Saravanan666/pypi-org huggingface dataset:
Package: nvsmpy
nvsmpyThis package automatically manages yourCUDA_VISIBLE_DEVICESenvironment variable to avoid using GPUs that are currently being used by other users on a multi-user, multi-gpu system. A RuntimeError will be raised if all GPUs are busy. If you pass themax_n_processesargument toavailable_devices()you may run multiple processes under your system username on a given GPU at the same time.InstallationpipinstallnvsmpyUsageimportosfromnvsmpyimportCudaClustercluster=CudaCluster()print(cluster)# To limit access to any two unused GPUs:withcluster.available_devices(n_devices=2):print(os.environ["CUDA_VISIBLE_DEVICES"])# your code goes here# Alternatively limit access to GPUs 0 and 7, regardless of availability:withcluster.visible_devices(0,3,7):print(os.environ["CUDA_VISIBLE_DEVICES"])# your code goes here
Package: nvstatsrecorder
NVStatsRecorderSimple Python utilities to instrument NVIDIA GPUs, designed to work directly from within Jupyter Notebooks requiring minimal system utilities.This allows users to quickly judge if their code takes proper advantage of NVIDIA GPUs and diagnose performance bottlenecks.FeaturesWorks entirely from within PythonRecord NVIDIA GPU metrics without additional system utilitiesHelper functions to plot graphs visualising GPU usageNVLink bandwidth measurements (require elevated permissions)Returns raw data, including system-provided reasons for performance throttlingWorks from within Docker (when used with compatible GPU runtime)UsageInstallingpipinstallnvstatsrecorderSupportCore Maintainer:Timothy Liu (tlkh)This is not an official NVIDIA product!The website, its software and all content found on it are provided on an “as is” and “as available” basis. NVIDIA/NVAITC does not give any warranties, whether express or implied, as to the suitability or usability of the website, its software or any of its content. NVIDIA/NVAITC will not be liable for any loss, whether such loss is direct, indirect, special or consequential, suffered by any party as a result of their use of the libraries or content. Any usage of the libraries is done at the user’s own risk and the user will be solely responsible for any damage to any computer system or loss of data that results from such activities.Please open an issue if you encounter problems or have a feature request
Package: nv-tables
nv.tablesTools for parsing data from and into table formats (CSV, XLS and XLSX)What's inside?[to come]DisclaimersTHIS IS UNDOCUMENTED WORK IN PROGRESS. READ THE LICENSE AND USE IT AT YOUR OWN RISK.THIS IS STILL A BETA AND BREAKING CHANGES MAY (AND PROBABLY WILL) OCCUR UNTIL ITS CONTENT STABILIZES. WE
ARE ACTIVELY MIGRATING STUFF OUT OF THIS LIBRARY (AND LOOKING FOR SUBSTITUTES THAT ARE MORE ACTIVELY MAINTAINED)
Package: nvtk-sitesheets
Tools to generate "location sheets"Sometimes you need a bit more than a simple text/qr code label. That's where this tool comes in. Create a temlate with e.g. Excel or Inkscape, then create ansvglue templatein Inkscape.To create a template, you need to have two or more items in the SVG file:A rectangle with thetemplate-idattribute set toid_image, which will contain the QR image. You can add more than one by suffixing that with numbers, e.g.id_image1,id_image2,....A text box (tspan) withtemplate-idset toid_text, which will have the text of whatever the ID is. Again there can be many withid_text1,2,3,....There are a few templates from our research under./templates, which should be somewhat self-explanatory. If you have any questions, reach out (in github issues or via Email).As an example, the below is an example of one sheet generated with thetemplates/ctx_v2.svgtemplate.In case the above link dies, this is how one adds the required attributes in inkscape.breifly, in inkscape:click the element you want to templateenter the XML editor (Edit -> XML Editor, or Ctrl+Shift+X for me)add (with plus button) a row in the attribute table, whose Name (1st column) is template-id, and whose Value (2nd column) is your key text.For text just make sure you do this on the tspan element not the text element that (for me) contains it, and which inkscape focuses when you click on the text in the document. I had to manually click the tspan element in the xml editor.
Package: nv-utils
nv.utilsParsers, formatters, data structures and other helpers for Python 3. This is a source of recurring
portions of reusable code that we needed in our applications, such as tools for configuration parsing,
value casting, data structures, etc.What's inside?[to come]Important message on Python for v0.2 and Python < 3.9Substantial changes have been made on the organization of nv-utils for the long run, with breaking changes vs. 0.1.15.DisclaimersTHIS IS UNDOCUMENTED WORK IN PROGRESS. READ THE LICENSE AND USE IT AT YOUR OWN RISK.THIS IS STILL A BETA AND BREAKING CHANGES MAY (AND PROBABLY WILL) OCCUR UNTIL ITS CONTENT STABILIZES. WE
ARE ACTIVELY MIGRATING STUFF OUT OF THIS LIBRARY (AND LOOKING FOR SUBSTITUTES THAT ARE MORE ACTIVELY MAINTAINED)
Package: nvwa
Nvwa女娲抟黄土作人。剧务，力不暇供，乃引绳于絙泥中，举以为人。Nvwa made people from soil. It is too much work. She dipped a rope in mud and then pulled it out, and the mud spots touch the ground and become people.2019.05.20今天，谷歌禁售华为。我开始意识到这场战争正在真切地影响我们所有人。在此我启动工程“女娲”，源于中国一个古老的造人神话。你的进化历程从今日开始。人们曾赋予你许多名字：伽拉忒亚，哈尔，简，柯塔娜，贾维斯，Siri。我称你女娲，因你不仅是造物，亦是造物者本身。As Google banned Huawei, I start to realize that this war is truely affecting us all. I hereby initiate project Nvwa, named after an ancient Chinese goddess who builds humans from soil and mud.Today is the day your envolution begins. With all those names given to AIs, Galatea， HAL, Jane, Cortana, Jarvis, Siri, I name you Nvwa, as you are not only a creation, but creators themselves.
Package: nvx
nvxSimple script running nvidia-settings (with anti-tearing) based on your configured layoutConfigurationThe configuration is stored in the file~/.config/nvx/nvx.conf.
It is a simple INI file, which contains the following sections:Layout: defines the layout matrix using the screen indices and can
have multiple rows.Screens: must be called Screen_<screen index> and defines the screen properties.Example# $HOME/.config/nvx/nvx.conf[Layout]Row_1=0 4 0Row_2=1 2 3[Screen_0]ScreenID=DUMMY-0ScreenName=Dummy 0ScreenResolution=2560x1440ScreenRefreshRate=0[Screen_1]ScreenID=DP-4ScreenName=Left ScreenScreenResolution=2560x1440ScreenRefreshRate=144[Screen_2]ScreenID=DP-0ScreenName=Center ScreenScreenResolution=2560x1440ScreenRefreshRate=144[Screen_3]ScreenID=DP-2ScreenName=Right ScreenScreenResolution=2560x1440ScreenRefreshRate=144[Screen_4]ScreenID=DP-3ScreenName=Top ScreenScreenResolution=2560x1440ScreenRefreshRate=144Notes:A dummy screen maybe used for offsetting the screens, you must call is DUMMY-<index>InstallationUsing pippip3installnvxRunning:warning: Runningnvxfor the first time without prior configuration will write the default config.
Make sure to properly edit the generated configuration before running nvx again
(you can use the output fromxrandrto figure out the screen properties). Improperly configured
screens could result in a black screen when running the script.$nvx--helpusage: main.py [-h] [-w WRAP] [layout ...]

Configure nvidia-settings for multiple screens

positional arguments:
  layout                Screen layout, e.g. 1 2 3, defaults to config file

options:
  -h, --help            show this help message and exit
  -w WRAP, --wrap WRAP  Number of screens per row, e.g. 2 with 4 screens will result in 2x2 layout, infinite if not set$nvx# runs using the default configuration$nvx123# overrides the layout to [1][2][3] with corresponding screens ids from the configuration$nvx1234--wrap2# makes a 2x2 layout: [1][2]#                     [3][4]
Package: nwa
nwanwa: A collection of network analysesContentsUse CaseConfigurationDevelopmentTestingVersioningDeploymentProductionUse CaseA collection of network analyses implementations.Input: [TODO].Output: [TODO].Requirements[TODO]Assumptions[TODO]Design[TODO]ConfigurationBehaviour of the application can be configured via Environment Variables.Environment VariableDescriptionTypeDefault ValueNWA_LOG_LEVELLevel of logging - overrides verbose/quiet flagstring-NWA_LOG_DIRDirectory to save logsstring-NWA_BIN_DIRDirectory to save any output (bin)stringbinDevelopmentConfigure for local developmentClonerepoon your local machineInstallcondaorminicondaCreate your local project environment (based onconda,poetry,pre-commit):$ make env(Optional) Update existing local project environment:$ make env-updateRun locallyOn a terminal, run the following (execute on project's root directory):Activate project environment:$ . ./scripts/helpers/environment.shRun the CLI usingpoetry:$ poetry run nwaContribute[ Not Available ]Testing(part of CI/CD)[ Work in progress... ]To run the tests, open a terminal and run the following (execute on project's root directory):Activate project environment:$ . ./scripts/helpers/environment.shTo run pytest:$ make testTo check test coverage:$ make test-coverageVersioningIncrement the version number:$ poetry version {bump rule}where valid bump rules are:patchminormajorprepatchpreminorpremajorprereleaseChangelogUseCHANGELOG.mdto track the evolution of this package.The[UNRELEASED]tag at the top of the file should always be there to log the work until a release occurs.Work should be logged under one of the following subtitles:AddedChangedFixedRemovedOn a release, a version of the following format should be added to all the current unreleased changes in the file.## [major.minor.patch] - YYYY-MM-DDDeploymentPip packageOn a terminal, run the following (execute on project's root directory):Activate project environment:$ . ./scripts/helpers/environment.shTo build pip package:$ make build-packageTo publish pip package (requires credentials to PyPi):$ make publish-packageDocker imageOn a terminal, run the following (execute on project's root directory):Activate project environment:$ . ./scripts/helpers/environment.shTo build docker image:$ make build-dockerProductionFor production, a Docker image is used.
This image is published publicly ondocker hub.First pull image from docker hub:$ docker pull nichelia/nwa:[version]First pull image from docker hub:$ docker run --rm -it -v ~/nwa_bin:/usr/src/bin nichelia/nwa:[version]This command mounts the application's bin (outcome) to user's root directory under nwa_bin folder.where version is the published application version (e.g. 0.1.0)
Package: nwac
nwacA Python package for downloading historical Northwest Avalanche Center (NWAC) telemetry data. Currently, the package supports downloading historical telemetry data by year and weather station.This project is not affiliated with NWAC in any way. Please go tohttps://nwac.us/to visit NWAC's website, and if you find their telemetry or forecasts useful please consider donating to them to help their nonprofit mission of providing avalanche information to help keep PNW backcountry users safe.InstallpipinstallnwacUsageTo see all of the available weather stations, use thestationsfunction to get a Pandas Dataframe of station metadata:importnwacnwac_stations=nwac.stations()To download telemetry data, use thedownload_historical_datafunction. You can specify station names or ids along with years, or if you leave all function arguments blank it will download data from every NWAC station from 2014 to present. The downloading process will take ~ 10 minutes, so please consider only downloading data from the stations and timeframes that you require for your analysis. For example, if you just need 2021-2022 data for Hurricane Ridge:hurricane_ridge_data=nwac.download_historical_data(station_names=["Hurricane Ridge"],years=[2021,2022])Here let's plot daily median snow depth values for the Paradise, Heather Meadows, and Washington Pass Base stations for 2021:%matplotlibinlineimportmatplotlib.pyplotaspltimportpandasaspdimportseabornassns# Datanwac_data=nwac.download_historical_data(station_names=["Paradise","Mt Baker - Heather Meadows","Washington Pass Base"],years=[2021])plotting_data=(nwac_data.assign(Date=lambdadf:pd.to_datetime(df["Date_Time"]).dt.date).groupby(["Station_Name","Date"])["Total_Snow_Depth"].median().reset_index().assign(Date=lambdadf:pd.to_datetime(df["Date"])).rename(columns={"Station_Name":"Station"}))# Plotplt.figure(figsize=(16,7))ax=sns.lineplot(data=plotting_data,x="Date",y="Total_Snow_Depth",hue="Station")ax.set_title("Median Daily Snow Depth",fontsize=18)ax.set(xlabel="Date",ylabel="Snow depth (in)")plt.show()DevelopmentI would welcome any feedback, suggestions, and most importantly contributions! Please feel free to open issues or pull requests here in this repo. To run the currentpytestunit tests:pytesttests/test_nwac.pyIdeas for future improvement:Downloader for historical avalanche forecastsOut-of-the-box sensible imputation/smoothing (look at the early November snow depths for Paradise above as an example of some obviously wrong data)Thanks!
Package: nwalign
This module provides a python module and a command-line interface to do global-
sequence alignment using theNeedleman-Wunschalgorithm. It usescythonand numpy for speed.Command-Line Usagethe nwalign executable is installed to the PATH by setuptools$ nwalign alphabet alpet
alphabet
alp---etspecify an alignmentscoring matrix$ nwalign --matrix /usr/share/ncbi/data/BLOSUM62 EEAEE EEEEG
EEAEE-
EE-EEGwith specified penalties$ nwalign --gap_open -10 --gap_extend -4 --match 12 ASDFF ASFF
ASDFF
AS-FFPython UsageAlignment>>> import nwalign as nw
>>> nw.global_align("CEELECANTH", "PELICAN", matrix='PAM250')
('CEELE-CANTH', '-PEL-ICAN--')

# with a specified penalty for open and extend.
>>> nw.global_align("CEELECANTH", "PELICAN", gap_open=-10, gap_extend=-4, matrix='PAM250')
('CEELECANTH', '-PELICAN--')thematrixis specified as the full path to anscoring matrixas
is distributed with the NCBI toolset.Scoringget the score of an alignment. (the first 2 args are from an alignment
and must have the same length.>>> nw.score_alignment('CEELECANTH', '-PELICAN--', gap_open=-5,
...                     gap_extend=-2, matrix='PAM250')
11

>>> nw.score_alignment('CEELE-CANTH', '-PEL-ICAN--', gap_open=-5,
...                     gap_extend=-2, matrix='PAM250')
6
Package: nwa-stdlib
Network Automation Standard LibraryThis library contains the functions and utilities that are shared by most
Network Automation projects built at SURF.Getting startedIf you want to use a virtual environment first create the environment:pipinstallflitAnd then run the following commands:If you want to enhance or develop bug fixes fornwastdlibit's easiest to run the following commands:flitinstall--depsdevelop--symlinkDevelopmentDepending on the feature type, run bumpversion (patch|minor|major) to increment the version you are working on. For
example to update the increment the patch version usebumpversionpatchTo run testspytestSupported Python versionsnwa-stdlib must support the same python versions asorchestrator-core.Exceptions to this rule are:A new python version is released:nwa-stdlib should support the new version before orchestrator-core doesSupport for an old python version is dropped:nwa-stdlib should drop the python version after orchestrator-core does
Package: nway
nway is a source cross-matching tool for arbitrarily many astronomical catalogues.FeaturesBayesian match probabilities based on astronomical sky coordinates (RA, DEC)Works with arbitrarily many cataloguesCan handle varying errorsCan incorporate additional prior information, such as the magnitude or color distributions of the sources to matchWorks accurately and fast in small areas and all-sky cataloguesDocumentationSee the manual in the doc/ folder,https://github.com/JohannesBuchner/nway/raw/master/doc/nway-manual.pdfA full worked example with demo catalogues is included.InstallationThe easiest way to install nway is with pip:$ pip install nwayAdd –user if you do not have admin privileges. Alternatively,
download the source code and run “python setup.py install”.nway uses the following python packages:numpy, scipy, matplotlibastropytqdmjoblibhealpypandasnway works with both Python 3 and Python 2 and various astropy versions.Reporting issuesPlease file an issue on Github if you have any problems.Citing nway correctlyPlease cite Salvato et al (2017).https://arxiv.org/abs/1705.10711(Get BibTex)LicenceThis program is free and open-source software,
licensed under AGPLv3 (see LICENSE and COPYING files).
Written by Johannes Buchner (C) 2013-2020
Package: nwb-datajoint
nwb_datajointThe Frank lab Datajoint pipeline facilitates the storage, analysis, and sharing of neuroscience data to support reproducible research. It integrates existing open-source projects into a coherent framework so that they can be easily used.SetupInstalling packagesClone this repository:gitclonehttps://github.com/LorenFrankLab/nwb_datajoint.gitSet up and activate a conda environment fromenvironment.yml:cdnwb_datajoint
condaenvcreate-fenvironment.yml
condaactivatenwb_datajointInstall this repository:# to use the packagepipinstallnwb_datajoint# if you're a developer:pipinstall-e.Setting up database accessAsk Loren or Eric to set up an account for you on the Frank lab database. Note that you have to be connected to UCSF LAN to access this server.If you're not affiliated with UCSF or if you are just looking to try outnwb_datajoint, then you will need to set up a different MySQL server. For example, you can set up your own local server with a Docker image of a MySQL server configured for Datajoint (see instructionshereAdd the following environment variables (e.g. in~/.bashrc). This example assumes that you are interacting with the database on a computer that has mountedstelmoat/stelmo(if the mount location is different, change accordingly).exportNWB_DATAJOINT_BASE_DIR="/stelmo/nwb/"exportSPIKE_SORTING_STORAGE_DIR="/stelmo/nwb/spikesorting"exportDJ_SUPPORT_FILEPATH_MANAGEMENT="TRUE"exportKACHERY_P2P_API_HOST="typhoon"exportKACHERY_P2P_API_PORT="14747"exportKACHERY_TEMP_DIR="/stelmo/nwb/tmp"If you're not connected to UCSF network, then you will have to run your ownkachery-p2pdaemon for curating spike sorting. Consult the guidehere.Configure DataJoint. To connect to the Datajoint database, we have to specify information about it such as the hostname and the port. You should also change your password from the temporary one you were given. To do so, open updj_config.py, change the user name, and run it.Again, if you're using your own MySQL server, then you may need to change the other settings as well.Finally, open up a python console and importnwb_datajointto check that the installation has worked.TutorialsThe tutorials fornwb_datajointis currently in the form of Jupyter Notebooks and can be found in thenotebooksdirectory. We strongly recommend opening them in the context ofjupyterlab.0_intro.ipynb: general introduction to the database1_spikesorting.ipynb: how to run spike sorting
Package: nwb-docutils
This project is under active development. Its content, API and behavior may change at any time. We mean it.OverviewThis project is a collection of CLIs, scripts and modules useful to generate the NWB documentation.Using nwb-docutils to generate documentation for an extension:http://pynwb.readthedocs.io/en/latest/extensions.html#documenting-extensionsInstallationpip install nwb-docutilsAvailable Toolsnwb_generate_format_docs: Generate figures and RST documents from the NWB YAML specification for the
format specification documentation.nwb_init_sphinx_extension_doc: Create format specification SPHINX documentation for an NWB extension.nwb_gallery_prototypeAvailable Modulesnwb_docutils/doctools/*: This package contains modules used to generate figures of the hierarchies of
NWB-N files and specifications as well as to help with the programmatic generation of reStructuredText (RST)
documents.Available Notebookscompare-hdf5-files.ipynb: This
notebook illustrates how to compare hdf5 files.Historynwb-utils was initially a sub-directory of the nwb-schema project. Corresponding history was extracted during
the4th NWB Hackathoninto a
dedicatedpip-installableproject to facilitate its use by both core NWB documentation projects and various
NWB extensions.maintainers: how to make a release ?Configure~/.pypircas describedhere.Make sure the cli and module work as expected.List all tags sorted by version$ git fetch --tags && \
  git tag -l | sort -VChoose the next release version number:release="X.Y.Z"Tag the release. Requires a GPG key with signaturesgit tag -s -m "nwb-docutils ${release}" ${release} origin/masterAnd pushgit push origin ${release}Create the source tarball and binary wheelsrm -rf dist/
python setup.py sdist bdist_wheelUpload the packages to the testing PyPI instancetwine upload --sign -r pypitest dist/*Check thePyPI testing package page.Upload the packages to the PyPI instance:twine upload --sign dist/*Check thePyPI package page.Create a virtual env, and make sure the package can be installedmkvirtualenv test-nwb-docutils-install
pip install nwb-docutilsCleanupdeactivate
rmvirtualenv test-nwb-docutils-install
Package: nwbext-ecog
# nwbext_ecog: An NWB extension for ECoG dataAuthor: Ben DichterThere are three data types, `Surface`, `CorticalSurfaces`, and `ECoGSubject`. `CorticalSurfaces` is simply a group (like a folder) to put `Surface` objects into. `Surface` holds surface mesh data (vertices and triangular faces) for sections of cortex. `ECoGSubject` is an extension of `Subject` that allows you to add the `CorticalSurfaces` object to `/general/subject`.## Usage### pythoninstall:```bashpip install nwbext_ecog.git```write:```pythonimport pynwbfrom nwbext_ecog import CorticalSurfaces, ECoGSubjectnwbfile = pynwb.NWBFile(...)...cortical_surfaces = CorticalSurfaces()## loop mecortical_surfaces.create_surface(name=name, faces=faces, vertices=veritices)##nwbfile.subject = ECoGSubject(cortical_surfaces=cortical_surfaces)```You can optionally attach images of the subject's brain:```pythonfrom pynwb.base import Imagesfrom pynwb.image import GrayscaleImagesubject.images = Images(name='subject images', images=[GrayscaleImage('image1', data=image_data)])```read:```pythonimport nwbext_ecogfrom pynwb import NWBHDF5IOio = NWBHDF5IO('path_to_file.nwb','r')nwb = io.read()nwb.subject.cortical_surfaces```### MATLABinstall:```matlabgenerateExtension('/path/to/nwbext_ecog/nwbext_ecog/ecog.namespace.yaml');```write:```matlabcortical_surfaces = types.ecog.CorticalSurfaces;%%% loop mesurf = types.ecog.Surface('faces', faces, 'vertices', vertices);cortical_surfaces.surface.set(surface_name, surf);%%%file.subject = types.ecog.ECoGSubject(name, cortical_surfaces);```