Saravanan666/pypi-org huggingface dataset:
Package: sepplotlib
sepplotlibSeparation plots for binary classification problems.CreditsThe one-dimensional separation plot is adapted from code originally produced byBrian Greenhill, Michael D. Ward, and Audrey Sacks.
The bi-separation plot and model criticism plot are adapted from code originally produced byMichael Colaresi and Zuhaib Mahmood.Installationpip install sepplotlibto install.Example usagePlease see the accompanied notebook for an example using mock data.The included figures are objects that expect a pandas DataFrame and strings for the relevant columns. To generate a one-dimensional separation plot for instance, simply run:importsepplotlibassplspl.SeparationPlot(df=df,y_true="y_true",y_pred="y_pred",title="Example")Similarly to generate a model criticism plot:importsepplotlibassplspl.ModelCriticismPlot(df=df,y_true="y_true",y_pred="y_pred",lab="lab",title="Example")And finally, to generate a two-dimensional, bi-separation plot:importsepplotlibassplspl.BiseparationPlot(df=df,x="y_pred_a",y="y_pred_b",obs="y_true",lab="lab",title="Example",)Please runhelpon any of these classes to learn what can be customized (e.g.help(spl.SeparationPlot)).
Package: seppmail-converter
SEPPMail ConverterThis python tool allows you to convertSEPPMailencrypted email files (html) to.emlfiles.UsageUsage: seppmail-converter [OPTIONS] INPUT_FILE

Options:
  -u, --username TEXT
  -p, --password TEXT
  -o, --output PATH
  -f, --force          Skip SEPPMail input file validation
  -d, --delete         Delete input file after conversion
  -o, --overwrite      Overwrite output file if it exists
  -e, --extract        Extract attachments from .eml file
  -q, --quiet          Suppress all output except final path
  -v, --version        Show the version and exit.
  --help               Show this message and exit.Relevant environment variables:NameDescriptionSEPPMAIL_USERNAMEEmail supplied during loginSEPPMAIL_PASSWORDPassword supplied during loginUnless specified, the script will place the output file next to the input file and name it after the original file.
Package: seppy
This package is in development status! Intended for internal use only, as the syntax is in a floating state and the documentation is incomplete.A compendium of Python data loaders and analysis tools for in-situ measurements of Solar Energetic Particles (SEP)So far combines loaders for the following instruments into one PyPI package:Parker Solar Probe: ISOISSOHO: CELIAS, COSTEP-EPHIN, ERNESolar Orbiter: EPD (STEP, EPT, HET)*, MAGSTEREO: HET, LET, SEPT, MAGWind: 3DP(* Note thatsolo-epd-loaderis aPyPI package itselfthat just is loaded here for completeness.)DisclaimerThis software is provided “as is”, with no guarantee. It is no official data source, and not officially endorsed by the corresponding instrument teams.Please always refer to the official data description of each instrument before using the data!Installationseppy requires python >= 3.8.It can be installed fromPyPIusing:pipinstallseppyUsageThe standard usecase is to utilize the***_loadfunction, which returns Pandas dataframe(s) of the corresponding measurements and a dictionary containing information on the energy channels. For example the SOHO/ERNE data from Apr 16 to Apr 20, 2021, can be obtained as follows:fromseppy.loader.sohoimportsoho_loaddf,meta=soho_load(dataset="SOHO_ERNE-HED_L2-1MIN",startdate="2021/04/16",enddate="2021/04/20")Note that the syntax is different for each loader!Please refer to this Notebook for more info and examples for the different data sets!CitationPlease cite the following paper if you useseppyin your publication:Palmroos, C., Gieseler, J., Dresing, N., Morosan, D.E., Asvestari, E., Yli-Laurila, A., Price, D.J., Valkila, S., Vainio, R. (2022).
Solar Energetic Particle Time Series Analysis with Python.Front. Astronomy Space Sci.9.doi:10.3389/fspas.2022.1073578
Package: sepsesam
sepsesamSEP Sesam Python APITHIS PROJECT IS NOT ASSOCIATED WITH SEP IN ANY WAYSEP Homepage:https://www.sep.de/This library is a wrapper for the v1 and v2 APIs of the SEP Sesam backup solution.
See:https://wiki.sep.de/wiki/index.php/4_4_3_Beefalo:Using_SEP_sesam_REST_APIInstall with pippython3-mpipinstallsepsesamExample:importpprintimportsepsesam.apicred={"url":"http://sesam.my.domain:11401","username":"Administrator","password":"Abcd1234!"}data={}withsepsesam.api.Api(**cred)asapi:data=api.location_list()pprint.pprint(data)ATTENTION: This project does not implement all API endpoints offered by the
SEP Sesam REST API (neither v1 nor v2) but also includes ones that are undocumented
and therefore may be not supported by SEP or could even be removed without any
further notice.Use this library at your own risk.In case you find and bugs or if you have any extensions, please open an issue / pull
request.
Package: septentrion
OverviewMaybe you’re looking for a tool to take care of Database migrations in your project. For
Django projects, that tool used to beSouthand then it became Django
itself.But maybe you’re looking for a tool that just focuses on running existing SQL migrations
and keeping track of what was applied. Your tool of choice would not generate those
migrations, because you prefer your migrations to be manually written in SQL. Then your
tool would bedjango-north.But maybe you’re not using Django. You would like a standalone migration tool. You’re
looking for Septentrion. Congratulations, you’ve found it.Septentrion supports PostgreSQL 9.6+ & Python 3.7+, and requires thepsqlexecutable to be present on the system.Very quick startStep 0: Install withpip install septentrion[psycopg2_binary](orpip install septentrion[psycopg2]if you know what you’re doing)Step 1: Create a folder for the version, and add some migration files.migrations/
└──  1.0
    ├── 1.0-0-version-dml.sql
    ├── 1.0-author-1-ddl.sql
    └── 1.0-author-2-dml.sqlStep 2: Run septentrion$septentrion--target-version1.0migrateStep 3: That’s it.We’re currently working on this tool, and it’s been used internally since 2018, but
for now, if you want to use it without a direct access to the people who
wrote it, you’re going to have a lot of questions. We expect a proper documentation
to be ready by mid-2020. Please feel free to contact us meanwhile.Where to go from hereThe completedocsis probably the best place to learn about the project.You can check thequickstartguide to start running your first migrations.If you encounter a bug, or want to get in touch, you’re always welcome to open aticket.
Package: sep-text
sep-textSeparate two texts, paragraph by paragraphInstall itpipinstallsep-text# pip install git+https://github.com/ffreemt/sep-text# poetry add git+https://github.com/ffreemt/sep-text# git clone https://github.com/ffreemt/sep-text && cd sep-textIf your environment does not have a C compiler, install fasttext.whl first. You can find fasttext.whl fromhttps://www.lfd.uci.edu/~gohlke/pythonlibs/.Use itfromsep_textimportsep_textprint(sep_text("This is a text\nMachen wir etwas"))# ('Machen wir etwas', 'This is a text')print(sep_text.langs)# ['de', 'en']print(sep_text.conf)# [0.994, 0.004]
Package: sepulsa
Sepulsa ClientUnofficial Sepulsa Client using PythonThis library allows you to quickly and easily use the Sepulsa v3 via Python.List of implemented API:Pulsa PrabayarPulsa PascabayarPaket DataPLN PrabayarPLN PascabayarBPJS KesehatanPDAMTelkomreference:https://horven.sumpahpalapa.com/swagger/kraken/index.html#!/Table of ContentsInstallationQuick StartInstallationpip3 install sepulsaQuick Startfrom sepulsa import build_client

# get balance
sepulsa = build_client("https://horven-api.sumpahpalapa.com/api", "username", "api-key")
sepulsa.get_products()more example checkout:Example CodeBuilt WithRequests- HTTP Library UsedContributingPlease readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull requests to us.VersioningWe useSemVerfor versioning. For the versions available, see thetags on this repository.AuthorsKelvin Desman-VousmeevoyezSee also the list ofcontributorswho participated in this project.LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details
Package: sepy
SEPA-python3-APIsClient-side libraries for the SEPA platform (Python3)Installation and usage$ pip3 install sepyClone the repository.cd SEPA-python3-APIs
sudo python3 setup.py build
sudo python3 setup.py sdist
sudo python3 setup.py installTo use the classes you have to import them in this way:from sepy.<the class you want to import> import *For example, if you want to import the SAPObject (used to handle JSAP files)
you have to write:fromsepy.SAPObjectimport*This library consists of 5 modules that can be used for different purposes:SAPObject: An handler class for SAP filesSEPA: A low-level class used to develop a client for SEPAConnectionHandler: A class for connection handlingExceptionstablaze: A runnable script (also callable as a function, to nicely print SEPA output)Let's talk about some classes deeply:SEPAThese APIs allow to develop a client for the SEPA platform using a simple interface.
First of all the class SEPA must be initialized. Then the standard methods
to interact with the broker are available.Parameters:sapObject :
A SAPObject file Default = NonelogLevel :
A number indicating the desired log level. Default = 40
The parameters are optional. If present, they activate query, update, subscribe,
methods by SAPObject pick. If absent, only the equivalntsparql_*methods
are available, giving the host communication information each time.Attributes:loggersap:
the SAPObjectconnectionManager :
The underlying responsible for network connectionsCreating a SEPA clientmySAP=open(path_to_sap,"r")sap=SAPObject(yaml.load(mySAP))sc=SEPA(sapObject=sap)Query and UpdateThese four methods (query,sparql_query,update,sparql_update,query_all,clear) expect either a sap entry or a SPARQL query/update.
In addition, it is possible to overwrite the sap communication parameters
with sepa. When a new query/update is issued, it may be preferrable to
catch theRegistrationFailedExceptions,TokenExpiredExceptionandTokenRequestFailedExceptionerrors. The query methods return the SEPA answer.Subscribe and UnsubscribeThesubscribeandsparql_subscribeprimitive requires a sap entry or
a SPARQL query, an alias for the subscription, an handler (a lambda expression
or a method with two parameters, one for added, the other for removed)
and if needed the overwriting params for communication.
Theunsubscribeprimitive only needs to know the ID of the subscription.SAPObjectThis package supports Semantic Application Profiles. The package is encoding
free, since it expects a dictionary in input. Therefore, for a ysap we havemySAP = open(path_to_sap,"r")
sap = SAPObject(yaml.load(mySAP))while for a jsap we havemySAP = open(path_to_sap,"r")
sap = SAPObject(json.load(mySAP))Something else?Documentation is being written...Foreseen changesMinors, plus the addition of some utilities.
Package: seq2annotation-for-deliverable
seq2annotation_for_deliverablePython Boilerplate contains all the boilerplate you need to create a Python package.Free software: MIT licenseDocumentation:https://seq2annotation-for-deliverable.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutter.=======
History0.1.0 (2020-03-04)First release on PyPI.
Package: seq2class
seq2class is a one stop solution for text classification. Text classification is made easy via seq2class package using sequential model(LSTM). This package is build by harnesing the capabilities of  LSTM(Long short term Memory) model.Requirementseq2class support Python 3.6 or newer.Installationpip install seq2classExampleThis package is being developed for text classification using sequential model.data = 'movies.csv'
labels = 'title'
text = 'genres'

s = Sequence2class()
X_train, X_test, y_train, y_test = s.train_test_split(data, labels, text)
trained_model = s.fit_train(X_train, y_train, 500, 50, 7789, 5, 4)
prediction = s.predict(trained_model, X_test, y_train, 4)wheremovies.csvis a training file containing text and labels.ReferencesDataSet information[1]  movies.csv dataset have been used for research purpose from this `*link* <http://files.grouplens.org/datasets/movielens/ml-latest-small.zip>`.
Package: seq2ftr
This project handles series dataSupport Series data Feature Calculation.InstallationYou need Python installed on your system to able to use seq2ftr.This package contains many feature extraction methods.Support different type (continues/class) features calculation.>>> $ pip install seq2ftrInstall RequirementsnumpypandassklearnFeature CalculationSupport Functionmeanmaxminfreq_of_maxfreq_of_minmedianmedian_mean_distancepercentage_below_meanvarstduniqueCount…Support Type0 - boolean1 - numericla2 - categoricalExampleTo start , we load data to python>>>
import pandas as pd
df = pd.DataFrame([[1,200,"1"],[1,500,"2"],[2,300,"2"],[2,600,"2"]],columns=['id','stock_price',"type"])
df = df.set_index("id")>>>
from seq2ftr import SequenceTransformer
st_num = SequenceTransformer()
st_num.transformer(df['stock_price']）
# output all features
Package: seq2onehot
Descriptionseq2onehotis a command-line tool encoding DNA/RNA/protein sequences to a one-hot numpy array.z:warning: All sequences must be the same lengths.To decode a one-hot numpy array to sequences, useonehot2seq.https://github.com/akikuno/onehot2seqInstallationYou can installseq2onehotusing pip:pipinstallseq2onehotUsageseq2onehot[options]-t/--type<dna/rna/protein>-i/--input<in.fasta>-o/--output<out.npy>Options-a/--ambiguous:includeambiguouscharactersThe ambigous characters are:XBZJfor amino acidNVHDBMRWSYKfor DNA and RNAThe detail of ambiguous characters is described here:https://meme-suite.org/meme/doc/alphabets.htmlExamples# DNA sequencesseq2onehot-tdna-iexample/dna.fasta-odna.npy# RNA sequencesseq2onehot-trna-iexample/rna.fasta-orna.npy# Protein sequencesseq2onehot-tprotein-iexample/protein.fasta-oprotein.npyOne-hot arrayThe output file contains 3d one-hot array ofRxNxL(Read x Nucreotide/Amino acid x Letter)The order of nucreotide isACGT(+NVHDBMRWSYK) for DNA,ACGU(+NVHDBMRWSYK) for RNAThe order of amino acid isACDEFGHIKLMNPQRSTVWY(+XBZJ)# Original sequences:## ACGTACGTACGTACGT## CCCCCCCCTTTTTTTTonehot=np.load("dna.npy")onehot.shape# (2, 16, 4) <- 2 reads x 16 nucreotides x 4 letters (ACGT)onehot# array([[[1., 0., 0., 0.],#         [0., 1., 0., 0.],#         [0., 0., 1., 0.],#         [0., 0., 0., 1.],#         [1., 0., 0., 0.],#         [0., 1., 0., 0.],#         [0., 0., 1., 0.],#         [0., 0., 0., 1.],#         [1., 0., 0., 0.],#         [0., 1., 0., 0.],#         [0., 0., 1., 0.],#         [0., 0., 0., 1.],#         [1., 0., 0., 0.],#         [0., 1., 0., 0.],#         [0., 0., 1., 0.],#         [0., 0., 0., 1.]],#        [[0., 1., 0., 0.],#         [0., 1., 0., 0.],#         [0., 1., 0., 0.],#         [0., 1., 0., 0.],#         [0., 1., 0., 0.],#         [0., 1., 0., 0.],#         [0., 1., 0., 0.],#         [0., 1., 0., 0.],#         [0., 0., 0., 1.],#         [0., 0., 0., 1.],#         [0., 0., 0., 1.],#         [0., 0., 0., 1.],#         [0., 0., 0., 1.],#         [0., 0., 0., 1.],#         [0., 0., 0., 1.],#         [0., 0., 0., 1.]]])
Package: seq2seq
Real valued sequence to sequence autoencoderMost sequence to sequence autoencoders I can find are suitable for categorical sequences,
such as translation.This auto encoder is for real valued sequences.The input and output can be multi dimensional, have different dimensions and could even be totally differentCompatibilityThis package is compatible with both Python 2 and 3Example scripts inmain.pyandmain.ipynbare also compatible with bothI have no plan to use tensorflow 2.0, so all the nonsense about future obsolescence is mutedInstallationpipinstallseq2seqUsageFirst create a factory:frommodelsimportNDS2SAEFactoryn_iterations=3000factory=NDS2SAEFactory()factory.set_output('toy.zip')factory.input_dim=2#  Input is 2 dimensiona;factory.output_dim=1#  Output is one dimensionalfactory.layer_sizes=[50,30]# Use exponential decaying learning rate, in here it starts at 0.02 then decreases exponentially to 0.00001factory.lrtype='expdecay'factory.lrargs=dict(start_lr=0.02,finish_lr=0.00001,decay_steps=n_iterations)# Alternatively, set a constant rate is also possible, e.g. 0.001# factory.lrtype = 'constant'# factory.lrargs = dict(lr=0.001)# For the dropout layer. Default is None. If None, the dropout layer is not usedfactory.keep_prob=0.7# The hidden layer will be symmetric (in this case: 50:30:30:50)# otherwise it'll be repeated (50:30:50:30)factory.symmetric=True# Save or load (and resume) from this zip fileencoder=factory.build()Create a training sample generator and a validation sample generator. Both should have the same signature:defgenerate_samples(batch_size):""":return in_seq: a list of input sequences. Each sequence must be a np.ndarrayout_seq: a list of output sequences. Each sequence must be a np.ndarrayThese sequences don't need to be the same length and don't need any paddingThe encoder will take care of thatlast_batch: True if this batch is the last of the iteration.E.g. if you have 70000 samples and the batch size is 20000, you'll have 4 batchesthe last batch contains 10000 samples. You should return False for the first 3batches and True for the last one"""...returnin_seq,out_seq,last_batchTrainencoder.train(train_generator,valid_generator,n_iterations=3000,batch_size=100,display_step=100)Predict# test_seq is a list of np.ndarrayspredicted=encoder.predict(test_seq)# predicted is a list of np.ndarrays. Each sequence will have the same length (due to padding)# Look for the stop token to truncate the padding outEncode# test_seq is a list of np.ndarraysencoded=encoder.encode(test_seq)# encoded is a list of hidden-layer states corresponding to each input sequenceJupyter notebookOpenmain.ipynbto run the exampleLicenceMITPRs are welcome
Package: seq2seq-lstm
The Seq2Seq-LSTM is a sequence-to-sequence classifier with the
sklearn-like interface, and it uses the Keras package for neural
modeling.Developing of this module was inspired by Francois Chollet’s tutorialA ten-minute introduction to sequence-to-sequence learning in KerasThe goal of this project is creating a simple Python package with the
sklearn-like interface for solution of different seq2seq tasks: machine
translation, question answering, decoding phonemes sequence into the
word sequence, etc.Getting StartedInstallingTo install this project on your local machine, you should run the
following commands in Terminal:git clone https://github.com/bond005/seq2seq.git
cd seq2seq
sudo python setup.pyYou can also run the tests:python setup.py testBut I recommend you to use pip and install this package from PyPi:pip install seq2seq-lstmor (usingsudo):sudo pip install seq2seq-lstmUsageAfter installing the Seq2Seq-LSTM can be used as Python package in your
projects. For example:from seq2seq import Seq2SeqLSTM  # import the Seq2Seq-LSTM package
seq2seq = Seq2SeqLSTM()  # create new sequence-to-sequence transformerTo see the work of the Seq2Seq-LSTM on a large dataset, you can run a
demopython demo/seq2seq_lstm_demo.pyor (with saving model after its training):python demo/seq2seq_lstm_demo.py some_file.pklIn this demo, the Seq2Seq-LSTM learns to translate the sentences from
English into Russian. If you specify the neural model file (for example,
aforementionedsome_file.pkl), then the learned neural model will be
saved into this file for its loading instead of re-fitting at the next
running.The Russian-English sentence pairs from the Tatoeba Project have been
used as data for unit tests and demo script (seehttp://www.manythings.org/anki/).
Package: seq2seq-pytorch
seq2seqIndustrial-grade implementation of seq2seq algorithm based on Pytorch, integrated beam search algorithm.seq2seq is based on other excellent open source projects, this project has the following highlights:easy to train, predict and deploy;lightweight implementation;multitasking support (including dialogue generation and machine translation).Model descriptionEncoder: Bidirectional GRUDecoder: GRU with Attention MechanismBahdanau Attention:Neural Machine Translation by Jointly Learning to Align and TranslateLuong Attention:Effective Approaches to Attention-based Neural Machine TranslationDiversity Promoting Beam Search:A Simple, Fast Diverse Decoding Algorithm for Neural GenerationInstallseq2seq is dependent on PyTorch. Two ways to install:Install seq2seq from Pypi:pip install seq2seq-pytorchInstall seq2seq from the Github source:git clone https://github.com/Chiang97912/seq2seq.git
cd seq2seq
python setup.py installUsageTrainfromseq2seq.modelimportSeq2Seqsources=['...']targets=['...']model=Seq2Seq('seq2seq-model',embed_size=256,hidden_size=512,lang4src='en',lang4tgt='en',device='cuda:0')model.fit(sources,targets,epochs=20,batch_size=64)Testfromseq2seq.modelimportSeq2Seqmodel=Seq2Seq('seq2seq-model')outputs=model.generate('...',beam_size=3,method='greedy')print(outputs)Dependenciespythonversion 3.6pyTorchversion 1.9.0torchtextversion 0.3.1numpyversion 1.19.5nltkversion 3.5jiebaversion 0.42.1ReferencesPyTorch Tutorial@spro/practical-pytorch@AuCson/PyTorch-Batch-Attention-Seq2seq@keon/seq2seq
Package: seqal
SeqALSeqAL is a sequence labeling active learning framework based on Flair.InstallationSeqAL is available on PyPI:pip install seqalSeqAL officially supports Python 3.8+.UsageTo understand what SeqAL can do, we first introduce the pool-based active learning cycle.Step 0: Prepare seed data (a small number of labeled data used for training)Step 1: Train the model with seed dataStep 2: Predict unlabeled data with the trained modelStep 3: Query informative samples based on predictionsStep 4: Annotator (Oracle) annotate the selected samplesStep 5: Input the new labeled samples to labeled datasetStep 6: Retrain modelRepeat step2~step6 until the f1 score of the model beyond the threshold or annotation budget is no leftSeqAL can cover all steps except step 0 and step 4. Because there is no 3rd part annotation tool, we can run below script to simulate the active learning cycle.$python examples/run_al_cycle.py --text_column 0  --tag_column 1 --data_folder ./data/sample_bio --train_file train_seed.txt --dev_file dev.txt --test_file test.txt --pool_file labeled_data_pool.txt --tag_type ner --hidden_size 256 --embeddings glove --use_rnn False --max_epochs 1 --mini_batch_size 32 --learning_rate 0.1 --sampler MaxNormLogProbSampler --query_number 2 --token_based False --iterations 5 --research_mode TrueWe setresearch_mode=True. This means that we simulate the active learning cycle. You can also find the script inexamples/run_al_cycle.pyorexamples/active_learning_cycle_research_mode.py. If you want to connect SeqAL with an annotation tool, you can see the script inexamples/active_learning_cycle_annotation_mode.py.You can find more explanations about the parameters in the following tutorials.TutorialsWe provide a set of quick tutorials to get you started with the library.Tutorials on Github PageTutorials on MarkownTutorial 1: IntroductionTutorial 2: Prepare CorpusTutorial 3: Active Learner SetupTutorial 4: Prepare Data PoolTutorial 5: Research and Annotation ModeTutorial 6: Query SetupTutorial 7: Annotated DataTutorial 8: StopperTutorial 9: Output Labeled DataTutorial 10: Performance RecorderTutorial 11: Multiple Language SupportPerformanceActive learning algorithms achieve 97% performance of the best deep model trained on full data using only 30% of the training data on the CoNLL 2003 English dataset. The CPU model can decrease the time cost greatly only sacrificing a little performance.Seeperformancefor more detail about performance and time cost.ContributingIf you have suggestions for how SeqAL could be improved, or want to report a bug, open an issue! We'd love all and any contributions.For more, check out theContributing Guide.CreditsCookiecutterbrowniebroke/cookiecutter-pypackageflairNLP/flairmodal