Saravanan666/pypi-org huggingface dataset:
Package: vibration-toolbox
Joseph C. Slater and Raphael TimbóWelcome to theVibration Toolbox.
ThisPythonversion is a completely new design build for modern education. This is aneducationalset of codes intended primarily for
demonstration of vibration analysis and phenomenon. You may find them useful for application, but that isn’t the intent of this toolbox. If you have professional-level needs pleasecontact the authors.Fulldocumentation is available, but please excuse that it is still under development. Such documentation has never existed for the other ports of the toolbox so this is taking some time. We don’t need feedback at this time, but we will take assistance in improving documentation and code.Pleaseclone the repository and support use by submitting pull requests fixing typos and clarifying documentation.Try now!You won’t get everything, but you can try parts of the toolbox immediately by riunning thetutorial on mybinder.org, right in your browser window!InstallationIf you aren’t familiar at all with Python, please seeInstalling Python.Installation is made easy withpip(orpip3), with releases as we have time while we try
to create a full first release. Much of it works already, but we certainly need
issue reports (ongithub).To install type:pip install --user vibration_toolboxat your command prompt(not the python prompt)where--userisn’t necessary if you are using a locally installed version of Python such asAnaconda.To run, I recommend you open aJupyternotebook by usingjupyter notebookat your command prompt/terminal prompt/Anaconda prompt and then type:import vibration_toolbox as vtbFor examples, see theJupyterNotebooks folder. Some of these have interactive capabilities that are only apparent when you run them yourself instead of just looking at them on GitHub. Unfortunately our organization of these still leaves a little to be desired. Help accepted!Installation of current code/contributingThe usage documentation is far behind the current code, while the reference is way ahead of the released code due to theautodoccapability ofSphinx. Especially as of 2017, the code is still in rapid development. So is the documentation. Releases topypi.If you wish to install the current version of the software, and especially contribute, please follow the instructions inContributing.rstThat should be it. Please note issues on theissues tabon GitHub.
Package: vibrato
🐍 python-vibrato 🎤Vibratois a fast implementation of tokenization (or morphological analysis) based on the Viterbi algorithm.
This is a Python wrapper for Vibrato.InstallationInstall pre-built package from PyPIRun the following command:$ pip install vibratoBuild from sourceYou need to install the Rust compiler followingthe documentationbeforehand.
daachorse usespyproject.toml, so you also need to upgrade pip to version 19 or later.$ pip install --upgrade pipAfter setting up the environment, you can install daachorse as follows:$ pip install git+https://github.com/daac-tools/python-vibratoExample Usagepython-vibrato does not contain model files.
To perform tokenization, followthe document of Vibratoto download distribution models or train your own models beforehand.Check the version number as shown below to use compatible models:>>>importvibrato>>>vibrato.VIBRATO_VERSION'0.5.0'Examples:>>>importvibrato>>>withopen('tests/data/system.dic','rb')asfp:...tokenizer=vibrato.Vibrato(fp.read())>>>tokens=tokenizer.tokenize('社長は火星猫だ')>>>len(tokens)5>>>tokens[0]Token{surface:"社長",feature:"名詞,普通名詞,一般,*"}>>>tokens[0].surface()'社長'>>>tokens[0].feature()'名詞,普通名詞,一般,*'>>>tokens[0].start()0>>>tokens[0].end()2Note for distributed modelsThe distributed models are compressed in zstd format. If you want to load these compressed models,
you must decompress them outside the API.>>>importvibrato>>>importzstandard# zstandard package in PyPI>>>dctx=zstandard.ZstdDecompressor()>>>withopen('tests/data/system.dic.zst','rb')asfp:...withdctx.stream_reader(fp)asdict_reader:...tokenizer=vibrato.Vibrato(dict_reader.read())LicenseLicensed under either ofApache License, Version 2.0
(LICENSE-APACHEorhttp://www.apache.org/licenses/LICENSE-2.0)MIT license
(LICENSE-MITorhttp://opensource.org/licenses/MIT)at your option.
Package: vibrav
VIBRAtional AVeraging (vibrav)A tool to perform vibrational averaging of molecular properties on molecules.InstallationTo use this code package you must download the development version and executepip intsall -r requirements.txt -e .and to build the documantationpip install -r requirements.publish.txt -r requirements.txt -e .Building the documentationThe docs are built with the sphinx-apidoc module. It will generate .txt
files with the contents of the docstrings in the python code. We use
thesphinx_bootstrap_themefor our documentation. To install it runpip install sphinx_bootstrap_theme.To build the documentation,cddocs/
makehtmlTo view the built documentation (assuming already in docs directory),cdbuild/html
xdg-openindex.htmlOr, on the Windows Subsystem Linux,cdbuild/html
cmd.exe/Cstartindex.htmlRequirementsnumpypandasnumbaexatomicCalculations availableVibronic Coupling:Zero-point vibrational correctionsVibrational Raman Optical ActivityResearch Publications:Abella, L; Ludowieg, H D; Autschbach, J. Theoretical Study of the Raman Optical Activity Spectra of $\ce{[M(en)3]^{3+}}$ with M = Co, Rh.Chirality2020, 32, 6, 741 $-$ 752. DOI:10.1002/chir.23194Ganguly, G; Ludowieg, H D; Autschbach, J. Ab Initio Study of Vibronic and Magnetic 5f-to-5f and Dipole-Allowed 5f-to-6d and Charge-Transfer Transitions in $\ce{[UX6]^{n−}}$ (X = Cl, Br; n = 1, 2).J. Chem. Theory Comput.2020, 16, 8, 5189 $-$ 5202. DOI:10.1021/acs.jctc.0c00386Atzori, M; Ludowieg, H D; et. al. Validation of microscopic magnetochiral dichroism theory.Science Advances2021, 7, 17, eabg2859. DOI:10.1126/sciadv.abg2859Morgante, P; Ludowieg, H D; Autschbach, J. Comparative Study of Vibrational Raman Optical Activity with Different Time-Dependent Density Functional Approximations: The VROA36 Database.J. Phys. Chem. A2022, 126, 9, 2909 $-$ 2927. DOI:10.1021/acs.jpca.2c00951To cite:Ludowieg, H D. Vibrav: a tool for vibrational averaging.https://github.com/herbertludowieg/vibravMort, B C; Autschbach, J. Magnitude of Zero-Point Vibrational Corrections to Optical Rotation in Rigid Organic Molecules:  A Time-Dependent Density Functional Study.J. Phys. Chem. A2005, 109, 38, 8617 $-$ 8623. DOI:10.1021/jp051685yVibronic CouplingHeit, Y N; Gendron, F; Autschbach, J. Calculation of Dipole-Forbidden 5f Absorption Spectra of Uranium(V) Hexa-Halide Complexes.J. Phys. Chem. Lett.2018, 9, 4, 887 $-$ 894. DOI:10.1021/acs.jpclett.7b03441Ganguly, G; Ludowieg, H D; Autschbach, J. Ab Initio Study of Vibronic and Magnetic 5f-to-5f and Dipole-Allowed 5f-to-6d and Charge-Transfer Transitions in $\ce{[UX6]^{n−}}$ (X = Cl, Br; n = 1, 2).J. Chem. Theory Comput.2020, 16, 8, 5189 $-$ 5202. DOI:10.1021/acs.jctc.0c00386CopyrightCopyright (c) 2023, Herbert D Ludowieg
Package: vibrio
vibrio-python (documentation)Python library for interfacing with osu!lazer functionality. Acts as bindings forhttps://github.com/notjagan/vibriounder the hood.Installationpip install vibrioSupports Python 3.9+.Tested (throughcibuildwheeldeployment) and published onpipon the following platforms:Ubuntu (via manylinux and musl) (x86)macOS (x86, arm64)arm is currently untested due to unavailability through GitHub actions hostingWindows (x86 and AMD64)If you do not have one of the supported platforms (or otherwise want to build from source), simply clone the repository and use thebuildsubcommand (and/or any superset ofbuildlikesdist) insetup.pyto produce an installable package, then usepip installon the result. This will require having thedotnetC# SDK on the system path to compile the server solution.
Package: vibromaf
VibroMAF - Vibrotactile Multi-Method Assessment FusionVibrotactile Quality MetricsThis package provides implementations of existing vibrotactile quality metrics and machine learning approach that fuses the individual metric scores into a single final quality score.InstallationInstallvibromaffromPyPI:pipinstallvibromafFor development versions and alternative installations see theinstallation documentation.UsageGet StartedFull DocumentationCitationIf you use this work, please cite our paper (PDF)@inproceedings{noll_vibromaf,
  author    ={Noll, Andreas and Hofbauer, Markus and Muschter, Evelyn and Li, Shu-Chen and Steinbach, Eckehard},
  booktitle ={2022 IEEE Haptics Symposium (HAPTICS)},
  title     ={Automated Quality Assessment for Compressed Vibrotactile Signals Using Multi-Method Assessment Fusion},
  year      ={2022},
  volume    ={},
  number    ={},
  pages     ={1-6},
  doi       ={10.1109/HAPTICS52432.2022.9765599},
  address   ={Santa Barabara, California, USA}}ContributeSee ourCONTRIBUTING.mdChangelog
Package: viburnum
ViburnumViburum- it's a small framework built on top of AWS CDK to simplify development and deploying AWS Serverless web applications.InstallingPackage consist of two patsprimitivesthat help to describe your handlers and resources anddeployerthat will convert primitives into CloudFormation using CDK.Installing only primitivespipinstallviburnumInstalling with deployerpipinstall"viburnum[deployer]"Lambda function will require only primitives to work correctly. That's why it's recommended to addviburnumintorequirements.txtandviburnum[deployer]intorequirements-dev.txtProject structureEach Lambda function handler is represented as folder withhandler.pyinside and other files if required.Examplehandler.py:fromviburnum.applicationimportRequest,Response,route@route("/tests/{id}",methods=["GET"])defget_test(request:Request,test_queue):print(f"Get test:{request.path_params.get('id')}")returnResponse(200,{})In the root folder you need to haveapp.pyfile withApplication, this file used by deployer and CDK to determine all related resources.Exampleapp.pyimportaws_cdkascdkfromviburnum.deployer.buildersimportAppConstructfromviburnum.applicationimportApplication,Sqsfromfunctions.api.get_test.handlerimportget_testapp=Application("TestApp")# Handlersapp.add_handler(get_test)context=cdk.App()AppConstruct(context,app)context.synth()All logic that shared across all lambdas, must be placed insidesharedfolder, and it will plugged into Lambda as a Layer.Recommended structure├── functions
│   ├── __init__.py
│   ├── api
│   │   ├── __init__.py
│   │   ├── some_api
│   │   │    ├── __init__.py
│   │   │    ├── handler.py
│   │   │    └── ...
│   │   │
│   │   └── ...
│   │   
│   ├── jobs
│   │   ├── __init__.py
│   │   ├── some_job
│   │   │    ├── __init__.py
│   │   │    ├── handler.py
│   │   │    └── ...
│   │   │
│   │   └── ...
│   │   
│   └── workers
│       ├── __init__.py
│       ├── some_job
│       │    ├── __init__.py
│       │    ├── handler.py
│       │    └── ...
│       │
│       └── ...
│      
├── shared
│   ├── __init__.py
│   └── ...
│
├── app.py
├── requirements-dev.txt
└── requirements.txtCLI toolViburnum deployer include CLI tool that helps initializing project and creating a new handlers.
After initializing project folder withcdk init app --language pythonyou can callvirburnum init, that command will change some files so Virburnum can work correctly.
There is command for creating new handlersvirburnum add [HANDLER_TYPE]that will create a handler.SupportedHANDLER_TYPE:apiworkerjobExample appSimpleexample app
Package: vic
This is a Python 3 implementation of the pen-and-paper cipher used by the Soviet spy Reino Häyhänen, codenamed “VICTOR”, in the 1950s.If you have 3to2 from the PyPI installed you can just runmake 3to2and you will end up with a copy of the cipher implementation in the3to2/subdirectory converted to run under Python 2.UsageThevicentrypoint has two subcommands,encryptanddecrypt.  To encrypt something you have to pass the following as arguments to theencryptsubcommand:A 10 character key for the straddling checkerboard, with two of those characters being spaces that are used for the second and third layer of the checkerboard.A 5 digit unique and random message ID.A 20 character passphrase used to derive keys.A date used to derive keys and to insert the message ID into the ciphertext. (DDMMYYYY)A 1 or 2 digit personal identifier, unique per person.A cleartext message to encrypt.To decrypt a message you have to pass the following as arguments to thedecryptsubcommand:A 10 character key for the straddling checkerboard, with two of those characters being spaces that are used for the second and third layer of the checkerboard.A 20 character passphrase used to derive keys.A date used to derive keys and to insert the message ID into the ciphertext. (DDMMYYYY)A 1 or 2 digit personal identifier, unique per person.A cleartext message to encrypt.
Package: vicdocs
vicdocsvicdocsis a documentation generator forVisionScript.vicdocsreads a VisionScript file (.vic), parses the comments, and generates a HTML document with:The program code;The title, description, and author information specified (if any);A list of function definitions.vicdocsonly works with single-file scripts.Installationvicdocsis available on PyPI, so you can install it withpip:pipinstallvicdocsUsageTo generate a documentation file, runvicdocswith the path to the VisionScript file as the first argument:vicdocsfile.vicThis will generate a HTML file in a{file}-docs, where{file}is the name of the VisionScript file for which you are generating documentation.LicenseThis project is licensed under anMIT license.
Package: vicdyf
vicdyf: Variational Inference of Cell state Dynamics with fluctuationvicdyf is intended to estimated cell state dynamics with fluctuation from spliced and unspliced transcript abundance.InstalationYou can install vicdyf using pip command from your shell.pipinstallvicdyfUsageYou need to prepare anAnnDataobjectwhich includes raw spliced and unspliced counts aslayersnamed  assplicedandunsplicedlike ascvelo data set. Applyvicdyfworkflow on the object:importvicdyfadata=vicdyf.workflow.estimate_dynamics(adata)vicdyf.workflow.estimate_dynamicshave optional parameters as below:use_genes: gene names for dynamics estimation (default:None)first_epoch: number of epochs for deriving latent representation (default:500)second_epoch: number of epochs for optimizing dynamics (default:500)param_path: a path where the optimized parameters ofvicdyf.modules.VicDyfare stored (default:.vicdyf_opt_pt)lr: Learning rate forAdam optimizerofpytorchbatch_size: Size of mini batches in the optimization procedurenum_workers: Number of workers indata loaderofpytorchval_ratio: proportion of validation data settest_ratio: proportion of test data setmodel_params: a dictionary which describe the configuration ofvicdyf.modules.VicDyf. The keys of the dictionary is as below:z_dim: dimension of latent representation (default10)enc_z_h_dim: dimension of hidden units in encoder layers (default50)enc_d_h_dim: dimension of hidden units in dynamics encoder layers (default50)dec_z_h_dim: dimension of hidden units in encoder layers (default50)num_enc_z_layers: the layer number of the encoder (default2)num_enc_z_layers: the layer number of the dynamics encoder (default2)num_dec_z_layers: the layer number of the decoder (default2)Here, theAnnDataobject acuires sevral elements inlayers,obsm,obspandobs.layers:vicdyf_expression: Expected gene expression levelvicdyf_mean_velocity: Expected gene expression changevicdyf_velocity: Stochasticaly sampled  gene expression changevicdyf_fluctuation: Fluctuation level for each geneobsm:X_vicdyf_z: Stochasticaly smapled latent representationX_vicdyf_zl: Expected latent representationX_vicdyf_d: Stochasticaly smapled changes of latent representationX_vicdyf_dl: Expected changes of latent representationX_vicdyf_umap: 2D UMAP embeddings of expected latent representation for visualizationX_vicdyf_sdumap: 2D UMAP embeddings ofX_vicdyf_dfor visualizationX_vicdyf_mdumap: 2D UMAP embeddings ofX_vicdyf_dlfor visualization
Package: vicdyf-tiisaishima
vicdyf: Variational Inference of Cell state Dynamics with fluctuationvicdyf is intended to estimated cell state dynamics with fluctuation from spliced and unspliced transcript abundance.InstalationYou can install vicdyf using pip command from your shell.pipinstallvicdyfUsageYou need to prepare anAnnDataobjectwhich includes raw spliced and unspliced counts aslayersnamed  assplicedandunsplicedlike ascvelo data set. Applyvicdyfworkflow on the object:importvicdyfadata=vicdyf.workflow.estimate_dynamics(adata)vicdyf.workflow.estimate_dynamicshave optional parameters as below:use_genes: gene names for dynamics estimation (default:None)first_epoch: number of epochs for deriving latent representation (default:500)second_epoch: number of epochs for optimizing dynamics (default:500)param_path: a path where the optimized parameters ofvicdyf.modules.VicDyfare stored (default:.vicdyf_opt_pt)lr: Learning rate forAdam optimizerofpytorchbatch_size: Size of mini batches in the optimization procedurenum_workers: Number of workers indata loaderofpytorchval_ratio: proportion of validation data settest_ratio: proportion of test data setmodel_params: a dictionary which describe the configuration ofvicdyf.modules.VicDyf. The keys of the dictionary is as below:z_dim: dimension of latent representation (default10)enc_z_h_dim: dimension of hidden units in encoder layers (default50)enc_d_h_dim: dimension of hidden units in dynamics encoder layers (default50)dec_z_h_dim: dimension of hidden units in encoder layers (default50)num_enc_z_layers: the layer number of the encoder (default2)num_enc_z_layers: the layer number of the dynamics encoder (default2)num_dec_z_layers: the layer number of the decoder (default2)Here, theAnnDataobject acuires sevral elements inlayers,obsm,obspandobs.layers:vicdyf_expression: Expected gene expression levelvicdyf_mean_velocity: Expected gene expression changevicdyf_velocity: Stochasticaly sampled  gene expression changevicdyf_fluctuation: Fluctuation level for each geneobsm:X_vicdyf_z: Stochasticaly smapled latent representationX_vicdyf_zl: Expected latent representationX_vicdyf_d: Stochasticaly smapled changes of latent representationX_vicdyf_dl: Expected changes of latent representationX_vicdyf_umap: 2D UMAP embeddings of expected latent representation for visualizationX_vicdyf_sdumap: 2D UMAP embeddings ofX_vicdyf_dfor visualizationX_vicdyf_mdumap: 2D UMAP embeddings ofX_vicdyf_dlfor visualization
Package: vici
AboutThe strongSwan VICI protocol allows external applications to monitor, configure
and control the IKE daemon charon. This Python package provides a native client
side implementation of the VICI protocol, well suited to script automated tasks
in a reliable way.Example Usage>>>importvici>>>s=vici.Session()>>>s.version()OrderedDict([('daemon',b'charon'),('version',b'5.4.0'),('sysname',b'Linux'),('release',b'3.13.0-27-generic'),('machine',b'x86_64')])>>>s.load_pool({"p1":{"addrs":"10.0.0.0/24"}})OrderedDict([('success',b'yes')])>>>s.get_pools()OrderedDict([('p1',OrderedDict([('base',b'10.0.0.0'),('size',b'254'),('online',b'0'),('offline',b'0')]))])
Package: vickers_hardness
VickersHardnessPredictionPredicting load dependent Vickers hardness based on chemical compositionCitationsTo cite the prediction of load dependent Vickers hardness, please reference the following work:Zhang. Z, Tehrani. A.M., Oliynyk. A.O., Day. B and Brgoch. J, Finding Superhard Materials through Ensemble Learning, Adv. Mater. 2020, 33, 2005112.PrerequisitesTo use the script provided here requires:pymatgenXGBoostscikit-learnpandasNumPyxlrdUsageIMPORTANTTo use all scripts smoothly, please clone the entire repo with all files in one folder and work within that folder.To train the model and predict the hardness of some materials you are interested in, simply following these steps:1 Generate descriptorsFirstly, prepare your compositions in an excel file, and name itpred_hv_comp.xlsxso that the script can recognize this file. The first column of thepred_hv_comp.xlsxfile should be named asComposition.To generate descriptors for your compositions, simply run:pythongenerate_des.pyYou will have an output file namedpred_hv_descriptors.xlsxcontaining all compositional descriptors.IMPORTANT STEP:now please manualy add a new column with load values (unit: N) at the end of the descriptor file you just generated. It is up to you at which load you want to predict the hardness.2 Train the model and make prediction of your compoundsWe have provided the training dataset in the filehv_comp_load.xlsxwhere you will find chemical compositions, hardness value and corresponding load value. We also provided the descriptors of our training set (hv_des.xlsx). The training process of our model will be automatically done when you run the prediciton script as this:pythonhv_prediction.pyResults will be stored in a file namedpredicted_hv.xlsx. Basically the script will first train the model using the dataset we constructed, then read thepred_hv_descriptors.xlsxfile you just generated and give you the predicted hardness at any load value you would be interested in.AuthorsThis code was created byZiyan Zhangwho is advised byJakoah Brgoch.
Package: viclint
viclintviclint is the official code linter forVisionScript.You can useviclintto ensure your code conforms to the VisionScript style.InstallationTo install viclint, run the following command:pipinstallviclintUsageTo use viclint, run:viclint<path><path>can be a single file or a directory whose.vicfiles you want to lint.A file must end in.victo be linted.Here is an example output from the linter:🪄 Linting ./camera.vic...
✨ Your code is now prettier! ✨If the lint failed, you will see an error message:🪄 Linting ./camera.vic...
🚨 ./camera.vic failed to lint.LicenseThis project is licensed under anMIT license.Related ResourcesVisionScript Website and DocumentationVisionScript GitHub repositoryVisionScript Visual Studio Code extension
Package: vicmiko
vicmikoPython library  to interact with Junos devices. Based on jnpr-pyez and jsnapy.Installpip install vicmikoFeature supportedjunos_get: get from junos box, support pipejunos_compare: show diffs of configjunos_commit: commit config to boxjsnapy_prejsnapy_postjsnapy_checkjunos_rpcjunos_viewjunos_pingjunos_traceroutejunos_commit_filejunos_compare_filejunos_installExamplefrom vicmiko.junos import JunOSDriver
from vicmiko import junos

jnpr = JunOSDriver(hostname='192.168.0.1',username="admin",password="admin")
jnpr.open()

jnpr.junos_get(['show version'])
jnpr.junos_rpc('get-software-information')
jnpr.junos_rpc('get-software-information',to_str=0)
jnpr.junos_compare(['set interfaces ge-0/0/0 description "to SRX300 port 01"'])
jnpr.load_junos_view('view.yml')
mem = junos.junos_routing_engine_table(jnpr.device) 
jnpr.jsnapy_pre(['test_memory.yml'])
jnpr.junos_commit(mode='exclusive', commands=['set interfaces ge-0/0/0 description "to SRX300 port 00"'], commit_comments='vicmiko test')
jnpr.junos_ping(host= '111.69.2.29')
jnpr.junos_traceroute(host= '111.69.2.29')
jnpr.junos_scp(mode='put',local = 'test.txt', remote = '/var/tmp/')
jnpr.junos_scp(mode='get',local = '.', remote = '/var/tmp/test.txt')
Package: vicNeuralNetwork
AboutThis is a self-designed deep learning framework created by victor @HangzhouFeaturesDefine Dynamic Computing GraphDefine Neural Network Operatordefine Linear, Sigmoid, L2_lossAuto_Diff ComputingAuto-Feedforward and BackwardRemainClassificaitonCross-EntropyCNN, RNNConnectionwhatchat:alwayslearn
Mail:2534535@qq.com
Package: vic-NeuralNetwork
AboutThis is a self-designed deep learning framework created by victor @HangzhouFeaturesDefine Dynamic Computing GraphDefine Neural Network Operatordefine Linear, Sigmoid, L2_lossAuto_Diff ComputingAuto-Feedforward and BackwardRemainClassificaitonCross-EntropyCNN, RNNConnectionwhatchat:alwayslearn
Mail:2534535@qq.com