Saravanan666/pypi-org huggingface dataset:
Package: ecc-messaging-scheme-package
ECC-Messaging-Scheme-PackageA system that encrypts and decrypts data packets with the elliptic curve cryptography (ECC) for others
to utilize and download.Where to download:typescript/javascripthttps://www.npmjs.com/package/ecc-messaging-scheme-packagePythonhttps://pypi.org/project/ecc-messaging-scheme-package/ClassesWe have one main class, ECCM class, which combines our ECC class and our Encrypt/Decrypt, and
curve initialization(initializePublicEnv) functions.The ECC Class:Responsible for generating/loading private keyGet current users public key from cookies or create a new one and saveGets shared key using diffie hellman when given another users public keyClear cookies containing private key for current instance.Encrypt/Decrypt:Given a key and text it encrypts/decrypts text using AESinitializePublicEnv function:Inizalites a point and an ecc curve: secp256k1Examples:#Key exchange
C = initialize_public_env()
a_ecc_instance = ECC_Instance(C,'a')
a_ecc_instance.get_public_key()

b_ecc_instance = ECC_Instance(C,'b')
b_ecc_instance.get_public_key()

a_ecc_instance.generate_shared_key(b_ecc_instance.get_public_key())
b_ecc_instance.generate_shared_key(a_ecc_instance.get_public_key())

assert(a_ecc_instance.get_shared_key() == b_ecc_instance.get_shared_key())
#Share messages
key_b = a_ecc_instance.get_shared_key().x.to_bytes(24, byteorder='big')
plaintext = "test 12312 "
ciphertext,nonce = encrypt(key_b,plaintext)

plaintext_out = decrypt(key_b,ciphertext,nonce)

assert(plaintext==plaintext_out)DisclaimerUse at Your Own Risk.
Package: ecco
Ecco is a python library for explaining Natural Language Processing models using interactive visualizations.It provides multiple interfaces to aid the explanation and intuition ofTransformer-based language models. Read:Interfaces for Explaining Transformer Language Models.Ecco runs inside Jupyter notebooks. It is built on top ofpytorchandtransformers.The library is currently an alpha release of a research project. Not production ready. You’re welcome to contribute to make it better!Installation# Assuming you had PyTorch previously installedpipinstalleccoDocumentationTo use the project:importecco# Load pre-trained language model. Setting 'activations' to True tells Ecco to capture neuron activations.lm=ecco.from_pretrained('distilgpt2',activations=True)# Input texttext="The countries of the European Union are:\n1. Austria\n2. Belgium\n3. Bulgaria\n4."# Generate 20 tokens to complete the input text.output=lm.generate(text,generate=20,do_sample=True)# Ecco will output each token as it is generated.# 'output' now contains the data captured from this run, including the input and output tokens# as well as neuron activations and input saliency values.# To view the input saliencyoutput.saliency()This does the following:It loads a pretrained Huggingface DistilGPT2 model. It wraps it an eccoLMobject that does useful things (e.g. it calculates input saliency, can collect neuron activations).We tell the model to generate 20 tokens.The model returns an eccoOutputSeqobject. This object holds the output sequence, but also a lot of data generated by the generation run, including the input sequence and input saliency values. If we setactivations=Trueinfrom_pretrained(), then this would also contain neuron activation values.outputcan now produce various interactive explorables. Examples include:output.saliency()to generate input saliency explorable [Input Saliency Colab Notebook]output.run_nmf()to to explore non-negative matrix factorization of neuron activations  [Neuron Activation Colab Notebook]# To view the input saliency explorableoutput.saliency()# to view input saliency with more details (a bar and % value for each token)output.saliency(style="detailed")# output.activations contains the neuron activation values. it has the shape: (layer, neuron, token position)# We can run non-negative matrix factorization using run_nmf. We pass the number of factors/components to break down intonmf_1=output.run_nmf(n_components=10)# nmf_1 now contains the necessary data to create the interactive nmf explorable:nmf_1.explore()Changelog0.0.8 (2020-11-20)Allowing the project some fresh air.
Package: eccodes-python
Please note:This package has been deprecated in favour of the packageeccodes. Please update your scripts to usepip install eccodesto benefit from future developments. (August 2020)LicenseCopyright 2017- ECMWF.This software is licensed under the terms of the Apache Licence Version 2.0
which can be obtained athttp://www.apache.org/licenses/LICENSE-2.0.In applying this licence, ECMWF does not waive the privileges and immunities
granted to it by virtue of its status as an intergovernmental organisation nor
does it submit to any jurisdiction.Changelog for eccodes-python0.9.9 (2020-08-04)Support for ecmwflibs. An additional way to find ECMWF libraries (if available)ECC-1140: Segfault from invalid pointer reference in grib_set_double_array()0.9.8 (2020-06-26)ECC-1110: Removed obsolete function codes_close_file()Provide missing argument to exceptionsFix codes_set_definitions_path() typoFix grib_get_double_element(). Missing last argumentAdd more tests to increase coverageAdd .__next__() method to eccodes.CodesFile class (pull request #15)ECC-1113: Python3 bindings under Windows: codes_get_long_array returns incorrect valuesECC-1108: Python3 bindings under Windows: use of handle causes crashECC-1121: Segfault when closing GribFile if messages are closed manually0.9.6 (2020-03-10)Update Copyright noticesFunction-argument type checks: Improve error messageFix C function calls for codes_gribex_mode_on/codes_gribex_mode_off0.9.5 (2020-01-15)ECC-1029: Function-argument type-checking should be disabled by default.To enable these checks, export ECCODES_PYTHON_ENABLE_TYPE_CHECKS=1ECC-1032: Added codes_samples_path() and codes_definition_path()ECC-1042: Python3 interface writes integer arrays incorrectlyECC-794: Python3 interface: Expose the grib_get_data function0.9.4 (2019-11-27)Added new function: codes_get_version_infoECC-753: Expose the codes_grib_nearest_find_multiple function in PythonECC-1007: Python3 interface for eccodes cannot write large arrays0.9.3 (2019-10-04)New exception added: FunctionalityNotEnabledErrorBUFR decoding: support for multi-element constant arrays (ECC-428)0.9.2 (2019-07-09)All ecCodes tests now passSimplify the xx_new_from_file callsFix for grib_set_string_arrayUse ECCODES_DIR to locate the libraryRemove the new-style high-level interface. It is still available incfgrib.0.9.1 (2019-06-06)codes_get_long_arrayandcodes_get_double_arraynow return anp.ndarray.
See:#3.0.9.0 (2019-05-07)Declare the project asBeta.0.8.0 (2019-04-08)First public release.
Package: ecco-v4-py
Synopsisecco_v4_py is a Python package that includes tools for loading and manipulating the ECCO v4 ocean and sea-ice state estimate (http://ecco-group.org)Extensive documentation is provided on our readthedocs page:http://ecco-v4-python-tutorial.readthedocs.io/index.html#InstallationInstallation instructions can be found here!https://ecco-v4-python-tutorial.readthedocs.io/Installing_Python_and_Python_Packages.htmlContributorsIf you would like to contribute, consider forking this repository and making pull requests via git!Supportcontactecco-support@mit.eduor Ian.Fenty at jpl.nasa.govLicenseMIT LicenseNote on version numbersecco_v4_py uses the 'semantic versioning' scheme described here:https://packaging.python.org/guides/distributing-packages-using-setuptools/#semantic-versioning-preferredThe essence of semantic versioning is a 3-part MAJOR.MINOR.MAINTENANCE numbering scheme:MAJOR version when they make incompatible API changes,MINOR version when they add functionality in a backwards-compatible manner, andMAINTENANCE version when they make backwards-compatible bug fixes.Note on testing withpytest(credit to Tim Smith)You can run the tests locally with the pytest package, which is available through conda-forge. With that installed, you can navigate to ECCOv4-py/ecco_v4_py/test and either:Run all the tests exactly as they are on travis (this takes a while, like 12 minutes!):py.test . -v --cov=ecco_v4_py --cov-config .coveragerc --ignore=ecco_v4_py/test/test_generate_ecco_netcdf_product.pyOr you can run any individual module e.g. to run the few tests in ecco_utils:py.test test_ecco_utils.py(and you can add any of the -v or whatever flags you want).
Package: ecdf-estimator
Welcome to ecdf_estimatorIt contains a Python based framework for parameter estimation, which is currently under
construction.It may be installed using$ pip install git+https://github.com/AndreasRupp/ecdf_estimator.gitfor the latest version, which is located in the GitHub repository. Alternatively, you can use$ python3 -m pip install ecdf_estimatorto obtain the latest stable version fromPyPI.Copyright, License, and Contribution PolicyThis directory contains the ecdf_estimator library.The ecdf_estimator library is copyrighted by the authors ofecdf_estimator. This term currently
refers to Andreas Rupp.This library is free software; you can use it, redistribute it, and/or modify it under the terms
of theGNU Lesser General Public Licenseas published by the Free Software Foundation; eitherversion 2.1of the License, or (at your option) any later version. The full text of the GNU
Lesser General Public version 2.1 is quoted inLicense.txt.ContributionsAs a contributor to this project, you agree that all of your contributions be governed by theDeveloper Certificate of Origin version 1.1. This project does not require copyright
assignments for contributions. This means that the copyright for code contributions in this project
is held by its respective contributors who have each agreed to release their contributed code under
a compatible open source license (LGPL v2.1 for library code). The full text of the Developer
Certificate of Origin version 1.1 is quoted inDeveloperCertificateOfOrigin.txt.Referencing the libraryIn addition to the terms imposed by the LGPL v2.1 or later, we ask for the following courtesy:Every publication presenting numerical results obtained with the help ofecdf_estimatorshould
state the name of the library and cite one or more of the following referencesA. Kazarnikov, N. Ray, H. Haario, J. Lappalainen, and A. RuppParameter estimation for cellular automataarXiv preprint, doi:10.48550/arXiv.2301.13320This is the usual, fair way of giving credit to contributors to a scientific result. In addition, it
helps us justify our effort in developingecdf_estimatoras an academic undertaking.ContactFor further questions regarding licensing and commercial use please contact Andreas Rupp directly
usingEmail.LinksThe license can be found inLicense.txt. It contains theGNU Lesser
General Public License version 2.1.The developer certificate of origin can be found inDeveloperCertificateOfOrigin.txt. It
contains theDeveloper Certificate of Origin version 1.1.
Package: ecdh
ecdh - ElectroChemical Data Handlingedch provides easy solutions for simple handling of electrochemical data. Most emphasis is so far put on how to display the data using matplotlib.Installationecdh is hosted on pypi, and can be installed via pip by:pip install ecdhUsage:runecdh.Command line arguments:initinitializes a toml file with the nameecdh.toml. Any supported datafiles in the local directory will be added to it.run config.tomlruns with the configuration set in config.tomlExample use:ecdh
ecdh init
ecdh run config.tomlFeatures:Galvanostatic CyclingPlot Charge/Discharge curves over cycle lifeRange, specific or all cycles can be plotted in addition to trimming of the firstxcycles.Plot Capacity retention over cycle lifePlot dQ/dVPlot data from any number of input filesCyclic VoltammetrySupported Input filetypes:Biologic (.mpt)BatSmall (.txt)Coming:- Neware backup files (.xlsx)
- Neware export as general report (.csv)DependenciesStandard Packages:ossysdatetimemathinspectshutilCustom Packages:MatplotlibNumpyScipyPandastomlExamplesI have three datafiles from different equipment, but since they are all in the same folder, I can initiate a config file with theinitcommand, and run it afterwords withrun ecdh.toml.The terminal looks like:$ ecdh init
[INFO] Wrote example configuration to 'ecdh.toml' with 3
files found
$ ecdh run ecdh.toml 
[INFO] Reading file: 'Neware_commasep.csv'
[INFO] Reading file: 'Biologic-text.mpt'
[INFO] Active mass found in file to be: 1e-06g
[INFO] Datafile ended with a Discharge
[INFO] Reading file: 'batsmall_data.dat'Which yields the following output:If I now enter theecdh.tomlfile and setdqdvplot = true,qcplot = falseand remove the entry of the ugly files, The output is:TODO listEnable specific cycles on a per-file basisAdd Withaker-despiker smoothing algorithm to dQ/dV dataMake dQ/dV and V/Q plot in the same figure if both are to be plottedFix Neware general report .csv readerProgram structureFlowchartSubModulesreadersFunctions:Read(): Takes a filepath and returns a dataframe with eventual metadataDataframe headers: ['mode', 'time/s', 'Ewe/V', '<I>/mA', 'cycle number']Internal functions:BioLogic: read_MPT()Neware: read_xlsx(), read_csv()Batsmall: read_txt()cellContains the Cell class with functions:get_data():   Runs readers.read(filename)edit_data(): Runs any of the internal functions according to the cfg.toml and what experiment type the file is.Internal functions:_edit_cyclelife(): Edits data for easy cyclelife plotting_edit_CV(): Edits data for easy cyclic voltammetry plotting_edit_GC(): Edits data for easy galvanostatic cycling plotting_edit_dQdV(): Edits data for easy dQdV plottingplotContains the Plot class with functions:plot(): Takes list of cell objects as input parameter, reads the cfg.toml and plots it all according to that.
Package: ecdtools
AboutElectronic circuit design in Python 3.IBISparsing. Currently only a subset of theIBIS 6.1specification is supported.Project homepage:https://github.com/eerimoq/ecdtoolsDocumentation:http://ecdtools.readthedocs.org/en/latestInstallationpipinstallecdtoolsExample usageIn this example we load an IBS-file and access some of its content.>>>frompprintimportpprint>>>importecdtools>>>ibs_file=ecdtools.ibis.load_file('tests/files/ibis/pybis/bushold.ibs')>>>model=ibs_file.get_model_by_name('TOP_MODEL_BUS_HOLD')>>>model.model_type'Input'>>>pprint(model.gnd_clamp)[('-2.0000e+00','-6.158e+17','NA','NA'),('-1.9000e+00','-1.697e+16','NA','NA'),('-1.8000e+00','-4.679e+14','NA','NA'),('-1.7000e+00','-1.290e+13','NA','NA'),('-1.6000e+00','-3.556e+11','NA','NA'),('-1.5000e+00','-9.802e+09','NA','NA'),('-1.4000e+00','-2.702e+08','NA','NA'),('-1.3000e+00','-7.449e+06','NA','NA'),('-1.2000e+00','-2.053e+05','NA','NA'),('-1.1000e+00','-5.660e+03','NA','NA'),('-1.0000e+00','-1.560e+02','NA','NA'),('-9.0000e-01','-4.308e+00','NA','NA'),('-8.0000e-01','-1.221e-01','NA','NA'),('-7.0000e-01','-4.315e-03','NA','NA'),('-6.0000e-01','-1.715e-04','NA','NA'),('-5.0000e-01','-4.959e-06','NA','NA'),('-4.0000e-01','-1.373e-07','NA','NA'),('-3.0000e-01','-4.075e-09','NA','NA'),('-2.0000e-01','-3.044e-10','NA','NA'),('-1.0000e-01','-1.030e-10','NA','NA'),('0.','0','NA','NA'),('5','0','NA','NA')]Alternatively, withtransform=True, the same input as above gives:>>>frompprintimportpprint>>>importecdtools>>>ibs_file=ecdtools.ibis.load_file('tests/files/ibis/pybis/bushold.ibs',transform=True)>>>model=ibs_file.get_model_by_name('TOP_MODEL_BUS_HOLD')>>>model.model_type'Input'>>>pprint(model.gnd_clamp)[(Decimal('-2.0000'),Decimal('-6.158E+17'),None,None),(Decimal('-1.9000'),Decimal('-1.697E+16'),None,None),(Decimal('-1.8000'),Decimal('-4.679E+14'),None,None),(Decimal('-1.7000'),Decimal('-1.290E+13'),None,None),(Decimal('-1.6000'),Decimal('-3.556E+11'),None,None),(Decimal('-1.5000'),Decimal('-9.802E+9'),None,None),(Decimal('-1.4000'),Decimal('-2.702E+8'),None,None),(Decimal('-1.3000'),Decimal('-7.449E+6'),None,None),(Decimal('-1.2000'),Decimal('-2.053E+5'),None,None),(Decimal('-1.1000'),Decimal('-5660'),None,None),(Decimal('-1.0000'),Decimal('-156.0'),None,None),(Decimal('-0.90000'),Decimal('-4.308'),None,None),(Decimal('-0.80000'),Decimal('-0.1221'),None,None),(Decimal('-0.70000'),Decimal('-0.004315'),None,None),(Decimal('-0.60000'),Decimal('-0.0001715'),None,None),(Decimal('-0.50000'),Decimal('-0.000004959'),None,None),(Decimal('-0.40000'),Decimal('-1.373E-7'),None,None),(Decimal('-0.30000'),Decimal('-4.075E-9'),None,None),(Decimal('-0.20000'),Decimal('-3.044E-10'),None,None),(Decimal('-0.10000'),Decimal('-1.030E-10'),None,None),(Decimal('0'),Decimal('0'),None,None),(Decimal('5'),Decimal('0'),None,None)]ContributingFork the repository.Install prerequisites.pip install -r requirements.txtImplement the new feature or bug fix.Implement test case(s) to ensure that future changes do not break
legacy.Run the tests.make testCreate a pull request.
Package: ecdysis
Interface to pyprocessmacroFeaturesCommand-line interfaceHandle loading data from common formats (csv, tsv, xlsx, sav, …)Convenience function for examining dataSave output in familiar PROCESS text formatSupports most or all pyprocessmacro optionsExamples$ecdysis-h...$ecdysis-idata.csv--summarize...$ecdysis-idata.csv-Xxvar-Mmvar-Yyvar-oresults.txt...LimitationsRelies on pandect which relies on pyreadstat, which has issues with
reading dta (Stata) format filesExpects variables to be numericCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: eceibs
eceibs Python SDK。安装目前 eceibs 支持的 Python 环境有 3.4, 3.5, 3.6, 3.7 和 pypy。为了简化安装过程，推荐使用 pip 进行安装pipinstalleceibs升级 eceibs 到新版本:pip install -U eceibs如果需要安装 GitHub 上的最新代码:pip install https://github.com/007gzs/eceibs/archive/master.zip
Package: eces-eg-weather-package
ECES Weather PackageWelcome to the ECES Weather Package â€“ the first official release from the ECES Data Analytics Unit led by Ahmed Dawoud. This Python package provides a simple and intuitive interface to interact with weather data using the OpenWeatherMap API. With it, you can fetch weather forecasts and analyze weather patterns.InstallationTo install the ECES Weather Package, run the following command in your terminal:pipinstalleces-weather-packageMake sure you have Python and pip already installed on your system.FeaturesRetrieve Weather Forecasts: Easily get weather forecasts for the next five days.Temperature and Wind Data: Access detailed temperature and wind data in a structured format.Data Analysis Ready: Integrate with Pandas for further data analysis.Quick StartHere's how to use the ECES Weather Package to retrieve weather forecast data for a specified city:fromeces_weather_packageimportWeather# Initialize the Weather object with your API key and desired cityweather=Weather(apikey='your_openweathermap_api_key_here',city='Cairo')# Get temperature forecast for the next 5 daystemperature_data=weather.temp_next_5_days()print(temperature_data)# Get wind forecast for the next 5 dayswind_data=weather.wind_next_5_days()print(wind_data)UsageInitializing the Weather ObjectBefore you can retrieve any data, you'll need to initialize theWeatherobject with your OpenWeatherMap API key and the city you're interested in:weather=Weather(apikey='your_api_key_here',city='Cairo')Getting Temperature DataTo get the temperature forecast for the next five days:temperature_data=weather.temp_next_5_days()print(temperature_data)This will return a Pandas DataFrame with the temperature and time data.Getting Wind DataSimilarly, to get the wind forecast:wind_data=weather.wind_next_5_days()print(wind_data)This will return a Pandas DataFrame with wind speed and direction data.Future WorkWe are excited to continue developing the ECES Weather Package. Future updates will include more features for weather analysis and prediction, harnessing the power of data science and machine learning.SupportFor support, bug reports, or feature requests, please contactsupport_email@example.com.LicenseThis project is licensed under theMIT License- see the LICENSE file for details.ContributingWe welcome contributions from the community. To contribute to the ECES Weather Package, please fork the repository, make your changes, and submit a pull request.AuthorAhmed Dawoud-Initial work-AhmedDawoud
Package: ecf
Empirical Christoffel function for anomaly detectionThis python module is an free and open source implementation of the empirical Christoffel function applied to outlier detection as proposed by Lasserre and Pauwels inThe empirical Christoffel function with applications in data analysis.SetupThis package is available onPyPI. If you usepip, simply execute:python3 -m pip install --user ecfAcknowledgementsI would like to thank Jean-Bernard Lasserre, Edouard Pauwels and Cyrius Nugier for their help in understanding and implementing this anomaly detector.
Package: ec-finder
EcFinderEcFinder tries to find an EC number for an enzyme name
using exact and fuzzy search.It automatically downloads the EC data file from Expasy.InstallationIt can be installed usingpip install ec_finder.CLIThe CLI toolec-findis provided by the package.
The first time the tool is called from the command line, the enzyme
data are downloaded from Expasy.APIThe first time thatec_finderis imported, the package
data is downloaded from Expasy.Theec_finder.update()function can be used to check if new
enzyme data is avalaible at Expasy and, if so, download it and update
the data.Theec_finder.search()function is used for searching
for an enzyme name.
Package: ecgai-logging
ecgai-loggingLogging decorator for both sync and async functions. Logging is carried out at logging levels DEBUG and INFO.INFO logs Module name, Method name, Variables, Returns, Elapsed timeDEBUG logs Module name, Method name, Variables, Returns, Elapsed time, Synchronous or Asynchronous function type,
Working directory, Start time, End time
All times recorded in UTCExceptions are logged if the exception is not handled in code or the exception is handled and raised. If the exception
is handled in code and not raised it is not logged by this decorator
Package: ecgai-training-data-physionet
ecgai-training-data-physionetFunction to access and retrieve ECG data from physionet.org and return the data in a specified format.Currently, only access the PTB-XL, a large publicly available electrocardiography dataset:The PTB-XL ECG dataset is a large dataset of 21837 clinical 12-lead ECGs from 18885 patients of 10 second
length. The raw signal data has been annotated by up to two cardiologists with 71 different ECG statements
and is supplemented by rich metadata.
Package: ecgaugmentation
ECG AugmentationECG Augmentation is a Python library for augmenting 12-lead ECG signals.InstallationUse the package managerpipto install ECG Augmentation.pipinstallecgaugmentationUsageimportecgaugmentationin_path="."# path to -fecg.npy ECG filesiterations=100anomaly_type="RBBB"ecgs,annotations=ecgaugmentation.augment(in_path,out_path,anomaly_type)ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: ecgdatasets
EcgdatasetsTable of contentsAuthorsInstallationA simple exampleAvailable datasetsCitingAuthorsRostislav Epifanov— Researcher in NovosibirskInstallationInstallation from PyPI:pip install ecgdatasetsInstallation from GitHub:pip install git+https://github.com/rostepifanov/ecgdatasetsA simple exampleimportecgdatasetsasedbfromtorch.utils.dataimportDataLoader# torch.Datasetdataset=edb.PTBXL('.','1.0.3',download=True,mapper=None)# torch.DataLoader that is ready to use with torch.nn.Moduledataloader=DataLoader(dataset,batch_size=32,shuffle=True)Available datasetsNameLabelsSourceLUDB-PhysionetPTBXL-PhysionetINCARTDB-PhysionetCitingIf you find this library useful for your research, please consider citing:@misc{epifanov2023ecgdatasets,
  Author = {Rostislav Epifanov},
  Title = {Ecgdatasets},
  Year = {2023},
  Publisher = {GitHub},
  Journal = {GitHub repository},
  Howpublished = {\url{https://github.com/rostepifanov/ecgdatasets}}
}