Saravanan666/pypi-org huggingface dataset:
Package: sentinelflow
Search, download and patchCopernicusSentinel-2data into appealing color
images. Colors are adapted to human perception and fit for dark oceans as well
as bright snow surfaces. Check out the example glacier animations in the
examples directory, and more examples at mywebpage. Support for archived
(offline) products is not yet available.RequiresGDAL,ImageMagick,XMLStarlet, and aregistrationto the
Copernicus Open Access Hub. A Python port is under preparation.Installation:pip install sentinelflowTo fetch the latest cloud-free image over the Aletsch Glacier use:sentinelflow.sh --user USERNAME --pass PASSWORD --cloudcover 10 \
                --intersect 46.4,8.1 --maxrows 1 --tiles 32TMS \
                --extent 417000,5138000,432000,5158000Please refer to the Sentinel-2data productsdocumentation to find your
corresponding tile(s). The image extent is given inUTMcoordinates of the
local zone. For additional command-line help use:sentinelflow.sh --helpPlease acknowledge usage in all derivative products, for instance with the
mention:Contains modified Copernicus Sentinel data (year).
Processed with Sentinelflow (version).
Package: sentinelhub
IntroductionThesentinelhubPython package is the official Python interface forSentinel Hub services. It supports most of the services described in theSentinel Hub documentationand any type ofsatellite data collections, including Sentinel, Landsat, MODIS, DEM, and custom collections produced by users.The package also provides a collection of basic tools and utilities for working with geospatial and satellite data. It builds on top of well known packages such asnumpy,shapely,pyproj, etc. It is also a core dependency ofeo-learnPython package for creating geospatial data-processing workflows.The main package resources areGitHub repository,documentation page, andSentinel Hub forum.InstallationThe package requires a Python version >= 3.8. The package is available at the PyPI package index and can be installed with$ pip install sentinelhubor with an extension tag for additional functionalities$ pip install sentinelhub[AWS]  # extra dependencies for interacting with Amazon Web ServicesAlternatively, the package can be installed with Conda fromconda-forgechannel$ conda install -c conda-forge sentinelhubTo install the package manually, clone the repository and run$ pip install .Before installingsentinelhubonWindowsit is recommended to installshapelypackage fromUnofficial Windows wheels repositoryOnce installed the package can be configured according toconfiguration instructionsin documentation.ContentA high-level overview of the main functionalities:Sentinel Hub servicesProcess API,Catalog API,Batch Processing API,BYOC API,Statistical API,OGC services (WMS/WCS/WFS),FIS,authentication and rate-limit handling,geospatial utilitiesinterface for geospatial objects and transformations,large area splitting,data collection objects,IO tools,GeopediaWMS and REST API.DocumentationFor more information on the package and to access the documentation, visitreadthedocs.ExamplesThe package has a collection of Jupyter notebooks with examples. They are available in theexamples folderon GitHub and converted into documentation underExamples section.Additionally, some examples are explained in Sentinel Hub webinar videos:Process API in PythonOGC API in PythonBlog postsThe package played a key role in many projects and use cases described atSentinel Hub blog. The following blog posts are about the package itself:Upgrading the sentinelhub Python packageRelease of sentinelhub Python Package 2.0Questions and IssuesFeel free to ask questions about the package and its use cases atSentinel Hub forumor raise an issue onGitHub.You are welcome to send your feedback to the package authors, Sentinel Hub research team, through any ofSentinel Hub communication channels.LicenseSeeLICENSE.
Package: sentinel-local
Sentinel: LocalPurposeConservation X Labs aims to make the deployment of customized machine learning models as simple as possible across all endpoints (Sentinel Field-Hardware, Offline Laptops, Cloud).This software is designed to run custom offline machine learning models across many images/videos on customer laptops or desktops. This will likely be used in field scenarios where:Data sorting is required without reliable internet connection.Privacy is paramountHigh Level OverviewBasic Controls (via Python)This repo is the high-level functionality of the system, such as selecting:Organization/Model SelectionInput FoldersDocker: Organization-Specific AlgorithmsWe use Docker to manage the difficulties of different dependencies (Operating Systems, existing tensorflow installations) that will inevitably be present on people's systems. It also allows us to update/fix systems, algorithms on-the-go.
This wil be downloaded by the python script, so dont worry about downloading this, it will be done automatically. Each Conservation  X Labs customer will have a docker container with their most up-to-date algorithms pre-loaded with the latest TensorFlow libraries. As new/updated algorithms are made, your new algorithms can be found here.===Please run the python script at least once before being offline to ensure your org's docker container is downloaded===Installation InstructionsInstallPythonDownload Sentinel Python Packagepip install sentinel_localIf using private algorithms (you should know if this is the case) - add the provided .json key to your machineFollow Usage InstructionsCLI (Command Line Interface)sentinel_download --org <ORG_NAME> --key <PATH_TO_JSON_KEY>sentinel_run --org <ORG_NAME> --model <model_name> --input_folder <PATH_TO_INPUT_FODLER>Python Scriptingimport sentinel_local
  sentinel_local.download(org,key)
  sentinel_local.run()
Package: sentinels
OverviewThe sentinels module is a small utility providing the Sentinel class, along with useful instances.What are Sentinels?Sentinels are objects with special meanings. They can be thought of as singletons, but they service the need of having ‘special’ values in your code, that have special meanings (see example below).Why Do I Need Sentinels?Let’s takeNOTHINGfor example. This sentinel is automatically provided with the sentinels import:>>> from sentinels import NOTHINGLet’s say you’re writing a wrapper around a Python dictionary, which supports a special kind of method,get_default_or_raise. This method behaves likeget, but when it does not receive a default and the key does not exist, it raises aKeyError. How would you implement such a thing? The naive method is this:>>> class MyDict(dict):
...     def get_default_or_raise(self, key, default=None):
...         if key not in self and default is None:
...             raise KeyError(key)
...         return self.get(key, default)Or even this:>>> class MyDict(dict):
...     def get_default_or_raise(self, key, default=None):
...         returned = self.get(key, default)
...         if returned is None:
...             raise KeyError(key)
...         return returnedBut the problem with the above two pieces of code is the same – when writing a general utility class, we don’t know how it will be used later on. More importantly,None might be a perfectly valid dictionary value!This is where NOTHING comes in handy:>>> class MyDict(dict):
...     def get_default_or_raise(self, key, default=NOTHING):
...         returned = self.get(key, default)
...         if returned is NOTHING:
...             raise KeyError(key)
...         return returnedAnd Tada!SemanticsSentinels are always equal to themselves:>>> NOTHING == NOTHING
TrueBut never to another object:>>> from sentinels import Sentinel
>>> NOTHING == 2
False
>>> NOTHING == "NOTHING"
FalseCopying sentinels returns the same object:>>> import copy
>>> copy.deepcopy(NOTHING) is NOTHING
TrueAnd of course also pickling/unpickling:>>> import pickle
>>> NOTHING is pickle.loads(pickle.dumps(NOTHING))
True
Package: sentinel-s3
This packages includes a few functions that facilitates extraction of
Sentinel-2’s metadata from Amazon S3.Installation$ pip install sentinel-s3or$ python setup.py installTests$ python setup.py testExampleGenerating metadata for a date rangeimportloggingfromdatetimeimportdatefromsentinel_s3importrange_metadatadefmain():start_date=date(2016,2,1)end_date=date(2016,3,22)returnrange_metadata(start_date,end_date,'.',20)if__name__=='__main__':logger=logging.getLogger('sentinel.meta.s3')logger.setLevel(logging.DEBUG)ch=logging.StreamHandler()ch.setLevel(logging.INFO)formatter=logging.Formatter('%(message)s')ch.setFormatter(formatter)logger.addHandler(ch)result=main()print(result)Generating metadata for a single productimportloggingfromdatetimeimportdatefromsentinel_s3importsingle_metadatadefmain():product='S2A_OPER_PRD_MSIL1C_PDMC_20160311T194734_R031_V20160311T011614_20160311T011614'returnsingle_metadata(product,'.')if__name__=='__main__':logger=logging.getLogger('sentinel.meta.s3')logger.setLevel(logging.DEBUG)ch=logging.StreamHandler()ch.setLevel(logging.INFO)formatter=logging.Formatter('%(message)s')ch.setFormatter(formatter)logger.addHandler(ch)result=main()print(result)
Package: sentinel-value
sentinel-valueis a Python package, that helps to createSentinel Values-
special singleton objects, akin toNone,NotImplementedandEllipsis.It implements thesentinel()function (described byPEP 661),
and for advanced cases it also provides theSentinelValue()class (not a part ofPEP 661).Usage example:fromsentinel_valueimportsentinelMISSING=sentinel("MISSING")defget_something(default=MISSING):...ifdefaultisnotMISSING:returndefault...Or, the same thing, but using theSentinelValueclass
(slightly more verbose, but allows to have nice type annotations):fromtypingimportUnionfromsentinel_valueimportSentinelValueclassMissing(SentinelValue):passMISSING=Missing(__name__,"MISSING")defget_something(default:Union[str,Missing]=MISSING):...ifdefaultisnotMISSING:returndefault...LinksRead the Docs:https://sentinel-value.readthedocs.ioGitHub repository:https://github.com/vdmit11/sentinel-valuePython package:https://pypi.org/project/sentinel-value/
Package: sentinex
Sentinex - An (Experimental) Object Oriented Deep Learning Library Built on top of JAX.Sentinex is a comprehensive deep learning library that aims to provide an intuitiveobject oriented apithat is accelerated using JAX primitives.Sentinex aims to provide a simplied and intuitive api that doesn't increase programming fatigue, when developing models. It offers low level abstractions likesx.Module, while offering higher level subclasses likenn.Layers,nn.Model,nn.Activation,nn.Initializers,nn.Losses, etc. Since everything is a PyTree, it is compatible with a wide variety of JAX ecosystem tools, like Optax, Equinox, Keras, and so much more.Sharp Bits:Currently, Sentinex is an immature framework that heavily utilizes external libraries for many core features. For example, Equinox is used to supply many of the filtered/lifted transformations, while Optax optimizers have been wrapped for extra-convenience.
This implies that Sentinex's internals are not maintained completely from this repo and is dependent on the support of other jax libraries. Therefore, there may be bugs in such interops, though Sentinex is aiming to prevent that and migrate to a more
independent status.
Package: sentio-prober-control
SENTIO® Prober Control - Python BindingsThis archive contains a package with python bindings to control aMPI SENTIO® probe station.Instructions for installing the SENTIO® prober control Python packageThe package for controlling MPI probe stations running the MPI SENTIO Software suite is now available viapythons package index. To install the
package simply type:python -m pip install sentio-prober-controlYou no longer need to download the package by yourself, just use pip. If you cannot access the internet from your machine you can still download the archives fromthe release section of this project.Example-ScriptsA set of example scripts for python is maintained in a separate archive at GitHub.https://github.com/SentioProberDev/Examples-PythonTroubleshootingMost problems arise from having multiple python environments on the local machine and installing the sentio-prober-control package into the wrong python environment. You can list all installed packages with the command:python -m pip listMake sure that the sentio-prober-control package in the latest version is in that list.Instructions for Package maintainerManually Building a Package
Package: sentipy
Python implementations of Sentinel 2 Toolbox L2 products & spectral indicessentipy is a small package that aims to do one thing well: provide an easy-to-use python API for processing sentinel
satellite raster data into useful biophysical estimates and spectral indices.InstallationThe sentipy package is built & published (with enormous gratitude) usingPoetry$pipinstallsentipyDocumentationDocumentation for the project is hosted atRead the DocsProject statusThis is a hobby project in the early stages of development, please do not rely on feature or API stability!
Package: sentistrength
Python-SentiStrengthPython 3 Wrapper for SentiStrength, reads a single or multiple input with options for binary class or scale output.Ensure that you have SentiStrength.jar file and SentiStrengthData Language folders, otherwise you can download them fromhttp://sentistrength.wlv.ac.uk/. For jar file, you will have to email Dr. Mike Thelwall.InstallationPip:pipinstallsentistrengthExamplesExample use (single string):>>>fromsentistrengthimportPySentiStr>>>senti=PySentiStr()>>>result=senti.getSentiment('What a lovely day')>>>print(result)...[0.25]Example use (list of strings or pandas Series):>>>fromsentistrengthimportPySentiStr>>>senti=PySentiStr()>>>str_arr=['What a lovely day','What a bad day']>>>result=senti.getSentiment(str_arr,score='scale')>>>print(result)...[1,-1]# OR, if you want dual scoring (a score each for positive rating and negative rating)>>>result=senti.getSentiment(str_arr,score='dual')>>>print(result)...[(2,-1),(1,-2)]# OR, if you want binary scoring (1 for positive sentence, -1 for negative sentence)>>>result=senti.getSentiment(str_arr,score='binary')>>>print(result)...[1,-1]# OR, if you want trinary scoring (a score each for positive rating, negative rating and neutral rating)>>>result=senti.getSentiment(str_arr,score='trinary')>>>print(result)...[(2,-1,1),(1,-2,-1)]Path SetupSpecify the paths as such:>>>senti=PySentiStr()>>>senti.setSentiStrengthPath('C:/Documents/SentiStrength.jar')# Note: Provide absolute path instead of relative path>>>senti.setSentiStrengthLanguageFolderPath('C:/Documents/SentiStrengthData/')# Note: Provide absolute path instead of relative pathLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for detailsAcknowledgmentsBig thanks to Dr. Mike Thelwall for access to SentiStrength.
Package: sentivi
A Simple Tool For Sentiment AnalysisSentivi- a simple tool for sentiment analysis which is a wrapper ofscikit-learnandPyTorch Transformersmodels (for more specific purpose, it is recommend to use native library instead). It is made for easy and faster pipeline to train and evaluate several
classification algorithms.Documentation:https://sentivi.readthedocs.io/en/latest/index.htmlClassifiersDecision TreeGaussian Naive BayesGaussian ProcessNearest CentroidSupport Vector MachineStochastic Gradient DescentCharacter Convolutional Neural NetworkMulti-Layer PerceptronLong Short Term MemoryText Convolutional Neural NetworkTransformerEnsembleLexicon-basedText EncodersOne-hotBag of WordsTerm Frequency - Inverse Document FrequencyWord2VecTransformer Tokenizer (for Transformer classifier only)WordPieceSentencePieceInstallInstall legacy version from PyPI:pipinstallsentiviInstall latest version from source:gitclonehttps://github.com/vndee/sentivicdsentivi
pipinstall.ExamplefromsentiviimportPipelinefromsentivi.dataimportDataLoader,TextEncoderfromsentivi.classifierimportSVMClassifierfromsentivi.text_processorimportTextProcessortext_processor=TextProcessor(methods=['word_segmentation','remove_punctuation','lower'])pipeline=Pipeline(DataLoader(text_processor=text_processor,n_grams=3),TextEncoder(encode_type='one-hot'),SVMClassifier(num_labels=3))train_results=pipeline(train='./data/dev.vi',test='./data/dev_test.vi')print(train_results)pipeline.save('./weights/pipeline.sentivi')_pipeline=Pipeline.load('./weights/pipeline.sentivi')predict_results=_pipeline.predict(['hàng ok đầu tuýp có một số không vừa ốc siết. chỉ được một số đầu thôi .cần ''nhất đầu tuýp 14 mà không có. không đạt yêu cầu của mình sử dụng','Son đẹpppp, mùi hương vali thơm nhưng hơi nồng, chất son mịn, màu lên chuẩn, ''đẹppppp'])print(predict_results)print(f'Decoded results:{_pipeline.decode_polarity(predict_results)}')Take a look at more examples inexample/.Pipeline ServingSentivi useFastAPIto serving pipeline. Simply run a web service as follows:# serving.pyfromsentiviimportPipeline,RESTServiceGatewaypipeline=Pipeline.load('./weights/pipeline.sentivi')server=RESTServiceGateway(pipeline).get_server()# pip install uvicorn python-multipartuvicornserving:server--host127.0.0.1--port8000Access Swagger athttp://127.0.0.1:8000/docsor Redochttp://127.0.0.1:8000/redoc. For example, you can usecurlto send post requests:curl--location--requestPOST'http://127.0.0.1:8000/get_sentiment/'\--form'text=Son đẹpppp, mùi hương vali thơm nhưng hơi nồng'# response{"polarity":2,"label":"#POS"}Deploy using DockerFROMtiangolo/uvicorn-gunicorn-fastapi:python3.7COPY./appENVPYTHONPATH=/appENVAPP_MODULE=serving:serverENVWORKERS_PER_CORE=0.75ENVMAX_WORKERS=6ENVHOST=0.0.0.0ENVPORT=80RUNpipinstall-rrequirements.txtdockerbuild-tsentivi.
dockerrun-d-p8000:80sentiviFuture ReleasesLexicon-basedCharCNNEnsemble learning methodsModel serving (Back-end and Front-end)
Package: sentm
senTMPronounced "sen-T-M".senTM is a lexical approach to sentiment analysis in Danish. The approach is inspired by theafinnpackage, but using theDanish Sentiment Lexicon version 0.2 (2022-12-20).The approach use the part-of-speech (POS) model from Spacy and then matches any tokens from the Danish Sentiment Lexicon.License: Same asDanish Sentiment Lexicon version 0.2 (2022-12-20): CC-BY-SA 4.0 Internationalhttps://creativecommons.org/licenses/by-sa/4.0/InstallationInstall from pip:pip install sentmQuickstartFirst initialize model.from sentm.sentm import senTM

sentm_model = senTM()You can both get sentiment score:sentm_model.score('Du er en kæmpe idiot!')You can also use it as a classifier.
Here, the labels are determined by:Score larger than 1: "positiv"Score between -1 and 1: "neutral"Score lower than -1: "negativ"sentm_model.classify('Du er en kæmpe idiot!')
Package: sentop
SenTopSenTop combines sentiment analysis and topic modeling into a single capability allowing for sentiments to be derived per topic and for topics to be derived per sentiment.InstallationTo install with pypi, use:pip install sentopQuick StartCreate a SenTop object and pass your list of documents torun_analysis().st = SenTop()
st.run_analysis(docs, annotation="My dataset")Sentiment AnalysisSentiment analysis is performed usingAdaptNLPwith state-of-the-art (SOTA)Hugging Face Transformers.  SenTop provides multiple sentiment analyses (confidence scores also available):RoBERTa Base Sentimentfor 3-class sentiment -- based on Facebook AI'sRoBERTaBERT Base Multilingual Uncased Sentimentfor 5-class sentiment -- based on Google'sBidirectional Encoder Representations from Transformers (BERT)Twitter roBERTa-base for Emotion Recognitionfor 4-class emotion recognitionBERT-base-cased Geomotions (Original)for 28-class emotion recognitionTwitter roBERTa-base for Offensive Language Identificationfor 2-class offensive language detectionTopic ModelingSenTop provides two types of topic modeling:Latent Dirichlet Allocation (LDA)usingTomotopyand transformer-basedBERTopic. While LDA provides de facto, statistical-based topic modeling, BERTopic provides SOTA-level performance usingHugging Face Transformers. Transformers that have been tested include:BERT Base Uncased-- based on Google'sBidirectional Encoder Representations from Transformers (BERT)XLM RoBERTa Base-- based onXLM-RoBERTaCombining Sentiment Analysis and Topic ModelingSenTop combines sentiment analysis and topic modeling by copmuting both at the document level for a corpus, the results of which can then be represented by a table as shown below.DocumentBERT TopicLDA Topic3-Class Sentiment5-Class Sentiment"Having to report to work without being provided PPE."30negative1_star"Teleworking at home."12neutral3_stars"Things are good. Im ready to do the mission."21positive4_stars
Package: sent-pattern
Sent PatternThis package categorizes English sentences into one of five basic sentence patterns and identifies the subject, verb, object, and other components. The five basic sentence patterns are based on C. T. Onions's Advanced English Syntax and are frequently used when teaching English in Japan.Influence of His Grammar on English Language Education in JapanUniverse ProjectQuick Startfastapi docker Example CodeHow To UseInstallationpipinstallsent-patternUsageimportspacynlp=spacy.load("en_core_web_lg")nlp.add_pipe("span_noun")nlp.add_pipe("sent_pattern")text="he gives me something"doc=nlp(text)pattern=doc._.sentpatternprint(pattern)# FourthSentencePattern (class)print(pattern.subject.root)# he (Token)print(pattern.verb.root)# give (Token)Cases without pipelineIf you want to know the sentence pattern without using components, we recommend using method of tags module.
The following three methods must be followed in order.create_dep_list,create_elements,create_sent_pattern.
execute in order to generate the sentpattern class.merit: can get sentpattern typeimportspacyfromsent_patternimporttagsnlp=spacy.load("en_core_web_lg")doc=nlp("he gives me something")dep_list=tags.create_dep_list(doc)elements=tags.create_elements(dep_list=dep_list)p=tags.create_sent_pattern(elements=elements)pattern=p.pattern_type# FourthSentencePattern(class)print(pattern.subject.root.text)# he (string)print(pattern.verb.root)# gives(spacy.Token)print(dep_list)# {'ROOT': [gives], 'dative': [me], 'dobj': [something], 'nsubj': [he]}print(pattern.abbreviation)# SVO (str)how to get prep phrasenlp=spacy.load("en_core_web_lg")text="The Eureka client handles all aspects of service instance registration and deregistration"doc=nlp(text)dep_list=tags.create_dep_list(doc)custom=ElementsFactory.make_custom_elements(dep_list,doc=doc,option="prep")phrase=custom.optionprint(phrase.prep_groups)# [of service instance registration and deregistration]LicenseDistributed under the terms of the MIT license, "sent-pattern" is free and open source software
Package: sentry
Users and logs provide clues. Sentry provides answers.What's Sentry?Sentry is a developer-first error tracking and performance monitoring platform that helps developers see what actually matters, solve quicker, and learn continuously about their applications.Official Sentry SDKsJavaScriptElectronReact-NativePythonRubyPHPLaravelGoRustJava/KotlinObjective-C/SwiftC#/F#C/C++DartPerlClojureElixirUnityUnreal EngineResourcesDocumentationCommunity(Bugs, feature requests,
general questions)DiscordContributingBug TrackerCodeTransifex(Translate
Sentry!)
Package: sentry-10-dingding
Sentry-DingDingForkhttps://github.com/anshengme/sentry-dingdingFixSentry 10 or the same errorsentry-dingding issues'Event' object has no attribute 'id'安装# requirement.txt

sentry-10-dingdingdocker-compose 安装的直接改成这个插件包就好了，如果帮你解决了问题，麻烦给个小星星.
Package: sentry10-dingding
Sentry-DingDingSentry的钉钉通知插件安装$pipinstallsentry-dingding2使用在项目的所有集成页面找到DingDing插件，启用，并设置Access Token在插件上使用Test Plugin进行测试，当配置好Access Token后，在钉钉群内会得到以下警告点击href按钮，打开异常详情页面。