Saravanan666/pypi-org huggingface dataset:
Package: esi-syncopy
Systems Neuroscience Computing in PythonSyncopy aims to be a user-friendly toolkit forlarge-scaleelectrophysiology data-analysis in Python. We strive to achieve the following goals:Syncopy is afully open source Pythonenvironment for electrophysiology
data analysis.Syncopy isscalableand built forvery large datasets. It automatically
makes use of available computing resources and is developed with built-in
parallelism in mind.Syncopy iscompatible with FieldTrip. Data and results can be loaded into
MATLAB and Python, and parameter names and function call syntax are as similar as possible.Syncopy is developed at theErnst Strüngmann Institute (ESI) gGmbH for Neuroscience in Cooperation with Max Planck Societyand released free of charge under theBSD 3-Clause “New” or “Revised” License.ContactTo report bugs or ask questions please use ourGitHub issue tracker.
For general inquiries please contact syncopy (at) esi-frankfurt.de.InstallationWe recommend to install SynCoPy into a new conda environment:Install theAnaconda Distribution for your Operating Systemif you do not yet have it.Start a new terminal.You can do this by starting`Anaconda navigator`, selecting`Environments`in the left tab, selecting the`base (root)`environment, and clicking the green play button and then`Open Terminal`.Alternatively, under Linux, you can just type`bash`in your active terminal to start a new session.You should see a terminal with a command prompt that starts with`(base)`, indicating that you are
in the conda`base`environment.Now we create a new environment named`syncopy`and install syncopy into this environment:condacreate-y--namesyncopycondaactivatesyncopycondainstall-y-cconda-forgeesi-syncopyGetting StartedPlease visit ouronline documentation.Developer InstallationTo get the latest development version, please clone our GitHub repository and change to thedevbranch. We highly recommend to install into a new conda virtual environment, so that this development version does not interfere with your existing installation.gitclonehttps://github.com/esi-neuroscience/syncopy.gitcdsyncopy/condaenvcreate--namesyncopy-dev--filesyncopy.ymlcondaactivatesyncopy-devpipinstall-e.We recommend to verify your development installation by running the unit tests. You can skip the parallel tests to save some time, the tests should run in about 5 minutes then:python-mpytest-k"not parallel"You now have a verified developer installation of Syncopy. Please refert to ourcontributing guideif you want to contribute to Syncopy.
Package: esi-utils-rupture
esi-utils-ruptureIntroductionUtility package with some helper functions for representing ruptures and computing
rupture distances used by USGS earthquake hazard products such as ShakeMap and gmprocess.See tests directory for usage examples.InstallationIsolating installations with virtual environments to prevent dependency conflicts is recommended.  You can use the Python3venvmodule orconda, which is shown below.Create and activate the virtual environment, checking that the chosen Python version is compatible with package requirementscondacreate--namerupturepython=3.10
condaactivateruptureFrom PyPiThe easiest way to install the package is via PyPi. For users interested in a more hands on development environment, instructions for a source installation with optional additional dependencies for development and testing further down.pipinstallesi-utils-ruptureFrom SourceFirst clone the repostory locallygitclonehttps://code.usgs.gov/ghsc/esi/esi-utils-rupture.gitcdesi-utils-ruptureNext, install the code with pippipinstall.Note that this will install the minimum requirements to run the code.
There are additional optional packages that can be installed that support running the unit tests (test), code development (dev), and building wheels (build).
To install these, you need to add the relevant option in brackets:pipinstall.[test,dev,build]For developers, it is also convenient to install the code in "editable" mode by adding the-eoption:pipinstall-e.[test,dev,build]TestsIf you are installing from soruce and included the optionaltestdependencies in the install step, then you can run the unit tests in the root directory of the repository:pytest.
Package: esi-utils-transfer
esi-utils-transferIntroductionUtility package for transferring files via pdl, ftp, email and other protocols as
used by ShakeMap and PAGER.InstallationFrom repository base, runconda create --name transfer pip
conda activate transfer
pip install git+https://code.usgs.gov/ghsc/esi/esi-utils-io
pip install -r requirements.txt .Testspip install pytest
pytest .
Package: eskiz-sms
eskiz-smseskiz-sms package for eskiz.uz/sms:warning:Please use the latest version. In previous versions, there are a lot of mistakes, bugsInstallationpip install eskiz_smsQuickstartfromeskiz_smsimportEskizSMSemail="your_email@mail.com"password="your_password"eskiz=EskizSMS(email=email,password=password)eskiz.send_sms('998991234567','message',from_whom='4546',callback_url=None)Using pre-saved tokenfromeskiz_smsimportEskizSMSyour_saved_token='eySomething9320'eskiz=EskizSMS('email','password')eskiz.token.set(your_saved_token)eskiz.send_sms('998901234567',message='message')Saving token to env fileIf you setsave_token=Trueit will save the token to env filefromeskiz_smsimportEskizSMSeskiz=EskizSMS('email','password',save_token=True,env_file_path='.env')# Don't forget to add env file to .gitignore!response=eskiz.send_sms('998901234567',message='message')Async usageimportasynciofromeskiz_sms.async_importEskizSMSasyncdefmain():eskiz=EskizSMS('email','password')response=awaiteskiz.send_sms('998901234567','Hello, World!')asyncio.run(main())
Package: eskiz-sms-client
Eskiz SMS Gateway Python SDK (BETA)FeaturesToken Management: Auto auth and refresh expired tokens.Message Building: Construct messages efficiently withMessageBuilder.Token Context Management: Manage token contexts easily for temporary changes.Synchronous Wrapper: Utilize synchronous capabilities withsync.force_sync.[!WARNING]
We're currently in beta, actively refining our features.Installationpip install eskiz-sms-clientQuickstartExample for auth get token:importasynciofromeskizimportSMSClientasyncdefmain():client=SMSClient()awaitclient.get_token('test@eskiz.uz','password')print(client.token)# NEW TOKENasyncio.run(main())Example for refresh token:ifclient.token.is_expired:awaitclient.refresh_token()print(client.token)# REFRESHED TOKENExample for send SMS:fromeskizimportSMSClientclient=SMSClient(token='TOKEN',as_dict=False)response=awaitclient.send_sms(mobile_phone=998991234567,message="test from sdk")print(response.status)# 'waiting'# current response as dictprint(response.model_dump())# { "id": "<id>", "status": "waiting", "message": "Waiting for SMS provider" }[!TIP]
setas_dicttoTrue, all responses will be returned as dictMore ExamplesIn examples diriectory:seeDocumentationhttps://documenter.getpostman.com/view/663428/RzfmES4z
Package: eskrim
ESKRIM: EStimate with K-mers the RIchness in a MicrobiomeESKRIM is a reference-free tool that compares microbial richness in shotgun metagenomic samples by counting k-mersInstallation via pippip install eskrimUsageBasic usageIn this example, k-mer richness in a sample (sample1) consisting in two paired-end runs (run1 and run2) is computed.Forward fastq files are taken as input. Results are saved in the filesample1.eskrim_stats.tsveskrim -i sample1.run1_1.fastq.gz sample1.run2_1.fastq.gz -n sample1 -s sample1.eskrim_stats.tsvQuality control(adapters removal, read trimming) andcontaminant removal(reads from the host genome) should be performed before using ESKRIM.Run ESKRIM similarly for each sample to be compared. All TSV output files can be merged manually.Advanced usageAdjusting target read count for subsamplingDepending on the sequencing depth, the target number of reads to randomly draw from each sample (default = 10M) can be adjusted with the-rparameter.eskrim -i sample1.run1_1.fastq.gz sample1.run2_1.fastq.gz -n sample1 -s sample1.eskrim_stats.tsv -r 5000000Adjusting read lengthAll reads are trimmed to a given length (default = 80) because read length can vary between samples.This length can be changed with the-lparameter.eskrim -i sample1.run1_1.fastq.gz sample1.run2_1.fastq.gz -n sample1 -s sample1.eskrim_stats.tsv -l 100ReproducibilityESKRIM ensures reproducibility when using the same random number generator seed (default = 0).To make read subsampling vary across executions, the parameters --seed can be used.eskrim -i sample1.run1_1.fastq.gz sample1.run2_1.fastq.gz -n sample1 -s sample1.eskrim_stats.tsv --seed 1234Interpreting the output fileESKRIM saves the results in a TSV file consisting in several columns (-sparameter).sample_name: sample name specified with-nparameter.total_num_reads: number of reads in the sample before subsampling.num_Ns_reads_ignored: number of reads with undetermined bases that were discarded.num_too_short_reads_ignored: number of reads with undetermined bases that were discarded.target_num_reads: target number of reads to draw during the subsampling step.num_selected_reads: number of reads actually drawn after subsampling.read_length: length at which reads were trimmed (-lparameter).kmer_length: length of counted k-mers (-kparameter).num_distinct_kmers: number of distinct kmers in subsampled reads.num_solid_kmers: number of kmers seen at least twice.num_mercy_kmers: number of non-solid kmers occuring in a read where all k-mers are not solid.From our experience, the sum 'num_solid_kmers + num_mercy_kmers' is an accurate proxy to compare microbial richness between samples.WARNING: Do not consider results whennum_selected_readsis strictly lower thantarget_num_reads.In this case, ignore the samples concerned or decrease the number of reads to be drawn randomly (-rparameter).AuthorsFlorian Plaza Oñate:florian.plaza-onate@inrae.frEmmanuelle Le Chatelier:emmanuelle.le-chatelier@inrae.fr
Package: esky
Esky is an auto-update framework for frozen Python applications.  It provides
a simple API through which apps can find, fetch and install updates, and a
bootstrapping mechanism that keeps the app safe in the face of failed or
partial updates.Esky is currently capable of freezing apps with py2exe, py2app and cxfreeze.
Adding support for other freezer programs should be straightforward;
patches will be gratefully accepted.Seehttps://github.com/cloudmatrix/esky/for more information:
Package: eslearn
Easylearn is designed for machine learning mainly in resting-state fMRI, radiomics and other fields (such as EEG). Easylearn is built on top of scikit-learn, pytorch and other packages. Easylearn can assist doctors and researchers who have limited coding experience to easily realize machine learning, e.g., (MR/CT/PET/EEG)imaging-marker- or other biomarker-based disease diagnosis and prediction, treatment response prediction, disease subtyping, dimensional decoding for transdiagnostic psychiatric diseases or other diseases, disease mechanism exploration and etc.We focus on machine learning rather than data preprocessing. Many software, such as SPM, GRETNA, DPABI, REST, RESTPlus, CCS, FSL, Freesufer, nipy, nipype, nibabel, fmriprep and etc, can be used for data preprocessing.Citing information:If you think this software (or some function) is useful, citing the easylearn software in your paper or code would be greatly appreciated!
Citing link:https://github.com/lichao312214129/easylearnInstallpip install -U eslearnUsageimport eslearn as el
el.run()DevelopmentWe hope you can join us!Email:lichao19870617@gmail.comWechat: 13591648206Supervisors/ConsultantsKe Xukexu@vip.sina.com  
Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China.  
Department of Radiology, The First Affiliated Hospital of China Medical University.Yanqing Tangyanqingtang@163.com  
1 Brain Function Research Section, The First Affiliated Hospital of China Medical
University, Shenyang, Liaoning, PR China.  
2 Department of Psychiatry, The First Affiliated Hospital of China Medical University,
Shenyang, Liaoning, PR China.Yong Heyong.he@bnu.edu.cn  
1 National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China  
2 Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China  
3 IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, ChinaMaintainersChao Lilichao19870617@gmail.com
Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China.Mengshi Dongdongmengshi1990@163.com  
Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China.
Package: es-lemmatizer
Spanish lemmatizerRead data/LICENSE firstInstall itpip install es-lemmatizerHow to use it:from es_lemmatizer import lemmatize
import spacy
nlp = spacy.load("es")
nlp.add_pipe(lemmatize, after="tagger")How to publish:rm -rf dist
python setup.py clean --all
python setup.py sdist bdist_wheel
twine upload --repository-url https://test.pypi.org/legacy/ --verbose dist/*
Package: esl-jupyterlab-codemirror
esl_jupyterlab_codemirrorCodeMirror mode for the Elephant Specification Language.RequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstallesl_jupyterlab_codemirrorUninstallTo remove the extension, execute:pipuninstallesl_jupyterlab_codemirrorContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the esl_jupyterlab_codemirror directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstallpipuninstallesl_jupyterlab_codemirrorIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedesl-jupyterlab-codemirrorwithin that folder.Packaging the extensionSeeRELEASE
Package: eslogging
esloggingThis is a fork ofCMRESHandler.pyproject. We didn't contribute to it as there's no maintenance of that project anymore.Python Elasticsearch Log handlerThis library provides an Elasticsearch logging appender compatible with thepython standardlogging <https://docs.python.org/2/library/logging.html>_ library.The code source is in github at `https://github.com/cmanaha/python-elasticsearch-loggerhttps://github.com/cmanaha/python-elasticsearch-logger`_Installation============Install using pip::pip install esloggingRequirements Python 3=====================This library requires the following dependencieselasticsearchrequestsAdditional requirements for Kerberos support============================================Additionally, the package support optionally kerberos authentication by adding the following dependecyrequests-kerberosAdditional requirements for AWS IAM user authentication (request signing)=========================================================================Additionally, the package support optionally AWS IAM user authentication by adding the following dependecyrequests-aws4authBuilding the sources & TestingTo create the package follow the standard python setup.py to compile.To test, just execute the python tests within the test folderWhy using an appender rather than logstash or beatsIn some cases is quite useful to provide all the information available within the LogRecords as it containsthings such as exception information, the method, file, log line where the log was generated.If you are interested on understanding more about the differences between the agent vs handlerapproach, I'd suggest readingthis conversation thread <https://github.com/cmanaha/python-elasticsearch-logger/issues/44/>_The same functionality can be implemented in many other different ways. For example, consider the integrationusingSysLogHandler <https://docs.python.org/3/library/logging.handlers.html#sysloghandler>_ andlogstash syslog plugin <https://www.elastic.co/guide/en/logstash/current/plugins-inputs-syslog.html>_.Contributing backFeel free to use this as is or even better, feel free to fork and send your pull requests over.
Package: esl-redis
Read continuously from a redis list(s) and index to elasticsearchInstallationpip installesl-redisUsage$ elasticsearch_loader --index lol --type cake redis --help
Usage: elasticsearch_loader redis [OPTIONS] LIST_NAMES...

Options:
  --host TEXT                  Redis host
  --port INTEGER               Redis port
  --db INTEGER                 Redis db
  --list-read-timeout INTEGER  block for X seconds, or until a value gets
                               pushed on to one of the lists. If timeout is 0,
                               then block indefinitely.
  -h, --help                   Show this message and exit.Exampleselasticsearch_loader--indexindex--typetype redis my_list_name
Package: esl-s3
Plugin for listing and indexing files from S3Installationpip installesl-s3Usage$ elasticsearch_loader --index lol --type cake s3 --help
Usage: elasticsearch_loader s3 [OPTIONS] BUCKET

Options:
  --prefix TEXT  AWS_S3_PREFIX
    -h, --help     Show this message and exit.Exampleselasticsearch_loader--indexindex--typetype s3 mybucket
Package: esm-analysis
ESM AnalysisAnalysis Scripts for ESM SimulationsQuickstartesm_analysisis the sugar for your bitter CDO-coffee. Check it out:Installesm_analysiswith:pip install esm_analysisGo to an experiment:cd /work/ba0989/a270077/AWICM_PISM/LGM_011Get a climatology:esm_analysis newest-climatology temp2
esm_analysis newest-climatology thetaoCalculate AMOC:esm_analysis amocIsn’t that better than this?outdata_dir=...# oh man now I need to figure out where the outdata isexpid=PI# ...uh...PI, I guess?...outfile=test.nc# ...I guess I should think of a real name for my outputvars=temp2# Oh! I know that one!filepattern="echam6_echam"fileext=".nc"# Aww geez I have no idea. Is it nc? grb? Let's just guess...
# How did cdo work again?cdo-hselectcdo-htimmmean# Ok, now I think I know what I want:cdo-fnc-techam6-timmean-select,name=$vars${outdata_dir}/${expid}_${filepattern}${fileext}$outfileI’d support the following hypothesis: you clearly don’t want to type all that stuff
out. Just say what you want. Easy, right?The computer should be able to figure out the rest for you. If Google can
predict where I get my breakfast, why can’t my computer figure out whatCDOcommand I want to use??FeaturesCommand line interface to quickly create common analysesPython library usageFree software: GNU General Public License v3Documentation:https://esm-tools-analysis.readthedocs.io.HistoryThis project was inspired by the many headaches caused by it not existing.
Here’s what has happened so far:0.4.2 (2020-02-04)Closes #40.4.1 (2020-02-04)Minor documentation update.0.4.0 (2020-02-03)FESOM 1.4climatologies0.3.4 (2020-01-31)Docs fixupLinks in wrong place in README.0.3.3 (2020-01-31)Adds version information with--versionFixup for multiple files having the requested variable0.3.2 (2019-12-18)Bad test fix0.3.1 (2019-12-18)Fixes a permission error0.3.0 (2019-12-18)Adds utility to parse compute log and provide some statistics on queuing time
and throughputIf multiple files have the same variable, ask user which filepattern to useNewest climatology forECHAM60.2.0 (2019-12-17)Asks user to create top level file if it isn’t found0.1.1 (2019-12-17)Process files in chunks forECHAM60.1.0 (2019-08-09)First release on PyPI.
Package: esmbc
EVE Ship Maintenance Bay CalculatorEsmbc is a command line ship volume calculator for EVE online. It would
probably be of most use to EVE pilots that own and fly carriers.Usage$ esmbc hound:3 talwar:2 sabre:1 rupture:1 hurricane:1 tornado:1Sabre: 43,000m3
Talwar: 86,000m3
Tornado: 216,000m3
Rupture: 96,000m3
Hound: 84,300m3
Hurricane: 216,000m3
Total: 741,300m3Esmbc accepts ship names and quantities with the colon character used
as a seperator.The pairs are seperated by a single whitespace.Ship names which contain a white space should have it replaced with an
underscore, all other special characters should not be entered.Republic Fleet Firetailshould be entered asrepublic_fleet_firetail.Case is not important.Rifter,RIFTERandrifterwill all be
matched correctly.InstallationIt’s possible to just use esmbc without installation by just calling the
module:python-mesmbcTo do sodownload a zipof
this repo or clone it.git clonegit://github.com/stuartdb/esmbc.gitIf you wish to actually install esmbc on your system the prefered way would
be to use pip.$ pip install esmbcor use obtain this repository as previously mentioned and:$ python setup.py installRequirementsEsmbc had been tested on Python 3.3 and Python 2.7.If you wish to install the module thendistributeis required.Ship/Volume Data FileThe ship and volume data file that is required is supplied (ships.json).You also have the option to build your own data file. This may be required in
the future as CCP release new ship types.A script is included to access a sqlite version of the CCP community data dump
and output a JSON string of the ships and their volumes.$ python sqlite.py > ships.jsonLicenseesmbc is released under theGPLv3 license
Package: esmcol-validator
Earth System Model (ESM)Collection specification ValidatorEarth System Model (ESM)Collection specification ValidatorInstallationUsageThis utility allows users to validate esm-collection json files against theesm-collection-spec.InstallationThe validator can be installed in any of the following ways:Using Pip via PyPI:python-mpipinstallesmcol-validatorUsing Conda:condainstall-cconda-forgeesmcol-validatorOr from the source repository:python-mpipinstallgit+https://github.com/NCAR/esmcol-validator.gitUsage$esmcol-validator--help
Usage:esmcol-validator[OPTIONS]ESMCOL_FILEAutilitythatallowsuserstovalidateesm-collectionjsonfilesagainsttheesm-collection-spec.

Options:--esmcol-spec-dirsTEXT--versionTEXT[default:master]--verbose[default:False]--timer[default:False]--log-levelTEXT[default:CRITICAL]--helpShowthismessageandexit.Example:$esmcol-validatorsample-pangeo-cmip6-collection.json{'collections':{'valid':1,'invalid':0},'catalogs':{'valid':1,'invalid':0},'unknown':0}{"collections":{"valid":1,"invalid":0},"catalogs":{"valid":1,"invalid":0},"unknown":0}
Package: esmerald-sessions
Esmerald🌟 An alternative SessionMiddleware for Esmerald with Pydantic at its core. 🌟Documentation:https://esmerald-sessions.dymmond.com📚Source Code:https://github.com/dymmond/esmerald-sessionsMotivationUsing the defaultSessionMiddlewarefrom Esmerald might not be enough for those applications that need a bit more
than just a simple caching, for example, where to store that same cookie.This package offers that possibility and allows the extension of it if needed.Inspired byStarlette Sessionand with
Pydantic at its core, Esmerald sessions offers the best of both worlds.RequirementsPython 3.7+Installation$pipinstallesmerald-sessionsHow to usePlease check thedocumentationhow to use the package.