Saravanan666/pypi-org huggingface dataset:
Package: idppnpdongle
Inmarsat IDP Plug-N-Play DongleThe Plug-N-Play dongle is a small programmable single board computer in a
black box intended to be able to quickly demonstrate and prototype
Internet-of-Things use cases enabled by satellite messaging connectivity.The dongle connects directly to anST2100satellite modem manufactured byORBCOMMand provides access to:Serial communications usingAT commandsModemevent notificationvia discrete output pin to a callback functionModem reset viamodem resetinput pinMicrocontroller reset event viaexternal resetoutput pin1 pulse-per-second (PPS) from GNSS timing via discrete output pinThe dongle supports connection to an external customer-supplied microcontroller
using RS-232 via thedeveloper breakout cable.The donglemodecan be configured as:transparentpass through serial commands to a separate third party
microcontroller (default hardware configuration)masteract as the application microcontroller(default when using this Python module)proxyact as a proxy intercepting responses from the modem to a third
party microcontrollerInstallationpip install idppnpdongleExample Usageimport asyncio

from idppnpdongle import PnpDongle

dongle = PnpDongle()
modem = dongle.modem
response = asyncio.run(modem.command('AT'))
Package: idptools-hoatzin
hoatzinA Python package for DNA-based evolution of protien sequences. Holistic apprOach for SequAnces To underZtand evolutIoN.Last updated August 2023Current version: v0.12InstallationThe current stable version ofhoatzinis available through GitHub or the Python Package Index (PyPI).To install from PyPI, run:pip install idptools-hoatzinYou can also install the current development version frompip install git+https://git@github.com/idptools/hoatzinTo clone the GitHub repository and gain the ability to modify a local copy of the code, rungit clone https://github.com/idptools/hoatzin.git
cd hoatzin
pip install -e .UsageFirst import hoatzinfrom hoatzin import evolveEvolving SequencesTheevolve.sequence()function lets you evolve protein or DNA sequences. If you input a protein sequence, it will be turned into a DNA sequence using the codon usage frequencies from humans. The probabilities for each mutation then use nucleotide mutation probabilities that are from COSMIC 2023, which examined the frequencies of non-synonymous mutations in the human genome. Theevolve.sequence()function requires that you input a sequence as the first argument and then the number of generations as the second argument. 1 DNA mutation per generation is assumed.sequence='QQQGSRGSGSGRRRGSGSGQGS'
evolved_sequence = evolve.sequence(sequence, number_generations=10)
print(evolved_sequence)Which would return something like:QQQGPSGSRNGRRRGFSGGLDSOptional Arguments:Using theevolve.sequence()function, you can specify additional parameters.mutations_per_generation- the number of DNA mutations in each 'mutation' generation.mutation_probs- The probabilities of each mutation. You can specify your own dictionary of mutations. See NUCLEOTIDE_MUTATION_PROBS in hoatzin_parameters to specify this.sequence_type- Lets you specify if you want to mutate a DNA sequence or a protein sequence. You must specify as 'nucleotide_sequence' if you are inputting a nucleotide sequence.codon_probs- Lets you specify the probabilities of each codon when going from a protein sequence to a DNA sequence.return_all_seqs- Lets you specify whether to return all sequences generated (one sequence per generation) or just get back a single final sequence.Examplesequence='QQQGSRGSGSGRRRGSGSGQGS'
evolved_sequence = evolve.sequence(sequence, number_generations=10, return_all_seqs=True)
print(evolved_sequence)Would return something like...{'original': 'QQQGSRGSGSGRRRGSGSGQGS', 1: 'QQQGSRGSGSGHRRGSGSGQGS', 2: 'QQQGSRGSGSGHRRGSGSGQGS', 3: 'QQQGSRGSGSGHKRGSGSGQGS', 4: 'QQQGSRGSGSGDKRGSGSGQGS', 5: 'QRQGSRGSGSGDKRGSGSGQGS', 6: 'QRQGSRGSGSGDKRGSGSGQGL', 7: 'QRQGSRGFGSGDKRGSGSGQGL', 8: 'QRQGSRGFRSGDKRGSGSGQGL', 9: 'QREGSRGFRSGDKRGSGSGQGL', 10: 'QREG*RGFRSGDKRGSGSGQGL'}CopyrightCopyright (c) 2023, Ryan Emenecker - Holehouse LabAcknowledgementsProject based on theComputational Molecular Science Python Cookiecutterversion 1.1.
Package: idpyoidc
idpyoidcThis project is a Python implementation of everything OpenID Connect and OAuth2.Introductionidpyoidc is the 2nd layer in the JwtConnect stack (cryptojwt, idpyoidc).
As OIDC OP Idpy implements the following standards:OpenID Connect Core 1.0 incorporating errata set 1Web FingerOpenID Connect Discovery 1.0 incorporating errata set 1OpenID Connect Dynamic Client Registration 1.0 incorporating errata set 1OpenID Connect Session Management 1.0OpenID Connect Back-Channel Logout 1.0OpenID Connect Front-Channel Logout 1.0OAuth2 Token introspectionOAuth 2.0 Form Post Response ModeIt also comes with the followingadd_onmodules.Custom scopes, that extendsOIDC standard ScopeClaimsProof Key for Code Exchange by OAuth Public Clients (PKCE)OAuth2 PAROAuth2 RAROAuth2 DPoPOAuth 2.0 Authorization Server Issuer IdentificationUsageIf you want to add or replace functionality the official documentation should be able to tell you how.
If you are just going to build a standard OP you only have to understand how to write your configuration file.
Inexample/folder you'll find some complete examples based on flask and django.Please read theOfficial Documentationfor getting usage examples and further informations.ContributeYour contribution is welcome, no question is useless and no answer is obvious, we need you.Contribute as end userPlease open an issue if you've discoveerd a bug or if you want to ask some features.Contribute as developerPlease open your Pull Requests on thedevelopbranch.
Please consider the following branches:main: where we merge the code before tag a new stable release.develop: where we push our code during development.other-custom-name: where a new feature/contribution/bugfix will be handled, revisioned and then merged to dev branch.CertificationsLicenseThe entire project code is open sourced and therefore licensed under theApache 2.0.
Package: idquant
IDQuant (Isotopic Dilution Quantification) is a software that calculates sample concentrations
of metabolites from C12 and C13 Mass Spectrometry Integrated DataIt uses linear and quadratic regression equations to predict concentrations using calibration data
from (unlabeled and fully labeled) Standard Molecules.
It takes as input integrated C12/C13 MS data from Emzed C13 Profiler.It is one of the routine tools used byMetaToul platform.The code is open-source, and available onGitHubunder a GPLv3 license.The documentation relative to the usage of IDQuant can be found on it’sReadTheDocs page.
Package: idr
[![Build Status](https://travis-ci.org/idr/idr-python.png)](http://travis-ci.org/idr/idr-python)IDR PythonLibrary with helper methods for accessing the Image Data Resource (IDR).RequirementsOMERO.py 5.3.xPython 2.6+LicenseThis project, similar to many Open Microscopy Environment (OME) projects, is
licensed under the terms of the GNU General Public License (GPL) v2 or later.
Package: idrac-wrapper
This wraps Dell’s Redfish API DMTF in a Python library. It supports iDrac 9.Basic UsageSee the manage.py script for example use.IdracAccessor stores authenticationsession data to a file provided in the constructor. It
defaults to /tmp/idracaccessor{uid}.dat.FunctionalityThe current implementation has a very small subject of iDRAC functionality, but could be
extended using the example code inhttps://github.com/dell/iDRAC-Redfish-Scripting.Alternative packagesThe code is based heavily onhttps://github.com/dell/iDRAC-Redfish-Scripting. We just wrapped
it for our convenience.
Package: idris-python
idris-camFinally, we reached dependent types in Python community.Installpip install idris-pythonUsageCommand: Idris-PythonCommand: Run-Cam## ExampleQuite verbose for the lack of encapsulations, not a good example but I’m so busy..Following example just revealed that I’ve alredy implmented such a big task.moduleMainimportCam.FFIimportCam.IOimportCam.Data.CollectionsimportCam.Data.FCollectionsimportCam.Data.CompatimportData.VectimportData.HVect%accessexportmain:IO()main=doputStrLn$showvectsklearn<-camImport$TheModule"sklearn.datasets"load_iris<-camImportFromsklearn"load_iris"iris<-unsafeCallload_iris$zero_arydata'<-getattriris"data"tag<-getattriris"target"rfc<-letensemble=camImport$TheModule"sklearn.ensemble"incamImportFrom!ensemble"RandomForestClassifier"clf<-unsafeCallrfczero_aryfit<-getattrclf"fit"unsafeCallfit.unsafe$the(FList_)[data',tag]score<-getattrclf"score"value<-unsafeCallscore.unsafe$the(FList_)[data',tag]-- overfitprintlnvaluewherevect:HVect[Int]vect=the(HVect_)[1]zero_ary:Unsafezero_ary=unsafe$the(FListUnsafe)$[]getattr':IOUnsafegetattr'=dob<-camImport$TheModule"builtins"camImportFromb"getattr"getattr:Unsafe->String->IOUnsafegetattrobjs=lets=unsafe.the(BoxedString)$sinletargs=unsafe.the(FHVect[_,_])$[obj,toTexts]inunsafeCall!getattr'argsYou might got[1]
0.99If you run it as a file with commandidris-python.
Package: idrlnet
IDRLnetIDRLnetis a machine learning library on top ofPyTorch. Use IDRLnet if you need a machine learning library that solves both forward and inverse differential equations via physics-informed neural networks (PINN). IDRLnet is a flexible framework inspired byNvidia Simnet.DocsFull docsTutorialPaper:IDRLnet: A Physics-Informed Neural Network Library.arXivInstallationChoose one of the following installation methods.PyPISimple installation from PyPI.pipinstall-UidrlnetNote: To avoid version conflicts, please use some tools to create a virtual environment first.DockerPull latest docker image from Dockerhub.dockerpullidrl/idrlnet:latest
dockerrun-itidrl/idrlnet:latestbashNote: Available tags can be found inDockerhub.Anacondacondacreate-nidrlnet_devpython=3.8-y
condaactivateidrlnet_dev
pipinstallidrlnetFrom Sourcegit clone https://github.com/idrl-lab/idrlnet
cd idrlnet
pip install -e .FeaturesIDRLnet supportscomplex domain geometries without mesh generation. Provided geometries include interval, triangle, rectangle, polygon, circle, sphere... Other geometries can be constructed using three boolean operations: union, difference, and intersection;sampling in the interior of the defined geometry or on the boundary with given conditions.enables the user code to be structured. Data sources, operations, constraints are all represented byNode. The graph will be automatically constructed via label symbols of each node. Getting rid of the explicit construction via explicit expressions, users model problems more naturally.builds computational graph automatically;user-defined callbacks;solving variational minimization problem;solving integral differential equation;adaptive resampling;recover unknown parameters of PDEs from noisy measurement data.It is also easy to customize IDRLnet to meet new demands.Main DependenciesMatplotlibNumPySympy==1.5.1pytorch>=1.7.0Contributing to IDRLnetFirst off, thanks for taking the time to contribute!Reporting bugs.To report a bug, simply open an issue in the GitHub "Issues" section.Suggesting enhancements.To submit an enhancement suggestion for IDRLnet, including completely new features and minor improvements to existing functionality, let us know by opening an issue.Pull requests.If you made improvements to IDRLnet, fixed a bug, or had a new example, feel free to send us a pull-request.Asking questions.To get help on how to use IDRLnet or its functionalities, you can as well open an issue.Answering questions.If you know the answer to any question in the "Issues", you are welcomed to answer.The TeamIDRLnet was originally developed by IDRL lab.CitationFeel free to cite this library.@article{peng2021idrlnet,title={IDRLnet: A Physics-Informed Neural Network Library},author={Wei Peng and Jun Zhang and Weien Zhou and Xiaoyu Zhao and Wen Yao and Xiaoqian Chen},year={2021},eprint={2107.04320},archivePrefix={arXiv},primaryClass={cs.LG}}
Package: id-roles
id-rolesId-manager roles librarySimple library for managing access roles. Inspired bij OAUTH2 scopes.Installationpip install id-rolesRole structureAccess roles can be described as keys with optional values using a single string.roles_str = "connect admin order[*] customer[read,update]"Multiple roles are seperated by spaces and may contain an optional values string[value1,value2].Values are separated by.,Role names may only contain alphanumeric characters and-,_or:.Role value may only contain alphanumeric characters and-,_or..[*]is a special value string that indicated that all values are valid.Role names and values are case sensitive
Package: idr-py
IDR-PYLibrary with helper methods for accessing the Image Data Resource (IDR).RequirementsOMERO.py 5.6.xPython 3.6+Installing with condaThis requiresAnaconda/Miniconda with Python 3.6.conda env create -f environment.yml -n idr-omero
conda activate idr-omeroInstalling from PyPIThis section assumes that an OMERO.web is already installed.Install the app usingpip:$ pip install -U idr-pyLicenseThis project, similar to many Open Microscopy Environment (OME) projects, is licensed under the terms of the GNU General Public License (GPL) v2 or later.Copyright2017, The Open Microscopy Environment
Package: idservice
idserviceID-Serviceis a distributed ID generator microservice based on FastAPI.Installationpip3installidserviceStart Serviceidservice-startList all availableuvicornoptions byidservice-start --helpAPI EndpointSeveral endpoints:/api/snowflake/api/random/64/api/uuidBrowse/docs(default tohttp://localhost:8000/docs) to read all APIs.ConfigurationEnvironment variable:Environment VarsUsageDefaultID_SERVICE_SNOWFLAKE_MACHINE_IDSnowflake Machine ID (10 bits)Random
Package: idsim
Identity SimilarityThis repository can helps researchers that want to use face recognition in their researches. You can easly implement powerfull face recognition in your project. I motivated for this repository fromLPIPS. The models are borrowed fromInsigtface.Warning :Please, be careful when chosing your criterion. Lower is more similar in MSE while higher is more similar in CosineSimilarity.Usage1. Training with preprocessed dataset.In this case, we assume that you have aligned images using a keypoint template and you want to calculate identity similarity between two aligned images or a image and a saved identity vector.importtorchimportnumpyasnpfromidsimimportIdentitySimilarityidsim=IdentitySimilarity()template=np.array([[35.066223,34.23266],[84.1586,33.96113],[59.768444,62.152763],[39.60066,90.89288],[80.255,90.66802]],dtype=np.float32)idsim.set_ref_point(template)# dummy variablesv1=torch.rand(1,512)im1=torch.rand(5,3,128,128)# useful functionssim_v2v=idsim.forward_v2v(v1,v1)sim_im2im=idsim.forward_img2img(im1,im1)sim_v2im=idsim.forward_v2img(v1,im1)print("\nsim_v2v :",sim_v2v,"\nsim_im2im :",sim_im2im,"\nsim_v2im :",sim_v2im)2. Face recognitionIn this case, Idsim can caculate identity similarity of your images.importcv2fromidsimimportIdentitySimilarityidsim=IdentitySimilarity(criterion="Cosine")img1=cv2.imread("a.jpg")img2=cv2.imread("b.jpg")v1=idsim.extract_identity(img1)v2=idsim.extract_identity(img2)sim=idsim.forward_v2v(v1,v2)print("Similarity :",sim)Note:You can check theproving_differentiability.ipynbfor an example training.Todo[] Release pypi packageContributingContributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make aregreatly appreciated.Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Runmake style && make qualityin the root repo directory, to ensure code quality.Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull Request
Package: idspiece
IDSpiece漢字/汉字-Tokenizer with Ideographic Description Sequence fromCHISE-IDS.Only nine IDCs (U+2FF0, U+2FF1, U+2FF4 to U+2FFA) are used.IDCs never occur instantly after another IDC.Instantly after IDCs, Kanxi Radicals and Supplement (U+2E80 to U+2FD5) are preferred.Otherwise, CJK Unified Ideographs and Extension A (U+3400 to U+9FFC) are preferred.Basic usage>>>fromidspieceimportidstable>>>deftokenize(text):...tokens=[]...whiletext>"":...c=text[0]...ifcinidstable:...tokens.append(idstable[c][0:2])...text=idstable[c][2]+text[1:]...else:...tokens.append(c)...text=text[1:]...returntokens...>>>t=tokenize("羯諦羯諦波羅羯諦波羅僧羯諦菩提薩婆訶")>>>print(t)['⿰⽺','⿱⽈','⿹⼓','亾','⿰⾔','帝','⿰⽺','⿱⽈','⿹⼓','亾','⿰⾔','帝','⿰⺡','皮','⿱⺲','⿰⽷','隹','⿰⽺','⿱⽈','⿹⼓','亾','⿰⾔','帝','⿰⺡','皮','⿱⺲','⿰⽷','隹','⿰⺅','曾','⿰⽺','⿱⽈','⿹⼓','亾','⿰⾔','帝','⿱⺾','⿱⽴','口','⿰⺘','⿱⽇','⿱⼀','龰','⿱⺾','⿰⻖','⿸产','生','⿱波','女','⿰⾔','可']Installationpip3installidspieceAuthorKoichi Yasuoka (安岡孝一)
Package: idspy-dictionaries
IDSPy_DictionariesIMAS Data Dictionaries converted to Python Dataclass used in the  IDSPy suite.PrerequisitesTo use this script, you need to have Python3.9or later installed. You can download Python fromhttps://www.python.org/downloads/.
Please note that Python at least3.10is recommended.InstallationTo install the necessary packages, run the following command:python -m pip install idspy_dictionariesUsageTo load the desired IDS :fromidspy_dictionariesimportids_gyrokinetics_local# or any other available IDSnew_ids=ids_gyrokinetics_local.Gyrokinetics()FAQQ:What is the minimum required version of Python to run this script?A:The  recommended version of Python is 3.10. It can be used with Python >= 3.9but there is no support in that case.Q:Can I add new members to the dataclasses?A:This option is not possible to be sure that the dataclasses follow the IMAS conventions.Q:Is the package compatible with pydantic and/or attrs?A:Short answer, no ;)Q:I would really like to use python <3.9 is it really impossible?A:IDSPy_dictionaries used mainly python dataclasses and the slot property which had been added in python 3.10 only.The main reason to use __slots__ is to avoid addition of members in the IDS and remains fully compliant with IMAS.
A version of the package had been published “as it is” without the slots dependency but need at least python 3.9.
The way dataclasses are generated with the associated fields, at the opposite, is not compatible at all with python 3.8.Q:Can I load all the dictionaries at once?A:For performances reasons, it’s not possible right now.
Package: idspy-toolkit
This module contains a serie of function to help mange and manipulate IDSPy dataclassesfill_missing_valuesids_to_hdf5hdf5_to_idsget_ids_value_from_stringset_ids_value_from_stringlist_ids_memberscopy_idsPlease note that this work is still under progress/heavy development and as experimental status.
This means that functions arguments/signatures as long as HDF5 structure might be totally redesigned in the next updates.## Quick example1importpprint2importdataclasses34importidspy_toolkit5fromidspy_dictionariesimportids_gyrokinetics67pp=pprint.PrettyPrinter(indent=2)89ids_test=ids_gyrokinetics.Gyrokinetics()10# you can directly print the class to see what it looks like  :11pp.pprint(ids_test)1213# if you want to see all the available classes in the current module :14ids_dict=idspy_toolkit.list_ids_members(gkids)15pp.pprint(ids_dict)1617#to fill an IDS with default values18idspy_toolkit.fill_missing_values(ids_test)1920# you can use the . to access ids members :21pp.pprint(ids_test.ids_properties)2223# and to set a value :24ids_test.ids_properties.comment="a comment"2526# if in a script you want to reach a "deeper" value, you can use the function *get_ids_value_from_string*27idspy_toolkit.get_ids_value_from_string(ids_test,"ids_properties/comment")28# and for list element, put the element index after an #29idspy_toolkit.get_ids_value_from_string(ids_test,"tag#0/name")3031# same kind of function exist to set a value :32idspy_toolkit.set_ids_value_from_string(ids_test,"tag#0/name","a new tag name")3334# pour afficher la classe sous forme de dictionnaire (conseil mettez l'ecran en vertical ;)):35ids_dict=dataclasses.asdict(ids_test)36pp.pprint(ids_dict)
Package: idsred
IDSRED: INT-IDS data-reduction pipelineThis is a spectroscopic data-reduction pipeline for the Isaac Newton Telescope (INT) Intermediate Dispersion Spectrograph (IDS) instrument.
It is optimised for the blue detector (EEV10).InstallationIt is recommended to install it on an anaconda environment:conda create -n idsred pip
conda activate idsredand install it using pip:pip install "idsred"or from source:git clone https://github.com/temuller/idsred.git
cd idsred
pip install .Developer mode:pip install -e .Usage exampleA notebook that explains how to use the pipeline is found in this repositoryhere.ContributingTo contribute, either open an issue or send a pull request (preferred option). You can also contact me directly.Citing IDSRED@software{tomas_e_muller_bravo_2023_7851772,
  author       = {Tomás E. Müller Bravo},
  title        = {temuller/idsred: First Release!},
  month        = apr,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {v0.1.0},
  doi          = {10.5281/zenodo.7851772},
  url          = {https://doi.org/10.5281/zenodo.7851772}
}AcknowledgementThis pipeline is based on theGROWTH school github repository, which nicely explains the entire reduction process for images and spectra, and onINT-IDS-DataReductionfor (long-slit) spectra reduction.
Package: idstool
idstoolPython Tool to create IDS (Information delivery specification) files for openBIM workflows.Currently built on IDS version 0.9.6ExampleAn example on how to use this library to create an IDS:importidstoolasidsspec1=ids.Specification(name='Wall Specifications',ifc_version='IFC4',applicability=ids.Applicability(facets=[ids.EntityFacet(ifc_class='IfcWall')]),requirement=ids.Requirement(facets=[ids.AttributeFacet(name='Name')]))spec2=ids.Specification(name='Hackathon Pset',ifc_version='IFC4',applicability=ids.Applicability(facets=[ids.EntityFacet(ifc_class='IfcWall')]),requirement=ids.Requirement(facets=[ids.PropertyFacet(property_set='Hackathon2024',property_name='AEC_Hack',datatype='IFCTEXT')]))ids_data=ids.InformationDeliverySpecification(title="AEC Hackathon Test IDS",version="0.1",purpose="Testing",specifications=[spec1,spec3])ids.IDSSerializer.save_ids(filepath='example_ids',ids=ids_data)LimitationsThis README is still under construction =)Currently not all metadata for IDS files are actually written to file. author and date are skippedNext StepsThe following features will be added next:Utility functions to easily create simple Applicabilities and Requirements
Package: idstring
an IDstring is a string-like object which can be incremented, to produce
a series of serial-number-like strings, consisting of Arabic digits and upper case letters,
skipping those which can be easily misinterpreted (“oh”, “I”, “Q”, and Zed) and which has
an optional Luhn-N check digit, capable of detecting any single-letter error and most other errors.Programmers may define alternate alphabets, alternate lists of “dirty” words (which are skipped),
a fixed subfield for generating serial number from multiple sources, and a hash code to create unique check
digits algorithms for multiple projects. Passing a hash code of None will surpress check digit operation.The class will also perform generic binary to alpha conversions in your alphabet (base-32 for the default).