.
Package: pyMelt
pyMelt mantle melting libraryIntroductionpyMelt is a python library for calculating the melting behaviour of mantle comprising multiple lithologies. The pyMelt library implements the melting equations developed by Phipps Morgan (2001), alongside many existing lherzolite and pyroxenite melting parameterisations.Parameters that can be calculated:The geotherm for decompressing mantleMelt fractions for each lithologyCrustal thickness for passive-upwelling at a mid-ocean ridgeCrystallisation temperatures (following the method in Matthews et al., 2016)Magma flux at intra-plate settingsLava trace element concentrationsThehydrousLithologymodule allows hydrous melting to be approximated given any anhydrous lithology melting model using a modified version of the Katz et al. (2003) hydrous melting equations.DocumentationFull documentation, further information about the package, and a tutorial for getting started are
provided atpymelt.readthedocs.io.InstallationpyMelt is available on pip, and can be installed by runningpip install pyMeltin a terminal.Run pyMelt on the cloud with myBinderYou can use pyMelt and go through the tutorials right now without installing anything.pyMelt_MultiNestpyMelt can be used in conjunction with the MultiNest algorithm (Feroz and Hobson, 2008; Feroz et al., 2009, 2013) via its python frontend, pyMultinest (Buchner et al., 2014). This permits the inversion of measured data (e.g. crystallisation temperature, crustal thickness) to obtain unknowns (e.g. potential temperature) via Bayesian inference. More details of the inversion methods are provided in Matthews et al., 2021.For pyMelt_MultiNest to work, MultiNest and pyMultinest must be installed. The user is directed to thepyMultinest installation instructionsfor further guidance.Note that the pyMelt_MultiNest library is built for an old version of pyMelt and has not yet been updated.Citing pyMeltIf pyMelt enables or aids your research please cite thepyMelt paper, published in Volcanica. To ensure reproducibility please also state the version of pyMelt that you used. The latest release is v2.00, which is archived in a Zenodo repository with DOI:You should also cite the relevant publications for the pure-lithology melting models. If you use our models, you should cite:Matthews, S., Wong, K., Shorttle, O., Edmonds, M., & Maclennan, J. (2021). Do olivine crystallization temperatures faithfully record mantle temperature variability?. Geochemistry, Geophysics, Geosystems, 22(4), e2020GC009157.https://doi.org/10.1029/2020GC009157See the documentation for the citations for the other models included in pyMelt.
Package: pymemapi
Memrise APIInstallationpython -m pip install PyMemAPIMajor FeaturesAPI Memrise with some actions such as create new level, add bulk, rename level, ...Automaticially generate the audio and upload to MemriseAutomaticially translate, get the International Phonetics Alphabet from database and sync with MemriseDocumentationsThe library have 3 main classesMemrise,CourseandSQLite.MemriseMemrise object will control your connection to Memrise. It's required your username and password to take permissions. And then it'll grant the necessary permission for the further process.classMemrise:username:str=field(init=False)password:str=field(init=False)session:requests.Session=requests.Session()deflogin(self,username,password)->bool:...defcourses(self)->List[Course]:...defget(self,path:str,params:Optional[Dict[str,Any]]=None):...defpost(self,path:str,payload:Dict[str,Any],headers:Dict[str,str],files:Optional[Dict[str,Tuple[str,bytes,str]]]=None,)->requests.Response:...defselect_course(self)->Course:...CourseSQLite
Package: pymemdb
pymemdbwill soon be available withpip install pymemdbDescriptionVery simple RDMBS that is supposed to serve as a drop-in replacement for a conventional DB during build-up. It is very fast, completely written in python und relies heavily on dictionaries. It features ato_sqliteexport method - more DBs will follow.UsageInsert into a tablefrom pymemdb import Table

table = Table()
row1 = dict(firstname="John", lastname="Smith")
row2 = dict(firstname="Jane", lastname="Smith")
row3 = dict(firstname="John", lastname="Doe")

for row in [row1, row2, row3]:
    table.insert(row)iterate over the entire tableprint(list(table.all()))

[{'id': 0, 'firstname': 'John', 'lastname': 'Smith'},
 {'id': 1, 'firstname': 'Jane', 'lastname': 'Smith'},
 {'id': 2, 'firstname': 'John', 'lastname': 'Doe'}]update rowstable.update(where={"firstname": "Jane"}, firstname="Joanne")
print(list(table.all()))

[{'id': 0, 'firstname': 'John', 'lastname': 'Smith'},
 {'id': 1, 'firstname': 'Joanne', 'lastname': 'Smith'},
 {'id': 2, 'firstname': 'John', 'lastname': 'Doe'}]search for rowsprint(list(table.find(firstname="John")))

[{'id': 0, 'firstname': 'John', 'lastname': 'Smith'},
{'id': 2, 'firstname': 'John', 'lastname': 'Doe'}]search for values in iterableprint(list(table.find(firstname=["John", "Joanne"])))

[{'id': 0, 'firstname': 'John', 'lastname': 'Smith'},
{'id': 1, 'firstname': 'Joanne', 'lastname': 'Smith'},
{'id': 2, 'firstname': 'John', 'lastname': 'Doe'}]delete rowstable.delete(firstname="John", lastname="Smith")
print(list(table.all()))

[{'id': 1, 'firstname': 'Joanne', 'lastname': 'Smith'},
 {'id': 2, 'firstname': 'John', 'lastname': 'Doe'}]
Package: pymementodb
Memento Database Python SDKThis is the python SDK for Memento Database API. Pull requests and bug reports are welcomed.Latest VersionThe latest version of Memento Database API SDK is PyMementoDB-1.0.0InstallationInstall using pip :$pipinstallpymementodbGet access tokensInstall desktop application and get authorisation token in Account menu.Include memento in your applicationfrompymementodbimportMementoGet memento instanceTo get an instance of memento just provide the access token.server=Memento('<access token>')Optionally, the custom server can be specified.server=Memento('<access token>','<memento token>')UsageOnce you have the access token you can make the calls to the server.
Get the memento instance and set the access token and then make authorized calls.List library infoYou can uselist_libraries()to list all available libraries and their main info.server=Memento('<access token>')libs_info=server.list_libraries()Get LibraryYou can useget_library()to get the specific library.
It returns aLibraryobject.server=Memento('<access token>')lib=server.get_library('<library id>')Get EntriesYou can useget_entries()to get entries of the library.
It returns a list ofEntryobjects.server=Memento('<access token>')lib=server.get_entries('<library id>')Optional parameters:limit (int, all by default) - limits the query result to the limit number of the newest entriesfield_ids (list of ints, all by default) - include only the specified entry field values in resultsstart_revision (int, oldest by default) - return entries since the provided library revision onlyGet EntryYou can useget_entry()to get a specific entry.
It returns anEntryobject.server=Memento('<access token>')entry=server.get_entry('<library id>','<entry id>')Create EntryUsecreate_entry()to create a new entry.
It returns anEntryobject that represents the newly created entry.'<field values>'is the list of dictionaries:[{'id': <field id>, 'value': <field value>}, ...].
Datetime fields to be provided in the'2023-08-31T16:47:00+00:00'format.server=Memento('<access token>')new_entry=server.create_entry('<library id>','<field values>')Edit EntryUseedit_entry()to modify a specific entry.
It returns anEntryobject that represents the modified entry.server=Memento('<access token>')server.edit_entry('<library id>','<entry id>','<field values>')Delete EntryUsedelete_entry()to delete a specific entry.server=Memento('<access token>')lib=server.delete_entry('<library id>','<entry id>')ObjectsAll objects are derived fromdataclasses.dataclass.LibraryProperties:idcreatedTimemodifiedTimefieldsnameownerrevisionsizeEntryProperties:idcreatedTimemodifiedTimefieldsstatusauthorrevisionsizeMethods:get_field_value(field_id) - returns the value of the field.FieldProperties:idnameoptionsorderroletypeLicenseMIT
Package: pymemfd
pymemfd -- memfd_create wrapper for Python==========================================This modules provides a Python wrapper for the new memfd_create() syscalland the new file-sealing fcntl() operators.Low level interface-------------------memfd.memfd_create(name, flags) -> intmemfd.MFD_ALLOW_SEALINGmemfd.MFD_CLOEXECmemfd.F_ADD_SEALSmemfd.F_GET_SEALSmemfd.F_SEAL_SEALmemfd.F_SEAL_GROWmemfd.F_SEAL_SHRINKmemfd.F_SEAL_WRITEHigh level interfaces---------------------memfd.open(name, *, flags=MFD_CLOEXEC, mode='wb+', buffering=0, closefd=True,**kwargs) -> io.FileIOmemfd.F_SEAL_ALLmemfd.MFD_ALLOW_SEALING_CLOEXECMore----http://man7.org/linux/man-pages/man2/memfd_create.2.htmlhttps://dvdhrm.wordpress.com/tag/memfd/
Package: pymemo-cache
PyMemoSimple Pythonmemoizationlibrary. Decrease your app's response time!Installationpipinstallpymemo-cacheUsagefrompymemoimportPyMemomemo=PyMemo()# Creates collections with optional expiration intervalarticles_cache=memo.create_collection('articles',expiration_interval=15)# Expires after 15 secondscomments_cache=memo.create_collection('comments',expiration_interval=60)# Expires after 60 secondscountry_list_cache=memo.create_collection('countries')# Never expiresarticles_cache.set_item('article-1',{'id':1,'title':'Article Example'})# Any data typearticles_cache.get_item('article-1')# -> {'id': 1, 'title': 'Article Example'}# After 15 secondsarticles_cache.get_item('article-1')# -> None# Set customized expiration interval for individual itemscomments_cache.set_item('comment-1',{'id':1,'content':'Lorem ipsum'},expiration_interval=120)# Expires after 120 seconds instead of 60 seconds
Package: pymemoq
Python module to facilitate accessing thememoQ API.InstallationThis module is available on the Python Package Index (PyPI) and can be installed as follows:pip install pymemoqDependenciesThis module is dependent on the following additional packages:zeepAvailable classesThe following classes are currently available (eventual goal is to provide wrappers for all of the APIs):Undermemoq.server:MemoQServer- Wraps around a memoQ server and exposes a limited subset of the APIUndermemoq.webservice:MemoQAsynchronousTasksService- Asynchronous Tasks management APIMemoQELMService- License (ELM) management APIMemoQFileManagerService- File upload/download APIMemoQLightResourceService- Light resource management APIMemoQLiveDocsService- LiveDocs management APIMemoQSecurityService- Security APIMemoQServerProjectService- Server projects APIMemoQTBService- Term base management APIMemoQTMService- Translation memory management APIExample API Usage>>> from memoq import MemoQServer
>>> memoq_server = MemoQServer('http://localhost:8080')
>>> memoq_server.api_version
'9.2.5'
>>> from memoq.webservice import MemoQServerProjectService
>>> project_service = MemoQServerProjectService('http://localhost:8080')
>>> project_service.GetApiVersion()
'9.2.5'
>>> from memoq.util import response_object_to_dict
>>> projects = [response_object_to_dict(project) for project in memoq_server.projects]
>>> from collections import Counter
>>> Counter([proj['DocumentStatus'] for proj in projects])
Counter({'TranslationInProgress': 65, 'TranslationFinished': 43, 'ProofreadingFinished': 21})
>>>Implementation NotesPer the recommendation toenable caching of WSDL and XSD files, we are using an instance of
thezeep.CachingClient()under the hood.  Since the memoQ WSDL and XSD files should not be changing except when
the server is upgraded, this should improve performance without causing any issues.ReferencesmemoQ Server Resources API documentationmemoQ server Web Service API documentationLicenseThis is released under an MIT license.  See theLICENSEfile in this repository for more information.
Package: pymemorymodule
PyMemoryModulePyMemoryModule is a Python binding forMemoryModule.By this module, you can load DLL completely from memory - without
storing on the disk first - all thanks toMemoryModulewritten by
Joachim Bauch.How to installpip install pymemorymodulePackages are available atPyPIandPyPI
test.How to useimport pymemorymodule as pymm
from ctypes import cast, c_int, CFUNCTYPE

with open("path/to/library.dll", "rb") as fp:
    # Load DLL from bytes object
    handle = pymm.MemoryLoadLibrary(fp.read())

    # __declspec(dllexport) int add(int a, int b)
    add = cast(
        pymm.MemoryGetProcAddress(handle, "add"),
        CFUNCTYPE(c_int, c_int, c_int)
    )

    # Use function exported from DLL
    assert add(1, 2) == 3

    # Free loaded DLL
    pymm.MemoryFreeLibrary(handle)How to build and testpython setup.py build_ext -i testHow to run code checkpython -m pip install flake8
python -m flake8 --show-source setup.py test.pyHow to prepare README.rstpython setup.py md2rstMemoryModulehttps://github.com/fancycode/MemoryModuleLicenseMozilla Public License Version 2.0 (MPL2.0)See alsolicense of
MemoryModule.Note: PyMemoryModule also distributes a few lines of MIT licensed codes
taken frompy3c project.
Package: pymem-osx
pymem-osxA python library for MacOS, providing the base functions required for memory manipulation (read/write).FeaturesMemory read/write helpers.Base address searcherModules name and address dumperLive in-memory modules macho-o address parser/dumper (64-bit implementation).Useful for finding address of different sections (__DATA, __TEXT) and even their respective segments.InstallationYou can install pymem from source.git clone https://github.com/qtkite/pymem-osx.git
cd pymem-osx
python3 -m pip install -e .Alternatively you can do:pip install pymem-osxNoteWhen running your python script, it must be elevated (in sudo mode) for the api calls to work.DonateBuy me a coffee to support some of the work I put out
Package: pymempool
mempool.space API wrapperPython3 wrapper around themempool.spaceAPI (V1)InstallationPyPIpipinstallpymempoolor from sourcegitclonehttps://github.com/holgern/pymempool.gitcdpymempool
python3setup.pyinstallUsagefrompymempoolimportMempoolAPImp=MempoolAPI()API Documentationhttps://mempool.space/docs/api/restTest SuiteSet up the test environmentInstall the test-runner dependencies:pip3 install -r requirements-test.txtThen make thepymempoolpython module visible/importable to the tests by installing the local dev dir as an editable module:# from the repo root
pip3 install -e .Running the test suiteRun the whole test suite:# from the repo root
pytestRun a specific test file:pytest test/test_this_file.pyRun a specific test:pytest test/test_this_file.py::test_this_specific_testRunning tests with toxInstall toxpip install toxRun teststoxLicenseMITPre-commit-configInstallation$ pip install pre-commitUsing homebrew:$ brew install pre-commit$ pre-commit --version
pre-commit 2.10.0Install the git hook scripts$ pre-commit installRun against all the filespre-commit run --all-files
pre-commit run --show-diff-on-failure --color=always --all-filesUpdate package rev in pre-commit yamlpre-commitautoupdate
pre-commitrun--show-diff-on-failure--color=always--all-files
Package: pymemri
PymemriPymemri is a python library for creatingPluginsfor the Memri Personal online datastore(pod). Pymemri has a PodClient to communicate with the pod, and tools to build and test plugins.Plugins connect and add the information to your Pod. Plugins thatimport your data from external servicesare calledImporters(Gmail, WhatsApp, etc.). Plugins thatconnect new data to the existing dataare calledindexers(face recognition, spam detection, object detection, etc.). Lastly there are plugins thatexecute actions(sending messages, uploading files).InstallingAs a packagepipinstallpymemriDevelopmentTo install the Python package for development, run:pipinstall-e.ContributingIf you would like to contribute to Pymemri, have a look at ourcontibuting guidelinesto get started and join our Discord if you have any questions about using or contributing to the library.Quickstart: Pod ClientAll interaction between plugins and the pod goes via the PymemriPodClient. To use this client in development, we first need to have a pod running locally. The quickest way to do this is to install from thepod repo, and run./examples/run_development.sh.If you have a running pod, you can define and add your own item definitions:frompymemri.data.schemaimportItemfrompymemri.pod.clientimportPodClientclassDog(Item):name:Optional[str]=Noneage:Optional[int]=None# Connect to the pod and add the Dog item definitionclient=PodClient()client.add_to_schema(Dog)# Add a Dog to the poddog=Dog(name="Rita",age=3)client.create(dog)Quickstart: Running a pluginAfter installation, users can use the plugin CLI to manually run a plugin. For more information on how to build a plugin, seerun_plugin.With the pod running, run in your terminal:store_keys
run_plugin--metadata"example_plugin.json"This stores a random owner key and database key on your disk for future use, and runs the pymemri example plugin. If everything works correctly, the output should readPlugin run success.Docspymemri docs
Package: pymem-snapshot
PyMem - Get Memory Image on WindowsWhat is this ?It is a software that you can take "memory image with AFF4 format" from your device with Python.How is it working ?Before taking your memory copy, it obtains information about your memory size. Then, based on this information, it addresses your memory image according to the buffer size and buffer size, and then starts making memory copies of all your applications.Tested Image Forensic SoftwaresAccessData FTK ImagerTested OS (on Virtual Machine)Windows 11 Build Number 22621.2283ImagesInstallation------------------------------------------------------------------------------------------------
  IMPORTANT : This program can CURRENTLY take a maximum of 2 GB memory image.
  ÖNEMLİ : Bu program ŞU ANLIK maksimum 2 GB bellek imajı alabilmektedir.
  ------------------------------------------------------------------------------------------------
  On CMD or PowerShell (Administrator)
  cd pymem_current_directory
  bcdedit /set testsigning on
  Check Memory Compression with "Get-MMAgent" command
  Disable Memory Compression with "Disable-MMAgent -mc" command
  Restart...winget install python --source=msstore
OR
winget install python
python -m pip install -r requirements.txt
python example.py
OR
pip install pymem_snapshot (PyPi Link)
python example.pyDisclaimerIt should not be forgotten that taking a memory image is a serious process. In this process, you may encounter numerous errors, BSODs (Blue Screen of Death), and even memory errors. For this reason, we declare that we are not responsible for any damage that may arise.For this reason, we recommend that you run your tests in demo environments.Unutulmamalıdır ki, bellek imajı almak ciddi bir süreçtir. Bu süreçte çok sayıda hata, BSOD (Blue Screen of Death / Mavi Ekran Hataları) ve hatta bellek hatalarıyla karşılaşabilirsiniz. Bu nedenle doğabilecek herhangi bir zarardan sorumlu olmadığımızı beyan ederiz.Bu nedenle testlerinizi demo ortamlarda yapmanızı tavsiye ederiz.LinksPyPi LinkThanksGreat thanks to theVelocidex (WinPMEM)team for providing drivers
Package: pymemuc
pymemucA Python API for MEmu Android Emulator.
The API exposesMEmu Command (MEMUC)functionality in Python.Allows for easy interaction with MEmu VMs, including VM image management, VM control, running VM commands and ADB interaction.InstallationpipinstallpymemucExample usage# import the PyMemuc classfrompymemucimportPyMemuc# create a PyMemuc instance, doing so will automatically link to the MEMUC executablememuc=PyMemuc()# create a new vm, saving the indexindex=memuc.create_vm()# list out all vmsprint(memuc.list_vm_info())# start the vmmemuc.start_vm(index)# stop the vmmemuc.stop_vm(index)Seethe demo notebookfor more examples.DocumentationSee theAPI documentation.
Package: pymenable
pymenablePython classes to render accessible HTML elementsInstallation$pipinstallpymenableUsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensepymenablewas created by Joel Dodson. It is licensed under the terms of the MIT license.Creditspymenablewas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.The initial code forpymenableand theexamples, came from theuianduiTest(respectively) packages from theaclu(Accessible Command Line Utilities) projectI started while at RingCentral.The structure of the code including boilerplate for documentation and packaging came from thebook on Python Packaging,Python Packages.
Package: pymender
👷‍♀️ PyMenderPerform entire codebase refactors in a way that isreproducible,testableandreviewable. Obeys.gitignoreby default.Usagepipinstallpymender==0.2.0

pymender<codemod><path_to_project>What codemods are available?⚡FastAPIAnnotatedConverts FastAPI endpoints to use the preferredAnnotated[<type>, Depends(<dependency>)]syntax rather than: <type> = Depends(<dependency>).Why?Defaultvalue of the function parameter is theactual defaultvalue.Reuseof the function is now possible.Type-safeusage of the functions, previously 'default' values where not type checked.Multi-purposee.g.Annotated[str, fastapi.Query(), typer.Argument()]is now possible.pymenderFastAPIAnnotated<folder-to-upgrade>BeforeAfter@router.get('/example')def example_function(value: int,query: str = Query("foo"),zar: str = Query(default="bar", alias="z"),foo: str = Depends(get_foo),*,bar: str = Depends(get_bar),body: str = Body(...),) -> str:return 'example'@router.get('/example')def example_function(value: int,foo: Annotated[str, Depends(get_foo)],query: Annotated[str, Query()] = "foo",zar: Annotated[str, Query(alias="z")] = "bar",*,bar: Annotated[str, Depends(get_bar)],body: Annotated[str, Body()],) -> str:return 'example'Developer Guide# Run a particular codemodpython3-mpymender<codemod><path_to_project># e.g.python3-mpymenderFastAPIAnnotated<path_to_project># Run the codemod directlypython3-mlibcst.toolcodemodefastapi_annotated.FastAPIAnnotated<path_to_project># Run testspytest-vvThanks to:libCSTwhich does a lot of the hardwork for this.autotypingfor showing what was possible.