Saravanan666/pypi-org huggingface dataset:
Package: denoc
DeprecatedThis would make more sense if it was written using Deno, or a compiled language
like Go or Rust.Email me toeliaz.bobadilladev@gmail.comif you create a similar tool, so I
will link it here.If no one does, I will try to do it in my free time later,sponsor me😩DenocCompile Deno executables and compress them for all platforms easily 🚀InstallYou can installdenocfrom PyPI like any other
package:pipinstalldenocFrom Github 👇pipinstallgit+https:/github.com/UltiRequiem/denocUsageBasic usagedenoccompileMe.tsThis will make a directory (deno_builds) with executables for all the
supported platforms.Optional flags:denoc--outputDirdeno_dir_output--compressTruefile.tsoutputDir: The directory where the binaries will be, by default the
directory isdeno_buildcompress: Compress the binaries directoryBuild and Publish on GitHub Actionsname:Compileon:push:tags:-"*"jobs:build:runs-on:ubuntu-lateststeps:-name:Checkoutuses:actions/checkout@v2-uses:denoland/setup-deno@v1with:deno-version:v1.x-name:Install denocrun:pip install denoc-name:Build for all platformsrun:denoc cli.ts-name:Releaseuses:softprops/action-gh-release@v1with:files:|deno_builds/x86_64-unknown-linux-gnudeno_builds/aarch64-apple-darwindeno_builds/x86_64-apple-darwindeno_builds/x86_64-pc-windows-msvc.exetoken:${{ secrets.GITHUB_TOKEN }}Example Repository 👉ultirequiem/deno-clearAuthorsEliaz Bobadilla (a.k.a UltiRequiem)- Creator and
Maintainer 💪See also the full list ofcontributorswho
participated in this project.VersioningWe useSemVerfor versioning. For the versions available,
see thetags.LicenceLicensed under the MIT License.
Package: denodo-sqlalchemy
This package includes the Denodo Dialect for SQLAlchemy, which offers an easy way to connect to Denodo databases from SQLAlchemyhttps://www.sqlalchemy.org/Denodo documentation and software resources are available athttps://community.denodo.comThis dialect supports SQLAlchemy v1.4 and higher and Denodo 7.0 and higher.Pre-requisitesThis dialect requires the following Python modules:SQLAlchemy version 1.4 or higherpsycopg2 version 2.7 or higher.However, these dependencies do not need to be installed because when you install Denodo Dialect for SQLAlchemy both should be installed automatically.Alternatively, these packages can be manually installed by means of:pip install sqlalchemy
pip install psycopg2Important: note also that psycopg2 has its own requirements for installation which need to be satisfied:https://www.psycopg.org/docs/install.html#prerequisitesInstallationThe Denodo SQLAlchemy package can be installed from the public PyPI repository usingpip:pip install --upgrade denodo-sqlalchemypipautomatically installs all required modules.UsageTo connect to Denodo VDP Server with SQLAlchemy, the following URL pattern can be used:denodo://<username>:<password>@<host>:<odbcport>/<database>
Package: denoisers
DenoisersDenoisers is a denoising library for audio with a focus on simplicity and ease of use. There are two major types of architectures available. WaveUNet for waveform denoising and UNet for spectrogram denoising.DemoGradio demohereUsage/ExamplespipinstalldenoisersimporttorchimporttorchaudiofromdenoisersimportWaveUNetModelfromtqdmimporttqdmmodel=WaveUNetModel.from_pretrained("wrice/waveunet-vctk-24khz")audio,sr=torchaudio.load("noisy_audio.wav")ifsr!=model.config.sample_rate:audio=torchaudio.functional.resample(audio,sr,model.config.sample_rate)chunk_size=model.config.max_lengthpadding=abs(audio.size(-1)%chunk_size-chunk_size)padded=torch.nn.functional.pad(audio,(0,padding))clean=[]foriintqdm(range(0,padded.shape[-1],chunk_size)):audio_chunk=padded[:,i:i+chunk_size]withtorch.no_grad():clean_chunk=model(audio_chunk[None]).logitsclean.append(clean_chunk.squeeze(0))denoised=torch.concat(clean,1)[:,:audio.shape[-1]]
Package: denomme
dénommé : Multilingual Name Detection using spaCy v3Supported LanguagesInstallationpip install denomme https://denomme.s3.us-east-2.amazonaws.com/xx_denomme-0.3.1/dist/xx_denomme-0.3.1.tar.gzUsing the denomme-pipefrom spacy.lang.xx import MultiLanguage
from denomme.name import person_name_component

nlp = MultiLanguage()
nlp.add_pipe("denomme")
doc = nlp("Hi my name is Meghana S.R Bhange and I want to talk Asha")
print(doc._.person_name)
Package: denonavr-cli
denonavr-cli is a trivial CLI client for Denon AV receivers based
on thedenonavrlibrary.  Its biggest advantage is receiver
autodiscovery, so you don’t have to specify the IP address.When run without arguments, it scans the network for AVRs.  If it finds
exactly one, it displays its status:$ denonavr-cli
Power: ON       Volume: -54.0 dB         Input: HEOS MusicNote that if there are more AVRs on the network, you need to specify
the host explicitly:$ denonavr-cli -H 192.168.1.6After every call, the executable caches the host used, eliminating
the autodiscovery delay for subsequent commands (but-Halways
overrides the cached value).  If the cached host is no longer
responding, it is discarded and autodiscovery is reenabled.  You can
also manually clear the cache via:$ denonavr-cli --host-cache=resetTo see the available commands and options:$ denonavr-cli --helpIn general, commands without additional arguments print the current
status in a machine-readable format, e.g.:$ denonavr-cli volume
-54.0The commands that alter the state generally print the new value
after performing the action:$ denonavr-cli volume up 4
-50.0
Package: denon-avr-serial-over-ip
Python Library for Denon AVR Serial over IP ControlDescriptionConnects to an older Denon AVR serial port using an IP to Serial convertorNoteThis is in no way affiliated with Denon.IssuesI don't have access to a Denon AMP any more directly - so most of this is from old Protocol documentationLogging / DebuggingThis library usesloggingjust set the log level and format you need.ExampleThe examples below may look a little complex - because this library relies on functions like.connect()need to beawaited.Connect and turn on Zone 2importasynciofromdenon_avr_serial_over_ipimportDenonAVRapi=DenonAVR(host=10.10.10.10,longitude=5001,)asyncdefzone_change(zone):"""Alert about a zone change"""_LOGGER.info("Zone%schanged",zone.zone_number)asyncdefconnect_turn_on_z2():awaitAPI.connect()API.zone2.subscribe(zone_change)awaitasyncio.sleep(2)awaitAPI.zone2.turn_on()awaitasyncio.sleep(2)awaitAPI.zone2.set_volume_level(0.5)awaitasyncio.sleep(2)awaitAPI.turn_off()asyncio.get_event_loop().run_until_complete(connect_turn_on_z2())Support
Package: denopy
DenoPy (WIP)Bare-minimum binding ofdeno_core, for embedding JavaScript in Python.Example:importdenopyr=denopy.Runtime()r.eval("['1', '2', '3'].map(parseInt)")NotesThis supports only blocking JavaScript, notasync/await.Most JavaScript embedding use cases I've seen so far involve pure logic, not I/O.Jugglingasync/awaitacross 3 languages is a lot of work.Deno has multiple layers, in decreasing order of functionalities:cli->deno_runtime->deno_core->v8. The first 2 are not stable, so we bind the 3rd one. This means:No TypeScript.Nonpm module import.
Package: denorm
DenormDenormalized and aggregated tables for PostgreSQL.Keywords: PostgreSQL, denormalization, aggregation, incremental view
maintenance, materialized viewOverviewInstallUsageOperationsPerformanceMigrationLimitationsDevelopingOverviewDernom is similar to PostgreSQL'sREFRESH MATERIALIZED VIEW,
except that it updates materialized table incrementally.How it works: Define the query, the tables, and their relationships in JSON.
Denorm generates the SQL DDL statements that create the necessary functions and
triggers. Apply the generated SQL statements to the database. Now, the
materialized target is automatically kept in sync with the source tables.FeaturesEfficient incremental updatesArbitrarily complex SQL features and expressionsConfigurable consistencyDeadlock-freeInstallPippip3 install denormUsageFor CLI usage, seeUsage.OperationsDenorm has two operations:AggregateCreate a materalized aggregate of a single table.SeeAggregate.JoinCreate a materialized join of several tables.SeeJoin.PerformanceMaterialized views exchange slower write performance for higher read
performance.While it's impossible to escape the fundamental trade-off, Denorm is as fast or
faster than hand-written triggers. It uses statement-level transitions tables to
make batch updates especially efficient.In deferred mode, Denorm uses temp tables to defer updates until the end of the
transaction. Using temp tables andON DELETE COMMITreduces I/O overhead and
obviates the need for vacuuming. Since PostgreSQL does not support global
temporary tables, the tables are created as necessary for each session. Thus the
first saliant update in a session may have several millseconds of overhead as
the trigger creates the temporary tables. Pool connections to reduce overhead,
and vacuum reguarly to prevent system tables from bloating.MigrationDenorm does not generate migration scripts.Consider a tool likemigrato help generate
migration scripts.LimitationsDenorm mangles names for generated objects, Long IDs and table names may run
into the PostgreSQL limit of 63 characters for identifiers.DevelopingInstall:make installGenerate JSONSchema:make schemaTest:make testGenerate documentation:make docFormat:make formatPublishUpdate denorm/version.py.Create commitVersion <version>.Tagv<version>.Push master branch and tag.Publish to PyPI:make publish.
Package: deno-version-manager
Deno Version ManagerSimple CLI to manageDenoversionsCreated withtyperandpoetryUsageCommandDescriptiondvm uselook for a .dvm file and use the specified deno versiondvm install [version]install a specific deno version or auto-install latestdvm upgrade [version]upgrade to a specific version or auto-install latestdvm uninstallremove denoDevelopmentIn VSCode$ cd dvm && poetry shell && code .
  $ poetry install
  $ poetry run python main.py [ARGS]Linting & FormattingVSCode GuidePackagingPython packaging Guide
Package: deno-vm
A Python 3 to Deno + worker-vm binding, helps you execute JavaScript safely.How it worksThe module launches a Deno REPL server, which can be communicated with JSON. All JavaScript code are encoded in JSON and sent to the server. After the server executing the code in vm, the result is sent back to Python.InstallInstall DenoFollow the instruction here:https://docs.deno.com/runtime/manual/getting_started/installationAlso make suredenocommand works as expected:https://docs.deno.com/runtime/manual/getting_started/installation#testing-your-installationInstall deno_vmpipinstalldeno_vmUsageMost of the APIs are bound toworker-vm.Simple eval:fromdeno_vmimportevalprint(eval("['foo', 'bar'].join()"))Use VM:fromdeno_vmimportVMwithVM()asvm:vm.run("""
      var sum = 0, i;
      for (i = 0; i < 10; i++) sum += i;
   """)print(vm.run("sum"))It is possible to do async task with Promise:fromdatetimeimportdatetimefromdeno_vmimportVMjs="""
function test() {
   return new Promise(resolve => {
      setTimeout(() => {
         resolve("hello")
      }, 3000);
   });
};
"""withVM()asvm:vm.run(js)print(datetime.now())print(vm.call("test"))print(datetime.now())API referencehttp://deno_vm.readthedocs.io/Changelog0.5.1 (Oct 10, 2023)Fix: unable to pass initial code toVM().0.5.0 (Oct 10, 2023)Switch to deno_vm.
Package: dense-ev
dense_evdense_evimplements expectation value measurements in Qiskit using optimal
dense groupingarXiv:quant-ph/2305.xxxx. For an $m$-qubit operator
that includes all Pauli strings in its decomposition,
it provides a speedup of $2^m$ compared to naive evaluation of all strings,
and $(3/2)^m$ compared to grouping based on qubit-wise commuting (QWC) families.InstallationCreate a virtual environment to sandbox the installation (optional):python3-mvenvtest-env&&source./test-env/bin/activateTo install,pipinstalldense-evInstall from GitHub:pipinstallgit+ssh://git@github.com/atlytle/dense-ev.gitTo run unit tests,fromdense_ev.test_opimportrun_unit_testsrun_unit_tests()Qiskit version compatibilityNote thatdense_evspecifiesqiskit < 0.43.0, as theOpflowandQuantumInstancepackages have been deprecated as ofQiskit 0.43.0.
We plan to update the code to function with the newprimitivesas this
migration continues and more documentation becomes available.UsageFunctionality for naive and QWC groupings is provided in Qiskit
by thePauliExpectationclass.DensePauliExpectationextends the functionality
to dense optimal grouping, and may be used as a replacement forPauliExpectation:fromdense_EVimportDensePauliExpectation# Simple expectation values:...ev_spec=StateFn(H).compose(psi)expectation=DensePauliExpectation().convert(ev_spec)...# VQE example...vqe=VQE(ansatz,optimizer=spsa,quantum_instance=qi,callback=store_intermediate_result,expectation=DensePauliExpectation())result=vqe.compute_minimum_eigenvalue(operator=H)...
Package: dense-fusion
dense-fusionPrerequisiteDownloadYCB Video Datasetand extract it.Also set,exportYCB_VIDEO_DATASET_PATH=<YOURYCB_Video_DatasetPATH>Installationpipinstalldense-fusionSamplecdexamples
pythoneval_ycb.pyROS InterfaceProviding ROS Interfacehttps://github.com/iory/dense-fusion-ros.
If you are ROS User, Please try it.FeaturesInferenceROS Interface (https://github.com/iory/dense-fusion-ros)Training YCB Video DatasetTraining custom Dataset
Package: densenet
DenseNet implementation using Tensorflow 2Quickstart$./bin/startSetup and use dockerBuild the docker image,$dockerbuild--rm-fdockerfiles/cpu-jupiter.Dockerfile-tsign-language-recognition:latest.and now run the image$dockerrun-v"$(pwd)/notebooks:/tf/notebooks"--rm-u$(id-u):$(id-g)-p6006:6006-p8888:8888sign-language-recognition:latestVisit that link, hey look your jupyter notebooks are ready to be created.
Changes in ./notebooks will be saved.If you want, you can attach a shell to the running container$dockerexec-it<container-id>/bin/sh-c"[ -e /bin/bash ] && /bin/bash || /bin/sh"
Package: densenlpt
`densenlpt` is a python package for creating dictionary and vector from scentences & training model to predict.  mainly it is used for Japanese Language AnalysisRequirements------------* Python 3.x(you may work by Python2.x)Features--------* nothingHistory-------0.0.1 (2017-7-20)0.0.2 (2017-7-29)0.0.3 (2017-7-29)0.0.4 (2017-7-29)0.0.5 (2017-7-29)~~~~~~~~~~~~~~~~~~* first releaseUsage------------please look at Github README~~~~~~~~~~~~~~~~~~Plan to upgrade-------*add some more maltiple method when you create dictionary*add some more maltiple method when you create corpus, vector*add some method when you plot some results of analysisand more, if i can~~~~~~~~~~~~~~~~~~* first release
Package: denseweight
DenseWeightThis package implements the method for imbalanced regressionDenseWeight. The corresponding paper "Density-based weighting for imbalanced regression" is availablehere.The goal of DenseWeight is to allow training machine learning models for regression tasks that emphasize performance for data points with rare target values in comparison to data points with more common target values. This can be useful when rare samples are of particular interest e.g. when estimating precipitation and you are interested in estimating rare, extreme precipitation events as well as possible. The parameter alpha controls the intensity of the density-based weighting scheme (alpha = 0.0 -> uniform weighting; larger alpha -> more emphasis on rare samples).DenseWeight judges the rarity of a target value based on its density, which is obtained through Kernel Density Estimation (KDE). This package uses the fast convolutional-based KDE implementation FFTKDE fromKDEpyto allow the application of DenseWeight for large datasets.InstallationDenseWeight is available atPyPIand can be installed via pip:pip install denseweightUsageimport numpy as np
from denseweight import DenseWeight

# Create toy target variable with 1000 samples
y = np.random.normal(size=1000)

# Define DenseWeight
dw = DenseWeight(alpha=1.0)

# Fit DenseWeight and get the weights for the 1000 samples
weights = dw.fit(y)

# Calculate the weight for an arbitrary target value
weights = dw([0.1206])These weights can be used as sample weights for machine learning algorithms which support them. They can also be easily integrated into loss function for models like Neural Networks to create a cost-sensitive learning solution to data imbalance in regression tasks which we callDenseLoss(more details on this in the paper).
Package: densify
densifydensifyis an algorithm foroversamplingpoint clouds. It creates synthetic data points that "fill the gaps" in the cloud, making it more dense. This can be a usefultechnique for reducing overfittingwhen training machine learning models on point cloud datasets.InstallationYou can installdensifyfrom PyPi:$pipinstalldensifyUsagedensifyis simple to use. The function expects an array of points representing your cloud and a "radius" that dictates the minimum distance each synthetic point must be from all other points. The smaller the radius, the higher the density.importnumpyasnpfromdensifyimportdensifypoint_cloud=np.array([[4.6,6.5],[1.5,4.1],[6.1,5.0],[1.1,2.9],[10.0,5.0]])new_points,iter_results=densify(point_cloud,radius=0.15)The function returnsnew_points, a numpy array of the synthetic points, anditer_results, a list of algorithm outputs to plug intovisualize_densify.Constrained Point GenerationBy default,densifyacts within theconvex hullof the point cloud and will not create points outside that boundary. But if the point cloud is non-convex, you can define a boundary to generate points within. To do this, pass in a list of points in the cloud representing the boundary:point_cloud=np.array([[0,0],[4,0],[4,-3],[6,-3],[6,3],[3,5],[2,1],[3,3],[5,0],[4,1]])hull=np.array([[0,0],[4,0],[4,-3],[6,-3],[6,3],[3,5]])new_points,iter_results=densify(point_cloud,radis=0.15,exterior_hull=hull)Note that these points must define asimplepolygon that enclosesallthe points in the cloud.Visualizing Point Generationdensifylets you visualize the point generation process for 2D point clouds. Simply plug thepoint_cloudanditer_resultsobjects intoanimate_densify:fromdensifyimportanimate_densifyanimate_densify(point_cloud,iter_results,dark=True,filename="ani.gif")How it Worksdensifycomputes aDelaunay triangulationof the point cloud and creates synthetic points from the centroids of each simplex in the triangulation. These points are added to the cloud, and the process is repeated recursively until no new points can be created.If a boundary is given,densifyenforces it by using thewinding number algorithmto identify simplices that contain edges outside of the boundary, and then dropping them.Authorsdensifywas created by Jonathan Shobrook with the help ofPaul C. Bogdanas part of our research in theDolcos Labat the Beckman Institute for Advanced Science and Technology.
Package: densityestimation
This package provides functions for three specific types of density estimators.
You might find it most useful for tasks involving plotting kernel density estimates,
histograms and nearest-neighbor density estimates.Typical intallation often looks like this:!pip install densityestimationDependenciesNumpyMatplotlibExamplesimport numpy as npsample = np.random.normal(0,1,1000)kde(sample)import numpy as npsample = np.random.normal(0,1,1000)hist(sample)import numpy as npsample = np.random.normal(0,1,1000)nnde(sample)ContributorsWe would like to thank our professor, Brian Chapman, for guiding us
through our first ever endeavors involving Python.  It is because of
you, this class and this project that density estimation in Python
will never be the same, or at least in our eyes.
Package: dentitox-pro-reviews
What is Dentitox Pro?Dentitox Pro,
 a natural formula, is an effective way to improve dental health. It 
also prevents common problems like bad breath, swelling, receding gums 
and decay. Contrary to popular belief, brushing your teeth every night 
and morning is not enough to prevent you from developing dental disease.
 While dental hygiene is an important part of overall health 
maintenance, it is not the only component. Relying on it 100% is foolish
 and dangerous. Regular brushing of teeth can lead to gum and tooth 
disease.Dental hygiene does not just 
involve brushing and cleaning. There is ample evidence that dental 
hygiene is not the only factor. Given the current circumstances, where 
everyone is busy, it can be difficult to find time to do something about
 it.People don't 
like taking supplements for their teeth because they don't believe they 
will work. This Dentitox review will dispel this myth and show how teeth
 and gums need vital nutrients to keep their structure and function.Dentitox, a liquid formulation 
that can be applied to the gums and teeth, is called Dentitox. It is 
quick to absorb and begins working immediately. Dentitox.com states that
 it can take between two and three weeks for it to start working, while a
 full dental transformation may take up to six to eight months. It is 
currently available at a discount price. You can also purchase bundle 
packs to save even more. Learn more about how it works, and what the 
ingredients mean for your teeth.Buy Dentitox Pro from the Official Site >>dentitox pro reviewspip3installdentitoxproreviews
Package: denton
Denton proportional procedureThe package provide a function to make an interpolation using the denton proportional procedure. It uses 2 arrays: the first one is the indicator array (high frequency data) and the second one is the aggregate array (low frequency data). Notice that the first array must have multiple number of elements of the second one. In this case, it is consider that the second array as means of a subsets elements of the first array. Boths array must be order ascending in time.An example is included in the repositoryThe methodology was taken from:https://www.imf.org/external/pubs/ft/qna/pdf/2017/chapter6.pdfThe package use the FOC of the minimization problem to get the optimal solution.Requirements:
python 3+
numpyInstallation:pip install dentongithub:https://github.com/MaxLugo/denton
Package: dentoscrapper
Useful tools for websrapping textbook centre imagesDescriptionIt consists of two main modules:beautifulsoup4: tool for webscappingpysimplegui: tools to work with python GUIInstallationNormal installationpipinstalldentoscrapperUsagepython3-mtbcscrapperDevelopment installationgitclonehttps://github.com/Denniskamau/TextbookcentreWebScrapperChange log[0.1.1] - 2020-04-11Initial release
Package: denv
Run yourscriptwith yourenvUltra simple runner that injects vars from the implicit.envfile
into your commands of choice.denv COMMAND [ARGS]Expects a.envfile in the current directory of format:KEY=VALUE
FOO=BAR
AGE=29
host=hostname=foo user=bar password=falseExamplesCreate an.envfile holding your vars$cat.envCONNECTION_STRING=host=foouser=barpassword=bazUse them in posix commands:$denvenv|grepCONNECTION_STRINGhost=foouser=barpassword=bazUse them when running python scripts:$denvpython-c'import os;print(os.environ["host"])'foouser=barpassword=bazWhy?At Trustpilot we strictly follow the12-factorsof designing software-as-a-service
apps, especially the concept ofisolating your
configurationoutside your app.Using environment vars is the accepted practice for injecting these
configs into apps during deployment. But running locally i wanted
something very simple to inject theseenv-varsinto the runtime
myself.Looking around you will quickly find many implementations:https://direnv.net/https://github.com/bkeepers/dotenvhttps://github.com/motdotla/dotenvhttps://github.com/pedroburon/dotenvhttps://github.com/josegonzalez/php-dotenvBut i tend to disagree with them in two ways:They tend to aim for being a replacement for your runtimes defaultenvironmentlib and just overload with reading from a fileSomeof them lack support forconnection-stringswhere there can be many=’s per line.I wanted to seperate it completely so the runtime only cared aboutenv-varsand its standard way of accessing these, and then have therunnerinject these vars as the only thing it did.- `sloev <sloev.github.io>`__