Saravanan666/pypi-org huggingface dataset:
Package: n-const
N-CONSTNecst Constants and ObservatioN Specification Translator.FeaturesThis library provides:constants of the telescope system as useful python objectsparsers for parameter files unique to NECSTInstallationpipinstalln-constUsageBe careful of the package name! Use underscore instead of hyphen.ConstantsSolid constants such as location of the telescope are declared inconstantsmodule. To use the constants:>>>importn_const>>>n_const.LOC_NANTEN2EarthLocation(2230866.39573496,-5440247.68222275,-2475554.41874542)m>>>n_const.XFFTS.ch_num32768Constantsobjects support both keys and dot notations to access its components. So you can write:>>>n_const.XFFTS['ch_num']32768You now can get all the parameters packed in theConstantsusingdictmethod:>>>n_const.XFFTS.keys()dict_keys(['ch_num','bandwidth'])>>>n_const.REST_FREQ.values()dict_values([<Quantity115.27GHz>,<Quantity110.20GHz>,...,<Quantity219.56GHz>])>>>n_const.XFFTS.items()dict_items([('ch_num',32768),('bandwidth',<Quantity2.GHz>)])ParametersPointing error parameter (parameters to correct pointing error) and observation parameters are extracted viapointingandobsparammodules respectively.To get the pointing error parameters:>>>fromn_const.pointingimportPointingError>>>params=PointingError.from_file("path/to/param_file")>>>params.dAzQuantity5314.24667547arcsec# This module also supports keys to access the components:>>>params['dAz']Quantity5314.24667547arcsecTo get the observation parameters:>>>fromn_constimportobsparams>>>params=obsparams.OTFParams.from_file("path/to/obs_file")>>>params.Beta_on<Angle15.51638889deg>>>>params['Beta_on']<Angle15.51638889deg>For conventional style obsfiles, this module provides a parser. This is a conventional one, so it provides very limited functionality;Dot notation is not supported, keys only.Return values are not combined with units.>>>params=obsparams.obsfile_parser("path/to/obs_file")>>>params['offset_Az']0This library is usingSemantic Versioning.
Package: ncoreparser
NcoreparserIntroductionThis module provides python API-s to manage torrents from ncore.pro eg.: search, download, rssfeed, etc..InstallpipinstallncoreparserExamplesSearch torrentGet most seeded torrents from all categoryfromncoreparserimportClient,SearchParamWhere,SearchParamType,ParamSort,ParamSeqif__name__=="__main__":client=Client()client.login("<username>","<password>")fort_typeinSearchParamType:torrent=client.search(pattern="",type=t_type,number=1,sort_by=ParamSort.SEEDERS,sort_order=ParamSeq.DECREASING)[0]print(torrent['title'],torrent['type'],torrent['size'],torrent['id'])client.logout()Download torrentThis example download Forest gump torrent file and save it to temp folderfromncoreparserimportClient,SearchParamWhere,SearchParamType,ParamSort,ParamSeqif__name__=="__main__":client=Client()client.login("<username>","<password>")torrent=client.search(pattern="Forrest gump",type=SearchParamType.HD_HUN,number=1,sort_by=ParamSort.SEEDERS,sort_order=ParamSeq.DECREASING)[0]client.download(torrent,"/tmp")client.logout()Download torrent by rssfeedThis example get all torrents and their informations from an ncore bookmark (rss feed)fromncoreparserimportClientif__name__=="__main__":client=Client()client.login("<username>","<password>")torrents=client.get_by_rss("<rss url>")fortorrentintorrents:print(torrent['title'],torrent['type'],torrent['size'],torrent['id'])client.logout()Get torrents by activityThis example get all torrents and their informations from the Hit&run pagefromncoreparserimportClientif__name__=="__main__":client=Client()client.login("<username>","<password>")torrents=client.get_by_activity()fortorrentintorrents:print(torrent['title'],torrent['type'],torrent['size'],torrent['id'],torrent['rate'],torrent['remaining'])client.logout()Get recommended torrentsThis example get all torrents and their informations from the recommended pagefromncoreparserimportClient,SearchParamTypeif__name__=="__main__":client=Client()client.login("<username>","<password>")torrents=client.get_recommended(type=SearchParamType.HD_HUN)fortorrentintorrents:print(torrent['title'],torrent['type'],torrent['size'],torrent['id'])client.logout()Async supportThe library also supports async calls. It works same as the sync version, but you have to use the AsyncClient class.importasynciofromncoreparserimportAsyncClient,SearchParamWhere,SearchParamType,ParamSort,ParamSeqasyncdefmain():client=AsyncClient()awaitclient.login("<username>","<password>")fort_typeinSearchParamType:torrent=awaitclient.search(pattern="",type=t_type,number=1,sort_by=ParamSort.SEEDERS,sort_order=ParamSeq.DECREASING)[0]print(torrent['title'],torrent['type'],torrent['size'],torrent['id'])awaitclient.logout()if__name__=="__main__":asyncio.run(main())
Package: nCoV
nCoVGet instant information about 2019-nCoV 获取并记录新冠病毒实时数据nCoV是一个可以在命令行查询并记录实时新型冠状病毒疫情信息的爬虫应用。
可以选择查看全国总览，各省各市详细信息或全部信息并且可以选择是否以表格文件存储数据使用方式 UsageUsage: nCoV [OPTIONS]

Options:
  -o      Output the overall data of China
  -d      Output detailed data of all provinces and some cities
  -a      Output all data. overall --> detailed (Default)
  --help  Show this message and exit.安装 Install$ pip install nCoVLicenseMIT
Package: ncov-parser
ncov_parserThencov_parserpackage provides a suite of tools to parse the files generated
in the Nextflow workflow and provide a QC summary file.  The package requires
several files including:.variants.tsv.variants.norm.vcf.pass.vcf.per_base_coverage.bed.primertrimmed.consensus.fa.consensus.fastaalleles.tsvAn optional metadata file with qPCR ct and collection date values can be
included.In addition,bedtoolsshould be run to generate a<sample>.per_base_coverage.bedfile to generate mean and median depth of
coverage statistics.InstallationAfter downloading the repository, the package can be installed usingpip:git clone https://github.com/jts/ncov-tools
cd ncov-tools/parser
pip install .UsageThe library consists of several functions that can be imported.import ncov.parserSeveral classes are available representing the different files that can
be processed.ncov.parser.Alleles
ncov.parser.Consensus
ncov.parser.Lineage
ncov.parser.Meta
ncov.parser.PerBaseCoverage
ncov.parser.Snpeff
ncov.parser.Variants
ncov.parser.Vcf
ncov.parser.primersSimilarly, wrapper scripts for creating a standard format output can be found inncov.parser.qcimport ncov.parser.qc as qc
qc.write_qc_summary_header()
qc.write_qc_summary()Top levels scriptsIn thebindirectory, several wrapper scripts exist to assist in generating
QC metrics.To create sample level summary qc files, use theget_qc.pyscript:get_qc.py --variants <sample>.variants.tsv or <sample>.pass.vcf
--coverage <sample>.per_base_coverage.bed --meta <metadata>.tsv
--consensus <sample>.primertrimmed.consensus.fa [--indel] --sample <samplename>
--platform <illumina or oxford-nanopore> --run_name <run_name> --alleles alleles.tsv
--indel --lineage <Pangolin lineage report> --aa_table <SNPEff annotation table>Note the--indelflag should only be present if indels will be used in the
calculation of variants.Once this is complete, we can use thecollect_qc_summary.pyscript to
aggregate the sample level summary files into a single run tab-separate file.collect_qc_summary.py --path <path to sample.summary.qc.tsv files>To create an amplicon BED file from a primer scheme BED file:primers_to_amplicons.py --primers <path to primer scheme BED file>
--offset <number of bases to offset> --bed_type <full or no_primers or unique_amplicons>
--output <full path to file to write BED data to>Credit and AcknowledgementsNote that this tool has been used in conjunction with the@jtsncov-toolssuite of tools.BED file importing and amplicon site merging obtained from the ARTIC pipeline:https://github.com/artic-network/fieldbioinformatics/blob/master/artic/vcftagprimersites.pyLicenseMIT
Package: ncpa-nvidiasmi-plugin
NCPA Nvidia-Smi PluginThis NCPA plugin for checks Nvidia GPU stats for all GPUS detected via the nvidia-smi executable on linux machinesRequirementsPython 3.5 or greaternagiosplugin module -https://pypi.org/project/nagiosplugin/Setuppip3 install nagiosplugininstallcheck_nvidiasmi.pyinto /usr/local/ncpa/plugins/check_nvidiasmi.pyensure/usr/local/ncpa/etc/ncpa.cfgconfigured to use python3 binary for plugin scriptsUsageusage: check_nvidiasmi.py [-h] [-a RANGE] [-A RANGE] [-u RANGE] [-U RANGE] [-m RANGE] [-M RANGE] [-t RANGE] [-T RANGE] [-p RANGE] [-P RANGE] [-v]

NCPA plugin to check Nvidia GPU status using nvidia-smi

optional arguments:
  -h, --help            show this help message and exit
  -a RANGE, --avg_gpu_warning RANGE
                        warning if threshold is outside RANGE for average of all GPUS
  -A RANGE, --avg_gpu_critical RANGE
                        critical if threshold is outside RANGE for average of all GPUS
  -u RANGE, --gpu_warning RANGE
                        warning if threshold is outside RANGE for any given GPU
  -U RANGE, --gpu_critical RANGE
                        critical if threshold is outside RANGE for any given GPU
  -m RANGE, --mem_warning RANGE
                        warning if threshold is outside RANGE for any given GPU
  -M RANGE, --mem_critical RANGE
                        critical if threshold is outside RANGE for any given GPU
  -t RANGE, --temp_warning RANGE
                        warning if threshold is outside RANGE for any given GPU
  -T RANGE, --temp_critical RANGE
                        critical if threshold is outside RANGE for any given GPU
  -p RANGE, --procs_warning RANGE
                        warning if threshold is outside RANGE for any given GPU
  -P RANGE, --procs_critical RANGE
                        critical if threshold is outside RANGE for any given GPU
  -v, --verbose         increase verbosity (use up to 3 times)
Package: ncplot
ncplot - Easy interactive viewing of NetCDF files in PythonAn easy to use Python (3.6 and above) package for quickly plotting the contents of NetCDF files or xarray datasets in Python or on the command line.This package is designed to work withCFcompliant NetCDF data.InstallationTo install from conda:condainstall-cconda-forgencplotTo install from pypi:pipinstallncplotInstall the development version using using pip:pipinstallgit+https://github.com/pmlmodelling/ncplot.gitHow to use in PythonThe package is made up of a simple and easy to use function: view. To visualize everything in a file:fromncplotimportview

view("example.nc")or to visualize a specific variable or list of variables:fromncplotimportview

view("example.nc",vars)where vars is either a string representing a variable or a list of variables.If you want to plot xarray datasets dataarrays, first import the xarray accessor, then plot in a similar way to xarray's built in plotting method:importncplot.xarray
importxarrayasxrds=xr.open_dataset("example.nc")ds.ncplot.view()How to use on the command lineCommand line usage is as simple as:ncplotexample.ncReference and tutorialsA full API reference, and a how-to guide are available atreadthedocs.
Package: ncp-wrapper
ncp_wrapper - Python Package for Naver Cloud Platform (NCP)ncp_wrapperis a versatile Python package that simplifies interaction with Naver Cloud Platform (NCP) services. It provides easy-to-use wrappers for various NCP functionalities, including sending emails, managing cloud resources, and more.InstallationYou can installncp_wrappervia pip. Use the following command:pipinstallncp_wrapperFeatures1. Naver Works EmailSend emails using Naver Works (smtp.worksmobile.com). The Email class encapsulates email composition and attachment handling.fromncp_wrapper.naver_worksimportEmail# Initialize the Email class with your Naver Works credentialsuser_id="your_email@example.com"user_password="your_password"email_sender=Email(user_id,user_password)# Compose and send an emailsubject="Hello, World!"body="This is a test email sent using ncp_wrapper."recipient_emails=["recipient1@example.com","recipient2@example.com"]cc=["cc@example.com"]bcc=["bcc@example.com"]attachments=[("document.pdf","file"),"image.jpg"]email_sender.send_email(subject=subject,body=body,recipient_emails=recipient_emails,cc=cc,bcc=bcc,attachments=attachments,)This code demonstrates how to create anEmailinstance, set the necessary email parameters, and send an email with attachments.DocumentationFor more details on theEmailclass and its methods, please refer to the source code or the package documentation.LicenseThis package is distributed under the MIT License. See theLICENSEfile for more information.Issues and ContributionsIf you encounter any issues or have suggestions for improvements, pleaseopen an issue. Contributions are welcome through pull requests.CreditsThis package is developed and maintained byJeongjin Kim.
Package: nc-py-api
Nextcloud Python FrameworkPython library that provides a robust and well-documented API that allows developers to interact with and extend Nextcloud's functionality.The key features are:Fast: High performance, and as low-latency as possible.Intuitive: Fast to code, easy to use.Reliable: Minimum number of incompatible changes.Robust: All code is covered with tests as much as possible.Easy: Designed to be easy to use with excellent documentation.Sync + Async: Provides both sync and async APIs.CapabilitiesCapabilityNextcloud 26Nextcloud 27Nextcloud 28Nextcloud 29Calendar✅✅✅✅File System & Tags✅✅✅✅Nextcloud Talk✅✅✅✅Notifications✅✅✅✅Shares✅✅✅✅Users & Groups✅✅✅✅User & Weather status✅✅✅✅Other APIs***✅✅✅✅Talk Bot API*N/A✅✅✅Settings UI API*N/AN/AN/A✅AI Providers API**N/AN/AN/A✅*available only forNextcloudApp**available only forNextcloudApp: SpeechToText, TextProcessing, Translation***Activity, NotesDifferences between the Nextcloud and NextcloudApp classesTheNextcloudclass functions as a standard Nextcloud client,
enabling you to make API requests using a username and password.On the other hand, theNextcloudAppclass is designed for creating applications for Nextcloud.It usesAppAPIto provide additional functionality allowing
applications have their own graphical interface, fulfill requests from different users,
and everything else that is necessary to implement full-fledged applications.Both classes offer most of the same APIs,
but NextcloudApp has a broader selection since applications typically require access to more APIs.Any code written for the Nextcloud class can easily be adapted for use with the NextcloudApp class,
as long as it doesn't involve calls that require user password verification.NextcloudAppavalaible only from Nextcloud 27.1.2 and greater version with installedAppAPI.Nextcloud skeleton app in PythonfromcontextlibimportasynccontextmanagerfromfastapiimportFastAPIfromnc_py_apiimportNextcloudAppfromnc_py_api.ex_appimportAppAPIAuthMiddleware,LogLvl,run_app,set_handlers@asynccontextmanagerasyncdeflifespan(app:FastAPI):set_handlers(app,enabled_handler)yieldAPP=FastAPI(lifespan=lifespan)APP.add_middleware(AppAPIAuthMiddleware)defenabled_handler(enabled:bool,nc:NextcloudApp)->str:ifenabled:nc.log(LogLvl.WARNING,"Hello from nc_py_api.")else:nc.log(LogLvl.WARNING,"Bye bye from nc_py_api.")return""if__name__=="__main__":run_app("main:APP",log_level="trace")SupportYou can support us in several ways:⭐️ Star our work (it really motivates)❗️ Create an Issue or feature request (bring to us an excellent idea)💁 Resolve some Issue or create a Pull Request (contribute to this project)🙏 Write an example of its use or correct a typo in the documentation.More InformationDocumentationFirst stepsMore APIsWriting a simple Nextcloud ApplicationUsing Nextcloud Talk Bot API in ApplicationUsing Language Models In ApplicationWriting a Nextcloud System ApplicationExamplesContributeDiscussionsIssuesSetting up dev environmentChangelog
Package: ncPyTools
Python tools for the handling ofnetcdffiles based on thepython-netCDF4library.
These tools currently only support files ofnetcdfclassicstructure.Tools inlcudedncdfViewSimple python wrapper around the python-netCDF4 library to readnetcdffiles
from the command line.UseageHelp-string:ncdfView.py [-h] [-o] [-q QUIET] [-n NOMASK] [filename]

Read netcdf files from command line.

positional arguments:
  filename              Filename of the netfdf file to open.

optional arguments:
  -h, --help            show this help message and exit
  -o, --object          Open file as pure netCDF4 object.
  -q QUIET, --quiet QUIET
                        Suppress header outputs.
  -n NOMASK, --nomask NOMASK
                        Don't mask fill valuesIn order to obatin an interactive prompt with the netcdf file loaded into a thencdfViewobject callednclaunch:python3 -i -m ncPyTools.ncdfViewncPyTools.ncdfViewnetCDFTemplate:UseageHelp-string:usage: netCDFTemplate [-h] [-c COMPRESS] yamlfile

Create netcdf file from yaml metadata.

positional arguments:
  yamlfile              yaml metadata file.

optional arguments:
  -h, --help            show this help message and exit
  -c COMPRESS, --compress COMPRESS
                        Compression level (0 = no compression, 9 = maximum
                        compression)Exampleyamlmetadata file:filename: month_flux_2006c.nc
dimensions:
    t: None
    y: 45
    x: 72
unlimited_shape:
    t: 12
variables:
    LAT:
        long_name: latitude
        units: degrees_north
        value: 1.e12
        fill_value: 1.e12
        dimensions: y
        type: f4
    LON:
        long_name: longitude
        units: degrees_east
        value: 1.e12
        fill_value: 1.e12
        dimensions: x
        type: f4Installation:After downloading the source fromgithubinstall via pip, descending
into the top-level of the source tree
and launching:pip install .or to install in developers mode:pip install -e .Or install the latest releaase from PyPI:pip install ncPyToolsDocumentation:Documentation of this package can be foundreadthedocs.
Package: ncrar-audio
NCRAR audioCopyright notice© 2021-2023, ncrar-audio development teamPortions of this project were funded by:The National Center for Rehabilitative Auditory Research at the Portland VA Medical Center on contract 36C26021P0557 awarded to Buran Consulting, LLC.Various NIH grants to Brad Buran, Stephen David, Naomi Bramhall and other investigators at the Oregon Hearing Research Center.
Package: ncrar-dkm-tools
If you don't already have Anaconda Python installed, you can download and
install it fromhttps://anaconda.org. Either Anaconda or Miniconda is fine, but
be sure to install the 64 bit version.To install the prgoram, open up an Anaconda Python prompt and type:conda create -n srm-aggregate python git
conda activate srm-aggregate
pip install srm-aggregate@git+https://github.com/NCRAR/srm-aggregate.gitTo run the program, open up an Anaconda Python prompt and type:conda activate srm-aggregate
srm-aggregateTo update the program, type (note the--upgradeflag):pip install --upgrade srm-aggregate @ git+https://github.com/NCRAR/srm-aggregate.git
Package: ncRNABert
ncRNABert: Deciphering the landscape of non-coding RNA using language modelModel detailsModel# of parameters# of hidden sizePretraining dataset# of ncRNAsModel downloadncRNABert303M1024RNAcentral26MDownloadncRNABert303M1024RNAcentral+ nt-DownloadInstallAs a prerequisite, you must have PyTorch installed to use this repository.You can use this one-liner for installation, using the latest release version# latest version
pip install git+https://github.com/wangleiofficial/ncRNABert

# stable version
pip install ncRNABertUsagencRNA sequence embeddingfrom ncRNABert.pretrain import load_ncRNABert, load_ncRNABert_ex
from ncRNABert.utils import BatchConverter
import torch

data = [
    ("ncRNA1", "ACGGAGGATGCGAGCGTTATCCGGATTTACTGGGCG"),
    ("ncRNA2", "AGGTTTTTAATCTAATTAAGATAGTTGA"),
]

ids, batch_token, lengths = BatchConverter(data)
model = load_ncRNABert()
model_ex = load_ncRNABert_ex()
with torch.no_grad():
    results = model(batch_token, lengths, repr_layers=[24])
    results_ex = model_ex(batch_token, lengths, repr_layers=[24])
# Generate per-sequence representations via averaging
token_representations = results["representations"][24]
token_representations_ex = results_ex["representations"][24]
sequence_representations = []
sequence_representations_ex = []
batch_lens = [len(item[1]) for item in data]
for i, tokens_len in enumerate(batch_lens):
    sequence_representations.append(token_representations[i, 1 : tokens_len - 1].mean(0))
    sequence_representations_ex.append(token_representations_ex[i, 1 : tokens_len - 1].mean(0))LicenseThis source code is licensed under the Apache-2.0 license found in the LICENSE file in the root directory of this source tree.
Package: ncrystal
NCrystal is a library and associated tools which enables calculations for Monte Carlo simulations of thermal neutrons in crystals and other materials, supporting a range of physics including both coherent, incoherent, elastic and inelastic scatterings in a wide range of materials, including crystal powders, mosaic single crystals, layered single crystals, amorphous solids, and liquids. Multiphase materials or isotopically enriched materials are supported as well, and the framework furthermore supports phase-contrast (SANS) physics.
Package: ncs2-device-plugin
Intel NCS2 device plugin for KubernetesA Kubernetes device plugin for the Intel Neural Compute Stick 2 (NCS2) / Intel Movidius MyriadXQuick StartTo directly installncs2-device-pluginas aDaemonSetinto the Kubernetes cluster:$ kubectl apply -f https://raw.githubusercontent.com/adaptant-labs/ncs2-device-plugin/ncs2-device-plugin.yamlPods will be scheduled on any node with afeature.node.kubernetes.io/usb-ff_03e7_2485.present(provided byNFD) oraccelerators/ncs2(provided byk8s-auto-labeller, in combination with NFD-based discovery) label set. These labels
can also be set manually on NCS2-capable nodes for simple deployments in order to enqueue the Pod.AnnotationsAnnotations are produced for the per-device full device name, allowing for different device types to be explicitly
targeted, regardless of their insertion order into the system:# For a single device
ncs2.intel.com/MYRIAD.name: Intel Movidius Myriad X VPU

# For multiple devices
ncs2.intel.com/MYRIAD.0.name: Intel Movidius Myriad X VPU
...Node LabelsNode labels are produced for the number of devices and per-device optimization capabilities:intel.com/ncs2=<number of NCS2 devices>

# For a single device
ncs2.intel.com/MYRIAD.FP16=true

# For multiple devices
ncs2.intel.com/MYRIAD.0.FP16=true
...USB Hotplug Event ReconciliationNode labels and annotations are reconciled on the node each time a USB device is added or removed from the system. This
can be seen below:2020-11-16 00:16:52 INFO     Discovered 1 NCS2 device(s):
2020-11-16 00:16:52 INFO     	MYRIAD: Intel Movidius Myriad X VPU, Optimizations: ['FP16']
2020-11-16 00:16:52 INFO     Applying initial labels and annotations to current node...
2020-11-16 00:16:52 INFO     Reconciling node sgx-celsius-w550power
2020-11-16 00:16:52 INFO     Starting USB monitor...
2020-11-16 00:17:04 INFO     Received a USB remove event for Device('/sys/devices/pci0000:00/0000:00:14.0/usb1/1-4')
2020-11-16 00:17:04 INFO     Reconciling node sgx-celsius-w550power
2020-11-16 00:17:26 INFO     Received a USB add event for Device('/sys/devices/pci0000:00/0000:00:14.0/usb1/1-4')
2020-11-16 00:17:26 INFO     Reconciling node sgx-celsius-w550powerFeatures and bugsPlease file feature requests and bugs in theissue tracker.AcknowledgementsThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant
agreement No 825480 (SODALITE).Licensencs2-device-pluginis licensed under the terms of the Apache 2.0 license, the full
version of which can be found in the LICENSE file included in the distribution.
Package: ncsdaemon
ncs-daemon [![Build Status](https://travis-ci.org/BrainComputationLab/ncs-daemon.png?branch=master)](https://travis-ci.org/BrainComputationLab/ncs-daemon)==========A service running on a master node that allows clients to interact with the NCS brain simulator using a restful API.0.01a--------------------------------------------------------------------------------Initial Pre-release alpha version. Until any significant progress is made thisversion will remain as the current working version.