Saravanan666/pypi-org huggingface dataset:
Package: inline-snapshot
InstallationYou can install "inline-snapshot" viapip:pipinstallinline-snapshotKey FeaturesIntuitive Semantics:snapshot(x)mirrorsxfor easy understanding.Versatile Comparison Support:Equipped withx == snapshot(...),x <= snapshot(...),x in snapshot(...), andsnapshot(...)[key].Enhanced Control Flags:Utilize variousflagsfor precise control of which snapshots you want to change.Preserved Black Formatting:Retains formatting consistency with Black formatting.External File Storage:Store snapshots externally usingoutsource(data).Seamless Pytest Integration:Integrated seamlessly with pytest for effortless testing.Comprehensive Documentation:Access detaileddocumentationfor complete guidance.UsageYou can usesnapshot()instead of the value which you want to compare with.frominline_snapshotimportsnapshotdeftest_something():assert1548*18489==snapshot()You can now run the tests and record the correct values.$ pytest --inline-snapshot=createfrominline_snapshotimportsnapshotdeftest_something():assert1548*18489==snapshot(28620972)inline-snapshot provides more advanced features like:frominline_snapshotimportsnapshot,outsource,externaldeftest_something():fornumberinrange(5):# testing for numeric limitsassertnumber<=snapshot(4)assertnumber>=snapshot(0)forcin"hello world":# test if something is part of a setassertcinsnapshot(["h","e","l","o"," ","w","r","d"])s=snapshot({0:{"square":0,"pow_of_two":False},1:{"square":1,"pow_of_two":True},2:{"square":4,"pow_of_two":True},3:{"square":9,"pow_of_two":False},4:{"square":16,"pow_of_two":True},})fornumberinrange(5):# create sub-snapshots at runtimeasserts[number]["square"]==number**2asserts[number]["pow_of_two"]==((number&(number-1)==0)andnumber!=0)assertoutsource("large string\n"*1000)==snapshot(external("8bf10bdf2c30*.txt"))Feedbackinline-snapshot provides some advanced ways to work with snapshots.I would like to know how these features are used to further improve this small library.
Let me know if you've found interesting use cases for this library viatwitteror in the githubdiscussions.IssuesIf you encounter any problems, pleasereport an issuealong with a detailed description.LicenseDistributed under the terms of theMITlicense, "inline-snapshot" is free and open source software.
Package: inline-sql
Inline SQLA simple embedded language for running inline SQL in Python programs.frominline_sqlimportsql,sql_valassertsql_val^"SELECT 1 + 1"==2x=5assertsql_val^"SELECT $x * 2"==10df=sql^"SELECT * FROM (VALUES (1, 10), (2, 20)) df (x, y)"assertsql_val^"SELECT SUM(x) + SUM(y) FROM df"==33Operations in theinline_sqllibrary run directly inside your process. You can query local datasets (pandas frames), CSV files, and even interpolate variables seamlessly. This is implemented as a small wrapper aroundDuckDB, so it isextremely fast.InstallationSupports Python 3.7+, tested on all major operating systems.pip install inline-sqlUsageThe exportedsqlandsql_valvariables are magic objects that can be used to run queries. Queries can read from local dataframes by name, and they can embed parameters using dollar-sign notation.>>>frominline_sqlimportsql,sql_val>>>sql_val^"SELECT 1 + 1"2>>>x=5>>>sql_val^"SELECT 2 * $x"10>>>sql^"SELECT * FROM 'disasters.csv' LIMIT 5"EntityYearDeaths0Allnaturaldisasters190012673601Allnaturaldisasters19012000182Allnaturaldisasters1902460373Allnaturaldisasters190365064Allnaturaldisasters190522758>>>disasters=sql^"SELECT * FROM 'disasters.csv'">>>deftotal_deaths(entity:str)->float:...returnsql_val^"SELECT SUM(deaths) FROM disasters WHERE Entity = $entity"...>>>total_deaths("Drought")11731294.0>>>total_deaths("Earthquake")2576801.0You can run any SQL query as described in theDuckDB documentation.Library UseYou can useinline_sqlas a library. Since results from queries are ordinarypandas.DataFrameobjects, they work in functions and application code. Here's a longer example:importpandasaspdfrominline_sqlimportsql,sql_valdefhead_data(count:int)->pd.DataFrame:returnsql^"SELECT * FROM 'cars.csv' LIMIT $count"cars=head_data(50)origin_counts=sql^"""SELECT origin, COUNT() FROM carsGROUP BY originORDER BY count DESC"""print(origin_counts)most_common=origin_counts.origin[0]print(sql_val^"""SELECT AVG(horsepower) FROM carsWHERE origin = $most_common""")In general,sql_valis used to run scalar queries, whilesqlis used to run queries that return tables.AcknowledgementsCreated by Eric Zhang (@ekzhang1). Licensed under theMIT license.
Package: iNLP
:rocket: iNLP :facepunch:Installpip install iNLPorpip install git+https://github.com/Jie-Yuan/iNLP.gitUsageinlp.convert简繁体转换frominlp.convertimportchinesechinese.s2t('忧郁的台湾乌龟')# chinese.simple2tradition('忧郁的台湾乌龟')chinese.t2s('憂郁的臺灣烏龜')# chinese.tradition2simple('憂郁的臺灣烏龜')全角半角转换frominlp.convertimportcharchar.half2full("0123456789")char.full2half("０１２３４５６７８９")inlp.explode汉字拆成字frominlp.explodeimportCharsChars().get_chars('袁')# ['土 口 衣']汉字拆成笔画frominlp.explodeimportStrokesStrokes().get_strokes('袁')# ['一', '丨', '一', '丨', 'フ', '一', 'ノ', 'フ', 'ノ', '丶']inlp.similarity基于词库的相似度frominlp.similarityimportthesauruss1=['周杰伦','是','一个','歌手']s2=['刘若英','是','个','演员']thesaurus.cilin(s1,s2)# 基于词林的相似度thesaurus.hownet(s1,s2)# 基于知网的相似度基于hash的相似度frominlp.similarityimportsimhashsimhash(s1,s2)计划：增加基于词向量相似词相似句的方法
Package: i-nlp
#============================================================inlp (innovata Natural Language Processing)#============================================================#============================================================사전작업.#============================================================
PJTS_PATH 를 OS-환경변수로 수동으로 저장.export PJTS_PATH=your_root_path#============================================================Packages description.#============================================================NLTKKoNLPyhttps://pypi.org/project/konlpy/설치 pakages :JPype1-0.6.3 konlpy-0.5.1
Package: inmanta-core
InmantaInmanta is an automation and orchestration tool to efficiently deploy and manage your software
services, including all (inter)dependencies to other services and the underpinning infrastructure.
It eliminates the complexity of managing large-scale, heterogeneous infrastructures and highly
distributed systems.The key characteristics of Inmanta are:Integrated: Inmanta integrates configuration management and orchestration into a single tool,
taking infrastructure as code to a whole new level.Powerful configuration model: Infrastructure and application services are described using a
high-level configuration model that allows the definition of (an unlimited amount of) your own
entities and abstraction levels. It works from a single source, which can be tested, versioned,
evolved and reused.Dependency management: Inmanta's configuration model describes all the relations between and
dependencies to other services, packages, underpinning platforms and infrastructure services.
This enables efficient deployment as well as provides an holistic view on your applications,
environments and infrastructure.End-to-end compliance: The architecture of your software service drives the configuration,
guaranteeing consistency across the entire stack and throughout distributed systems at any time.
This compliance with the architecture can be achieved thanks to the integrated management
approach and the configuration model using dependencies.Currently, the Inmanta project is mainly developed and maintained by Inmanta NV.LinksDocumentationQuickstartInstallInstall GuideRunning the tests using tox$ python3 -m venv env
$ source env/bin/activate
$ pip install -U pip tox
$ toxAdditional pytest arguments can be passed to tox via theINMANTA_EXTRA_PYTEST_ARGSenvironment variable.
In order to run the test suite in fast mode, setINMANTA_EXTRA_PYTEST_ARGS='--fast'.
Package: inmantals
Inmanta Language serverProvides the bridge between the Inmanta compiler and Visual Studio Code IDE, by implementing a Language Server.For recent versions ofinmanta-core(>=6), the server takes ownership of the Python environment it runs in and installs any
modules and module dependencies in that environment. For older versions ofinmanta-core, a separate compiler venv is used,
which can be configured with thecompilerVenvoption.This is alpha software, it may or may not workInstallationmkvirtualenv -p python3 lstestingpip install inmantalsinstall the inmanta extension via marketplacechange config for the extension (ctrl+,)setinmanta.pythonPathto the virtual env you just created~/.virtualenvs/lstesting/bin/python3setinmanta.ls.enabledtotrueFeaturesnavigate-to-definition on typesdocstring display on hoverfind references to a symbol (e.g. right-click > Find All References)supports working on an Inmanta project or an Inmanta moduleNot supported yetwork with incorrect models (needs to compile, all or nothing)TroubleshootingNo module named xIf compilation fails with the message "no module named x" where x is a Python module, you might need to clean up the virtual
environments used by the compiler. This issue can be caused by running the compiler from multiple different environments.
To clean up the virtual environment, remove the .env directory in the Inmanta project directory as well as the compiler venv
specified by the client, if it exists.Referenceshttps://microsoft.github.io/language-server-protocol/specification
Package: inmanta-tfplugin
inmanta-tfpluginThis package contains the generated python package for Terraform Plugin RPC protocolAt inmanta, this is used by the terraform module, it was originally packaged in the module's plugins but we had to move it out because of an incompatibility between the agent file loading mechanism (inmanta-core#2205andinmanta-core#2323) and the protobuf library:https://github.com/protocolbuffers/protobuf/issues/9535How to useimportgrpcimportinmanta_tfplugin.tfplugin5_pb2importinmanta_tfplugin.tfplugin5_pb2_grpcproto_addr=""channel=grpc.insecure_channel(proto_addr)stub=inmanta_tfplugin.tfplugin5_pb2_grpc.ProviderStub(channel)A more complete usage example can be found ininmanta's terraform module.Package versioningmajor: Major version of the source protobuf file used to generate the package.minor: Minor version of the source protobuf file used to generate the package.patch: Up to us, in case we want to re-release a generated package with a higher version.
Package: inmatelocator
Inmate Locator (ALPHA)======================This is a library and webservice for searching for people who are incarceratedin the US.  It searches various individual state lookup servicessimultaneously.Working demo: https://inmatelocator.herokuapp.comMost states (and the Federal Bureau of Prisons) have their own siloed webservices for searching for incarcerated people.  There is wide variation andinconsistency among the interfaces and features they support.This package aims to act as a compatibility layer and bridge.  It'sparticularly important for service and support organizations that maintainmailinglists of people in prison who frequently move.Future goals for this service include:- adding more state backends- normalizing key data across statesUse as a library----------------To use this as a standalone library, install it with pip:pip install inmatelocatorTODO: documentation.  Basic usage:from inmatelocator.stateparsers import floridadata = flroida.search(first_name="John", last_name="Dough")Use as a web app----------------To use as a web servicde, clone the git repository and install dependencieslisted in ``requirements.txt``.- ``/``: HTML interface for querying the API.- ``/search/``: REST endpoint for querying. Returns JSON.The search endpoint accepts the following parameters:- ``first_name``: String, part of first name to search for.- ``last_name``: String, part of last name to search for.- ``number``: String, the prisoner or DOC number assigned to the person.- ``state``: Which state to search in.Different states have different minimum requirements for parameters -- for example, some require both first and last names in order to search by name.Data is returned in the following format:{"name": "<string>",   // the name of the person, if available"url": "<url>",       // a URL to the source for these results// Additional keys vary depending on the provider}State parsers-------------The ``states/`` folder in the source repository contains parsers and lookup engines for each state.  Each parser should be named with a normalized lowercase name of the state with spaces removed, and should expose a ``search`` method that accepts the kwargs "first_name", "last_name", and "number".  It should return a dictionary with the following format:{'state': "<state name>",'results': [<array of search results>],'errors': [<array of errors encountered>],'url': "<url to state's search interface for humans>"}
Package: inmation-api-client
inmation API ClientThis API client can only be used with the new inmation Web API server.InstallInstall withpip install inmation-api-client.Dependencieswebsockets, will be installed automatically when installing withpip.DocumentationVisit theapi client documentationpage on the inmation's docs website for various usage examples and more.How to use itAdvanced authentication optionsauthority can be inmation, ad (Active Directory - domain account), machine (local account)username can be provided in 'User Principal Name' or 'Down-Level Logon Name'source:https://docs.microsoft.com/en-us/windows/desktop/secauthn/user-name-formatsauthauthentication andauthorizationfields:options={'auth':{'username':'','password':'','authority':'inmation|ad|machine','grant_type':'password','include_claims':['email','family_name','given_name','middle_name','phone_number']},'authorization':'BearereyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9...'}include_claimscan only be used in combination with authority set to ad. The token will not be returned
to the client but passed via the req argument intoExecFunctionimplementation.Note: check Web API version whichauthorizationoptions its supports.Example Active Directory authentication:options={'auth':{'username':'user@domain.com','password':'secret','authority':'ad','grant_type':'password'}}Example Bearer token authentication:options={'authorization':'BearereyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9...'}Example Basic authentication:options={'authorization':'BasicdXNlcm5hbWU6cGFzc3dvcmQ='}Connect and Read examplefrominmation_api_clientimportClient,Options,Itemdefcreate_client():options=Options({'auth':{'username':'user@domain.com','password':'secret','authority':'ad','grant_type':'password'}})client=Client()client.Connect('127.0.0.1',8002,options)defconnection_changed(conn_info):print('Connection state:{}, authenticated:{}'.format(conn_info.state_string,conn_info.authenticated))client.OnConnectionChanged(connection_changed)defon_error(err):iferr:print("Error:{}".format(err.message))client.OnError(on_error)returnclientdefmain():items_path="/System/Core/"# Make sure you have some items with historical dataitems=[Item(items_path+i)foriin['Item01','Item02','Item03']]client=create_client()# Read values syncroniouslyresponse=client.Read(items)print(response['data'])if__name__=='__main__':main()inmationinmation is a vendor-independent industrial system-integration specialist. Dedicated to modern technologies such as OPC UA (Unified Architecture) and document-oriented schema-less repositories, inmation opens up new horizons for enterprise real-time data management.More information oninmation.comLicenseMIT
Package: in-memory-table
WARNINGThis package is part of the development effort ofarbeitszeitapp. It it not meant for
public consumption. No though went into the security aspects of this
package as its intended use is in the test suite of arbeitszeitapp. If
you need something similar to this in your application we recommend
usingpandasor something
similar.in_memory_tableThe goal of this package is provide an easy way have the behavior of a
relational database without the performance penalty that comes from
using one. The target application of this code is in testing where we
deal with a low volume of data.
Package: inmetpy
InmetPy - A Python API Client for the Brazilian National Institute of Meteorology - INMet:warning:Package development in progress!This is anunofficial(from INMET) python package for the INMET API.Get all stations over Brazil (Manual and Automatic);Get historical data for each station;Search the stations near by at given location;InstallInstall using pippipinstallinmetpyMethodsget_stations: Get a list of details of all stations available at INMET API.get_all_stations: get data from all stations at given date;get_data_station: get data for a list of stations for a given date interval;search_station_by_state: search for all stations available for a given state(s);search_station_by_coords: search the closestnstations available for a given coordinate (latitude, longitude);Comand Line Library Usage# get data from all inmet station after a selected dateinmetpyget_all_stations2021-09-01# get data from a station or a list of stationsinmetpyget_data_station2021-09-012021-09-10'[A652,A667]'Basic usage# import the libraryfrominmetpy.stationsimportInmetStationinmet=InmetStation()# list all inmet stations availablestations=inmet.get_stations()# get details of all stations availableauto_stations=inmet.get_stations("A")# details of all automatic stationsmanual_stations=inmet.get_stations("M")# details of all manual stations# get data from all inmet station after a selected dateinmet.get_all_stations(date)# date in format YYYY-MM-DD"# get data from a station or a list of stationsinmet.get_data_station(start_date,end_date,by,station_id)# stations must be a list of strings with the "CD_STATION" (ID) of the stations desired.
Package: inmoose
InMooseInMoose is theIntegratedMultiOmicOpenSourceEnvironment.
It is a collection of tools for the analysis of omic data.InstallationYou can install InMoose directly with:pip install inmooseDocumentationDocumentation is hosted onreadthedocs.org.Batch Effect CorrectionInMoose provides features to correct technical biases, also called batch
effects, in transcriptomic data:for microarray data, InMoose supersedes pyCombat [1], a Python 3
implementation of ComBat [2], one of the most widely used tool for batch effect
correction on microarray data.for RNASeq, InMoose features a port to Python3 of ComBat-Seq [3], one of the
most widely used tool for batch effect correction on RNASeq data.To use these functions, simply import them and call them with default
parameters:frominmoose.pycombatimportpycombat_norm,pycombat_seqmicroarray_corrected=pycombat_norm(microarray_data,microarray_batches)rnaseq_corrected=pycombat_seq(rnaseq_data,rnaseq_batches)microarray_data,rnaseq_data: the expression matrices, containing the
information about the gene expression (rows) for each sample (columns).microarray_batches,rnaseq_batches: list of batch indices, describing the
batch for each sample. The list of batches should contain as many elements as
the number of samples in the expression matrix.Consensus clusteringInMoose provides features to compute consensus clustering, a resampling based algorithm compatible with any clustering algorithms which class implementation is instantiated with parametern_clusters, and possess afit_predictmethod, which is invoked on data.
Consensus clustering helps determining the best number of clusters to use and output confidence metrics and plots.To use these functions, import the consensusClustering class and a clustering algorithm class:frominmoose.consensus_clustering.consensus_clusteringimportconsensusClusteringfromsklearn.clusterimportAgglomerativeClusteringCC=consensusClustering(AgglomerativeClustering)CC.compute_consensus_clustering(numpy_ndarray)How to contributePlease refer toCONTRIBUTING.mdto learn more about the contribution guidelines.References[1] Behdenna A, Colange M, Haziza J, Gema A, Appé G, Azencot CA and Nordor A. (2023) pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. BMC Bioinformatics 7;24(1):459.https://doi.org/10.1186/s12859-023-05578-5.[2] Johnson W E, et al. (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118–12.https://doi.org/10.1093/biostatistics/kxj037[3] Zhang Y, et al. (2020) ComBat-Seq: batch effect adjustment for RNASeq count
data. NAR Genomics and Bioinformatics, 2(3).https://doi.org/10.1093/nargab/lqaa078
Package: innateml
Innate stands for Integrated Neural Network Automatic Trainer and Evaluator. It is a framework for training neural networks and optimizing their hyperparameters. The base concept is to provide, at the user level, a complete abstraction of the training of a neural network on a cluster. The user can use the interface to launch tasks and get results as if they were executed on its own machine. The runtime provided by the framework simplify also the necessary code for training.This package is the interface library to connect to an innate server and execute training.
Package: innatis
InnatisThis is a library of customRasa NLUcomponents that we (CarLabs) are building.What does the name mean?Viribus Innatismeans "innate abilities" in Latin. It's a joke..."Rasa" comes from "tabula rasa" -blank slatein Latin. We (just Sam) thought it would be funny for this project to have the opposite name, since this is meant to be a suite of tools to fill in functionality for Rasa... that is, make it a not-blank-slate. So "Innate Abilities" -> Viribus Innatis.Usage$ pip install innatisThen add to your pipeline in yourrasa_config.yml. Example pipeline can be found insample_rasa_innatis_config.yml.ComponentsClassifiersintent_classifier_bert- Pulls the bert model from TF HUB and pretrains on given data.Example configlanguage:enpipeline:-name:"tokenizer_whitespace"-name:"ner_crf"-name:"ner_synonyms"-name:"innatis.classifiers.BertIntentClassifier"pretrained_model_dir:'/path/to/uncased_L-24_H-1024_A-16'epochs:10batch_size:64Extractorscomposite_entity_extractor- Given entities extracted by another extractor (ner_crfseems to be the best for now), splits them into composite entities, similar toDialogFlow.EntitySynonymMapper (replacesner_synonyms) - this is thener_synonymsadapted forcomposite_entity_extractor. You most likely need it if you usecomposite_entity_extractor. It replaces the synonyms with the original entities inside composite entities. It can also do fuzzy matching when matching synonyms (enabled by default). See example config:config_composite_entities.ymlFeaturizersuniversal_sentence_encoder_featurizer- Pulls the smaller USE model from TF HUB and embeds inputs as document vectors, and that vector gets sent downstream to be used as a feature.Developmentgitclonegit@github.com:Revmaker/innatis.gitcdinnatis
pipenvinstall# pipenv install --skip-lock if locking takes too long# do some stuff# write some testspipenvrunpythontest.pyDependencies suck, as is always the case in Python. I started out usingpipenvbecause I thought that wasthepackage manager for Python. But I guess you need to put dependencies in theinstall_requiressection ofsetup.py. So, the (currently manual, but automatable) process for moving dependencies from thePipfiletosetup.pyis:$cdinnatis# you are now in parent/innatis, not innatis/innatis$pythonir_from_pipfile.py|pbcopy['scikit-learn','scipy','sklearn-crfsuite','tensorflow','word2number','rasa_nlu==0.13.8','tensorflow-hub','spacy']# that array is copied; paste it into setup.pyAlso, please manually bump the version in a semver-ish way.
Package: inn-digialert-Mylist
###Objective: This has been developed for learning only! The target audience is:Beginner for flask,sqliteTesting - API testing, understanding the api development and then automating API testing through postman or karate DSL###microservices built in flask for simple "todo list"
refer app:https://play.google.com/store/apps/details?id=inn.digialert.todo_flutter( flutter and sqlite)APIs - flaskDatabase - sqlite3(its comes with python , so not a dependency)Testing1.Postman ( attached)
2.Karate ( Still in development)
Note: for Postman, use import functionality of postman to import json "MyList.postman_collection.json"Download postman:https://www.postman.com/Git remote:git push origin master:origin --> new branchpush local changes to remote :  git push origin master