Saravanan666/pypi-org huggingface dataset:
Package: es-template-manager
es-template-managerA utility for managing Elasticsearch index templates in source controlTemplate DirectoryThe utility expects to be passed the path to a directory containing
Elasticsearch index template files. The utility will iterate over each
file, using the name of the file as the name of the index template. The
contents of each index template file should be a JSON payload defining the
index template.Usagepythones-template-manager.py$ARGUMENTSRunpython es-template-manager.py -hto see a full list of arguments.Required Arguments--es-hostname: The Elasticsearch hostname. Do not prefix with http/https--es-port: The Elasticsearch port number--template-directory: The path to the directory containing the index
template filesOptional Arguments--debug: Enable debug logging--es-use-ssl: Use SSL/TLS when connecting to Elasticsearch--es-cacert: The path to a CA certificate file for verifying the
Elasticsearch server certificate's authenticity--es-cert: The path to a certificate file for authencating against the
Elasticsearch server--es-key: The path to a key file for authenticating against the
Elasticsearch server--overwrite-templates: Whether to overwrite any templates that already
exist--pushgateway_enpoint: If you want to push the final status of the job
to a pushgateway endpoint
Package: estest
<br /><div align=”center”><img width=”25%” src=”../../icon_testing.svg”></img><h3 align=”center”>ESTEST</h3><p align=”center”>Quantumatic’s testing framework</p></div><!– TABLE OF CONTENTS –><details><summary>Table of Contents</summary><ol><li><a href=”#about-the-project”>About The Project</a></li><li><a href=”#license”>License</a></li><li><a href=”#contact”>Contact</a></li></ol></details><!– ABOUT THE PROJECT –>## About The ProjectESTEST - is a simple testing framework made inPython. It allows you to test things, you want without thinking of testing implementation. Feel free to contribute to this project 😁 (but only at github.com/hikelang/chike official repo).> Note: this repo was made by taking some old Quantumatic’s February C++ and Python codebase 🙂.<p align=”right”><a href=”#top”>back to top 👆🏻</a></p><!– LICENSE –>## LicenseDistributed under the GPL 3.0 License. SeeLICENSEfor more information.<p align=”right”><a href=”#top”>back to top 👆🏻</a></p><!– CONTACT –>## ContactVK: [👐🏾](https://vk.com/ne_pinguy_pzh)Discord:SnakeEatingItSelf#2004<p align=”right”><a href=”#top”>back to top 👆🏻</a></p>Keywords: python,testing,c,c++
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
Package: estimagic
estimagicIntroductionestimagic is a Python package for nonlinear optimization with or without constraints. It
is particularly suited to solve difficult nonlinear estimation problems. On top, it
provides functionality to perform statistical inference on estimated parameters.Optimizationestimagic wraps algorithms fromscipy.optimize,nlopt,pygmoand more.estimagic implements constraints efficiently via reparametrization, so you can solve
constrained problems with any optimzer that supports bounds.The parameters of an optimization problem can be arbitrary pytreesThe complete history of parameters and function evaluations can be saved in a database
for maximum reproducibility.Painless and efficient multistart optimization.The progress of the optimization is displayed in real time via an interactive
dashboard.Estimation and InferenceYou can estimate a model using method of simulated moments (MSM), calculate standard
errors and do sensitivity analysis with just one function call.Asymptotic standard errors for maximum likelihood estimation.estimagic also provides bootstrap confidence intervals and standard errors. Of course
the bootstrap procedures are parallelized.Numerical differentiationestimagic can calculate precise numerical derivatives usingRichardson extrapolations.Function evaluations needed for numerical derivatives can be done in parallel with
pre-implemented or user provided batch evaluators.InstallationThe package can be installed via conda. To do so, type the following commands in a
terminal:$condaconfig--addchannelsconda-forge
$condainstallestimagicThe first line adds conda-forge to your conda channels. This is necessary for conda to
find all dependencies of estimagic. The second line installs estimagic and its
dependencies.Installing optional dependenciesOnlyscipyis a mandatory dependency of estimagic. Other algorithms become available
if you install more packages. We make this optional because most of the time you will
use at least one additional package, but only very rarely will you need all of them.For an overview of all optimizers and the packages you need to install to enable them
see {ref}list_of_algorithms.To enable all algorithms at once, do the following:conda install nloptpip install Py-BOBYQApip install DFO-LSconda install petsc4py(Not available on Windows)conda install cyipoptconda install pygmopip install fides>=0.7.4 (Make sure you have at least 0.7.1)DocumentationThe documentation is hosted (on rtd)CitationIf you use Estimagic for your research, please do not forget to cite it.@Unpublished{Gabler2022,
  Title  = {A Python Tool for the Estimation of large scale scientific models.},
  Author = {Janos Gabler},
  Year   = {2022},
  Url    = {https://github.com/OpenSourceEconomics/estimagic}
}
Package: estimatecharm
# AboutEstimateCharm is a tool for estimating per-line charm in Python programs. Not tobe confused with [pycharm](https://www.jetbrains.com/pycharm/).# LicensingAssume that EstimateCharm is licensed under the [AGPL3+](LICENSE) unless otherwisespecified.&copy; 2012-2014 Joshua Charles Campbell, Abram Hindle, Alex WilsonUnnaturalCode is free software: you can redistribute it and/or modify it underthe terms of the GNU Affero General Public License as published by the FreeSoftware Foundation, either version 3 of the License, or (at your option) anylater version.UnnaturalCode is distributed in the hope that it will be useful, but WITHOUTANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESSFOR A PARTICULAR PURPOSE.  See the GNU Affero General Public License for moredetails.You should have received a copy of the GNU Affero General Public License alongwith UnnaturalCode.  If not, see <http://www.gnu.org/licenses/>.# Academic UseNow we aren't going to impose any extra requirements, but we ask kindly if youare using this academically that you should consider citing our relevant work:@article{campbell2015charming,title={The charming code that error messages are talking about},author={Campbell, Joshua C and Hindle, Abram},journal={PeerJ PrePrints},volume={3},pages={e1388},year={2015},publisher={PeerJ Inc. San Francisco, USA}}
Package: estimated-taxes
estimated-taxesEstimated taxes calculator.This is meant to help estimate additional tax payments throughout the year, i.e.
forIRS form 1040-ESandCA form
540-ES.Please review the logic yourself if you intend to use this. It intentionally
does not attempt to handle all forms and all scenarios; you will likely need to
augment it for your particular needs.See alsonkouevda/capital-gains.Installationpip install estimated-taxesOr:brew install nkouevda/nkouevda/estimated-taxesUsageusage: estimated-taxes [<options>] [--] <input file>

Estimated taxes calculator

optional arguments:
  -h, --help     show this help message and exit
  -v, --version  show program's version number and exitInput FormatSeeinput/example.yaml.The input file must start with!InputData, and must specifyyear,filing_status,fed_allowances, andca_allowances. All other values are
optional.Each value must be a number, or a list of numbers, or a map where the values are
numbers. Lists and maps are allowed for convenience, to split up categories into
multiple entries. For example, the following are all equivalent:supplemental_wages: 62500

supplemental_wages:
  - 12500
  - 50000

supplemental_wages:
  bonus: 12500
  rsu: 50000Examplesestimated-taxes input/example.yaml > output/example.txtTODOItemized deductions other than SALTAMTLicenseMIT License
Package: estinnnn
vidstreamUnder construction! Not ready for use yet! Currently experimenting and planning!Developed by Florian Dedov from NeuralNine (c) 2020Examples of How To Use (Buggy Alpha Version)Creating A ServerfromvidstreamimportStreamingServerserver=StreamingServer('127.0.0.1',9999)server.start_server()# Other Code# When You Are Doneserver.stop_server()Creating A ClientfromvidstreamimportCameraClientfromvidstreamimportVideoClientfromvidstreamimportScreenShareClient# Choose Oneclient1=CameraClient('127.0.0.1',9999)client2=VideoClient('127.0.0.1',9999,'video.mp4')client3=ScreenShareClient('127.0.0.1',9999)client1.start_stream()client2.start_stream()client3.start_stream()Check out:https://www.youtube.com/c/NeuralNine
Package: estipy
estipyEstimate time of availability of long running for loops.UsageimporttimefromestipyimportETA# Some data to be processeddata=list(range(42))fornum,_inETA(data):# Do something useful here that takes a little timetime.sleep(0.1)# Example output:# 13/42 = 31.0%, ETA Delta 0:00:02.685864, ETA 2021-07-04 14:50:12.486002Try it onlineOr disable auto print and access all eta datafornum,etainETA(data,auto_print=False):# Do something useful here that takes a little timetime.sleep(0.1)print(eta.json(indent='  '))# Example output:# {#   "total": {#     "time": "0:00:03.762402",#     "absolute": 42,#     "percentage": 100#   },#   "remaining": {#     "time": "0:00:02.956173",#     "absolute": 33,#     "percentage": 78.57142857142857#   },#   "done": {#     "time": "0:00:00.806229",#     "absolute": 9,#     "percentage": 21.428571428571427#   },#   "eta": "2021-07-04 18:05:35.103308"# }
Package: estival
estivalCalibration and optimization tools for summer2https://github.com/monash-emu/summer2Estival provides a simple API for using summer2 CompartmentalModels with a variety of optimization frameworks, includingpymcnevergradCHANGELOG0.2.2Add logprior/logposterior to BayesianCompartmentalModel0.2.3Include tensorflow-probability(jax) for more (and better tested) stats modules0.2.4
Bugfix (vector priors were not exported to pymc correctly)
Add Epoch support to allow DatetimeIndex targets0.2.5
Bugfix for BinomialTarget (wasn't indexing modelled data)0.2.6
Bugfix (reference index for models without date returned incorrect type)0.3.0
Note - breaking changes!
Remove old AuTuMN MCMC implementation
Move nevergrad/pymc -> wrappers
Expand likelihood output tools
Include parallelism framework0.3.1
Bugfix (submodules not properly exported)0.3.2
SampleIterator tools (better support for shaped priors)
Attempted map_parallel bugfix0.3.3
Requirements fix (update summerepi2)0.3.4
Swap modelled/target data in Normal and TruncatedNormal targets (incorrect results previously)0.3.5
Add sampling utils
Add gamma prior
Support multiple targets for each derived output0.3.6
Minor bugfix to 0.3.50.3.7
Reimplement BetaPrior, and get_series and finite_bounds for priors0.3.8
Fix BetaPrior.to_pymc, add testing0.3.9
Bugfix (BetaPrior .from_ method injected arrays into params)0.4.0
Improved sampling tools0.4.1
Extend sampling tools, more options for map_parallel0.4.2b
Experimental release using expanded transform for uniform priors0.4.3
Better exec_mode defaults for parallel helper functions0.4.4
Fix issues with xarray converting array parameters0.4.5
Fix nevergrad wrapper issue with infinite support priors0.4.8
Improved ergonomics and sample type support0.4.9
Add BetaTarget0.5.0
Correct loc and iloc methods for SampleIterator0.5.1
Make wrapper libraries (pymc/nevergrad) optional extras
Package: estndv
Learned NDV estimatorLearned model to estimate number of distinct values (NDV) of a population using a small sample. The model approximates the maximum likelihood estimation of NDV, which is difficult to obtain analytically.
See our VLDB 2022 paperLearning to be a Statistician: Learned Estimator for Number of Distinct Valuesfor more details.How to useInstall the packagepip install estndvImport and create an instancefromestndvimportndvEstimatorestimator=ndvEstimator()Assume your sample is S=[1,1,1,3,5,5,12] and the population size is N=100000. You can estimate population ndv by:ndv = estimator.sample_predict(S=[1,1,1,3,5,5,12], N=100000)If you have the sample profile e.g. f=[2,1,1], you can estimate population NDV by:ndv = estimator.profile_predict(f=[2,1,1], N=100000)If you have multiple samples/profiles from multiple populations, you can estimate population NDV for all of them in a batch by methodestimator.sample_predict_batch()orestimator.profile_predict_batch().How to train the ndv estimatorYou can directly use our packaged library for your datasets, as the pre-trained model in the packed library is agnostic to any workloads. However, if you want to train the model from scratch anyway, do the following:Go to the model_training foldercd model_trainingInstall requirementspip install requirements.txtGenerate training data. (This uses a lot of memory.)python training_data_generation.pyTrain modelpython model_training.pySave trained pytorch model parameters to numpy, this generates a file model_paras.npypython torch2npy.pyTest with your model parameters by specifying a path to your model_paras.npyestimator = ndvEstimator(para_path=your path to model_paras.npy)
Package: estnin
Python library for working with Estonian national identity numbers.InstallUsepipto install:pip install estninDocumentationFull documentation is athttps://estnin.readthedocs.io/Runnig testspython setup.py testUsageCreate a newestnininstance:>>> from estnin import estnin
>>> person = estnin(37001011233)
>>> print(person.sequence)
>>> 123
>>> print(estnin(estnin.MIN))
>>> 10001010002
>>> person = estnin.create(estnin.MALE, date(1970, 1, 1), 1)
>>> print(person)
>>> 37001010018str()>>> assert estnin(37001010018) == "37001010018"int()>>> assert estnin(37001010018) == 37001010018<, ==, >>>> assert estnin(37001011233) < estnin(estnin.MAX)
>>> assert estnin(37001011233) > estnin(estnin.MIN)
>>> assert estnin(37001011233) == estnin(37001011233)negationNegation is defined as changing the sex from male to female or vice verca.>>> assert -estnin(37001011233) == estnin(47001011234)addition and substractionAdding an integer increments the sequence value by given amount. If the sum of the sequence and the number given is greater than 999, then the day is incremented and the remainder is set as the new sequence value. The day, month and year values are incremented in chronological order until the defined maximum value is reached. If the year crosses the century boundary, then the century digit is also properly set.>>> assert estnin(37001011244) == estnin(37001011233)+1
>>> assert estnin(37001011244) == estnin(37001011255)-1
>>> # Create new person
>>> person = estnin.create(estnin.MALE, date(1999, 12, 31), 999)
>>> print(person)
>>> 39912319997
>>> assert estnin(50001010006) == person+1iterationIterating over a givenestnininstance creates new objects.>>> people = [p for _, p in zip(range(3), estnin(37001011233))]
>>> print(' '.join(map(str, people)))
>>> 37001011233 37001011244 37001011255Iterating in a reverse order can be done by using the methodreversed():>>> people = [p for _, p in zip(range(3), reversed(estnin(37001011233)))]
>>> print(' '.join(map(str, people)))
>>> 37001011233 37001011222 37001011211properties>>> person = estnin.create(estnin.MALE, date(1970, 1, 2), 3)
>>> person
37001020036
>>> person.century
3
>>> person.year
1970
>>> person.month
1
>>> person.day
2
>>> person.sequence
3
>>> person.checksum
6
>>> person.date
datetime.date(1970, 1, 2)
Package: estnltk-core
EstNLTK-core -- core components of the EstNLTK libraryThis package contains core components of the EstNLTK library:data structures:BaseText,BaseLayer,Layer,RelationLayer,Span,EnvelopingSpan,SpanList,Annotation;tagger component interfaces:Tagger,Retagger,RelationTagger,MultiLayerTagger;basic layer operations: flatten, merge, rebase, split layers etc.basic visualization and Jupyter Notebook support (HTML representations);functions for converting between EstNLTK's data structures and JSON / dict representations;skeleton for NLP pipeline (components for resolving layer dependencies and tagging layers sequentially);Note: this package does not include linguistic analysis tools / Estonian NLP pipeline. Please use theestnltkpackage for the pipeline.The EstNLTK project is funded by EKT (Eesti Keeletehnoloogia Riiklik Programm).InstallationEstNLTK-core is available as a PyPI wheel:pip install estnltk_core==1.7.2And as an Anaconda package:conda install -c estnltk -c conda-forge estnltk_core=1.7.2Supported Python versions: 3.8+DocumentationInformation about EstNLTK-core's API is provided in docstrings of classes and methods. Browse the source for details.SourceThe source of the package can be found atmain branch.License: GNU General Public License v2.0(C) University of Tartu
Package: estnltk-light
This package contains core components of the EstNLTK v1.6 library:data structures: Text, Layer, Span, EnvelopingSpan, SpanList, Annotation;tagger component interfaces: Tagger, Retagger;basic layer operations: flatten, merge, rebase, split layers etc.functions for converting between EstNLTK’s data structures and JSON/dict representations;The package is based on the EstNLTK v1.6 commit 1a2754611f (https://github.com/estnltk/estnltk/tree/1a2754611feec84cdef597a7f74a2cc806964d60), which has been heavily stripped down. Removed components:NLP tools and pipelines;all the visualization components and Jupyter Notebook support (HTML representations);storage components;tests;The source code of the light package:https://github.com/estnltk/estnltk/tree/version_1.6_lightThe full package:https://github.com/estnltk/estnltk
Package: estnltk-neural
EstNLTK neural -- EstNLTK's linguistic analysis based on neural modelsThis package contains EstNLTK's linguistic analysis tools that use neural models:neural morphological tagger ( disambiguator );bert embeddings tagger;bert NER tagger;stanza syntax tagger and stanza ensemble syntax tagger;pronominal coreference tagger v1 (relies on stanza for input preprocessing);Note: these tools require installation of deep learning frameworks (tensorflow,pytorch), and are demanding for computational resources; they also rely on large models which need to be downloaded separately.The EstNLTK project is funded by EKT (Eesti Keeletehnoloogia Riiklik Programm).InstallationEstNLTK-neural is available as a PyPI wheel:pip install estnltk_neuralAnd as an Anaconda package:conda install -c estnltk -c conda-forge estnltk_neuralSupported Python versions: 3.8+Neural modelsModels required by neural tools are large, and therefore cannot be distributed with this package.
However, our tagger classes are implemented in a way that once you create an instance of a neural tagger, you'll be asked  for a permission to download missing models, and if you give the permission, the model will be downloaded (and installed in a proper location) automatically.
If needed, you can also change the default location where downloaded models will be placed, seethis tutorialfor details.DocumentationEstNLTK'sNLP component tutorialsalso cover information about neural taggers.SourceThe source of the last release is available at themain branch.License: GNU General Public License v2.0(C) University of Tartu
Package: estonian-e-invoice
Estonian E-InvoiceEstonian e-invoice generatorFree software: MIT licenseDocumentation:https://estonian-e-invoice.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History1.0.1 (2020-04-29)Project meta data changes.1.0.0 (2020-04-29)First release on PyPI.
Package: estools
A set of commandline utilities for interacting with instances of theelasticsearchsearch engine.Installationestoolsis available onPyPIand can be installed usingpip:$ pip install estoolsUsageCurrently available tools withinestoolsCommandDescriptiones-rotateRotate a set of daily indices (creates “current”
aliases and deletes old indices)es-riverGet, create, delete and compare riverses-templateGet, create, delete and compare index templatesLicenseestoolsis released under the MIT license, a copy of which can be found inLICENSE.
Package: es-tori-sep
This is a tori name entity recognizer using spacy splitting names into FORENAMES and SURNAMESFeature | Description |— | — |Name|es_tori_sep|Version|0.0.1|spaCy|>=3.4.3,<3.5.0|Default Pipeline|tok2vec,ner|Components|tok2vec,ner|Vectors| 0 keys, 0 unique vectors (0 dimensions) |Sources| n/a |License|MIT|Author| [emptor]() |### Label Scheme<details><summary>View label scheme (2 labels for 1 components)</summary>Component | Labels |— | — |`ner`|FORENAMES,SURNAMES|</details>### AccuracyType | Score |— | — |ENTS_F| 98.88 |ENTS_P| 98.88 |ENTS_R| 98.89 |TOK2VEC_LOSS| 768.33 |NER_LOSS| 2213.93 |
Package: estory
estory — read from and write to event stores$estoryinit<IDENTIFIER>$estoryread<IDENTIFIER>$estorywrite<IDENTIFIER>$estoryguess<IDENTIFIER>IDENTIFIERis either:a proper DSN (driver://user:password@host:port/database) ;a file that will be interpreted as a DSN (sqlite:///<FILE>) ;a name matching an environment variable (ESTORY_DSN_<NAME>).To initialise an event store:$exportESTORY_DSN_TMP="sqlite:////tmp/event_store.sqlite"$estoryinitTMPTo write an event to a store:$jostream="uuid"version=1name="EventName"data=$(joid=1)unixtime=$(date+%s)|estorywriteTMPTo read events from a store:$estoryreadTMP{"stream": "uuid", "name": "EventName", "data": {"id": 1}, "unixtime": 1647876362, "who": "", "id": 1}To copy events from one store to another:$exportESTORY_DSN_TMP2="sqlite:////tmp/another_event_store.sqlite"$estoryinitTMP2$estoryreadTMP|estorywriteTMP2To copy only some events from one store to another:$estoryreadTMP|jq'.|select(.id==2)'|estorywriteTMP2To modify events while copying them from one store to another:$estoryreadTMP|jq'.|.name="EventNewName"'|estorywriteTMP2
Package: estoult
EstoultEstoult is a Python toolkit for data mapping with an integrated query builder for SQL databases. It currently supports MySQL, PostgreSQL, and SQLite.Features:Not an ORM. Estoult doesn't attempt to apply relational algebra to objects.Concise and composable (sub)queries.Easy debugging by displaying any generated SQL in a readable format.Fast. Estoult isNOTan ORM.Estoult only works with Python 3.6+ and is primarily tested on Python 3.9+.InstallationInstall Estoult through pip:pip install estoultDocumentationCheck thedocumentationfor help andgetting started.ContributingIf you have found a bug or would like to see a feature added to Estoult, please submit an issue or a pull request! Likewise if you found something in the documentation unclear or imprecise.TestsTests are run withpytest. Install it using pip:pip install pytestRun tests from the shell:pytestLicenseEstoult is licensed under the MIT license (seeLICENSE file).