Saravanan666/pypi-org huggingface dataset:
Package: eshop-prices
Nintendo Eshop PricesAvailable resourcesSearchGet prices from URLGet pricesGet top discountsGet availables currenciesInstall$pipinstalleshop-pricescontributeClone the repository project:$gitclonehttps://github.com/hudsonbrendon/eshop-prices.gitMake surePoetryis installed, otherwise:$pipinstall-UpoetryInstall the dependencies:$poetryinstallTo run the tests:$pytestUsageSearchfromeshop_pricesimportEshopPriceseshop=EshopPrices(currency="BRL")eshop.search("Zelda")Get prices from urlfromeshop_pricesimportEshopPriceseshop=EshopPrices(currency="BRL")eshop.get_prices_from_url("/games/164-mario-kart-8-deluxe/")Get pricesfromeshop_pricesimportEshopPriceseshop=EshopPrices(currency="BRL")eshop.get_prices("Zelda")Get top discountsfromeshop_pricesimportEshopPriceseshop=EshopPrices(currency="BRL")eshop.get_top_discounts()Get available currenciesfromeshop_pricesimportEshopPriceseshop=EshopPrices(currency="BRL")eshop.get_available_currencies()DependenciesPython >=3.8LicenseMIT
Package: esibd-explorer
A comprehensive data acquisition and analysis tool for Electrospray Ion-Beam Deposition experiments and beyond.ESIBD Explorer user interface.The ESIBD Explorer controls all aspects of an Electrospray Ion-Beam Deposition (ESIBD) experiment,
including optimization and characterization of the ion-beam energy, beam intensity, and beam size, as well as monitoring of the deposition.
At each step, results and metadata are saved to document the experiment and allow for later reproduction of all experimental conditions.DocumentationRead the documentation online athttp://esibd-explorer.rtfd.io/InstallationInstall directly from thePython Package Indexusing pip:pip install esibd-explorerRun the program using:python -m esibd.explorerVisit thedocumentationfor alternative methods.Source codehttps://github.com/ioneater/ESIBD-ExplorerContactioneater.dev@gmail.com
Package: esi-controls
pyesiIntroductionPython API client to theESI ControlsAPI for monitoring and controlling your thermostat.UsageAuthenticateYou need to log in and authenticate the client.fromesi_controlsimport*client=Esi()user=client.login()client.authenticate(user.token,user.user_id)Get device listYou can discover all of your devicesdevices=client.get_device_list()fordeviceindevices:print(f"Name:{device.name}, Temp:{device.inside_temperature}({device.current_temperature})")Set thermostatYou can update the current temperatureclient.set_thermostat(device.id,"180")
Package: esi-core
esi-coreRepository for compiled code used by ESI projects, namely C code used in the USGS Shakemap and Groundmotion-Processing programs.As of September 15th, this project is functional for the gmprocess portion of compiled code. Shakemap functionality will soon follow.gmprocessThe compiled code here is used ingmprocessboth in metrics calculations and waveform processing.Computation of waveform metrics utilizes theoscillatorsmodule, while waveform processing routines use theauto_fchpfor corner frequency determination, as well as thekonno-omachiandsmoothingmodules to perform spectral smoothingShakemapUnder development for future PyPi releaseProvides theShakemapproject with necessary C libraries for Shakemap production and contouring
Package: esigen
ESIgen: Supporting information generatorAutomatically generate supporting information documents for your
Chemistry publications online.Documentation and supportLatest documentation is always available atesigen.readthedocs.io.If you have problems using ESIgen, feel free tocreate an issue!
Also, make sure to visit our main webpage atinsilichem.com.CitationESIgen is scientific software, funded by public research grants: Spanish
MINECO (project CTQ2017-87889-P), Generalitat de Catalunya (project
2014SGR989), J.R.G.P.: Generalitat de Catalunya (grant 2017FI_B2_00168),
P.G.O.: Spanish MINECO (grant FPI BES-2015-074190). If you make use of
ESIgen in scientific publications, please citeour article in JCIM.
It will help measure the impact of our research and future funding!@article{doi:10.1021/acs.jcim.7b00714,
    author = {Rodríguez-Guerra Pedregal, Jaime and Gómez-Orellana, Pablo and Maréchal, Jean-Didier Pierre},
    title = {ESIgen: Electronic Supporting Information Generator for Computational Chemistry Publications},
    journal = {Journal of Chemical Information and Modeling},
    year = {2018},
    doi = {10.1021/acs.jcim.7b00714},
        note ={PMID: 29506387},
    URL = {
            https://doi.org/10.1021/acs.jcim.7b00714
    },
    eprint = {
            https://doi.org/10.1021/acs.jcim.7b00714
    }
}AcknowledgmentsInspired byChauncey Garrett’s collection of scripts, this project
was conceived as a Python-only CLI attempt at solving the same problem.
Then more features were added (like markdown reports or image
rendering), and finally was turned into a online service.ESIgen is possible thanks to great open-source projects:Backend:CCLib,Jinja.Web UI:NGL,Flask,requests,HTML5 UP,Dropzone,clipboard.js,FileSaver.js,JSZip,CloudFormatter.CLI:PyMol.Installer:Conda Constructor.
Package: esi-maker
esi_makerThis is a python module to make, load and unzip esi files.ESI is a lightweight sound source file format that stores a folder of audio files into a single binary file, together with a setting file, where you can customize the mappings between note names and audio file names, and other specific note control informations.ESI file stores the uncompressed data of each audio file in the folder you specify, and the information to unzip the audio files with their original file names, along with the settings.ESI stands forEasy Sampler Instrument, which is used inEasy Sampleras an audio samples sound source file format, together with SoundFont files and audio files.For more details about ESI format, you can refer to the musicpy sampler module's wiki,click hereInstallationMake sure you have installed python >= 3.7 first.Runpip install esi_makerin cmd/terminal.Importingimportesi_makerasesUsageThere are 3 functions in this python module,make_esi,load_esiandunzip_esi.make_esi(file_path,name='untitled.esi',settings=None,asfile=True,name_mappings=None,show_msg=True)# this function will make an esi file at the path that the parameter `name` specified,# the file extension of the esi file is esi# file_path: the directory of folder than contains all of the audio files you want to# include in the esi file# name: the name of the esi file# settings: the settings of the esi file, could be a string or a file path of a text file# asfile: if set to True, then read settings as a file path of a text file, otherwise read as a string# name_mappings: the dictionary maps note names to audio file names# show_msg: print progress messages or notload_esi(file_path,convert=True)# this function will load a esi file, return an esi class instance# file_path: the file path of the esi file you want to load# convert: if set to True, the audio files in the esi file will be converted to# pydub AudioSegment instances from binary dataunzip_esi(file_path,folder_name=None,show_msg=True)# this function will unzip the audio files in the esi file to the folder you specify# file_path: the file path of the esi file you want to unzip the audio files# folder_name: the path of the folder you want to unzip the audio files to# show_msg: print progress messages or notThe esi class:esi(samples,settings=None,name_mappings=None)# samples: a dictionary which keys are the audio file names, values are the audio file binary data# settings: the string represents settings# name_mappings: the dictionary maps note names to audio file names# file_names: this attribute is an auto-generated dictionary with audio file names# without file extension maps to audio file names with file extension when an esi instance is initialized
Package: e-sim-game-scraper
E-sim ScraperUnofficial scraper for thee-simbrowser game.Installationpipinstall-Ue-sim-game-scraperUsagefrome_sim_game_scrapperimportEsimScraper# or from e_sim_game_scrapper.EsimScraper import get_page_data# Example 1link="https://luxia.e-sim.org/battleDrops.html?id=1&showSpecialItems=yes"result=EsimScraper.get_page_data(link)# >>> {'pages': 2, 'drops': [{'nick': 'Taturuski', 'item': 'Bandage size A', 'citizen_id': 912},# >>>                        {'nick': 'Sverlio77_77', 'item': 'Bandage size C', 'citizen_id': 307}, ...]}# Example 2 (locked page)importrequestsfromlxml.htmlimportfromstringtree=fromstring(requests.get(link).text)# this is a locked page, so you actually have to log-in first.link="https://luxia.e-sim.org/showShout.html?id=1"result=EsimScraper.get_page_data(link,tree)# or call the function directly:result=EsimScraper.showShout(tree)List of supported base-links / functions (alphabetical order):achievementarticleauctionbattleDropsbattlesbattlesByWarcitizenStatisticscoalitionStatisticscompaniesForSalecongressElectionscountryEconomyStatisticscountryPoliticalStatisticscountryStatisticseventsjobMarketlawmonetaryMarketnewCitizenStatisticsnewCitizensnewsnewspapernewspaperStatisticspartypartyStatisticspresidentalElectionsproductMarketprofileregionshowShoutstockCompanystockCompanyMoneystockCompanyProducts(Note: not all of them are tested, some of them may not work properly, as those are dynamic pages that may change over time.)There are tests for most of them locally, but they are not included in the package for now.Support the Project:The development and maintenance of this bot are ongoing. If you find it valuable, please consider supporting it through
a donation:Buy Me a CoffeePatreonYour contributions will help ensure the bot's continued improvement and availability for the e-Sim community.
Package: es-indexer
es-indexeres-indexer(Elasticsearch Indexer) is a simple concurrent command line tool written in Python to help you quickly populate some json data into Elasticsearch.AboutUsually you'll have to use a third-party software or a client library to index data to Elasticsearch and setting that up can be really time consuming and tiresome (coughlogstashcough).es-indexerhelps in indexing raw contents of*.jsondocuments quickly with the help of multi-processing.es-indexercurrently doesn't provide any syncing of the data, you'll have to reindex the data if it changes, but will always populate a new index and then create an alias, the old data will be present while re-indexing until the new index is fully populated. A future update might include syncing.Since Elasticsearch exposes a REST-API on Port 9200, there's no need for es-indexer providing a REST-API itself.InstallationRequires Python 3.x and is compatible with Elasticsearch 7.x.x.es-indexer can be installed with the help of pip.$ pip install es-indexer(OR)Clone the repository.$ git clone https://github.com/itsron717/es-indexer.gitMove inside the repo.$ cd es-indexerInstall the package locally.$ pip install .UsageConfigYou need to create aconfig.ymlbefore running es-indexer:host: http://127.0.0.1:9200
index: twitter-example
type: documents
mapping:
    settings:
        number_of_shards: 1
        number_of_replicas: 0You can provide a custom mapping in the config file, es-indexer will convert the yaml mapping 1:1 to json.$ es-indexer --config path/to/config/file --source path/to/json/folderAdding more Data SourcesMore data sources other thanjsonsuch asSQL,Filesystem, etc are also to be added to the es-indexer tool such that it can be a one stop shop for all the indexing needs of Elasticsearch. Anybody who'd like to contribute in integrating other data sources can raise and issue and we can start working on it!.To-DOAddjsonsupport.AddSQLdata source integration.AddFileSystemdata source integration.Increase the speed of indexing.Add tests.Referenceses-indexer was built using the insipiration ofthisamazing tool written in Go.LicenseThe MIT License (MIT)Copyright (c) 2019Rounak Vyas
Package: esinet
esinet: Electric Source Imaging using Artificial Neural Networks (ANNs)esinetlet's you solve the EEG inverse problem using ANNs with the
mne-python framework. It currently supports three main architectures:ConvDipA convolutional neural network as described in ourfirst paper.Fully-connected networkA fully-connected neural network which is trained on single time instances of M/EEG data. This model was described in oursecond paperalongside the LSTM.Long-short term memory networkA Long-short-term memory (LSTM) model which is trained on sequences of EEG data. This model is described in detail and published in oursecond paper.Neural network design was createdhereDependencies:Python >= 3.8.3mneFollow theinstallation guideTensorflow>=2.4.1Follow theinstallation guideColorednoisejoblibpyvista>=0.24pyvistaqt>=0.2.0tqdmdillsklearnInstallation from PyPiUsepipto install esinet and all its
dependencies fromPyPi:pip install esinetQuick startThe following code demonstrates how to use this package:from esinet import Simulation, Net
from esinet.forward import create_forward_model, get_info

# Create generic Forward Model
info = get_info()
fwd = create_forward_model(info=info, sampling='ico2')

# Simulate M/EEG data
settings = dict(duration_of_trial=0.1)
sim = Simulation(fwd, info, settings=settings)
sim.simulate(n_samples=200)

# Train neural network (LSTM) on the simulated data
net = Net(fwd)
net.fit(sim)

# Plot
stc = net.predict(sim)[0]
sim.source_data[0].plot()
stc.plot()First stepsCheck out one of thetutorialsto learn how to use the package:Tutorial 1: The fastest way to get started withesinet. This tutorial can be used as an entry point. If you want to dig
deeper you should have a look at the next tutorials, too!Tutorial 2: Useesinetwith low-level
functions that allow for more control over your parameters with respect to
simulations and training of the neural network.Tutorial 3: A demonstration of simulation
parameters and how they affect the model performance.opm_source: Source imaging of optically pumped
magnetometer (OPM) data. The tutorial is based on the one provided bymneFeedbackPlease leave your feedback and bug reports atlukas_hecker@web.de.LiteraturePlease cite these publications if you are using this code:Hecker, L., Rupprecht, R., van Elst, L. T., & Kornmeier, J. (2022). Long-Short Term Memory Networks for Electric Source Imaging with Distributed Dipole Models. bioRxiv.Hecker, L., Rupprecht, R., Tebartz van Elst, L., & Kornmeier, J. (2021). ConvDip: A convolutional neural network for better EEG Source Imaging. Frontiers in Neuroscience, 15, 533.TroubleshootingHaving problems with the installation? Check thepackage requirements
Package: esi-oephys2nwb
oephys2nwb: Export Open Ephys binary data to NWB 2.xSummaryThis package can be used to export data saved inOpen Ephys binary format(the default for the Open Ephys GUI) to theNWB 2.0 format.
At the time of writing theNWBFormat pluginof Open Ephys only supports the deprecated NWB 1.0 format specification. The present
package is developed and maintained 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.InstallationThe package can be installed withpippipinstallesi-oephys2nwbTo get the latest development version, simply clone our GitHub repository and
(optionally) create a dedicated conda development environment:gitclonehttps://github.com/esi-neuroscience/oephys2nwb.gitcdoephys2nwb/
pythonsetup.py--version
condaenvcreate-foephys2nwb-dev.yml
condaactivateoephys2nwb-dev
pipinstall-e.UsageWe recommend setting up a dedicated conda environment foroephys2nwb. For general
information about conda, please refer to theofficial documentation.condacreate-noephys2nwb-envpython=3.8pip
condaactivateoephys2nwb-env
pipinstallesi-oephys2nwbAlternatively, we provide a conda environment file to automatically install all
required dependencieswgethttps://raw.githubusercontent.com/esi-neuroscience/oephys2nwb/main/oephys2nwb.yml
condaenvcreate-foephys2nwb.yml
condaactivateoephys2nwbOnce the package is installed the exporter can be used either embedded in Python
code or as a stand-alone command line utility.Command LineActivate the conda environment the package was installed in and invoke the exporter
as followscondaactivateoephys2nwb-env
python-moephys2nwb-i/path/to/recordingDir-o/path/to/outputFile.nwbCallingoephys2nwbwithout arguments prints the function helppython-moephys2nwbPython CodeAlternatively, the package can be used like any other Python modulefromoephys2nwbimportexport2nwbinput="/path/to/recordingDir"output="/path/to/outputFile.nwb"export2nwb(input,output)Documentation and ContactTo report bugs or ask questions please use ourGitHub issue tracker.
More usage details and background information is available in ouronline documentation.
Package: esi-releases
ESI ReleasesNote that relevant info on USGS code distribution and repository metadata can be found at <https://www.usgs.gov/products/software/software-management/distribution-usgs-code>DescriptionThis repository provides a CLI release tool for use in the repositories of the USGS Engineering Seismology and Impacts Gitlab group.At present the CLI is a minimally functionally tool to increment major, minor, or patch version numbers in the code and update the project metadata incode.jsonaccordingly.For our purposes, this amounts to changing theversionfield incode.jsonfollowed by 3 URLs:'downloardURL``This URL is tied to a specific release on Gitlab based on a tagdisclaimerURLAlicensesURL that exists under thepermissionsfieldAt the moment, this tool assumes that the version tag is of the form `v#.#.#`InstallationIt can be installed directly from source by callingpipin the cloned repository base directory:pipinstall.Developers or advanced users may which to include optional dependencies and install in editable mode:pipinstall-e.[dev,test,build]The package is also availbe on PyPI and can be installed with:pipinstallesi-releasesUsageThis CLI tool allows for three levels of version incrementation, depending on the scope of changes to a repostories code, and attempts to adhere to the conventions of Semantic Versioning<Major>.<Minor>.<Patch>Major version increments are used when new features that are backwards incompatible are introducedMinor version increments are used when new features that retain backwards compatibility are introducedPatch increments are used for bugfixes that do not introduce new featuresThe general behavior of this tool is the following:When a patch release is initiated, the patch number is simply incremented and adjusted across the metadataWhen a minor release is initiated, the minor number is incremented and the patch number is reset to zeroWhen a major release is initiated, the major number is incremented, while the minor and patch numbers are reset to zeroCommandsTo initiate a release the following syntax is used:releases<major|minor|patch>For example, for a package in apatchrelease at the a current version ofv1.0.0:releasespatchwill result inv1.0.1Should the same package have a following 'minor' release, i.e.:releasesminorthis will result inv1.1.0Finally, if the package then undergoes amajorrelease:releasesmajorthe version will be incremented tov2.0.0Help can be obtained in the CLI with the following:releases--helpIn DevelopmentThe intention of this CLI tool is to soon also support automateCHANGELOG.mdupdates based on commit messages in the repository, and to also prepare for a release by creating a new version entry. This is currently under development.
Package: esi-requests
esi_requestsesi_requests tries to userequestsstyle methods:>>>importesi_requests>>>r=esi_requests.get("/markets/{region_id}/orders/",region_id=10000002,type_id=1403)>>>r.status200>>>r.json(){[{'duration':90,'is_buy_order':False,...}withasyncenabled and simplified:>>>resps=esi_requests.get("/markets/{region_id}/orders/",region_id=10000002,type_id=[1403,12005,626])# equivalent to: esi_requests.get("/markets/{region_id}/orders/", region_id=10000002, params={"type_id": [1403, 12005, 626]})>>>resps[<Response[200]>,<Response[200]>,<Response[200]>]>>>resps[0].status200>>>resps[0].url'https://esi.evetech.net/latest/markets/10000002/orders/?datasource=tranquility&order_type=all&page=1&type_id=1403'which internally usesaiohttpto send requests asynchronously.FeaturesOne-lineasyncenabled: no need to masteraiohttpandasyncioSimplerequests-like apiSimplifiedOAuth2SSO authentication: all you need is to log in your accountSupportETagheaders: compliant withESI recommendation
Package: esis
Elasticsearch Index & SearchFree software: MIT licenseDocumentation:https://esis.readthedocs.org.FeaturesIndex content for every SQLite database row in ElasticsearchSearch indexed contentUsageIndex every SQLite database row under a given directory (recursively)esisindex<directory>Search for a given string in the indexed dataesissearch<query>Get information about the number of indexed documentsesiscountDelete all indexed documentsesiscleanDocker containersDocker files are included in the source code to run esis and elasticsearch in
their own containers. To build/pull the images needed to run esis and start the
elasticsearch server, use the following commands:docker-composebuilddocker-composestartAfter that, to launch esis in a container run:docker-composerunesis<subcommand>where<subcommand>is any of the subcommands in the previous section
(index,search,countorclean).Note:Ifdocker-compose runis executed too quickly, then a connection error
might be returning meaning that elasticsearch is still initializing.The entry point in the esis container uses the–hostcommand line option
to connect to the linked container where elasticsearch is running.The user home directory is mounted in the esis container as/data. This
must be taken into account when passing a directory to theindexsubcommand
using a path in the container, not in the host machine.History0.1.0 (2015-03-23)First release on PyPI.0.2.0 (2015-05-14)All documents indexed under the same index name.Docker files allow using the tool in a container.