Saravanan666/pypi-org huggingface dataset:
Package: ertis-auth-cli
Ertis Auth Initializer CLIInit your database (mongodb) for using ertis auth.If you are using ertis auth, you should create first membership, role and user.
This manually operation have some risks.You may not be sure about Membership, user and role database models.sys field. who is created that documents and when?You can forget database indexes before using.Ertis Auth CLI create first models and create mongodb indexes by your given mongodb connection string.UsageInstallation$pipinstallertis_auth_cliHelp$ertis_auth_cli--helpSetup$ertis_auth_cli-c<mogno_connection_string>-d<datbase_name>Then cli asks some questions to you.Initializeyouertisauthservice.
?Enteramembershipname:ertis
?Enterarolename:admin
?Enterausername:admin
?Enterapassword:mySecretP@assWord!
?Entertokenttlvalueasaminutes:60?Enterrefreshtokenttlvalueasaminutes:120?Entermaxactivetokencountbyuser:120?Doyouwantcreateindexesonmongodbforertisauth?[y|N]TrueAfter installation you can check your mongodb collections and indexes.Note:  if you have memberships, roles and users collections already this cli will be created only documents. s
And documents means new membership, new role under membership and new user under membership.
Package: ertis-auth-migrate
Ertis Auth Initializer CLIErtis Auth Command Line Interface.Package IndexMigrateInit your database (mongodb) for using ertis auth.If you are using ertis auth, you should create first membership, role and user.
This manually operation have some risks.You may not be sure about Membership, user and role database models.sys field. who is created that documents and when?You can forget database indexes before using.Ertis Auth CLI create first models and create mongodb indexes by your given mongodb connection string.UsageInstallation$pipinstallertis_auth_migrateHelp$ertis_auth_migrate--helpSetup$ertis_auth_migrate-c<mogno_connection_string>-d<datbase_name>Then cli asks some questions to you.Initializeyouertisauthservice.
?Enteramembershipname:ertis
?Enterarolename:admin
?Enterausername:admin
?Enterapassword:mySecretP@assWord!
?Entertokenttlvalueasaminutes:60?Enterrefreshtokenttlvalueasaminutes:120?Entermaxactivetokencountbyuser:120?Doyouwantcreateindexesonmongodbforertisauth?[y|N]TrueYou can check your mongodb collections and indexes after installation.Note:  if you have memberships, roles and users collections already this cli will be created only documents. s
And documents means new membership, new role under membership and new user under membership.
Package: erutils
ErutilsThe Erutils package includes a collection of utilities and pre-built neural networks that I frequently use in my
personal projects. While this package is mainly for my personal use, others may find it useful as well.InstallationYou can install Erutils using pip:pipinstallerutilsOpenerfan zare GithubOpenPackage Github🚀 APIs and Usagesimporterutilsfromerutils.loggersimport*# Loggers contain Logging stufffromerutils.nnimport*# NN contain Nural networks built in Pytorch# you can easily import erutils.nn and use many prebuilt neurons in the project# if you thing any refrence should be added tell me in discussionfromerutils.lightningimport*# Lightning Contain Some CV Packagesfromerutils.utilsimport*# Utils Contain Some Utils like reading files downloading and etc ...Neural NetworksErutils includes a diverse collection of over 80 pre-built neural networks that cover a wide range of applications, such
as natural language processing, computer vision, and more. Some of the neural networks included are:Transformers: Customized transformer encoder and decoder layers for natural language processing tasks and a lot of
customized Layers like RoEmbedding ...Feed-forward networks: Customized feed-forward layers for various applicationsCustom CNN backbones: A variety of pre-built CNN backbones for computer vision tasks, such as ResNet, VGG, and moreObject Detection: Pre-built networks for object detection and localization, including YOLOv5, RetinaNet, and Faster
R-CNNCustom CNN architectures: Custom CNN architectures designed for specific applications, such as image classification,
segmentation, and more.🚀 About MeHi there 👋
I like to train deep neural nets on large datasets 🧠.
Among other things in this world:)LicenseMITContributingWhile this package is mainly for my personal use, contributions and suggestions are welcome. If you find a bug or have
an idea for a new feature, please open an issue on the GitHub repository.Used ByPrivate ProjectAuthor@erfanzar
Package: ervdetective
ERVdetective: an efficient pipeline for identification and annotation of endogenous retroviruses (ERVs)1. Download and installervdetective is a command-line-interface program developed based onPython 3, and you can download and install ervdetective in a variety of ways.1.1. pip methodervdetective has been distributed to the standard library ofPyPI, and can be easily installed by the toolpip.pip install ervdetective
ervdetective -h1.2. Or local installationIn addition, ervdetective can also be installed manually using the file using the filesetup.py.Firstly, download this repository, then, run:python setup.py install
ervdetective -h1.3. Or run the source code directlyyou can also directly run the source code of ervdetective without installation. First, download this repository, then, install the required python environment of ervdetective:pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simplefinally, run ervdetective using the filemain.py. Please view the help documentation bypython main.py -h.2. Software dependenciesThe running ofervdetectiverelies on these softwares:blast(version >=2.9.0+), has to containmakeblastdbandtblastn.genometools(version >=1.6.1), has to containltrharvest.hmmer(version >=3.0), has to containhmmpressandhmmscan.Note, these dependencies need to be installed and added to environment variables of system (or user) beforehand, because ervdetective call them from the environment variables directly.3. Getting helpThe help documentation can be get by enteringervdetective -horervdetective --help.ParameterDescription-h, --helpshow this help message and exit-i HOSTThe file-path of host genome sequence, the suffix is generally *.fna, *.fas, *.fasta.-eb EBLASTSpecify threshold of e-value for BLAST search, default: 0.0001.-f FLANKSpecify length of extended flank sequence on either side of the blast hit-site, default: 15000.-l1 MINLTRSpecify minimum length of LTR, default: 100.-l2 MAXLTRSpecify maximum length of LTR, default: 1000.-s LTRSIMILARSpecify threshold(%) of the similarity of paired LTRs, default: 80.-d1 MINDISTLTRSpecify minimum interval of paired-LTRs start-positions, default: 1000.-d2 MAXDISTLTRSpecify maximum interval of paired-LTRs start-positions, default: 15000.-t1 MINTSDSpecify minimum length for each TSD site, default: 4.-t2 MAXTSDSpecify maximum length for each TSD site, default: 6.-motif MOTIFSpecify start-motif (2 nucleotides) and end-motif (2 nucleotides), default string: TGCA.-mis MISMOTIFSpecify maximum number of mismatches nucleotides in motif, default: 1.-ed EHMMERSpecify threshold of e-value using for HMMER search, default: 0.0001.-n THREADSpecify the number of threads used, default: 1.-p PREFIXSpecify the prefix of output file, default character: 'host'.-o OUTPUTThe path of output folder to store all the results.4. Example of usageThe bat Myotis myotis (GCA_004026985.1) , and its genome data was downloaded fromhttps://www.ncbi.nlm.nih.gov/datasets/taxonomy/51298/Then, run:ervdetective -i GCA_004026985.1_MyoMyo_v1_BIUU_genomic.fna -p myotis_myotis -o output
Package: ervin
ERVinThis is a tool to allow for the detection of ERVs in genome segmentsThis has been designed primarily with a view to be used on OSX, cross-compatibility with other UNIX-based architectures may exist, but it almost certainly will not run on Microsoft Windows systemsInstallationpip install ervinRequirementsPython 3.6+ (Download)NCBI BLAST suite must be installed locally (Download)Local genome db to be queriedThis can be located in a directory of your choosing, but must be named in aconfig.jsonfileThere is aconfig.json.templfile which will be used to create aconfig.jsonfile from with the contained defaults at first run if you do not provide your ownCurrent functionalityERViN Currently:When provided with a.fastafile of probe sequencesRuns localtblastnagainst the specified genome database, filtering the results based on alignment length and e-value (optional arguments which result in default values of >400 and <0.009 respectively when omitted)Parses and merges filtered results where appropriateRuns resultant fasta records against a local Viruses refseq database (a copy will be downloaded if not user provided, and will be kept up-to-date) usingtblastn, grouping the records in a final set of output files based on their top hitUsageArgumentsArgumentVerboseDescriptionTypeRequiredDefault-f--fileSource fasta file containing the sample probe records to run through tblastnFilepathTrue-gdb--genome_databaseName of the genome database against which the probe records are to be BLASTed (located in the genome db store specified in the config filestrTrue-o--output_dirLocation to which to write the result filesstrFalse<current_working_directory>/OUTPUT-a--alignment_len_thresholdMinimum length threshold that BLAST result alignment sequence lengths should exceedintFalse400-e--e_valueMaximum e-value threshold that BLAST result e-values should exceedfloatFalse0.009Exampleservin -f data/fasta_file.fasta -gdb genome_dbervin -f data/fasta_file.fasta -gdb genome_db -o results/probe_blaster_outputervin -f data/fasta_file.fasta -gdb genome_db -o results/probe_blaster_output -a 500ervin -f data/fasta_file.fasta -gdb genome_db -o results/probe_blaster_output -e 0.0008ervin -f data/fasta_file.fasta -gdb genome_db -o results/probe_blaster_output -a 800 -e 0.01
Package: ervsearch
ERVsearchFull documentation is available viaReadTheDocs.ERVsearch is a pipeline for identification of endogenous retrovirus like regions in a host genome, based on sequence similarity to known retroviruses.ERVsearch screens for endogenous retrovirus (ERV) like regions in any FASTA file using the Exonerate algorithm (Slater and Birney, 2005, doi:10.1186/1471-2105-6-31).In theScreensection, open reading frames (ORFs) resembling retroviralgag,polandenvgenes are identified based on their level of similarity to a database of known complete or partial retroviral ORFs.In theClassifysection, these ORFs are classified into groups based on a database of currently classified retroviruses and phylogenetic trees are built.In theERVRegionssection, regions with ORFs resembling more than one retroviral gene are identified.This is a updated and expanded version of the pipeline used to identify ERVs in Brown and Tarlinton 2017 (doi:10.1111/mam.12079), Brown et al. 2014 (doi:10.1128/JVI.00966-14), Brown et al. 2012 (doi:j.virol.2012.07.010) and Tarlinton et al. 2012 (doi:10.1016/j.tvjl.2012.08.011). The original version is availablehereas a Perl pipeline and was written by Dr Richard Emes.
Package: erzsol3Py
erzsol3PyA few useful Python functions to easily create erzsol3 input files and read output files.Erzsol3 is a Fortran code written by Brian Kennet. The original version can be downloaded athttp://www.quest-itn.org/library/software/reflectivity-method.htmlTwo files have been slighly changed here. In qbessel.f line 136 was changed from COMPLEX FUNCTION BESHS016(X, IFAIL) to  COMPLEX16 FUNCTION BESHS0(X, IFAIL) in order to overcome an error occuring during compiling. A copy of erzol3.f named erzsol3c.f was made and it was changed to allow a greater range of slownesses and frequencies from 600 and 2500 to 10000 and 3600, respectively.The fortran code is compiled in the following way form the directory of this README file
gfortran -mcmodel=medium -O2 -o erzsol3SourceCode/bin/erzsol3 erzsol3SourceCode/src/erzsol3c.f erzsol3SourceCode/src/qbessel.f erzsol3SourceCode/src/qfcoolr.ferzsol3Py can be installed via pip
pip install -ihttps://test.pypi.org/simple/erzsol3Pycan be imported in python as
import erzsol3Py.erzsol3Py as erz
Package: es2es
es2esTransfers data between Elasticsearch serversInstallationpipinstalles2esUsagefromes2esimportes2eses2es(origin_endpoint='https://PATH-TO-ENDPOINT',origin_index='ORIGIN_INDEX',dest_endpoint='https://PATH-TO-ANOTHER-ENDPOINT',dest_index='DESTINATION_INDEX')Advanced usageorigin_endpoint(str):"Path to the endpoint to transfer data from."origin_index(str):"Index at the endpoint to transfer data from."dest_endpoint(str):"Path to the endpoint to transfer data to."dest_index(str):"Index at the endpoint to transfer data to."origin_method(str,default='GET'):"Method for retrieving data from origin endpoint (e.g. 'GET' or 'POST')."chunksize(int,default=100):"Number of records to send to the new server in bulk."scroll(str,default='1m'):"Elasticsearch scroll window."origin_kwargs(dict,default={}):"Any additional kwargs to add to the http(s) request to the origin endpoint."dest_kwargs(dict,default={}):"Any additional kwargs to add to the http(s) request to the origin endpoint."do_transfer_index(bool,default=True):"Flag regarding whether to transfer the index, settings and mapping as well. Set this to False if the index already exists, or you're restarting a transfer."limit(int,default=None):"Maximum number of 'rows' of data to transfer."
Package: es3-modifier
es3-modifieres3-modifieris a Python package designed for handling stat files generated by Unity'sEasy Savetool. With it, you can seamlessly open, modify, and save new stats. This project was created for use ines3-editorInstallationTo get started, you can easily install the package using pip:pipinstalles3-modifierUsageBelow is a practical demonstration of how to use this package. In this instance, we'll be working with thePhasmophobiagame save file, extracting key details, and adjusting specific player stats:importos,re,jsonfromes3_modifierimportES3defformat_inventory_name(name):"""Format the inventory name for better readability."""name=re.sub(r'(?<=[a-z])([A-Z])',r' \1',name)name=re.sub(r'(?<=[A-Z])([A-Z][a-z])',r' \1',name)returnname.replace('Inventory','').rstrip()# Define the path to the Phasmophobia save filephasmophobia_path=f'{os.environ["USERPROFILE"]}\\AppData\\LocalLow\\Kinetic Games\\Phasmophobia\\SaveFile.txt'# Initialize ES3 with the Phasmophobia save datawithopen(phasmophobia_path,'rb')asfile:es3=ES3(file.read(),'t36gref9u84y7f43g')try:# Load and decrypt the save datadecrypted=es3.load()# Display player statsprint(f'Money: ${decrypted["PlayersMoney"]["value"]:,}')print(f'Legacy Level:{decrypted["Level"]["value"]}')print(f'Level:{decrypted["NewLevel"]["value"]}')print(f'Prestige:{decrypted["PrestigeIndex"]["value"]}')# Display inventoryprint('\n== Inventory ==')forkey,valueindecrypted.items():if'Inventory'notinkey:continueformatted_name=format_inventory_name(key)amount=value['value']print(f'{formatted_name}:{amount}')# Modify save data valuesdecrypted['PlayersMoney']['value']=99999decrypted['NewLevel']['value']=123# Save the modified datawithopen('new.txt','wb')asout_file:out_file.write(es3.save(json.dumps(decrypted)))exceptExceptionase:print(e)Remember to always create backups before editing your save files!PrerequisitesThis package usespycryptodomefor AES, it will automatically be installed.
Package: es7s.commons
es7s system shared codeInstallationpipinstalles7s-commonsContents@TODOLoggingMax verbosityimportlogginglogging.getLogger('es7s_commons').setLevel(logging.DEBUG)Silenceimportlogginglogging.getLogger('es7s_commons').setLevel(logging.CRITICAL)ChangelogCHANGES.rst
Package: esa
Easy SimAuto (ESA) is an easy-to-use Power System Analysis Automation
Platform atop PowerWorld’s Simulator Automation Server (SimAuto).
ESA wraps all PowerWorld SimAuto functions, supports Auxiliary scripts,
provides helper functions to further simplify working with SimAuto and
also turbocharges with native implementation of SOTA algorithms. Wherever
possible, data is returned as Pandas DataFrames, making analysis a breeze.
ESA is well tested and fullydocumented.DocumentationFor quick-start directions, installation instructions, API reference,
examples, and more, please check out ESA’sdocumentation.If you have your own copy of the ESA repository, you can also view the
documentation locally by navigating to the directorydocs/htmland
openingindex.htmlwith your web browser.If you want to use ESA or SimAuto from julia, definitely checkEasySimauto.jlfor a julia wrapper of ESA.CitationIf you use ESA in any of your work, please use the citation below.@article{ESA,
  doi ={10.21105/joss.02289},
  url ={https://doi.org/10.21105/joss.02289},
  year ={2020},
  publisher ={The Open Journal},
  volume ={5},
  number ={50},
  pages ={2289},
  author ={Brandon L. Thayer and Zeyu Mao and Yijing Liu and Katherine Davis and Thomas J. Overbye},
  title ={Easy SimAuto (ESA): A Python Package that Simplifies Interacting with PowerWorld Simulator},
  journal ={Journal of Open Source Software}}InstallationPlease refer to ESA’sdocumentationfor full, detailed installation
directions. In many cases, ESA can simply be installed by:python -m pip install esaSimulator CompatibilityCurrently ESA supports PW Simulator V17, V18, V19, V20, V21, V22 and V23.Testing CoverageThe ESA team works hard to ensure ESA is well tested, and we strive for
100% testing coverage (sometimes we cannot due to lack of specific add-ons). The table below shows the most up-to-date
testing coverage data for ESA, usingcoverage.ESA’s testing coverage as of 2023-05-31 (Git commit: 7180cc9)NameNum. StatementsMissing LinesCovered LinesPercent Coverageesa/__init__.py202100esa/saw.py12062120499.8342LicenseApache License 2.0ContributingWe welcome contributions! Please readcontributing.md.
Package: esa-cci-sm
Reading and reshuffling of CCI soil moisture Written in Python.InstallationInstalling the package can be done via pip:pipinstallesa_cci_smSetup of a complete development environment withcondacan be performed using the following
commands:gitclonegit@github.com:TUW-GEO/esa_cci_sm.git--recursiveesa_cci_smcd./esa_cci_smcondaenvcreate-fenvironment.ymlsourceactivateesa_cci_smpipinstall-e.To checkout our testdata files, you need to haveGit LFSinstalled on your machine.Supported ProductsAt the moment this package supports ESA CCI soil moisture data version
v02.x, v03.x, v04.x and v05.x, v06.x, v07.x in netCDF format (reading and time series creation)
with a spatial sampling of 0.25 degrees.ContributeWe are happy if you want to contribute. Please raise an issue explaining what
is missing or if you find a bug. We will also gladly accept pull requests
against our master branch for new features or bug fixes.GuidelinesIf you want to contribute please follow these steps:Fork the esa_cci_sm repository to your accountClone the repository, make sure you usegit clone ...--recursiveto also get
the test data repository.make a new feature branch from the esa_cci_sm master branchAdd your featurePlease include tests for your contributions in one of the test directories.
We use pytest so a simple function called test_my_feature is enoughsubmit a pull request to our master branchNoteThis project has been set up using PyScaffold 2.5. For details and usage
information on PyScaffold seehttp://pyscaffold.readthedocs.org/.
Package: esa-climate-toolbox
ESA CCI ToolboxThe ESA CCI Toolbox is designed to provide access to CCI data from the ESA
Open Data Portal.
Also, it contains functions to operate on this data.Installation into a new environment with condaThe ESA CCI Toolbox and all necessary dependencies are available
onconda-forge, and can be installed using theconda package manager.
The conda package manager itself can be obtained in theminiconda
distribution.
Once conda is installed, the ESA CCI Toolbox can be installed like this:$ conda create --name ect --channel conda-forge esa-climate-toolbox
$ conda activate ectThe name of the environment may be freely chosen.Installation into an existing environment with condaThe ESA CCI Toolbox can also be installed into an existing conda
environment.
To do so, execute this command with the existing environment activated:$ conda install --channel conda-forge esa-climate-toolboxAny necessary dependencies will be installed or updated if they are not already
installed in a compatible version.Installation into an existing environment from the repositoryIf you want to install the ESA CCI Toolbox directly from the git repository
(for example in order to use an unreleased version or to modify the code),
you can do so as follows:$ git clone https://github.com/esa-cci/esa-climate-toolbox.git
$ cd esa-climate-toolbox
$ conda env create
$ conda activate ect
$ pip install -e .TestingYou can run the unit tests for the ESA CCI Toolbox by executing$ pytestin theesa-climate-toolboxrepository.To create a test coverage report, you can usecoverage run --include='esa-climate-toolbox/**' --module pytest
coverage htmlThis will write a coverage report tohtmlcov/index.html.UseJupyter notebooks demonstrating the use of the ESA CCI Toolbox can be found
in thenotebooks/subdirectory of the repository.ReleasingTo release theesa-climate-toolbox, please follow the steps outlined in thexcube Developer Guide.
Package: esader
Aciklama=====Python programlari icin logo ve renklendirme.Modul Detayi=============Yazidgim python programlari icin genel renklendirme ve logoesLogo("programiYazanKisi", "programiYazanGrup", "programIsmi", "clear")oresLogo("programiYazanKisi", "programiYazanGrup", "programIsmi", "notClear")oresLogo("programiYazanKisi", "programiYazanGrup", "programIsmi") #NotClear!
Package: esak
Code on GithubPublished on PyPiMarvel API documentationThis project is a fork ofmarvelouswith the goal of supporting the full Marvel API.To install:$pip3install--useresakExample Usage:importesak# Your own config file to keep your private key local and secretfromconfigimportpublic_key,private_key# Authenticate with Marvel, with keys I got from http://developer.marvel.com/m=esak.api(public_key,private_key)# Get all comics from this week, sorted alphabetically by titlepulls=sorted(m.comics_list({'format':"comic",'formatType':"comic",'noVariants':True,'dateDescriptor':"thisWeek",'limit':100}),key=lambdacomic:comic.title)forcomicinpulls:# Write a line to the file with the name of the issue, and the# id of the seriesprint(f'{comic.title}(series #{comic.series.id})')DocumentationRead the project documentationBugs/RequestsPlease use theGitHub issue trackerto submit bugs or request features.ContributingWhen running a new test for the first time, set the environment variablesPUBLIC_KEYandPRIVATE_KEYto any Marel API keys. The result will be
stored in thetests/testing_mock.sqlitedatabase without your keys.