Saravanan666/pypi-org huggingface dataset:
Package: nutsflow
IntroductionInstallationTutorialDocumentationGithubnuts-flowis largely a thin wrapper around Python’sitertoolsthat allows
the chaining of iterators using the>>operator. This leads to more
readable code that highlights the flow of data. The following example shows
two implementations of a simple data processing pipeline; the first based onitertoolsand the second usingnuts-flow:>>> from itertools import islice, ifilter
>>> list(islice(ifilter(lambda x: x > 5, xrange(10)), 3))
[6, 7, 8]>>> from nutsflow import Range, Filter, Take, Collect, _
>>> Range(10) >> Filter(_ > 5) >> Take(3) >> Collect()
[6, 7, 8]Both examples extract the first three numbers within range [0, 9]
that are greater than five. However, thenuts-flowpipeline
is easier to understand than the nesteditertoolscode.nuts-flowis the base fornuts-ml,
which is describedhere.
Package: nutsml
IntroductionInstallationTutorialDocumentationExamplesGithubnuts-mlis a data pre-processing library for GPU based deep learning
that provides common pre-processing functions as independent, reusable units.
These so called ‘nuts’ can be freely arranged to build data flows that
are efficient, easy to read and modify.The following example gives a taste of anuts-mldata-flow that
trains a network on image data and prints training loss and accuracy(train_samples>>Stratify(1)>>read_image>>transform>>augment>>Shuffle(100)>>build_batch>>network.train()>>Print('train loss:{}acc:{}')>>Consume())nuts-mlis based onnuts-flow,
which is describedhere.
Package: nuttcp-parser
# python-nuttcp-parserParsing the output of NUTTCP in Python##Usageparser = nuttcp_parser.Parser(nuttcp_output) nuttcp_output must be string not bytes\result = parser.parse()##Returns* duration in seconds* transmitted_MB* speed_Mbs* RX* TX* and for TCP Additionally:* retrans* RTT in ms* and for UDP Additionally:* dropped* packets* loss in %
Package: nutui
nutUI - Flask WebUI for nut-serverTesting Build - Not considered production readynutUI Main Page with Light ThemenutUI Main Page with Dark Themegit clone https://github.com/beardedtek-com/nutui.gitnutui.py -h
usage: nutui.py [-h] [-c] [-f] [-v] [-u] [-a] [-n NUTHOST] [-ah APIHOST] [-ap APIPORT] [-i INTERVAL] [-l NUTLOGIN] [-p NUTPASSWORD]

Starts nutclient and nutui flask server

options:
  -h, --help            show this help message and exit
  -c, --clientonly      Runs nutclient only. Disables the flask server
  -f, --flaskonly       Runs the flask server only
  -v, --verbose         Verbose Output
  -u, --uwsgi           Launches web app with uwsgi instead of flask development server
  -a, --all
  -n NUTHOST, --nuthost NUTHOST
                        ip/hostname of nut server (defaults to 127.0.0.1)
  -ah APIHOST, --apihost APIHOST
                        ip/hostname of nutUI api server
  -ap APIPORT, --apiport APIPORT
                        port number of nutUI api server
  -i INTERVAL, --interval INTERVAL
                        number of seconds between polling nut-server
  -l NUTLOGIN, --nutlogin NUTLOGIN
                        nut-server login (default: None)
  -p NUTPASSWORD, --nutpassword NUTPASSWORD
                        nut-server login (default: None)
Package: nuudel-best-time-finder
Nuudel best time finderIn a poll like this:2023-06-052023-06-072023-06-082023-06-12...AliceYesNoNoYes...BobNoNoNoYes...CarlYesYesNoNo...DaveYesNoNoYes...EveNoNoNoYes...FrancisNoNoNoNo.....................a common task is to find the set of two or more days that cover as many people as
possible.This tool works with the poll service nuudel (https://nuudel.digitalcourage.de/),
reads the data directly from a given poll and finds the best combinations for any number
of times.InstallInstall using pip:pip install nuudel-best-time-finderHow to useSay we have a nuudel poll at:https://nuudel.digitalcourage.de/nuudel-poll-idand want
to find two dates that cover the most amount of people simply run the following in your
console:nuudel-find-best-times {nuudel-poll-id} 2 --results-file results.csvor alternatively as part of a script:fromnuudel_best_time_finderimportfind_best_timesfind_best_times(poll="{nuudel-poll-id}",n=2,results_file="results.csv")The results are then written to a file calledresults.csvwith three columns:Time combinationsNumber of people covered by the combinationPercent coverage out of all participants
Package: nuvla-cli
Nuvla Command-Line interface clientNuvla CLI client. Allows to control some Nuvla functionalities from a terminal. It
currently supports the creation of Edges and Fleets, as well as  geolocation.First stepsTo use this library it is required to have an account inhttps://nuvla.io. If you don't have one, go toNuvlaand start with the User Interface.Once the account is created, you will need to create an API Key credential in Nuvla/UI credentials sections. Due to security reasons, the CLI does not support user/password authentications.1. Install Nuvla CLIThe package can be installed directly from PyPi repository for convenience.$pipinstallnuvla-cliOr download the pre-compiled packages fromhereRequirementsAll the dependencies are installed with pip.Python >= 3.82. Create credentials in NuvlaAs mentioned before, to use the CLI it is required to have API credentials in Nuvla.io.To create them:Go tocredentialstab.Click on add in the top left corner.Select Nuvla API-Key and provide the name and description that suits better for your needs.Copy the key-secret as this is the only time it will be provided. If lost, you will need to delete this credential and create a new one.3. Login the CLIThe CLI provides two login possibilities: environmental variables or cli options.ENV Variables:$exportNUVLA_API_KEY='your_api_key'$exportNUVLA_API_SECRET='your_secret_key'$nuvla-cliloginCLI Options$nuvla-clilogin--key'your_api_key'--secret'your_secret_key'The session is persistent and stored in the user's path under ~/.nuvla/. To remove the session just logout using the CLI.For further details, the whole help depiction on the CLI can be foundhere
Package: nuvole
nuvoleNuvole is a simple wrapper of Pythontornado web framework- crogeo.orgInstallationFrom sourcesgitclonehttps://github.com/crogeo/nuvole.gitcdnuvole
pipinstall.From PyPipipinstallnuvoleDocumentationUsagefromnuvoleimportServer,ServiceclassMyService(Service):PATH=r'/page/*'defget(self):self.write('Hello World')if__name__=='__main__':server=Server([MyService,])server.run('localhost',8080)LicenseCrogeo nuvole isMIT licensed.
Package: nuvolos-cli
_   _                  _              ____ _     ___ 
| \ | |_   ___   _____ | | ___  ___   / ___| |   |_ _|
|  \| | | | \ \ / / _ \| |/ _ \/ __| | |   | |    | | 
| |\  | |_| |\ V / (_) | | (_) \__ \ | |___| |___ | | 
|_| \_|\__,_| \_/ \___/|_|\___/|___/  \____|_____|___|Nuvolos CLI is command-line interface (CLI) for theNuvolosplatform. It allows you to interact with the Nuvolos platform from your local terminal.It interacts with theNuvolos REST APIvia thenuvolos-client-apipackage.For installation and detailed usage, check out thedocumentation.InstallationYou can install the latest stable version using pip withpip install nuvolos-cli
Package: nuvolos-odbc
Nuvolos connector for Python (PyODBC version, for internal use)This package allows you to read table data from Nuvolos and upload Pandas dataframes as tables.Loading data to NuvolosThis package provides ato_sqlfunction, which bulk loads a Pandas DataFrame to Nuvolos.
This function is able to load large DataFrames quickly and efficiently, using the database engine's bulk data ingestion process.
It is based on thewrite_pandasfunction of thesnowflake-connector-pythonpackage.Note:It is recommended to use lowercase DataFrame column and index names.
Uppercase or mixed-case DataFrame column/index names will result in case-sensitive table name and column names, which can be queried with quoted identifiers.Syntax:defto_sql(df,name,con,database=None,schema=None,if_exists="fail",index=True,index_label=None,nanoseconds=False,):"""Load a DataFrame to the specified table in the database.Creates the table if it doesn't yet exist, with TEXT/FLOAT/DATE/TIMESTAMP columns as required.The name will be case sensitive (quoted) if it contains lowercase or special characters or is a reserved keyword.Based on the write_pandas function of snowflake-connector-python:https://docs.snowflake.com/en/user-guide/python-connector-api.html#write_pandas:param df: The Pandas DataFrame to insert/stage as a table.:param name: The name of the database table. It will only be quoted and case sensitive if it contains keywords or special chars.:param con: The pre-opened database Connection to use.:param database: The name of the database to which data will be inserted.:param schema: The name of the schema to which data will be inserted.:param if_exists: How to behave if the table already exists. {‘fail’, ‘replace’, ‘append’}, default ‘fail’* fail: Raise a ValueError.* replace: Drop the table before inserting new values.* append: Insert new values to the existing table.:param index: bool, default True: Write DataFrame index as a column. Uses index_label as the column name in the table.:param index_label: Column label for index column(s). If None is given (default) and index is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.:param nanoseconds: If True, nanosecond timestamps will be used to upload the data. Limits timestamp range from 1677-09-21 00:12:43.145224192 to 2262-04-11 23:47:16.854775807.:return: Returns the COPY INTO command's results to verify ingestion in the form of a tuple of whether all chunks wereingested correctly, # of chunks, # of ingested rows, and ingest's output."""Usage example:In this example, the DataFrame will be loaded to a table named "quotes_AND_index", which is a case-sensitive name.fromnuvolosimportget_connection,to_sqlimportpandasaspdconn=get_connection()to_sql(df=df,name="lowercase_is_best",con=conn,index=True,index_label="seq_num",if_exists="replace")df_r=pd.read_sql('SELECT * FROM lowercase_is_best;',con=conn,index_col="seq_num")df_c=df.compare(df_r)# Will be an empty DataFrame, as there are no differences.
Package: nuvoton-isp
Nuvoton ISP library and CLIFree software: MIT licenseDocumentation:https://fishman.github.io/nuvoton_ispInstallation:$pipinstallnuvoton-ispUSAGE:$nuvoisp-ffirmware.binFeaturesFlash APROMCreditsThis package was created withCookiecutterand thewboxx1/cookiecutter-pypackage-poetryproject template.
Package: nuwavsource
nuwavsourceThis package is supposed to be used to detect the sources in NuStar observations and generate a mask excluding the signal from the sources of any kind.Additionaly, it generates a table containing:Useful data about the observation:OBS_IDDetectorCoordinates in equatorial (ra,dec) and galactical (lon,lat) systemsTime of the observation in secondsExposureUseful algorythm-related data:Average count rate on unmasked areaPortion of unmasked areaSpecific statistical metric[1] before and after masking the detected sourcesRoot-mean-square of counts in unmasked areaInstallationThis package is to be used with Python 3.x.xTo install tha package writepipinstallnuwavsourceUsageTo use the package in your project, import it in by writingfromnuwavsourceimportnuwavsourceYou can process the cl.evt file by creating an Observation class object:obs=nuwavsource.Observation(path_to_evt_file)Additionally, the energy band in KeV to get events from can be passed as an argument. The default value is [3,20].obs=nuwavsource.Observation(path_to_evt_file,E_borders=[E_min,E_max])
Package: nuztf
NuZTFPython package for correlating ZTF data with external multi-messenger triggers, created by@robertdstein.
This package enables ZTF follow-up analysis of neutrinos/gravitational waves/gamma-ray bursts, built using theAMPEL platform.Installation InstructionsNuZTF can be directly installed with pip, giving the latest stable release:pip install nuztfAlternatively, the latest Github version of the code can be installed via pip:git clone https://github.com/desy-multimessenger/nuztf.gitcd nuztfpoetry installIn case you encounter problems with an ARM-based Mac, use conda and issue:conda install -c conda-forge python-confluent-kafka fiona pyproj lalsuite ligo.skymap -yThis should take care of all packages that have not yet been ported.You will need theIRSA login detailswith a ZTF-enabled account to fully utilise all features.Additionally, you need an AMPEL API token. This can be obtainedhere.Citing the codeIf you make use of this code, please cite it! A DOI is provided by Zenodo, which can reference both the code repository, or specific releases:ContributorsJannis Necker@JannisNeSimeon Reusch@simeonreuschRobert Stein@robertdsteinAcknowledgementsThis code stands on the shoulders of giants. We would particularly like to acknowledge:Ampel, created primarily by@wombaugh,@vbrinneland@jvansantenplanobs, created by@simeonreuschztfquery, created by@MickaelRigault
Package: nv
A tool to help users manage multiple environments.When launching a shell in the environment, it sets sets
environment variables automatically.It can also integrate with Python Virtual Environments,
AWS Profiles (eg. Assume Role).Once you’ve created an environment, edit the environment.json
to configure additional environment variables.Usage$ nv
Usage: nv [OPTIONS] COMMAND [ARGS]...

Options:
  -n, --environment-name _VALID_ENVIRONMENT_NAME
  -d, --project-dir DIRECTORY     Path to the project project (defaults to
                                  current directory)
  -P
  -K
  --help                          Show this message and exit.

Commands:
  create  Create a new environment in...
  rm      Remove an environment.
  run     Runs a command in the specified environment.
  shell   Launch a new shell in the specified...$ nv create --help

Usage: nv create [OPTIONS]

  Create a new environment in %PROJECT%/.nv-%ENVIRONMENT_NAME%

Options:
  -p, --project-name TEXT   Your project name (defaults to current directory
                            name)
  -py, --python-virtualenv  Activate a python virtualenv
  --aws-profile TEXT        Obtain credentials for the given profile.
  --env <TEXT TEXT>...      Name & Value of environment variables to set
  --help                    Show this message and exit.$ nv shell --help

Usage: nv shell [OPTIONS]

  Launch a new shell in the specified environment.

Options:
  --help  Show this message and exit.$ nv run --help

Usage: nv run [OPTIONS] COMMAND [ARGS]...

  Runs a command in the specified environment.

Options:
  --help  Show this message and exit.
Package: nv21
nv21Functions for reading and decoding the data of frames in NV21 format.Basically it wraps the cv2-based conversion with some check in order to avoid some confusing cv2 errors.Available functionsnv21_to_rgbread_nv21_from_fileyuv_to_nv21Exampleimportcv2importmatplotlib.pyplotasplt# do not forget to install `matplotlib` firstimportnv21# Check the "data" folder of this repository.img=cv2.imread('./data/example.png')[...,::-1]# to RGBh,w=img.shape[:2]nv21_data=nv21.yuv_to_nv21(cv2.cvtColor(img,cv2.COLOR_RGB2YUV))restored_img=nv21.nv21_to_rgb(nv21_data,w,h)# Draw resultsplt.subplot(1,3,1)plt.axis('off')plt.imshow(img)plt.subplot(1,3,2)plt.axis('off')plt.imshow(nv21_data,cmap='gray')plt.subplot(1,3,3)plt.axis('off')plt.imshow(restored_img)plt.show()
Package: nv2a-debug
Simulator/debugger for the original Xbox nv2a vertex shader.UseRunnv2adbgwith--helpto see command line options.MenusFile menuAllows inputs to be configured.Source windowThe source window is split into three areas:The program view on the left shows the shader instructionsThe inputs panel on the right shows the value of register inputs for the
currently selected line. Components of the register that are used will be
highlighted.The outputs panel at the bottom shows the computed values of the outputs.
Components that are written to will be highlighted.Cursor up/down, page-up/down, and home/end in the source window navigate
source lines."a" will toggle ancestry tracing for the currently selected line, marking
every line that contributes to the line's outputs.In tracing mode"space" will allow the current line to be locked so
moving the cursor will not change the root of the ancestor trace."f" will filter out any lines that do not contribute to the
instruction being traced. Press "f" again to return to full source
view.NOTE: At the time of this writing there is no way to choose between the
MAC and ILU component of a paired command, both will be traced. If you
only care about one or the other, the best option is to go to the first
ancestor that contributes solely to the one you care about and do the
trace from there.Helpful hintsYou can convert the contents of axemuvertex shader usingRenderDocto examine the draw
and running it
throughhttps://github.com/abaire/renderdoc_util/blob/main/util/xemu_shader_to_nv2a.pyto sanitize it.You can set initial values with csv dumps from RenderDoc (use-hto see the
appropriate commands, the mesh view will have the input register values and
you can expand the uniforms in the pipeline view to get the constant
registers)
Package: nv2a-vsh
Trivial assembler for the nv2a vertex shader.The Cg -> vp20 path performs various optimizations that sometimes make it hard
to force unusual test conditions. This assembler performs no optimizations, and
simply translates operands into machine code.InstructionsADDARLDP3DP4DPHDSTEXPPLITLOGPMADMAXMINMOVMULRCCRCPRSQSGESLTRegistersTemporary registersr0 - r11Constant/uniform registersc0 - c191Constant registers may also use relative addressing via bracket syntax. E.g.,c[A0 + 12]Address registera0May only be set via theARLinstruction. E.g.,ARL a0, v12Inputsv0, iPosv1, iWeightv2, iNormalv3, iDiffusev4, iSpecularv5, iFogv6, iPtsv7, iBackDiffusev8, iBackSpecularv9, iTex0v10, iTex1v11, iTex2v12, iTex3v13v14v15OutputsoB0, oBackDiffuseoB1, oBackSpecularoD0, oDiffuseoD1, oSpecularoFogoPosoPtsoTex0, oT0oTex1, oT1oTex2, oT2oTex3, oT3Swizzle/destination masksxyzwrgbaUniform macrosA simple macro syntax is supported to allow symbolic naming forc-register
access. Two types are currently implemented,vectorandmatrix4.Avectortype
aliases a singlecregister. E.g., to give a symbolic name to c10:#uniform_name vector 10. The macro can then be used in subsequent
instructions; e.g.,mov r0, #uniform_name.Amatrix4type aliases a contiguous set of 4cregisters. E.g., to give a
symbolic name to a model matrix passed at 'c96':#my_model_matrix matrix4 96.
To access the second row:mul r0, v0.y, #my_model_matrix[1].
Package: nvcc4jupyter
nvcc4jupyter: CUDA C++ plugin for Jupyter NotebookTestingCode QualityPackagenvcc4jupyteris a Jupyter Notebook plugin that provides cell and linemagicsto allow running CUDA C++ code from a notebook. This is especially
useful when combined with a hosted service such a Google'sColabwhich provide CUDA capable GPUs
and you can start learning CUDA C++ without having to install anything or even
to own a GPU yourself.Table of ContentsMain FeaturesInstallUsageLicenseDocumentationContributingMain FeaturesHere are just a few of the things that nvcc4jupyter does well:Easily run CUDA C++ codeProfile your code with NVIDIA Nsight ComputeCompile your code with external libraries (e.g. OpenCV)Share code between different programs in the same notebook / split your code into multiple files for improved readabilityInstallThe installer for the latest released version is available at thePython
Package Index (PyPI).pipinstallnvcc4jupyterUsageFirst, load the extension to enable the magic commands:%load_ext nvcc4jupyterRunning a quick CUDA Hello World program:%%cuda#include<stdio.h>__global__voidhello(){printf("Hello from block: %u, thread: %u\n",blockIdx.x,threadIdx.x);}intmain(){hello<<<2,2>>>();cudaDeviceSynchronize();}For more advanced use cases, seethe documentation.DocumentationThe official documentation is hosted onreadthedocs.LicenseMITContributingThe recommended setup for development is using the devcontainer in GitHub
Codespaces or locally in VSCode.If not using the devcontainer you need to install the package with the
development dependencies and install the pre-commit hook before commiting any
changes:pipinstall.[dev]pre-commitinstallGo to Top
Package: nvc-dhl-tracker
nvc-dhl-trackerAutomatically track and record records for NVC shipments!Installationpip install nvc-dhl-trackerOr clone from github and runpip install -e "."UsageFollow the gspread documentation to create a service account:https://docs.gspread.org/en/latest/Copy the URL of the google spreadsheetCreate a new sheet in the document and copy the ID out of the URL, this will be the tracking sheet ID.Then runi129f_nvc_trackerAnd answer the questions it asks. You should be able to let this run indefinitely, but eventually the database will fill up.As a SystemD serviceBefore running as a systemd service you should run the command with a–start-numberto seed the database. Otherwise if
the service crashes it will restart from the previous start number which may be significantly out of date./root/i129fvenv/bin/python -m i129f.nvc_dhl_tracker.cli --no-input --dhl-keys [..., ...] --start-number [...] --google-spreadsheet-url [...] --google-sheet-id [...]The command below does not include–start-numberto make it easier to copy and paste.[Unit]
Description=i129f SystemD Service

[Service]
ExecStart=/root/i129fvenv/bin/python -m i129f.nvc_dhl_tracker.cli --no-input --dhl-keys [..., ...] --google-spreadsheet-url [...] --google-sheet-id [...]
Restart=on-failure

[Install]
WantedBy=multi-user.targetno-input: This disables any input from the command, so that it won’t interrupt a daemonized servicedhl-keys: The keys to use with the DHL APIstart-number: The number from which to start pollinggoogle-spreadsheet-url: The URL of the Google spreadsheetgoogle-sheet-id: The ID of the sheet in the spreadsheet to use. This is appended to the URL if you view the spreadsheet in your browser.Making Changes & ContributingThis project usespre-commit, please make sure to install it before making any
changes:pip install pre-commit
cd nvc-dhl-tracker
pre-commit installIt is a good idea to update the hooks to the latest version:pre-commit autoupdateDon’t forget to tell your contributors to also install and use pre-commit.NoteThis project has been set up using PyScaffold 4.5. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: nvchecker
nvchecker(short fornew version checker) is for checking if a new version of some software has been released.This is the version 2.0 branch. For the old version 1.x, please switch to thev1.xbranch.DependencyInstall and RunDocumentationDependencyPython 3.8+Python library: structlog, platformdirs, tomli (on Python < 3.11)One of these Python library combinations (ordered by preference):tornado + pycurlaiohttphttpx with http2 support (experimental; only latest version is supported)tornadoAll commands used in your software version configuration filesInstall and RunTo install:pip3 install nvcheckerTo use the latest code, you can also clone this repository and run:pip install .To see available options:nvchecker --helpRun with one or more software version files:nvchecker -c config_fileYou normally will like to specify some “version record files”; see below.DocumentationFor detailed documentation, seehttps://nvchecker.readthedocs.io/en/latest/.