Saravanan666/pypi-org huggingface dataset:
Package: esp-serial-find
ESP Serial FindFind the ESP32 serial port name with device serial number on macOS and GNU/Linux.Documentation:https://ProfFan.github.io/esp-serial-findGitHub:https://github.com/ProfFan/esp-serial-findPyPI:https://pypi.org/project/esp-serial-find/Free software: MITFeaturesFind the ESP32 serial port name with device serial number on macOS and GNU/Linux.-soption to print the path for the given serial number-voption to print verbose outputWithout any option: print all serial ports and their serial numbersusage:esp-serial-find[-h][--verbose][--serialSERIAL]options:-h,--helpshowthishelpmessageandexit--verbose,-vPrintverboseoutput--serialSERIAL,-sSERIALPrintthepathforthegivenserialnumberExamplePrint all serial ports and their serial numbers$esp-serial-find70:EE:50:1C:1B:0C/dev/cu.usbserial-000170:EE:50:1C:1B:0D/dev/cu.usbserial-000270:EE:50:1C:1B:0E/dev/cu.usbserial-0003Print the path for the given serial number$esp-serial-find-s70:EE:50:1C:1B:0C
/dev/cu.usbserial-0001CreditsThis package was created withCookiecutterand thewaynerv/cookiecutter-pypackageproject template.
Package: esp-trainer
cli-esp-trainer 🧙‍♂️️ESP trainer that runs in your terminal. The idea is inspired byRussell Targ's ESP trainerand is based on sound parapsychological study design.How it works ✨There's 24 trials.Each trial has four numbers to select from. You guess which number is going to be next.If you're right, you score a hit.If you're wrong, it's a miss. The correct number is displayed.At the end, your results are tallied.This trains you to distinguish an intuitive hunch from mental noise. True intuition should feel like a magnetic pull towards the correct number.If your result is statistically significant, this is a clear indication of ESP. The scoring levels are 6, 8. 10, 12, or 14 hits. Frequently scoring 12 or more is considered highly developed ESP.Features 📔CurrentTrack your score against milestones of ESP abilityFast, minimal UICross-platformColoured textPretty table to display resultsFutureAbility to skip a trialCustomise amount of trialsExport results into .csv fileVisualise improvements in results over time with chartsInteractive shell (so you can play again without needing to reload the script)--helpcommand to provide info from the terminalCode quality improvements (tests, linting)WebsitePublic leaderboardsScientific evidence 🧪The proof of ESP is out of the scope of this README. The evidence is extremely strong despite the controversy. Look at the work of both Russell Targ and Dean Radin if you're not convinced.Improving your score 📈To improve your score, do consciousness work (especially meditation). Psychedelics also help (but do so at your own risk, I can't recommend it unless you know what you're doing).Installation 🚀DependenciesPython 3.8.2 (lower versions of 3 will probably work, but haven't been tested)InstructionsInstall the CLI client:pip3installesp-trainerRun the script:esp-trainerDevelopment 👨‍💻Install dependencies withPipenv:pipenvinstall--devThen open the project with your preferred editor.Open the pipenv shell:pipenvshellThat's it. Start coding.
Package: espy
Espy is a simple way to check the environment in which your code will run:# Boiler plate sanity checkpython >= 2.7# Make sure we have MongoDB and Driverspy pymongo >= 2.1.0cmd mongod >= 2.0.0# Track down Solaris GCC installcmd /usr/sfw/bin/gcc >= 4.0Espy makes it easy to check Python Modules and external commands.
Package: espyta
espytaThis software provides a webhook handler to automatically flash any ESP-based
devices on your network when the repository for their firmware gets updated.RequirementsPython 3.6 or up.PlatformIO Core (CLI)5.0 or up.InstallationI recommend you use a virtual environment and use that to installespyta. You
can then start theespytaHTTP server as follows:# python3.8 -m venv venv
# venv/bin/pip install espyta
# venv/bin/espyta httpConfigurationDocumentationYou can read more on our fulldocumentation website.
Package: esq
esqInstall-Init-Usage-DocumentationA python package that provides GraphQL API for Elasticsearch and makes it easier to query data without writing complex queries.Installpip install esqInit>>>fromesqimportEsq>>>fromelasticsearchimportElasticsearch# init your Elasticsearch client object here>>>ELASTICSEARCH_URI="http://localhost:9200">>>es_client=Elasticsearch([ELASTICSEARCH_URI])# init your Esq object here>>>esq=Esq(es_client)# now you can start making queriesUsage# to get aggregation on sentiment field on your twitter index, you can simply do:>>>sentiments=esq.get_aggr_data(index='twitter',data_type='keyword/text',#data_type is an enum('keyword/text', 'coordinates', 'timeseries')data_field='sentiment')>>>print(sentiments)[{'label':'positive','value':9},{'label':'neutral','value':7},{'label':'negative','value':1}]DocumentationRead the full usage documentation hereHistory1.0.3 (10-10-2022)Added full usage detailed documentation.1.0.2 (04-09-2022)Add Github Actions workflow for version bumping and release to PyPI.1.0.1 (30-08-2022)Fix Docs and add content README.1.0.0 (30-08-2022)First release on PyPI.
Package: esqb
ElasticSearch Query BuilderA Query Builder to build queries specially suited for ElasticSearch queriesExamplesBasic exampleThis code show how to define a simple query.fromesqb.queryimportBaseQueryclassSimpleQuery():size=0query={"bool":{"must":[{"term":{"name":{"value":"esqb"}}}]}}aggs={"by_logtime":{"date_histogram":{"field":"time","interval":"day","order":{"_key":"desc"}}}}and this is the generated query.{"query":{"bool":{"must":[{"term":{"name":{"value":"esqb"}}}]}},"size":0,"aggs":{"by_logtime":{"date_histogram":{"field":"time","interval":"day","order":{"_key":"desc"}}}},"sort":[]}Variables and filtersExample to create a query to show the the lastNdocuments ordered by asort_fieldbetween two dates (tsandte)v.pyThis file show how to define the query variables.fromesqb.variableimportVariablets=Variable('ts',None,str,True,'Time start')te=Variable('te','2017-12-01',str,True,'Time end')size=Variable('query_size',10,str,False,'Term size')sort_field=Variable('sort_field','',str,True,'Field to do the ordination')sort_order=Variable('sort_order','',str,True,'asc or desc')variables={v.name:v.nameforvin[ts,te,size,sort_field,sort_order]}filters.pyThis file show how to define a esqb query filter to add a date range.fromesqb.queryfilterimportQueryFilterclasstime_range_filter(QueryFilter):"""
    Query filter to filter between two dates.
    """def__init__(self,field,ts,te):self.field=fieldself.variables={'ts':ts,'te':te,}defapply(self,query,data):query.setdefault('bool',{}).setdefault('must',[]).append({'range':{self.field:{'gte':self.variables['ts'],'lte':self.variables['te'],}}})returnquerylast_docs.pyThis file show how to define a parameterized elasticsearch query using the filters and variables previously defined.fromesqb.queryimportBaseQueryfromfiltersimporttime_range_filterfromvimport(size,sort_field,sort_order,ts,te)classLastDocs(BaseQuery):size=sizesort=[{sort_field:{"unmapped_type":"float","missing":"_last","order":sort_order}}]def__init__(self):BaseQuery.__init__(self)self.filters=[time_range_filter('timestamp',ts,te)]defresult(self,response):return[r.get('_source',{})forrinself.dotget(response,'hits.hits')]__doc__=LastDocs().docs(variables)example.pyThis file show how to create a complete query ready to be used by elasticsearch.fromlast_docsimportLastDocsif__name__=='__main__':q=LastDocs().get_es_query({'ts':'1980','te':'1990','query_size':3,'sort_order':'asc','sort_field':'age'})print(q)And this is the query.$>pythonexample.py{"query":{"bool":{"must":[{"range":{"timestamp":{"gte":"1980","lte":"1990"}}}]}},"size":3,"aggs":{},"sort":[{"age":{"unmapped_type":"float","missing":"_last","order":"asc"}}]}FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: esql
ESQLESQL is a way to easily make simple datebases for your discord bot so you don't have to use JSON.how does it work?Its pretty simple. you create a datebase, then feed in the variables you want to have.the tool automatically generates a README specific to how to implement your DB system.What's going on under the hood?We populate a file using your given variables, then when you finish up, your file is created in your CWD.creating a DBwindows:esql*NIX systems:python3 -m esql
Package: esqlite
esqliteesqlite means 'easy sqlite', is a sqlite3 wrapper.
method 'execute' for execute any SQL and return result set.
method 'batch_save' for batch insert dict list data. Table will be created if the table not exists.installpip install esqliteexampleimportesqlitedb=esqlite.DB('test.db')data=[{'id':1,'name':'Bob'},{'id':2,'name':'Lena'}]# batch save, default init table with dict keysdb.batch_save('user',data,cleanup=True)# insertdb.execute('insert into user(id, name) values (?,?)',[3,'C++'])print(db.execute('select * from user'))# updatedb.execute('update user set name=? where id=?',['Python',3])print(db.execute('select * from user'))# deletedb.execute('delete from user where id=?',[3])# findprint(db.execute('select * from user order by id desc'))filetest.dbwill be created and console will print:[{'id': 1, 'name': 'Bob'}, {'id': 2, 'name': 'Lena'}, {'id': 3, 'name': 'C++'}]
[{'id': 1, 'name': 'Bob'}, {'id': 2, 'name': 'Lena'}, {'id': 3, 'name': 'Python'}]
[{'id': 2, 'name': 'Lena'}, {'id': 1, 'name': 'Bob'}]
Package: esque-wire
esque-wireA complete and user oriented implementation of the kafka wire protocol.FeaturesSupports all Api endpointsSince the code for the API endpoints is automatically generated, this library supportsallof them.
If a new one comes along, its implementation is just one code execution away. Also the field documentation is extracted
from Kafka source code if there is one.Type annotationsEverything is annotated! Enjoy autocomplete all the way to the last field.# run with mypyfromesque_wireimportBrokerConnection,ApiVersionsRequestDatarequest_data=ApiVersionsRequestData()connection=BrokerConnection("localhost:9092","test_client")response=connection.send(request_data)reveal_type(response)# Revealed type is '... AnsweredApiCall[... ApiVersionsRequestData, ... ApiVersionsResponseData]'reveal_type(response.response_data)# Revealed type is '... ApiVersionsResponseData*'
Package: esrapgra
EsrapgraThe opposite of argparse. Converts function arguments to command-line
arguments.Installationpip install esrapgraFrom sourcepip install git+https://github.com/audy/esrapgraOverviewSo you want to callwgetfrom Python, mapping Python variables to
command-line arguments:wget\http://example.com/file.zip\--directory-prefix=/path/to/directory\--retry-connrefused\--waitretry=5\--tries=3This often gets messy if you need to interpolate values into the arguments or
map Python logic into command logic:args=["http://example.com/file.zip","--directory-prefix",directory_prefix,f"--tries={tries}"]ifretry_connrefused:args.append("--retry-connrefused")subprocess.check_output(["wget"]+args)With Esrapgra (if you can remember how to spell it), this becomes a nice
function invocation:es=Esrapgra(kwargs_last=True)args=es.lex_arguments("http://example.com/file.zip",directory_prefix="/path/to/directory",retry_connrefused=True,waitretry=5,tries=3)subprocess.check_output(["wget"]+args)The following things are happening:String arguments are automatically escaped and quote to protect against
shell-escape attacksBoolean arguments are automatically turned into boolean flags (e.g.,retry_connrefused=Trueturns into--retry-connrefusedandretry_connrefused=Falseturns into nothing)Numeric flags are automatically converted into stringsExamplesBasic InitializationYou can initialize theEsrapgraobject with default parameters:esrap=Esrapgra()Custom SeparatorCustomize the separator between words in keyword arguments:# Using underscores as separatorsesrap=Esrapgra(separator="_")# Example usageesrap.lex_arguments(test_argument="test")# Output: ['--test_argument="test"']Custom PrefixDefine a custom prefix for the keyword arguments:# Using a single hyphen as a prefixesrap=Esrapgra(prefix="-")# Example usageesrap.lex_arguments(test_argument="test")# Output: ['-test_argument="test"']Keyword Arguments PositionControl the position of keyword arguments relative to positional arguments:# Putting keyword arguments before positional argumentsesrap=Esrapgra(kwargs_last=False)# Example usageesrap.lex_arguments("first_arg",test_argument="test")# Output: ['-test_argument="test"', 'first_arg']Quoting StringsYou can enable or disable the quoting of strings as per your needs:# Disabling quoting of stringsesrap=Esrapgra(quote_strings=False)# Example usage# Adjust the expected output based on the implementation of `lex_arguments`esrap.lex_arguments(test_argument="test")
Package: esridumpgdf
esridumpgdfSimple module usingpyesridumpandgeopandasto create GeoDataFrames from
ArcGIS Map and Feature layers and services.Installpip install esridumpgdfUsageFor exporting a single Map or Feature service to GeoDataFrame:fromesridumpgdfimportLayerlayer='https://sampleserver6.arcgisonline.com/arcgis/rest/services/911CallsHotspot/MapServer/1'gdf=Layer(layer).to_gdf()To export an entire service to a multiple GeoDataFrames:fromesridumpgdfimportServiceservice='https://sampleserver6.arcgisonline.com/arcgis/rest/services/Wildfire/MapServer'gdfs=Service(service).to_gdfs()
Package: esrijson
esrijsonIntroductionThis module is meant to build a bridge between GDAL__geo_interface__property viaShapely.
There is no concept ofGeometryCollectionin esrijson syntax, so there is only a limited support for this concept. Currently, we only take the first geometry and drop the rest.
This library is heavily inspired bypython-geojson.Usageimportesrijsonfromshapelyimportgeometry# Create an esrijson featurefeat=esrijson.Feature(geometry=geometry.Point(1,4),attributes={'name':'dummy','val':1},wkid=2056)# Dump to jsondumped=esrijson.dumps(feat)# Load to Feature back againloaded=esrijson.loads(dumped)print(loaded)print(type(loaded))>>>{'geometry':{'y':4.0,'x':1.0},'spatialReference':{u'wkid':2056}},'attributes':{u'name':u'dummy',u'val':1}}>>><class'esrijson.feature.Feature'># You can also transform an esri json like object into a shapely objectgeom=esrijson.to_shape({"paths":[[[0.0,0.0],[1.0,1.0]],[[-1.0,0.0],[1.0,0.0]]]})print(getattr(geom,'__geo_interface__')>>>{'type':'MultiLineString','coordinates':(((0.0,0.0),(1.0,1.0)),((-1.0,0.0),(1.0,0.0)))}# And of your do the same operation back againesri_geom=esrijson.from_shape(geom)print(esri_geom)>>>{'paths':(((0.0,0.0),(1.0,1.0)),((-1.0,0.0),(1.0,0.0)))}ESRI specs referencesGeometry ObjectFeature Object
Package: esrpoise
esrpoiseParameter Optimisation by Iterative Spectral Evaluation: an Xepr-compatible ESR packageInstallationESR-POISE requires Python 3.6 or later and can be installed usingpip:python -m pip install esrpoiseTestsInside the top-levelesrpoisedirectory, run:pip install tox
toxDocumentationFor more details on installation, use and troubleshoot, please seethe documentation.
Package: esrp-release-test
The Bing Ads Python Software Development Kit (SDK) Version 13.0 enhances the experience of developing Bing Ads applications
with the Python programming language.
The SDK includes proxy classes for all Bing Ads API web services and abstracts the low level details of authentication with OAuth.
You can also read and write bulk files with the SDK BulkFileReader and BulkFileWriter,
and use the high level BulkServiceManager interface to abstract and execute operations in the low level Bulk API. For more information, seeBing Ads Client Libraries.Getting StartedTo get started developing Bing Ads applications with Python, install the SDK and either start with theexamplesor follow one of the application walkthroughs. For more information, seeGetting Started Using Python with Bing Ads Services.External Dependenciessuds-community-requests-enum34Release History13.0.18(2023-11-13)
+++++++++++++++++++++++++ * Update Bing Ads API Version 13 service proxies to reflect recent interface changes. For details please see the Bing Ads API Release Notes:https://docs.microsoft.com/en-us/esrp-release-test/guides/client-libraries. * Added mappings for new fields in BulkOfflineConversion: HashedEmailAddress and HashedPhoneNumber.
Package: essahtmlgen
Python tool for creating HTML specific layouts with source code files.
Uses CSU images as the default.
The source code is formatted with a javasript library to be ‘pretty’Here are some examples (Im on OSX):Create a 6 part assignment (very basic no source)essahtmlgen –destination ~/Desktop –name bayleaf –id 2631542 –parts 6 –assignment 1This will create ‘Assignment1’ on the desktop with 6 basic parts.Create a the 3 part assignment 1  with a sourceA source directory contains folders which will become named parts (as well as the .html versions).
The files in each directory will be used to compose the specfic parts html file.
Java code will turn into pretty embedded source code.
Text code will turn into paragraph tags.
Pictures will be copied, linked and embedded (.png / .jpg).
There is no order right now so you may have to play around with it after but generally it follows the directory structure.Create a source directory structured like this:Assignment0Files ->FirstPart ->data.txtJavaCode.javascreenshot.pngSecondPart ->file.txtessahtmlgen –destination ~/Desktop –name bayleaf –id 2631542 –parts 2 –assignment 1  –source /path/to/Assignment0FilesThis will create ‘Assignment1’ on the desktop with two parts: FirstPart and SecondPart.
Package: essence
Essence is an ‘entity, component, system’ framework for Python.An example>>>importessence>>>world=essence.World()>>>alice=essence.new_entity()>>>position=PositionComponent(3,4)>>>alice.add(position)>>>printalice.get(Position)<PositionComponent(3,4)>TestsYou can usepy.testto run the tests for just the current Python
environment orpython setup.py testto run tox as well as various other
pre-release tests.
Package: essence-extractor
Essence Extractor 📜✨Essence Extractor is a dynamic tool designed to transform YouTube videos into engaging and concise blog posts. This utility automates the process of downloading videos, extracting audio, transcribing speech, and generating a summary to create a ready-to-publish blog post.Limitations ⚠️Due to the nature of llm, results may vary.Requirements 🛠️Before you get started, make sure you have the following installed on your machine:FFmpeg: A complete, cross-platform solution to record, convert, and stream audio and video.Installation 🖥️pipinstallessence-extractorUsage 🚀To unleash the power of Essence Extractor, you'll need an API key from OpenAI. Sign up on OpenAI's platform to get yours.Once you have your API key, run:essence-extractor"output_directory""YOUR_API_KEY"output_directory: Where the magic happens - all your output files will land here.YOUR_API_KEY: Your secret key to OpenAI's capabilities.Next, you'll be prompted to enter the YouTube video URL:PleaseentertheYouTubevideoURL:"https://www.youtube.com/watch?v=yourvideoid"What’s in the Box? 🎁Running Essence Extractor will populate your output directory with:Video File: The original YouTube video, downloaded for reference.Audio File: The extracted audio from the video.Transcription File: A text file with everything that was said in the video.Images Directory: A directory containing all the images used in the blog post.Blog Post File: Your brand new blog post, ready for the world to see.Documentation 📖To learn more about Essence Extractor, check out ourdocumentation.Join the Essence Extractor Community 🤝Got ideas or found a bug? We’d love to have you in our contributor community! 🚀 Check out ourcontributing guidelinesto get started.Roadmap 🗺️Wondering what's coming next for Essence Extractor? Check out ourproject roadmapto stay up to date with the latest features and improvements. We're constantly working to make Essence Extractor better, and we value your input! Feel free to share your suggestions and feedback to help shape the future of this project.
Package: essence-nlp
EssenceEssence is string processing library designed for advanced Natural Language Processing (NLP) and Generative AI tasks. Built in Rust, bridged to Python.FeaturesError Correction: Enhances text accuracy by correcting spelling and grammatical errorsNoise Removal: Eliminates irrelevant information, aiding in cost-effective prompt handling especially when using APIs with usage-based pricingKeyword Extraction: Extracts key terms from prompts facilitating fast searches, database lookups, and other system interactionsPrompt Compression: Reduces operational costs in AI-driven platforms by minimizing the text data processedHigh Performance: Leverages Rust's memory safety and speed for string processingInstallationBefore installing Essence, ensure that you have Rust and Python installed on your system.Install Rust:Visithttps://rustup.rs/and follow the instructions.Install Python:Download and install Python fromhttps://www.python.org/downloads/.Install Essence via pip:pipinstallessence-nlp# orpip3installessence-nlpUsagefromessence_nlpimportprocessingsentence='Hello i am sogtware engineer, and i designing smart mashine learning system.'# Example 1: Correcting textcorrected_sentence=processing.corrected(sentence)print(corrected_sentence)# Output: 'hello i am software engineer and i'm designing smart machine learning system'# Example 2: Cleaning textclean_sentence=processing.cleaned(sentence)print(clean_sentence)# Output: 'hello software engineer im designing smart machine learning system'# Example 3: Extracting keywordskeywords=processing.keywords(sentence)print(keywords)# Output: 'im designing smart machine learning system: 36\nsoftware engineer: 4\nhello: 1'BenchmarksComing soon...CreditsnlpruleRAKE
Package: essentia
Essentia is an open-source C++ library with Python bindings for audio analysis and audio-based music information retrieval. It contains an extensive collection of algorithms, including audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, a large variety of spectral, temporal, tonal, and high-level music descriptors, and tools for inference with deep learning models. Designed with a focus on optimization in terms of robustness, computational speed, low memory usage, as well as flexibility, it is efficient for many industrial applications and allows fast prototyping and setting up research experiments very rapidly.Website:https://essentia.upf.edu