Saravanan666/pypi-org huggingface dataset:
Package: ecghelper
ecghelperA utility package for working with electrocardiograms, particularly 12-lead electrocardiograms.QuickstartpipinstallecghelperDevelopmentAssumingcondaormambaare available in the shell, you can create a virtual environment for package development as follows.condacreate-y-necghelper"python>=3.9"condaactivateecghelper
pipinstallecghelper[dev]If you are usingzsh, you will need to escape the square brackets, i.e.:pipinstallecghelper\[dev\]Converting functionsDebug - directly call the entry point.python -m pdb src/ecghelper/__main__.py convert -i 82000.xml -f xml -o 82000_wfdb -t wfdb
Package: ecgmentations
EcgmentationsEcgmentations is a Python library for ecg augmentation. Ecg augmentation is used in deep learning to increase the quality of trained models. The purpose of ecg augmentation is to create new training samples from the existing data.Here is an example of how you can apply some augmentations from Ecgmentations to create new ecgs from the original one:Table of contentsAuthorsInstallationA simple exampleList of augmentationsCitingAuthorsRostislav Epifanov— Researcher in NovosibirskInstallationInstallation from PyPI:pip install ecgmentationsInstallation from GitHub:pip install git+https://github.com/rostepifanov/ecgmentationsA simple exampleimportnumpyasnpimportecgmentationsasE# Declare an augmentation pipelinetransform=E.Sequential([E.TimeReverse(p=0.5),E.ChannelShuffle(p=0.06),])# Create example ecg (length, nchannels)ecg=np.ones((12,5000)).T# Augment an ecgtransformed=transform(ecg=ecg)transformed_ecg=transformed['ecg']List of augmentationsThe list of time axis transforms:TimeReverseTimeShiftTimeSegmentShuffleRandomTimeWrapTimeCutoutTimeCropCenterTimeCropRandomTimeCropTimePadIfNeededThe list of pulse transforms:SinePulseSquarePulsePowerlineNoiseRespirationNoiseThe list of other transforms:AmplitudeInvertChannelShuffleChannelDropoutGaussNoiseBlurGaussBlurAmplitudeScaleCitingIf you find this library useful for your research, please consider citing:@misc{epifanov2023ecgmentations,
  Author = {Rostislav Epifanov},
  Title = {Ecgmentations},
  Year = {2023},
  Publisher = {GitHub},
  Journal = {GitHub repository},
  Howpublished = {\url{https://github.com/rostepifanov/ecgmentations}}
}
Package: ecg-noise-detector
ecg-noise-detectorThis package helps detect and manage noise in ECGs📚 Original publicationYet to be published🔧 InstalationThis package is available via pippip install ecg-noise-detector💻 Get startedIt is important that raw ECGs (i.e not bandpass filtered) are used when usingis_noisyandplot_ecgfromecg_noise_detectorimportnoiseDetector# Generate a noisy ECGecg=noiseDetector.get_example_ecg('noisy')# Plot the ecg with green highlights on where clean signal is presentnoiseDetector.plot_ecg(ecg)# Classify the ecgprint(noiseDetector.is_noisy(ecg))📒 Quick Specificationget_example_ecg(ecgType)'''RETURNSNumpy array of 30s raw ECG of specified typeINPUTS@ ecgType - ['clean' | 'noisy'], specifies which ecg to generate'''plot_ecg(ecg,fs=500,highlights=True,show=True)'''RETURNSpyplot figure of filtered (and highlighted) ecgINPUTS@ ecg - raw ecg@ fs - sampling frequency@ highlights - show highlights (green when segment is clean, grey when noisy)@ show - display figure when function is executed'''is_noisy(ecg,fs=500)'''RETURNSboolean (True if ecg is noisy, False if not)INPUTS@ ecg - raw ecg@ fs - sampling frequency'''
Package: ecg-plot
ECG plotPlot standard ECG chart from data.Support both direct plotting and plotting SVG preview in browser (currently only works on mac)Support saving PNG and SVG to diskSupport customer defined lead orderSupport customer defined column countInstallpip install ecg_plotNoticeInput data should be m x n matrix, which m is lead count of ECG and n is length of single lead signal.Default sample rate is 500 Hz.ExamplePlot 12 lead ECGparams:parameterdescriptionecgm x n ECG signal data, which m is number of leads and n is length of signal.sample_rateSample rate of the signal.titleTitle which will be shown on top off chartlead_indexLead name array in the same order of ecg, will be shown on left of signal plot, defaults to ['I', 'II', 'III', 'aVR', 'aVL', 'aVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']lead_orderLead display ordercolumnsdisplay columns, defaults to 2styledisplay style, defaults to None, can be 'bw' which means black whiterow_heighthow many grid should a lead signal haveshow_lead_nameshow lead nameshow_gridshow gridshow_separate_lineshow separate lineimport ecg_plot

ecg = load_data() # load data should be implemented by yourself 
ecg_plot.plot(ecg, sample_rate = 500, title = 'ECG 12')
ecg_plot.show()Plot single lead ECGimport ecg_plot

ecg = load_data() # load data should be implemented by yourself 
ecg_plot.plot_1(ecg[1], sample_rate=500, title = 'ECG')
ecg_plot.show()Save result as pngimport ecg_plot

ecg = load_data() # load data should be implemented by yourself 
ecg_plot.plot_12(ecg, sample_rate = 500, title = 'ECG 12')
ecg_plot.save_as_png('example_ecg','tmp/')License: MIT
Package: ecg-qc
ECG_QC (Quality Classification)Full Documentation:https://aura-healthcare.github.io/ecg_qc/Website:https://www.aura.healthcareGithub:https://github.com/Aura-healthcareVersion: 1.0b6ecg_qc is a python library that classifies ECG signal into good/bad quality thanks to Machine Learning.There are currently 4 trained models:NameType of model(training) ECG time window(training) ECG segments individual standardizationdfc_2s.pklDecision Tree Classifier2 secondsNorfc_2s.pklRandom Forest Classifier2 secondsNorfc_2s_norm.pklRandom Forest Classifier2 secondsYesxgb_9s.joblibXGboost Classifier9 secondsNoInstallation / PrerequisitesDependenciesecg_qc requires:Python (>= 3.6)biosppy>=0.6.1dill>=0.3.4pathtools>=0.1.2py-ecg-detectors>=1.0.2scikit-learn>=0.23.2wfdb>=3.1.1xgboost>=1.3.1User installationThe easiest way to install hrv-analysis is usingpip:$ pip install ecg-qcyou can also clone the repository:$ git clone https://github.com/Aura-healthcare/ecg_qc.git
$ python setup.py installGetting startedUsageImport:fromecg_qcimportEcgQcClass initialization:ecg_qc=EcgQc()Default parameters:ecg_qc=EcgQc(model='rfc_norm_2s.pkl',sampling_frequency=256,normalized=True)Predicting the quality of the signal:ecg_data=[1905.72,...-150.75995323,-134.14559104]# ECG values with same sampling frequency as class declarationsignal_quality=ecg_qc.get_signal_quality(ecg_data)Computing SQIs before making prediction:ecg_data=[1905.72,...-150.75995323,-134.14559104]# ECG values with same sampling frequency as class declarationsqi_scores=ecg_qc.compute_sqi_score(ecg_data)signal_quality=ecg_qc.predict_quality(sqi_scores)AuthorsAlexandre CHIROUZE- (https://github.com/achirouze)Alexis COMTE- (https://github.com/alexisgcomte)Laura DUMONT- (https://github.com/laudmt)LicenseThis project is licensed under theGNU GENERAL PUBLIC License- see theLICENSE.mdfile for detailsReferencesNemcova, A., Smisek, R., Opravilová, K., Vitek, M., Smital, L., & Maršánová, L. (2020). Brno University of Technology ECG Quality Database (BUT QDB) (version 1.0.0). PhysioNet.https://doi.org/10.13026/kah4-0w24.
Package: ecg-quality
Library descriptionThis library is meant to be used to check quality of ECG signal. Recordings of any length can be processed, memory is the only limit. The library work with three basic levels of quality:Quality 1 : All important segment of ECG can be detected in the signalQuality 2 : Noise is present, but the QRS complex can still be detectedQuality 3 : Noise is too large, not even QRS complex can be detectedEach model works with a sliding window. At each step, the window is evaluated using one of 4 models and one score from range 0 to 1 is returned. The length of sliding windows for individual models can be found in their names. The 4 supported models are:cnn2scnn5slstm2soscnn2sThe stride of the sliding window can be specified. The class limits so this value is at least 1 second and is a divisor of the length of the sliding window.There are currently 4 return modes supported:score : The model returns the score between 0 and 1. 0 is meant to represent quality 1, 0.5 to represent quality 2 and 1 to represent quality 3. This is however not given and the score distributions of different qualities can differ between models.three_value : Three qualities are given using thresholds.bin_clean : Signal is either marked as quality 1 or as quality 2/3bin_qrs: Signal is either marked as quality 1/2 or as quality 3The thresholds used in each of these modes, except of score, can be specified by the user. However, there are default thresholds, that aim to prioritize precision with the better classes. When using three_value, the returned values are one of 1, 2 and 3, depending on the quality of the signal. When using either of the binary modes, the returned values are either 1 or 2. 1 is for the better quality signal and 2 is for the worse quality.In terms of length of output, there are two return_types supported:intervals : value is returned for each window of the size stride, since the value of the quality ill not change in thesefull : value is returned for each value in the inputModels were trained on cleaned data. These data were cleaned using default NeuroKit2 ecg_clean method. The class can clean signal it is meant to process using ecg_clean. Currently only default version of ecg_clean us supported. It can be specified not to do this. However, it is advised to either use this option, or to pass cleaned signal to the method. We give no guarantee about the quality of the signal if uncleaned signal is processed and no cleaning is done..Signal processed must have frequency of 250 Hz. Signal of other frequencies is not supported as the models are not trained on such a signal.Full explanation of all the modes can be found inside the source code documentation.InstallationThe library can be easily installed through the pip utility.pip install ecg-qualityUsageThe library offers a high-level interface. The simplest example using all the default settings:from ecg_quality.ECGQualityChecker import ECGQualityChecker  

checker = ECGQualityChecker()  
 
signal = ...	 

output = checker.process_signal(signal)
Package: ecgtools
ecgtoolsCIDocsPackageLicenseMotivationThe critical requirement for usingintake-esmis having a data catalog. This package enables you to build data catalogs to be read in byintake-esm, which enables a user to easily search, discover, and access datasets they are interested in using.Seedocumentationfor more information.Installationecgtools can be installed from PyPI with pip:python-mpipinstallecgtoolsIt is also available from conda-forge for conda installations:condainstall-cconda-forgeecgtools
Package: ech
echis a tool to manage AWS EC2 instances. It canStart/stop instances in multiple regions and users.Add the instance to you SSH profile.InstallInstallaws-clifromhere.pip install echHow to useSuppose I have a instance with idi-01234567inus-west-2under default profile, and a instance with idi-76543210inus-east-1under profiletom. To manage thoseRunaws configureto add default profile if you haven't done so, and go to~/.aws/credentialsto add profile fortom.Createawsh.tomlin your home directory:[servers]my-server={id="i-01234567",region="us-west-2"}another-server={id="i-76543210",region="us-east-1"}Now you created two server aliasmy-serveranother-server.Now suppose you want to start your work onmy-server.Start your instanceechstartmy-serverConnect to your instanceFirst, add this server to your SSH profile.echbindmy-serverThe next step is straightforward:sshmy-serverTip:You can directly access this instance in your VSCode by selectingmy-serverinstalling SSH plugin and remote explorer.Stop your instanceechstopmy-serverThat's it.I am using this tool quite often in my personal study/work, but this tool may/should still have bugs. If so please submit an issue. Thanks!
Package: ech2901-pyaes
pyaesDescriptionThis is my attempt to implement the AES encryption algorithm in Python 3.7.This is my first real project that has taken a while to study and understand the mathematics behind the scenes in how it operates and also my first project uploaded to Github so that I can learn how to properly use version control software as well.End goal is to be used in the creation of a password manager system.InstallationPackage can be installed with pip via:pip install ech2901-pyaes==1.2.1Simple Use Casefrom pyaes.AES import ECBecb_mode = ECB()ciphertext, salt = ecb_mode.encrypt(plaintext=b'test', password=b'password', size=128)plaintext = ecb_mode.decrypt(ciphertext=ciphertext, password=b'password', size=128)
Package: echarts
A sample project that exists as an aid to thePython Packaging User Guide’sTutorial on Packaging and Distributing
Projects.This projects does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.This is the README file for the project.The file should use UTF-8 encoding and be written usingreStructuredText. It
will be used to generate the project webpage on PyPI and will be displayed as
the project homepage on common code-hosting services, and should be written for
that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not
a good idea, although a simple “What’s New” section for the most recent version
may be appropriate.
Package: echarts-china-cities-pypkg
The project packagesecharts-china-cities-jsand distributes it via pypi.InstallationYou can install echarts-china-cities-pypkg via pip:$pipinstallecharts-china-cities-pypkgor clone it and install it:$gitclonehttps://github.com/pyecharts/echarts-china-cities-pypkg.git$cdecharts-china-cities-pypkg$pythonsetup.pyinstallChange log0.0.8 - 09.11.2018Updatedloose the limit on lml0.0.8 - 04.03.2018Updatedcompressed 6 first line cities0.0.7 - 04.03.2018Updatedall 370 Chinese cities
Package: echarts-china-counties-pypkg
The project packagesecharts-china-counties-jsand distributes it via pypi.UsageInstallationYou can install echarts-china-counties-pypkg via pip:$pipinstallecharts-china-counties-pypkgor clone it and install it:$gitclonehttps://github.com/pyecharts/echarts-china-counties-pypkg.git$cdecharts-china-counties-pypkg$pythonsetup.pyinstallChange log0.0.2 - 31.07.2018Updatedpyecharts#640, failed
to install on windows0.0.1 - 01.04.2018Addedinitial release
Package: echarts-china-misc-pypkg
The project packagesecharts-china-misc-jsand distributes it via pypi.InstallationYou can install echarts-china-misc-pypkg via pip:$pipinstallecharts-china-misc-pypkgor clone it and install it:$gitclonehttps://github.com/chfw/echarts-china-misc-pypkg.git$cdecharts-china-misc-pypkg$pythonsetup.pyinstallChange log
Package: echarts-china-provinces-pypkg
The project packagesecharts-china-provinces-jsand distributes it via pypi.InstallationYou can install echarts-china-provinces-pypkg via pip:$pipinstallecharts-china-provinces-pypkgor clone it and install it:$gitclonehttps://github.com/chfw/echarts-china-provinces-pypkg.git$cdecharts-china-provinces-pypkg$pythonsetup.pyinstallChange log0.0.3 - 09.11.2018Updatedloose lml version0.0.2 - 26.04.2018Updatedpyecharts#492: pip 10
compactibility problem0.0.1 - 22.02.2018Addedfirst stable release
Package: echarts-cities-pypkg
The project packagesecharts-cities-jsand distributes it via pypi.InstallationYou can install echarts-cities-pypkg via pip:$pipinstallecharts-cities-pypkgor clone it and install it:$gitclonehttps://github.com/pyecharts/echarts-cities-pypkg.git$cdecharts-cities-pypkg$pythonsetup.pyinstallChange log
Package: echarts-countries-pypkg
The project packagesecharts-countries-jsand distributes it via pypi.InstallationYou can install echarts-countries-pypkg via pip:$pipinstallecharts-countries-pypkgor clone it and install it:$gitclonehttps://github.com/pyecharts/echarts-countries-pypkg.git$cdecharts-countries-pypkg$pythonsetup.pyinstallChange log0.1.6 - 09.11.2018Updateduse lml >= 0.0.20.1.4 - 26.04.2018Updatedpyecharts#492: pip 10
compactibility problem0.0.8 - 23.02.2018Addedfirst stable release
Package: echarts-python
An unofficial Echarts options generator with Python.Free software: MIT licenseDocumentation:http://echarts-python.readthedocs.io/en/stable/(NOT Ready)Online demo:https://yufeiminds.github.io/echarts-python/(NOT Ready)This repo still on developing (ALPHA), DON’T USE IT IN PRODUCTION.InstallationInstallingecharts-pythonwith pip$ pip install echarts-pythonCurrent version forEcharts 3.1.6Basic UsagefromechartsimportEchart,Legend,Bar,Axischart=Echart('GDP','This is a fake chart')chart.use(Bar('China',[2,3,4,5]))chart.use(Legend(['GDP']))chart.use(Axis('category','bottom',data=['Nov','Dec','Jan','Feb']))Thechart.jsonproperty will be{"title":{"text":"GDP","subtext":"This is a fake chart"},"series":[{"type":"bar","data":[2,3,4,5],"name":"China"}],"legend":{"y":"top","x":"center","data":["GDP"],"orient":"horizontal"},"xAxis":[{"position":"bottom","data":["Nov","Dec","Jan","Feb"],"type":"category"}],"yAxis":{}}on Mac OSX, you also can executechart.plot()and invoke a browser to display the chart.ContributionThis package authored by Hsiaoming Yang <me@lepture.com> in 2014.If you have any question or want to improve this repository, welcome to create
anissueorpull requests.This repo is maintained by Yufei Li <yufeiminds@gmail.com> now,
you can also send a email to me.
Package: echarts-themes-pypkg
The project packagesecharts-themes-jsand distributes it via pypi.InstallationYou can install echarts-themes-pypkg via pip:$pipinstallecharts-themes-pypkgor clone it and install it:$gitclonehttps://github.com/pyecharts/echarts-themes-pypkg.git$cdecharts-themes-pypkg$pythonsetup.pyinstallChange log0.0.3 - 13/08/2018Addedupgrade to echarts-themes-js 1.0.2, adding addtional romatic theme0.0.2 - 12/08/2018Addedupgrade to echarts-themes-js 1.0.1, adding addtional 7 themes: westeros,
wonderland, chalk, halloween, essos, walden and purple-passion0.0.1 - 5.2018Addedinitial release
Package: echarts-united-kingdom-pypkg
The project packagesecharts-united-kingdom-jsand distributes it via pypi.InstallationYou can install echarts-united-kingdom-pypkg via pip:$pipinstallecharts-united-kingdom-pypkgor clone it and install it:$gitclonehttps://github.com/pyecharts/echarts-united-kingdom-pypkg.git$cdecharts-united-kingdom-pypkg$pythonsetup.pyinstallChange log0.0.1 - 03.03.2018AddedUK electoral boundaries 2016
Package: ech-datastructures
ech-datastructuresComprehensive package of Python-native datastructures.Planned Datastructures:tree setordered list (?) = binary tree with duplicates (counts)triedisjoint set (forest)graph (weighted or not, directed or not, sparse or not)Implemented Datastructures:Heap (AKA priority queue)TreeMapWill Not Implement:fixed-size arraydynamic array (uselist)linked list (usecollections.deque)stack (usecollections.dequeorlist)queue (usecollections.deque)deque (usecollections.deque)hash set (useset)hash map (usedict)See AlsoStandard Library Time ComplexitiesStandard Librarycollections
Package: eche
UsageEche provides aClusterHelperclass to conveniently handle entity clusters.fromecheimportClusterHelperch=ClusterHelper([{"a1","b1"},{"a2","b2"}])print(ch.clusters){0:{'a1','b1'},1:{'a2','b2'}}Add an element to a clusterch.add_to_cluster(0,"c1")print(ch.clusters){0:{'a1','b1','c1'},1:{'a2','b2'}}Add a new clusterch.add({"e2","f1","c3"})print(ch.clusters){0:{'a1','b1','c1'},1:{'a2','b2'},2:{'f1','e2','c3'}}Remove an element from a clusterch.remove("b1")print(ch.clusters){0:{'a1','c1'},1:{'a2','b2'},2:{'f1','e2','c3'}}The__contains__function is smartly overloaded. You can check if an entity is in theClusterHelper:"a1"inch# TrueIf a cluster is present{"c1","a1"}inch# TrueAnd even if a link exists or not("f1","e2")inch# True("a1","e2")inch# FalseTo know the cluster id of an entity you can look it up withprint(ch.elements["a1"])0To get members of a cluster either useprint(ch.members(0)){'a1','b1','c1'}or simplyprint(ch[0]){'a1','b1','c1'}More functions can be found in theDocumentation.InstallationSimply usepipfor installation:pip install eche
Package: echelle
EchelleEchelle is a Python package for plotting and interacting with echelle diagrams.
In an echelle diagram, the amplitude spectrum of a star is stacked in equal
slices of delta nu, the large separation.Installationpip install echelleor download the git repository andpython setup.py installUsageFor a dynamic interface:from echelle import interact_echelle
interact_echelle(frequency, power, dnu_min, dnu_max)If you're using echelle in a Jupyter notebook, I suggest calling%matplotlib notebookfirst.To plot a non-interactive echelle diagram,from echelle import plot_echelle
plot_echelle(frequency, power, dnu)See the example.ipynb for common usage!CitingIf you make use of echelle in your work, please consider citing the Zenodo listing@software{daniel_hey_2020_3629933,
  author       = {Daniel Hey and
                  Warrick Ball},
  title        = {{Echelle: Dynamic echelle diagrams for 
                   asteroseismology}},
  month        = jan,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {1.4},
  doi          = {10.5281/zenodo.3629933},
  url          = {https://doi.org/10.5281/zenodo.3629933}
}Thanks to Adam Hamilton for making the echelle gif!
Package: echelle-spectra
Echelle SpectraGraphical tool for the extraction and analysis of calibrated spectra from 2D Echelle spectrometer imagesQuick start•Usage•Configuration•Authors•LicenseQuick startThe echelle_spectra module can be installed directly from PyPI viapip:pip install echelle_spectra --upgradeThe echelle_spectra app GUI can be invoked directly as a Python module:python -m echelle_spectraAlternatively, the module can imported and the GUI started from within a Python script:importechelle_spectraechelle_spectra.gui.start()If you instead wish to run the tool from its source, clone this repository usinggit, install the project requirements, and execute the__main__.pyscript file as follows:gitclonehttps://github.com/queezz/echelle_spectra.gitcdechelle_spectra
pipinstall-rrequirements.txt
python./src/echelle_spectra/__main__.pyUsageWith the Echelle Spectra GUI open, you will be presented with a set of blank image and spectra plots, as well as a control panel of settings and information on the left.Firstly, an image file containing data from an Echelle spectrometer needs to be loaded. During the loading process, each frame of the file is converted to a calibrated wavelength-intensity spectrum, and fitting to regions of interest in the spectrum is carried out if requested. Optionally, all spectral and fitting data is saved to disk, which can be imported by another application.After a file is successfully loaded, the first frame will be graphically displayed in the image viewer, and the corresponding computed spectrum will be shown below it. The control panel on the left can be used to select which frame from the file is being visualised.Other tabs are also available in the GUI, which contain plots populated by snippets of spectral data in common regions of interest. If fitting was enabled during loading, a convolution of one or more Gaussian curves is plotted that best approximates the emissive intensity in those wavelength regions.Data loading(Info box)CalibrationData exportBulk processingGUI for Echelle image processingTo convert Echelle images to spectra two options are avaliable: use this GUI, or importEchelleImageclass to read, calibrate, and produce aSpectrumclass, from wicht data could be exported. See examples forCCDandCMOSsensors.CCD imageCMOS image
Package: echelon-py
echelon-py: Echelon analysis in PythonInstall$ pip install echelon-pyFunctionalitiesEchelon analysisEchelon dendrogram plotEchelon scan (detection of spatial clusters)Quick StartCheck out someexamples (echelon-py-examples)!How to UseCheck out thedocumentation.Notes for ContributorsNotes for Contributors.ReferencesR Package‘echelon’