Saravanan666/pypi-org huggingface dataset:
Package: ntrfc
NTRfC READMENumerical Test Rig for Cascades.Free software: MIT licenseDocumentation:https://ntrfc.readthedocs.io.NTRfC is the base of the (NTRFlows)[https://gitlab.uni-hannover.de/tfd_public/tools/NTRFlows] repository, a workflow for cfd parameter studiesFeaturesEasy geometry and post-processing visualization and manipulation with pyvista.
Tested methods and functions for math, time-series, and mesh quality analysis.Dependencieslibgl1-mesa-glx (graphics driver)xvfb (virtual Display)libglu1-mesa (gmsh dependency)libxcursor1 (gmsh dependency)libxinerama1 (gmsh dependency)Current NTRfC versions are based on Python 3.10. Only versions <v0.1.0 can be used with older versions of Python. Library requirements will be installed with the package itself.
InstallationNTRfC is utilizing powerful and complex dependencies like pyvista and gmsh. We strongly recommend using virtual or conda environments for installation.For more information, see:virtualenv:https://pypi.org/project/virtualenv/miniconda:https://docs.conda.io/en/latest/miniconda.htmlanaconda:https://docs.anaconda.com/anaconda/install/index.htmlmamba:https://mamba.readthedocs.io/en/latest/installation.htmlInstallationInstallation from pypi` pip install ntrfc `Installation from gitlab with pip` pip installgit+https://gitlab.uni-hannover.de/tfd_public/tools/NTRfC.git`Installation from sourceAfter cloning the repository, go to the project root dir and type` python setup.py install `Editable installation from source with pipAfter cloning the repository, go to the project root dir and type` pip install-e. `This way you have NTRfC installed but the code is not installed, but linked to the source-code.
You don’t have to reinstall the package after your edits.
This speeds up testing and will lead to less debugging time.Singularity releasesUse a singularity container from ntrfc singularity releases:https://cloud.sylabs.io/library/nyhuma/ntrflows/ntr.sif].
The containers will come with a virtual graphics card and a xvfb display-server, enabling you to render on hpc-systems and any other unprepared system with limited graphics capability.CreditsThis package was created with Cookiecutter and theaudreyr/cookiecutter-pypackageproject template. It uses the following libraries:[pyvista](https://github.com/pyvista)[gmsh](http://gmsh.info/)[Cookiecutter](https://github.com/audreyr/cookiecutter)[audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage)History0.1.8 (2023-10-31)bladeloading fix: reliable variable name usage0.1.7 (2023-10-26)turbocascade fixes: usable meshing algorithmdependency updates0.1.6 (2023-10-13)WITHDRAWN RELEASEproductive meshing algorithmminor improvements0.1.5 (2023-07-04)bugfixes alphashapeinitial gmsh intigrationjupyternotebook testingimproved codecov0.1.4 (2023-06-25)cleaner structure, more reliabilitypypi deploymentsphynx docsremoved bugs0.1.3 (2023-03-11)bug fixesimplementation of a new timeseries stationarity analysisyplus postprocessingadditional tests0.1.2 (2022-20-12)meshquality methodsexample data setsjupyter notebook examples
Package: ntripbrowser
NTRIP BrowserA Python API for browsing NTRIP (Networked Transport of RTCM via Internet Protocol).RequirementspagergeopypycurlcchardettexttablePython 2.6–2.7 & 3.4–3.6Installationmake sure that you havelibcurlinstalledpip install ntripbrowseror clone and runmake installIf you are looking for the last Python2 version of this package, checkout onto v2.2.3 tag. Python2 support will be discontinued in future releases.git checkout v2.2.3libcurl installation hintsinstallation viaapt:apt-get install libssl-dev libcurl4-openssl-dev python-devUsagentripbrowser [-h] [-p] [-t] [-c] host

positional arguments:  
  host                  NTRIP source table host address

optional arguments:  
  -h, --help            Show this help message and exit  
  -p, --port            Set url port. Standard port is 2101  
  -t, --timeout         Add timeout  
  -c, --coordinates     Add NTRIP station distance to this coordinate
  -M  --maxdist         Only report stations less than this number of km away
                        from given coordinateCLI workflow example:ntripbrowser cddis-caster.gsfc.nasa.gov -p 443 -t 5 -c 1.0 2.0 -M 4000Package APIWorkflow example:browser=NtripBrowser(host,port=2101,timeout=5)browser.get_mountpoints()browser.host=another_hostbrowser.get_mountpoints()Arguments:hostNTRIP caster host.
Standard port is 2101, use:portoptional argument to set another one.Optional arguments:portNTRIP caster port.timeoutUsetimeoutto define, how long to wait for a connection to NTRIP caster.coordinatesUsecoordinatesto pass your position coordinates in function and get distance to NTRIP station.Form of coordiantes must be(x, y)or(x.x, y.y)of latitude, longitude.maxdistUsemaxdistto only report stations less than this number of km away from given coordinate.ResultAs a result you'll get a dictionary consisting of a lists of dictionaries with such structure:CAS stations:"Host", "Port", "ID", "Operator", "NMEA", "Country", "Latitude", "Longitude", "FallbackHost", "FallbackPort", "Site", "Other Details", "Distance"NET stations:"ID", "Operator", "Authentication", "Fee", "Web-Net", "Web-Str", "Web-Reg", "Other Details", "Distance"STR stations:"Mountpoint", "ID", "Format", "Format-Details","Carrier", "Nav-System", "Network", "Country", "Latitude", "Longitude", "NMEA", "Solution", "Generator", "Compr-Encryp", "Authentication", "Fee", "Bitrate", "Other Details", "Distance"Exceptionsntripbrowser.NtripbrowserError- base class for all ntripbrowser exceptions.ntripbrowser.UnableToConnect- raised when ntripbrowser could not connect to the assigned url.ntripbrowser.NoDataReceivedFromCaster- raised when ntripbrowser could not find any data on the page.ntripbrowser.ExceededTimeoutError- raised when connection timeout is exceeded.To testmake testKnown IssuesTests withtoxmay fail if python*-dev is not installed.
So, you need to install python2.7-dev and python3.6-dev:sudo apt-get install python2.7-dev
sudo apt-get install python3.6-dev
Package: ntropy-sdk
Ntropy SDKThis repository hosts the SDK for the Ntropy API.  To use the Ntropy API you require an API key which can be requested atntropy.com.The Ntropy API provides transaction enrichment and categorization, account ledger, metrics and custom model training. The full documentation is available at thedeveloper portal.Installation$python3-mpipinstall--upgrade'ntropy-sdk'Quick StartEnriching your first transaction requires anSDKobject and an inputTransactionobject. The API key can be set in the environment variableNTROPY_API_KEYor in theSDKconstructor:fromntropy_sdkimportSDK,Transactionsdk=SDK("YOUR-API-KEY")tx=Transaction(description="AMAZON WEB SERVICES",entry_type="outgoing",amount=12042.37,iso_currency_code="USD",date="2021-11-01",transaction_id="4yp49x3tbj9mD8DB4fM8DDY6Yxbx8YP14g565Xketw3tFmn",country="US",account_holder_id="id-1",account_holder_type="business")enriched_tx=sdk.add_transactions([tx])[0]print(enriched_tx.merchant)The returnedEnrichedTransactioncontains the added information by Ntropy API.  You can consult the Enrichment section of the documentation for more information on the parameters for bothTransactionandEnrichedTransaction.DocumentationYou can consult in-depth documentation and examples at thedeveloper portaland at theSDK reference.License:Free software: MIT license
Package: ntropy-timer
ntropy - Human readable function time measuringAbout·Usage·Testing🗣️ AboutA very simple utility to decorate your functions with that shows you how long that function took to run. Zero dependencies, uses only what Python already gives you.🛠️ UsageRequirementspython >= 3.8 (for TypedDict support)Usage instructionsInstall this package withpip install ntropy-timer;Import themeasure_timefunction and decorate the function you wish to measure:fromntropyimportmeasure_time###@measure_timedefmeasure_this_function(*args,**kwargs):# do somethingThen just execute the function and you should see the results in the standard output:measure_this_function()# > The function 'measure_this_function' took 12 seconds 389 miliseconds to run.ParametersDisabling the gargabe collector for more precise measurements:disable_gc(default:False)With thedisable_gcflag, you can temporarily disable Python's garbage collector while the decorated function runs. This can be useful for more accurately measuring time specific to that function, tuning outside noise down to a minimum.Showing a more straightforward run time message:message_format(possible values:"human", "complete"; default:"human")If you'd like to see a more standard message, showing even zero values, you can pass in themessage_format="complete"parameter. It will go from:The function 'measure_this_function' took 3 minutes 33 seconds 123 miliseconds to run.toThe function 'measure_this_function' took 0hr 3min 33sec 123ms to run.📋 DevelopingRequirementsntropyhas no requirements to run, but it has to be developed. Clone this repository and install the dev requirements withpip install -r dev-requirements.txtTestingTo run tests (after installing dev dependencies):pytest
Package: ntrprtr-bitlocker-forensics
Descriptionntrprtr configurations for forensic analysis of bitlocker volumesInstallationpip install ntrprtr_bitlocker_forensicsUsageShell:GeneralOptionShortTypeDefaultDescription--mode-mString-copy = Create a local copy of bitlocker forensics configuration filesmode = copyOptionShortTypeDefaultDescription--path-pString""Path for local copy of ntrprtr configuration filesExampleTo use this configuration files installntrprtrandntrprtr_bitlocker_forensics:pipinstallntrprtr
pipinstallntrprtr_bitlocker_forensicsTo use the files, create a local copy:python-mntrprtr_bitlocker_forensics-mcopy-p.It creates the following structure:./ntrprtr-bitlocker-config
â”œâ”€â”€â”€bitlocker-metadata-header.json
â”œâ”€â”€â”€bitlocker-volume-header.jsonNow just use the config as input forntrprtr:python-mntrprtr-minterpret-pbitlocker-volume-header.bin-c./ntrprtr-bitlocker-config/bitlocker-volume-header.json-rresult.txtLicenseMIT
Package: ntrprtr-fs-forensics
Descriptionntrprtr configurations for forensic analysis of file systemsInstallationpip install ntrprtr_fs_forensicsUsageShell:GeneralOptionShortTypeDefaultDescription--mode-mString-copy = Create a local copy of file system forensics configuration filesmode = copyOptionShortTypeDefaultDescription--path-pString""Path for local copy of ntrprtr configuration filesExampleTo use this configuration files installntrprtrandntrprtr_fs_forensics:pipinstallntrprtr
pipinstallntrprtr_fs_forensicsTo use the files, create a local copy:python-mntrprtr_fs_forensics-mcopy-p.It creates the following structure:./ntrprtr-fsf-config
|
â”œâ”€â”€â”€ext
â”‚       ext-group-descriptor-table.json
â”‚       ext-inode.json
â”‚       ext-super-block.json
â”‚
â”œâ”€â”€â”€fat
â”‚       fat-directory-entry.json
â”‚       fat-fs-info.json
â”‚       fat-long-filename.json
â”‚       fat-vbr-fat1216.json
â”‚       fat-vbr-fat32.json
â”‚       fat-vbr-type.json
â”‚       fat-vbr.json
|
â”œâ”€â”€â”€gpt
â”‚       gpt-entry.json
|       gpt-header.json
|
â”œâ”€â”€â”€mbr
â”‚       mbr.json
â”‚
â””â”€â”€â”€ntfs
        ntfs-attribute-file-name.json
        ntfs-attribute-header-general.json
        ntfs-attribute-header-non-resident.json
        ntfs-attribute-header-resident.json
        ntfs-attribute-standard-information.json
        ntfs-mft-entry-header.json
        ntfs-vbr.jsonNow just use the config as input forntrprtr:python-mntrprtr-minterpret-pdir-entry.bin-c./ntrprtr-fsf-config/fat/fat-directory-entry.json-rresult.txtLicenseMIT
Package: ntru
A sample Python projectA sample project that exists as an aid to thePython Packaging User
Guide'sTutorial on Packaging and Distributing
Projects.This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.The source for this project is available here.Most of the configuration for a Python project is done in thesetup.pyfile,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.This is the README file for the project.The file should use UTF-8 encoding and can be written usingreStructuredTextormarkdownwith the appropriatekey set. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, and should be
written for that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not a
good idea, although a simple “What's New” section for the most recent version
may be appropriate.
Package: ntsc-py
NTSC-pyctypesinterfaces to Blargg'sNTSC filter libraries.NES NTSCSNES NTSCSMS NTSCInstallationThe preferred installation ofntsc-pyis frompip:pipinstallntsc-pyDebianMake sure you have theclang++compiler installed:sudoapt-getinstallclangWindowsYou'll need to install the Visual-Studio 17.0 tools for Windows installation.
TheVisual Studio Communitypackage provides these tools for free.UsagePython APINES NTSCTo use the NES NTSC filter, first create an instance:fromntsc_pyimportNES_NTSCntsc=NES_NTSC()Thesetupfunction can be used to configure the parameters of the filter. The
same parameters of thesetupfunction can be passed to the constructor on
initialization of the filter. See the table below for a description of each of
the parameters for the setup functionntsc.setup(mode='composite',gamma=1,artifacts=2,sharpness=0.4,...)ParameterStable ValuesDescriptionmodergb,composite,svideo,monochromeSets all parameters to a preset value.hue[-1.0, 1.0]Controls the hue of the image in degrees[-180, 180].saturation[-1.0, 1.0]Controls the saturation from monochrome to over-saturated.contrast[-1.0, 1.0]Controls the contrast of the luminance in the image.brightness[-1.0, 1.0]Controls the brightness from dark to bright.sharpness[-1.0, 1.0]Controls edge / contrast enhancement and blurring effects.gamma[-1.0, 1.0]Adjusts the linearity of the luminance quantizer.resolution?Controls the resolution of the image.artifacts?Controls influence of artifacts caused by color changes.fringing?Controls influence of fringing caused by brightness changes.bleed?Controls the amount of color bleed (color resolution reduction)merge_fields[0, 1]If true, merges even and off fields to reduce flicker.Images can be filtered by assigning them to the input buffer of the image in
NES pixel format using the NES palette of 64 unique colors.ntsc.nes_pixels[:]=np.random.uniform(0,63,ntsc.nes_pixels.shape)Alternatively, RGB images can be converted to the NES palette using a mean
squared error fit.fromntsc_pyimportrgb2nes,nes2rgbntsc.nes_pixels[:]=rgb2nes(np.random.uniform(0,255,ntsc.nes_pixels.shape[:2]+(3,)))Oncenes_pixelshas been updated with new pixel data, callprocessto filter
the image and compute the RGB output inntsc_pixels.ntsc.filter()SNES NTSCComing Soon!SMS NTSCComing Soon!
Package: nts-everdrone
NTS Radio downloaderDownloadsNTSepisodes (with metadata) for offline listening.InstallationFirst install all the requirements.pip3installnts-everdroneUsageUsage: nts [options] args

Options:
  -h, --help            show this help message and exit
  -o DIR, --out-dir=DIR
                        where the files will be downloaded, defaults to
                        ~/Downloads on macOS and %USERPROFILE%\Downloads
  -v, --version         print the version number and quit
  -q, --quiet           only print errorsJust paste the episode url and it will be downloaded in your Downloads folder.ntshttps://www.nts.live/shows/myshow/episodes/myepisodeAlternatively, you can pass a show/host url to download all its episodes.ntshttps://www.nts.live/shows/myshowIf you have multiple urls, write them into a file line by line and pass the file to the script.
Show urls will be expanded and downloaded as well.ntslinks.txtYou can also pass files and urls (shows or episodes) at the same timentslinks.txthttps://www.nts.live/shows/myshowTo change the output directory use the--out-diroption, or the-oshorthandnts-o~/Desktop/NTSlinks.txt
Package: nttc
NTTC (Name That Twitter Community!): Process and analyze community-detected databy Chris Lindgrenchris.a.lindgren@gmail.comDistributed under the BSD 3-clause license. See LICENSE.txt orhttp://opensource.org/licenses/BSD-3-Clausefor details.Documentation:https://nttc.readthedocs.io/en/latest/OverviewA set of functions that process and create topic models from a sample of community-detected Twitter networks' tweets. It also analyzes if there are potential persistent community hubs (either/and by top mentioned or top RTers).It assumes you seek an answer to the following questions:What communities persist or are ephemeral across periods in the corpora, and when?What can these communities be named, based on their top RTs and users, top mentioned users, as well as generated topic models?Of these communities, what are their topics over time?Accordingly, it assumes you have a desire to investigate communities across periods and the tweets from each detected community across already defined periodic episodes with the goal of naming each community AND examining their respective topics over time in the corpus.It functions only with Python 3.x and is not backwards-compatible (although one could probably branch off a 2.x port with minimal effort).Warning:nttcperforms no custom error-handling, so make sure your inputs are formatted properly! If you have questions, please let me know via email.System requirementsarrowtsmnltknetworkxmatplotpandasnumpyemojipprintgensimspacytqdmsklearnjoblibMulticoreTSNEhdbscanseabornstop_wordsInstallationpip install nttcKnown Issues or LimitationsPlease contact me if you discover any issuess.Example notebooksSee theassets/examplesfolder for example uses.Distribution update terminal commands# Create new distribution of code for archiving
sudo python setup.py sdist bdist_wheel

# Distribute to Python Package Index
python -m twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
Package: ntt-command-service
ntt-command-serviceLibrary for implementing Command PatternExamplesfromntt_command_serviceimport*fromtypingimportListfScores:List[float]=[]classAppendDataCommand(ICommand):def__init__(self,fScores:List[float],fScore:float):self._fScores=fScoresself._fScore=fScoredefExecute(self)->None:self._fScores.append(self._fScore)defCanExecute(self)->bool:returnTruedefUndo(self)->None:self._fScore.pop()serCommandService=CommandService()serCommandService.AddCommand(AppendDataCommand(fScores,3))# ----> fScores = [3]serCommandService.AddCommand(AppendDataCommand(fScores,5))# ----> fScores = [3, 5]serCommandService.AddCommand(AppendDataCommand(fScores,1))# ----> fScores = [3, 5, 1]serCommandService.Undo()# ----> fScores = [3, 5]serCommandService.Clear()serCommandService.CanUndo()# ----> False
Package: ntt-injector
Library for creating Dependency Injection PatternUsagefromnttinjectorimportdependency_inject,InjectorclassModel:count=0def__init__(self,strName:str="")->None:self._strName=strNameself.CreateNewModel()def__repr__(self)->str:returnf"<Model name=\"{self._strName}\"/>"@classmethoddefCreateNewModel(cls):cls.count+=1@classmethoddefGetModelInstances(cls):returncls.count@dependency_inject(Model)classViewModel:count=0def__init__(self,mModel:Model,strName:str="")->None:self._mModel=mModelself._strName=strNameself.CreateNewModel()@classmethoddefCreateNewModel(cls):cls.count+=1@classmethoddefGetModelInstances(cls):returncls.countdef__repr__(self)->str:returnf"<ViewModel name=\"{self._strName}\"/>"@dependency_inject(ViewModel,Model)classView:def__init__(self,viewModel:ViewModel,mModel:Model,strName:str="")->None:self._viewModel=viewModelself._model=mModelself._strName=strNamedef__repr__(self)->str:returnf"<View name=\"{self._strName}\"/>"Then use the View as normalview=View()print(view._model)
Package: ntt-json-model
ntt-json-modelLibrary with model object which is observable and can be serialize and deserialize from json fileExampleWithprimitivedatafromntt_json_modelimport*classTestModel(ModelBase):def__init__(self,nValue:int=3,strName:str="Hello",fTemp:float=3.1,lstScores:List[int]=[])->None:super().__init__()IntegerProperty(self,nValue,"_nValue")FloatProperty(self,fTemp,"_fTemp")StrProperty(self,strName,"_strName")ListProperty(self,lstScores,"_lstScores")@propertydefValue(self)->int:returnself._nValue.GetValue()@Value.setterdefValue(self,nValue:int)->None:self._nValue.SetValue(nValue)@propertydefTemp(self)->str:returnself._fTemp.GetValue()@Temp.setterdefTemp(self,fTemp:float)->None:self._fTemp.SetValue(fTemp)@propertydefName(self)->str:returnself._strName.GetValue()@Name.setterdefName(self,strName:str)->None:self._strName.SetValue(strName)@propertydefScores(self)->List[int]:returnself._lstScores.GetValue()ModelBase.mSubModels[TestModel.__name__]=TestModeldefPrintIfChanged()->None:print("Model has changed")model=TestModel()model.Connect(PrintIfChanged)model.Temp=3# ---> "Model has changed"print(model.ToDict())# Output:# {#     "__class__": "TestModel",#     "_nValue": 3,#     "_fTemp": 3.0,#     "_strName": "Hello",#     "_lstScores": []# }model.FromDict({"__class__":"TestModel","_nValue":3,"_fTemp":3.0,"_strName":"Hello","_lstScores":[4,3]})ModelDataclassTestModelClass(ModelBase):def__init__(self,nScore:int=4,*args,**kwargs)->None:super().__init__()IntegerProperty(self,nScore,"_nScore")ModelProperty(self,TestModel(*args,**kwargs),"_mTestModel")@propertydefScore(self)->int:returnself._nScore.GetValue()@Score.setterdefScore(self,nNewScore:int)->None:self._nScore.SetValue(nNewScore)@propertydefTestModel(self)->TestModel:returnself._mTestModel.GetValue()ModelBase.mSubModels[TestModelClass.__name__]=TestModelClassModel ListDataclassTestModelListClass(ModelBase):def__init__(self,fScore:float=8.5)->None:super().__init__()FloatProperty(self,fScore,"_fScore")ModelListProperty(self,[],"_mModels")@propertydefScore(self)->float:returnself._fScore.GetValue()@Score.setterdefScore(self,fNewScore:float)->None:self._fScore.SetValue(fNewScore)@propertydefModels(self)->List[TestModelClass]:returnself._mModels.GetValue()ModelBase.mSubModels[TestModelListClass.__name__]=TestModelListClass
Package: ntt-json-model-parser
ntt-json-model-parserThe project for parsing the data from .json file (use json format) to a object in PythonUsageFirstly, define the model.fromntt_json_model_parserimportParser,ModelProperty,Model,Property@ModelclassLecture:def__init__(self)->None:self._strName=""self._nAttempts=0@PropertydefstrName(self)->str:returnself._strName@PropertydefnAttempts(self)->int:returnself._nAttempts@ModelclassPerson:def__init__(self)->None:self._strName=""self._nAge=0self._lecLecture:Lecture=Lecture()@PropertydefstrName(self)->str:returnself._strName@PropertydefnAge(self)->int:returnself._nAge@ModelProperty(Lecture)deflecLecture(self)->Lecture:returnself._lecLectureThen usingdictObjectData={"strName":"Thao Nguyen The","nAge":23,"lecLecture":None}perFirstStudent:Person=Parser.DeSerializeFromDict(Person,dictObjectData)# The student will have the name: `Nguyen The Thao` and the lecture with default valuedictObjectData={"strName":"Thao Nguyen The","nAge":23,}perFirstStudent:Person=Parser.DeSerializeFromDict(Person,dictObjectData)dictSerializedData=Parser.SerializeToDict(perFirstStudent)The same result for theSerializemethod
Package: ntt-observable-list
ntt-observable-listObservableList pythonExamplefromntt_observable_listimportObservableListdefPrintList(lst:list)->None:print(lst)lst=ObservableList([3,1,4])lst.Connect(PrintList)lst.append(5)# ---> [3, 1, 4, 5]lst[1]=3# ---> [3, 3, 4, 5]