Saravanan666/pypi-org huggingface dataset:
Package: noun-splitter
SECOSThis repo is a modular python implementation of the SECOS algorithm for decomposing composite nouns.Based on the SECOS algorithm:original python implementationoriginal paperHowever, the training data of the models have been distilled slightly to reduce the size of the models. More information on this can be found in the pretrained-models directory in the github repo. Typically though it involves trimming out words with low frequency counts, words with non-unicode characters etc.InstallationFrom Githubpip install git+https://github.com/mhaugestad/noun-splitter.git -UFrom Sourcegit clone
cd noun-splitter
pip install -e . -UFrom Pippip install noun-splitterInstalling models:The module relies on pretrained models to be passed in. These can be downloaded from command line as follows:python -m secos download --model deThe command line tool also takes an optional argument --overwrite. This is to be used if you would like to redownload a model for whatever reason, as follows:python -m secos download --model no --overwriteAlternatively, you can download models directly from a python script or notebook like this:from secos import Decomposition

Decomposition.download_model('de')Available models and their names are:LanguageModelDanishdaGermandeEnglishenSpanishesEstonianetFinnishfiHungarianhuLatinlaLatvianlvNetherlandnlNorwaynoSwedishsvBasic Usagefrom secos import Decomposition

model = Decomposition.load_model('de')

secos = Decomposition(model)

secos.decompose("Bundesfinanzministerium")

['bundes', 'finanz', 'ministerium']EvaluationThe evaluation folder in the github repo includes code for the evaluation of the pretrained models.
Package: nourish
Nourish is a Python API that enables data consumers and distributors to easily use and share datasets, and establishes a
standard for exchanging data assets. It enables:a data scientist to have a simpler and more unified way to begin working with a wide range of datasets, anda data distributor to have a consistent, safe, and open source way to share datasets with interested communities.Quick Example>>>importnourish>>>nourish.list_all_datasets(){'claim_sentences_search':('1.0.2',),...,'wikitext103':('1.0.1',)}>>>nourish.load_dataset('wikitext103'){...}# Content of the datasetInstall the Package & its DependenciesTo install the latest version of Nourish, run$pipinstallnourishAlternatively, if you have downloaded the source, switch to the source directory (same directory as this README file,cd/path/to/nourish-source) and run$pipinstall-U.Quick StartImport the package and load a dataset. Nourish will downloadWikiText-103dataset (version1.0.1) if it’s not already
downloaded, and then load it.importnourishwikitext103_data=nourish.load_dataset('wikitext103')View available Nourish datasets and their versions.>>>nourish.list_all_datasets(){'claim_sentences_search':('1.0.2',),...,'wikitext103':('1.0.1',)}To view your globally set configs for Nourish, such as your default data directory, usenourish.get_config.>>>nourish.get_config()Config(DATADIR=PosixPath('dir/to/dowload/load/from'),...,DATASET_SCHEMA_URL='file/to/load/datasets/from')By default,nourish.load_datasetdownloads to and loads from~/.nourish/data/<dataset-name>/<dataset-version>/. To change the default data directory, usenourish.init.nourish.init(DATADIR='new/dir/to/dowload/load/from')Load a previously downloaded dataset usingnourish.load_dataset. With the new default data dir set, Nourish now
searches for theGroningen Meaning Bankdataset (version1.0.2) innew/dir/to/dowload/load/from/gmb/1.0.2/.gmb_data=load_dataset('gmb',version='1.0.2',download=False)# assuming GMB dataset was already downloadedNotebooksFor a more extensive look at Nourish functionality, check out these notebooks:Early Nourish Features Walkthrough
Package: nous.migration
Yet another sqlalchemy based database schema migration tool===========================================================Installation------------To install it, add:[migration]recipe = zc.recipe.eggeggs=nous.migrationto your buildout.cfgYou might want to add your databse driver pakcage and your applicationpackage like this:[migration]recipe = zc.recipe.eggeggs=psycopg2nous.some_appnous.migrationConfiguration-------------Add to your development.ini or some other kind of ini:[nous.migration]app = app:busypackage = busy.migrationmigrator expects sqlalchemy configuration to be present in the appsection, for example:[app:busy]sqlalchemy.url = postgresql:///developmentI might add sqlalchemy configuration to the migration section too ifsomeone will ask.You can also add:vcs = gitschema_diff_cmd = git diff src/busy/models/schema.sqlso that add_script command would add the files to the version controlsystem, and populate them with the delta of your schema automatically.These two settings might go the setup.cfg as they are not reallydeployment related and are never required to run your application.Usage-----bin/migrate development.ini [upgrade|downgrade|setup|add_script] [version]Integrating with your application---------------------------------Add something like this:engine = engine_from_config(conf, 'sqlalchemy.')DBMigrator(engine, 'busy.migration').set_up_migration(init_migration=DBSetUp(), run_scripts=False)to your setup_app, or in your initialize_sqlDBSetUp is a class that has an "upgrade" method that initializes yourschema.You can do it in multiple ways, you can add a migration script thatsets up your tables and then migrate that using migration scripts:DBMigrator(engine, 'busy.migration').set_up_migration(run_scripts=True)Or you can have a canonical schema set up and initialize it all atonce and just mark all the scripts as 'done'DBMigrator(engine, 'busy.migration').set_up_migration(init_migration=DBSetUp(), run_scripts=False)Scripts-------Put your scripts in the package that you have set up in your migrationconfig.Scripts are tracked separately.Script files should be of this format:Timestamp    script title201006231515_python_migration_script.pyTimestamp    script title201006231516_sql_migration_script.sql201006231516_downgrade.sqlIf both, python and sql exist with the same timestamp I assume thatpython script is responsible for running sql scripts. I might addhelpers to make running an sql file later.201006231517_mixed_migration_script.py201006231517_upgrade.sql201006231517_downgrade.sqlSql files are just that, sql files that will be executed. Python filesshould contain callables: upgrade and downgrade, downgrade can bemissing if script is not intended to be reversable.TODO: add example migration scripts
Package: nouveau
Art Nouveau Design DataPublic domain lazy-downloading data samples.MorrisThis is a collection of designs by the famous Morris company artists of the 1800s.Formatimage: image in numpy array or PyTorch tensor formatname: a common name for the designyear: initial year of designExample Useimport nouveau
data = nouveau.Morris()

data.index.head()
>> shows pandas dataframe head

data[0]
>> {'year': 1862,
>>  'name': 'Fruit-Blue',
>>  'filename': '1862-Fruit-Blue.jpg',
>>  'image': array([[[254, 253, 249], ...

data.show(1)
>> <pyplot image>

data.show('Fruit-Blue')
>> <pyplot image>

tensors = data.torch()
tensors[0][0].shape
>> torch.Size([3, 1987, 1586])license: unlicense
task_categories:image-classification
pretty_name: Morris Co. Public Domain Art
size_categories:n<1K
Package: nova
OpenStack Nova provides a cloud computing fabric controller, supporting a wide
variety of compute technologies, including: libvirt (KVM, Xen, LXC and more),
Hyper-V, VMware and OpenStack Ironic.Use the following resources to learn more.APITo learn how to use Nova’s API, consult the documentation available online at:Compute API GuideCompute API ReferenceFor more information on OpenStack APIs, SDKs and CLIs in general, refer to:OpenStack for App DevelopersDevelopment resources for OpenStack cloudsOperatorsTo learn how to deploy and configure OpenStack Nova, consult the documentation
available online at:OpenStack NovaIn the unfortunate event that bugs are discovered, they should be reported to
the appropriate bug tracker. If you obtained the software from a 3rd party
operating system vendor, it is often wise to use their own bug tracker for
reporting problems. In all other cases use the master OpenStack bug tracker,
available at:Bug TrackerDevelopersFor information on how to contribute to Nova, please see the contents of the
CONTRIBUTING.rst.Any new code must follow the development guidelines detailed in the HACKING.rst
file, and pass all unit tests.Further developer focused documentation is available at:Official Nova DocumentationOfficial Client DocumentationOther InformationDuring eachSummitandProject Team Gathering, we agree on what the whole
community wants to focus on for the upcoming release. The plans for nova can
be found at:Nova Specs
Package: nova6
# Standalone NovasearchThis is a fork of [qBittorrent's Nova search engine](https://github.com/qbittorrent/qBittorrent/tree/master/src/searchengine).It has been patched to use modern [requests](http://docs.python-requests.org/en/master/) library, add a setuptools installer for standalone usage and support both Python2 and Python3 out of the box (though this should not be relied upon).Please note that even though this works on Python2, development will only focus on Python3.## How to use* Install it```shell$ pip install nova6```* Get some engines from qBittorrent: https://github.com/qbittorrent/search-plugins* Run it```shell$ nova6 -d search-plugins all all query```## SynopsisNova6 tries to maintain command-line compatibility with nova from qBittorrent. However, in order to make it distributable and still maintain compatibility with existing plugins, I had to make some changes.Notably, `nova6` accepts one extra command line argument that `nova2` does not: `--engines-dir`, `-d`.This argument can be specified multiple times with a list of directories to be scanned for plugins. If none is specified, it checks for an `engines` in the same directory of `nova6.py`, just like qBittorrent's.### Positional arguments* **engines**Select the engines to be used for search, comma-separated, or "all". Use `--capabilities` to list available plugins.* **category**Select the category to be used for search, or "all". Available categories are `movies`, `tv`, `music`, `games`, `anime`, `software`, `pictures` and `books`.* **keywords**Search keywords### Optional arguments* **--capabilities**Outputs an XML showing search engine plugins capabilities and exits immediately.* **--engines-dir**, **-d**See above. Specify custom directory for engine plugins. Can be specified multiple times.## Output### SearchSearch results are meant to be machine readable. The format is the following:```link|name|size|seeds|leech|engine_url```* **link** → a string corresponding the the download link (points to the .torrent file)* **name** → a unicode string corresponding to the torrent's name (i.e: "Ubuntu Linux 17.04")* **size** → a string corresponding to the torrent size (i.e: "6 MB" or "200 KB" or "1.2 GB"...)* **seeds** → the number of seeds for this torrent* **leech** → the number of leechers for this torrent* **engine_url** → the search engine url (i.e: http://www.mininova.org)* **desc_link** (optional) → if the search plugin provides it, a human-readable page describing the torrent### CapabilitiesOutputs an XML listing all the engines found in search directories and their capabilities. For example:```xml<capabilities><myengine><name>My Super Torrent Engine</name><url>https://www.mytorrentengine.com</url><categories>movies anime books software games tv music</categories></myengine>...</capabilities>```## How to write pluginsPlease see [qBittorrent's guide](https://github.com/qbittorrent/search-plugins/wiki/How-to-write-a-search-plugin).## Credits and licenseAll this code has been taken from qBittorrent. It's been adjusted by [Davide Depau](https://github.com/Depaulicious) to make it standalone and reusable.The license is the same as qBittorrent, GPLv2.
Package: novadata-utils
Package to facilitate your day to day as a Django developer.Getting StartedFollow the step by step below to install and configure the package.DependenciesDepends on the following packages (which will be installed automatically):DjangoDjango Rest FrameworkDjango Advanced FiltersDjango Admin List Filter DropdownDjango Object ActionsDjango Import ExportDjango CrumInstallation and Configurationpipinstallnovadata-utilsSettings.py:INSTALLED_APPS=[...'advanced_filters','django_admin_listfilter_dropdown','django_object_actions','import_export','novadata_utils','rangefilter','rest_framework',...]# After Django middlewaresMIDDLEWARE+=('crum.CurrentRequestUserMiddleware',)Main urls.py:urlpatterns=[...path('advanced_filters/',include('advanced_filters.urls')),...]FeaturesAccess thedocsto see the features and how to use them.
Package: nova-dpm
About this projectThis project provides a Nova virtualization driver for the PR/SM hypervisor of
IBM z Systems and IBM LinuxOne machines that are in the DPM (Dynamic Partition
Manager) administrative mode.The DPM mode enables dynamic capabilities of the firmware-based PR/SM
hypervisor that are usually known from software-based hypervisors, such as
creation, deletion and modification of partitions (i.e. virtual machines) and
virtual devices within these partitions, and dynamic assignment of these
virtual devices to physical I/O adapters.The z/VM and KVM hypervisors on z Systems and LinuxONE machines are supported
by separate Nova virtualization drivers:KVM is supported by the standard libvirt/KVM driver in theopenstack/novaproject.z/VM is supported by the z/VM driver in theopenstack/nova-zvm-virt-driverproject.LinksDocumentation:http://nova-dpm.readthedocs.io/en/latest/Source:http://git.openstack.org/cgit/openstack/nova-dpmGithub shadow:https://github.com/openstack/nova-dpmBugs:http://bugs.launchpad.net/nova-dpmGerrit:https://review.openstack.org/#/q/project:openstack/nova-dpmLicense: Apache 2.0 license
Package: novafitness-sds018
Novafitness SDS018 Particulate Sensor Python interfaceA basic python interface for interacting with the novafitness PM sensors.  This code has been tested with the following devices: Novafitness SDS018.It may work with other sensors from the novafitness range, if you try it please do let us know either way.  If there are any problems with it please either raise an issue or fix it and issue a pull request.This wrapper is based on:https://github.com/FEEprojects/plantower
Package: nova-graph
Svelte + TS + ViteThis template should help get you started developing with Svelte and TypeScript in Vite.Recommended IDE SetupVS Code+Svelte.Need an official Svelte framework?Check outSvelteKit, which is also powered by Vite. Deploy anywhere with its serverless-first approach and adapt to various platforms, with out of the box support for TypeScript, SCSS, and Less, and easily-added support for mdsvex, GraphQL, PostCSS, Tailwind CSS, and more.Technical considerationsWhy use this over SvelteKit?It brings its own routing solution which might not be preferable for some users.It is first and foremost a framework that just happens to use Vite under the hood, not a Vite app.vite devandvite buildwouldn't work in a SvelteKit environment, for example.This template contains as little as possible to get started with Vite + TypeScript + Svelte, while taking into account the developer experience with regards to HMR and intellisense. It demonstrates capabilities on par with the othercreate-vitetemplates and is a good starting point for beginners dipping their toes into a Vite + Svelte project.Should you later need the extended capabilities and extensibility provided by SvelteKit, the template has been structured similarly to SvelteKit so that it is easy to migrate.Whyglobal.d.tsinstead ofcompilerOptions.typesinsidejsconfig.jsonortsconfig.json?SettingcompilerOptions.typesshuts out all other types not explicitly listed in the configuration. Using triple-slash references keeps the default TypeScript setting of accepting type information from the entire workspace, while also addingsvelteandvite/clienttype information.Why include.vscode/extensions.json?Other templates indirectly recommend extensions via the README, but this file allows VS Code to prompt the user to install the recommended extension upon opening the project.Why enableallowJsin the TS template?WhileallowJs: falsewould indeed prevent the use of.jsfiles in the project, it does not prevent the use of JavaScript syntax in.sveltefiles. In addition, it would forcecheckJs: false, bringing the worst of both worlds: not being able to guarantee the entire codebase is TypeScript, and also having worse typechecking for the existing JavaScript. In addition, there are valid use cases in which a mixed codebase may be relevant.Why is HMR not preserving my local component state?HMR state preservation comes with a number of gotchas! It has been disabled by default in bothsvelte-hmrand@sveltejs/vite-plugin-sveltedue to its often surprising behavior. You can read the detailshere.If you have state that's important to retain within a component, consider creating an external store which would not be replaced by HMR.// store.ts// An extremely simple external storeimport{writable}from'svelte/store'exportdefaultwritable(0)
Package: novajoin-tempest-plugin
This project defines a tempest plugin containing tests used to verify the
functionality of a novajoin installation. The plugin will automatically load
these tests into tempest.DependenciesThe novajoin_tempest_plugin tests the novajoin API, which requires the
existence of an IPA server.DevelopersFor more information on novajoin, refer to:http://docs.openstack.org/developer/novajoin/For more information on tempest plugins, refer to:http://docs.openstack.org/developer/tempest/plugin.html#using-pluginsBugsPlease report bugs to:http://bugs.launchpad.net/novajoin
Package: noval
noval_
 _ __   _____   ____ _| |
| '_ \ / _ \ \ / / _` | |
| | | | (_) \ V / (_| | |
|_| |_|\___/ \_/ \__,_|_| Configurable novel downloader.Usagenoval -hto get help message.usage: noval [-h] [--sep SEP] [--save-to path] [--range RANGE RANGE] [--split SPLIT | --append] [-v] name

positional arguments:
  name                 fiction name.

optional arguments:
  -h, --help           show this help message and exit
  --sep SEP            sleep time.
  --save-to path       custom fiction save path.
  --range RANGE RANGE  Download chapter range, like:`--range 10 20`
  --split SPLIT        Download segmented storage.
  --append             Whether it is in append mode. It is recreated by default.
  -v, --version        Show version and exit.InstallationPippip3 install noval --upgradeSourcegit clone --dep 1 https://github.com/zlj-zz/noval.git
cd noval && python3 setup.py install
Package: nova_limits
This package provides thenova_limitsPython module, which
contains thenova_preprocess()preprocessor, theNovaClassLimitlimit class, and theNovaTurnstileMiddlewarereplacement middleware class, all for use with Turnstile.  These
pieces work together to provide class-based rate limiting integration
with nova.  To use, you must configure the Turnstile middleware with
the following configuration:[filter:turnstile]
use = egg:turnstile#turnstile
turnstile = nova_limits:NovaTurnstileMiddleware
preprocess = nova_limits:nova_preprocess
redis.host = <your Redis database host>Then, simply use thenova_limits:NovaClassLimitrate limit class
in your configuration.UsingNovaClassLimitIn addition to the other attributes provided byturnstile.limits:Limit, theNovaClassLimitlimit class
provides one additional required argument: therate_class.  Each
tenant is associated with a given rate-limit class through the Redis
database.  (If no such association is present, the rate-limit class
for a tenant isdefault.)  Settingrate_classonNovaClassLimitrestricts the limiting action to only those tenants
in the given rate-limit class.Also note that, for nova, the URIs used in configuring rate limiting
must include the version identifier, i.e.,
“/v2/{tenant}/servers/detail”.
Package: nova-lxd
nova-lxdAn OpenStack Compute driver for managing containers using LXD.nova-lxd on DevstackFor development purposes, nova-lxd provides a devstack plugin. To use it, just include the
following in your devstacklocal.conf:[[local|localrc]]
enable_plugin nova-lxd https://git.openstack.org/openstack/nova-lxd

# You should enable the following if you use lxd 3.0.
# In addition, this setting requires zfs >= 0.7.0.
#LXD_BACKEND_DRIVER=zfsChange git repositories as needed (it's probably not very useful to point to the main
nova-lxd repo). If you have a local tree you'd like to use, you can symlink your tree to/opt/stack/nova-lxdand do your development from there.The devstack default images won't work with lxd, as lxd doesn't support them. Once your
stack is up and you've configured authentication against your devstack, do the following::wget http://cloud-images.ubuntu.com/xenial/current/xenial-server-cloudimg-amd64-root.tar.gz
glance image-create --name xenial --disk-format raw --container-format bare --file xenial-server-cloudimg-amd64-root.tar.gzYou can test your configuration using the exercise scripts in devstack. For instance,DEFAULT_IMAGE_NAME=xenial ./exercises/volumes.shPlease note: the exercise scripts in devstack likely won't work, as they have requirements
for using the cirros images.Support and discussionsWe use the LXC mailing-lists for developer and user discussions, you can
find and subscribe to those at:https://lists.linuxcontainers.orgIf you prefer live discussions, some of us also hang out in#lxcontainerson irc.freenode.net.Bug reportsBug reports can be filed athttps://bugs.launchpad.net/nova-lxd
Package: nova-mksproxy
This is Nova console proxy for instances running on VMware.
It requires Nova to support microversion 2.31 to work.Usage with DevstackThere is a devstack plugin fornova-mksproxy, so simply add this to yourlocal.conf:[[local|localrc]]
enable_plugin nova-mksproxy https://github.com/openstack/nova-mksproxyThe plugin will enable MKS consoles in Nova and will start the proxy on port 6090. Use nova CLI to get a console URL:$ nova get-mks-console <instance_name>
Package: novaml
NovaMLBuild machine learning applications with NovaML; The NEW machine learning library!NoteThis library is for learning purposes and not ready for production use!InstallpipinstallnovamlUsageimportnumpyasnpfromnovaml.modelsimportLinearRegressionx=np.array([1.0,2.0])y=np.array([300.0,500.0])w_init=np.array([0.0])b_init=np.array([0.0])alpha=1.0e-2iterations=10000lambd=Nonelinear_regression=LinearRegression()final_w,final_b,_,_=linear_regression.train(x,y,w_init,b_init,alpha,iterations,lambd)yhat=linear_regression.predict(x,final_w,final_b)print(f"ytrain:{y}")print(f"yhat:{yhat}")ExamplesCheck theREADME.mdof examples folder.LicenseMIT License