Saravanan666/pypi-org huggingface dataset:
Package: hydrosim-sdk
HydroSim SDK PythonThis is a python package for connecting to the Shared Memory API of the boat racing simHydroSim.InstallThe package can be installed with pip.pipinstallhydrosim_sdkUsageFirst import the HydroSimSDK.fromhydrosim_sdkimportHydroSimSDKThen create an instance of the SDK.hydrosim=HydroSimSDK()This will automatically start a thread that reads from the
Memory Mapped Files that HydroSim writes to at 60hz.The data can be accessed through several member properties.Note: You can look at thehydrosim.runningproperty to check
if the game is currently running.hydrosim.hydro_sim- Returns the game and API version and a tick value that will increment as long as the game is running.hydrosim.telemetry- Telemetry data for the local playerhydrosim.timing- Live timing data for all drivers.hydrosim.course_info- Information about the current course and layout.hydrosim.session- Session time and status data.hydrosim.buoys- Location and state of all the buoys.Dealing with multiple instancesIn case you need to work with multiple instances of HydroSim, for instance, if running multiple dedicated servers on the same machine.You can pass the command line parameter-mmapNamewith any string
value when launching HydroSim. This will append the name to the
default memory mapped file names.For instance if you pass-mmapName "Server1", then the names will change
from:hydro_sim_file_name="Local\\HydroSim"telemetry_file_name="Local\\HydroSimTelemetry"session_file_name="Local\\HydroSimSession"timing_file_name="Local\\HydroSimTiming"course_info_file_name="Local\\HydroSimCourseInfo"buoys_file_name="Local\\HydroSimBuoys"tohydro_sim_file_name="Local\\HydroSim.Server1"telemetry_file_name="Local\\HydroSimTelemetry.Server1"session_file_name="Local\\HydroSimSession.Server1"timing_file_name="Local\\HydroSimTiming.Server1"course_info_file_name="Local\\HydroSimCourseInfo.Server1"buoys_file_name="Local\\HydroSimBuoys.Server1"To connect the python SDK to this instance, you then need to specify the
name to the HydroSimSDK constructor.hydrosim=HydroSimSDK(mmap_name='Server1')
Package: hydrostats
Tools for use in comparison studies, specifically for use in the field of hydrologyDocumentation:https://hydrostats.readthedocs.io/en/stable/Source:https://github.com/BYU-Hydroinformatics/HydrostatsBug reports:https://github.com/BYU-Hydroinformatics/Hydrostats/issuesHydrostats provides:Preprocessing ToolsVisualization ToolsAccess to over 70 error metrics through theHydroErr packagedependencyA library of relevant metrics to measure forecast skillPublications:Roberts, W., Williams, G., Jackson, E., Nelson, E., Ames, D., 2018. Hydrostats: A Python Package for Characterizing Errors between Observed and Predicted Time Series. Hydrology 5(4) 66, doi:10.3390/hydrology5040066We would request that if you use this package that you please cite the above paper. The journal paper is open access and can be found athttps://doi.org/10.3390/hydrology5040066. BibTeX, EndNote, and RIS files can be downloaded at the journal site.
Package: hydrotoolbox
hydrotoolbox - Quick GuideThe hydrotoolbox is a Python script for hydrologic calculations and analysis
or by function calls within Python.  Uses pandas (http://pandas.pydata.org/)
or numpy (http://numpy.scipy.org) for any heavy lifting.Requirementspython 3.7 or higherInstallationpippipinstallhydrotoolboxcondacondainstall-cconda-forgehydrotoolboxUsage - Command LineJust run ‘hydrotoolbox –help’ to get a list of subcommands:usage: hydrotoolbox [-h] [-v] {baseflow_sep,recession,about} ...

positional arguments:
  {baseflow_sep,recession,about}
    baseflow_sep        baseflow_sep subcommand
    recession           Recession coefficient.
    about               Display version number and system information.

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exitThe default for all of the subcommands is to accept data from stdin (typically
a pipe).  If a subcommand accepts an input file for an argument, you can use
“–input_ts=input_file_name.csv”, or to explicitly specify from stdin (the
default) “–input_ts=’-‘”.For the subcommands that output data it is printed to the screen and you can
then redirect to a file.Usage - APIYou can use all of the command line subcommands as functions.  The function
signature is identical to the command line subcommands.  The return is always
a PANDAS DataFrame.  Input can be a CSV or TAB separated file, or a PANDAS
DataFrame and is supplied to the function via the ‘input_ts’ keyword.Simply import hydrotoolbox:from hydrotoolbox import hydrotoolbox

# Then you could call the functions
ntsd = hydrotoolbox.baseflow_sep(method='broughton', input_ts='tests/test_fill_01.csv')
Package: hydrotools.caches
OWPHydroTools :: CachesThis subpackage implements different methods to cache objects generated byhydrotoolsmethods. See theCaches Documentationfor a complete list and description of the currently available caches. To report bugs or request additional features, submit an issue through theOWPHydroTools Issue Trackeron GitHub.InstallationIn accordance with the python community, we support and advise the usage of virtual environments in any workflow using python. In the following installation guide, we use python's built-invenvmodule to create a virtual environment in which the tool will be installed. Note this is just personal preference, any python virtual environment manager should work just fine (conda,pipenv, etc. ).# Create and activate python environment, requires python >= 3.8$python3-mvenvvenv
$sourcevenv/bin/activate
$python3-mpipinstall--upgradepip# Install caches$python3-mpipinstallhydrotools.cachesUsageThe following example demonstrates how one might usehydrotools.caches.hdfto cache apandas.dataframegenerated by a long running process.Codefromhydrotools.caches.hdfimportHDFCacheimportpandasaspdfromtimeimportsleep# Some long running process that returns a pandas.DataFramedeflong_process(cols,rows):sleep(1.0)data={f'col_{i}':[jforjinrange(rows)]foriinrange(cols)}returnpd.DataFrame(data)# Setup the cache with a context manager#  Similar to setting up a pandas.HDFStorewithHDFCache(path='mycache.h5',complevel=1,complib='zlib',fletcher32=True)ascache:# The first call runs long_process and stores the resultdf=cache.get(long_process,'data/results',cols=10,rows=1000000)# The second call retrieves the result from cache without#  running long_processdf=cache.get(long_process,'data/results',cols=10,rows=1000000)
Package: hydrotools.metrics
OWPHydroTools :: MetricsThis subpackage implements common evaluation metrics used in hydrological model validation and forecast verification. See theMetrics Documentationfor a complete list and description of the currently available metrics. To request more metrics, submit an issue through theOWPHydroTools Issue Trackeron GitHub.InstallationIn accordance with the python community, we support and advise the usage of virtual
environments in any workflow using python. In the following installation guide, we
use python's built-invenvmodule to create a virtual environment in which the
tool will be installed. Note this is just personal preference, any python virtual
environment manager should work just fine (conda,pipenv, etc. ).# Create and activate python environment, requires python >= 3.8$python3-mvenvvenv
$sourcevenv/bin/activate
$python3-mpipinstall--upgradepip# Install metrics$python3-mpipinstallhydrotools.metricsUsageThe following example demonstrates how one might usehydrotools.metricsto compute a Threat Score, also called the Critical Success Index, by comparing a persistence forecast to USGS streamflow observations. This example also requires thehydrotools.nwis_clientpackage.Codefromhydrotools.metricsimportmetricsfromhydrotools.nwis_client.ivimportIVDataServiceimportpandasaspd# Get observed dataservice=IVDataService()observed=service.get(sites='01646500',startDT='2020-01-01',endDT='2021-01-01')# Preprocess dataobserved=observed[['value_time','value']]observed=observed.drop_duplicates(['value_time'])observed=observed.set_index('value_time')# Simulate a 10-day persistence forecastissue_frequency=pd.Timedelta('6H')forecast_length=pd.Timedelta('10D')forecasts=observed.resample(issue_frequency).nearest()forecasts=forecasts.rename(columns={"value":"sim"})# Find observed maximum during forecast periodvalues=[]foridx,siminforecasts.itertuples():obs=observed.loc[idx:idx+forecast_length,"value"].max()values.append(obs)forecasts["obs"]=values# Apply flood criteria using a "Hit Window" approach#  A flood is observed or simluated if any value within the#  forecast_length meets or exceeds the flood_criteria## Apply flood_criteria to forecastsflood_criteria=19200.0forecasts['simulated_flood']=(forecasts['sim']>=flood_criteria)forecasts['observed_flood']=(forecasts['obs']>=flood_criteria)# Compute contingency tablecontingency_table=metrics.compute_contingency_table(forecasts['observed_flood'],forecasts['simulated_flood'])print('Contingency Table Components')print('============================')print(contingency_table)# Compute threat score/critical success indexTS=metrics.threat_score(contingency_table)print('Threat Score:{:.2f}'.format(TS))OutputContingency Table Components============================true_positive      148false_positive       0false_negative     194true_negative     1123dtype: int64Threat Score: 0.43
Package: hydrotools.-restclient
OWPHydroTools :: REST ClientSubpackage of objects and utilities suitable for quickly developing robust
REST api libraries.Main features:RestClient: a class which offers asynchronous performance via a convenient serial
wrapper, sqlite database GET request caching, GET request backoff, batch GET requests,
and flexible url argument encoding.Url: Treat urls like topathlib.Paths. Supports appending paths with/and
appending url query parameters with+.ClientSession: Extension ofaiohttp_client_cachethat adds
exponential backoff.Variadic: Join list or tuple of objects on some delimiter. Useful when query
parameters utilize a non-key=value&key=value2pattern.AliasandAliasGroup: Immutable utility classes that simplify common API library
development patterns (i.e. separation of backend uris from front end interfaces).See theREST Client
Documentationfor a
complete list and description of the currently available methods. To report bugs or
request new features, submit an issue through theOWPHydroTools Issue
Trackeron GitHub.InstallationIn accordance with the python community, we support and advise the usage of virtual
environments in any workflow using python. In the following installation guide, we
use python's built-invenvmodule to create a virtual environment in which the
tool will be installed. Note this is just personal preference, any python virtual
environment manager should work just fine (conda,pipenv, etc. ).# Create and activate python environment, requires python >= 3.8$python3-mvenvvenv
$sourcevenv/bin/activate
$python3-mpipinstall--upgradepip# Install _restclient$python3-mpipinstallhydrotools._restclientUsageThe following example demonstrates how one might usehydrotools._restclientto write a simple REST api interface.Codefromhydrotoolsimport_restclientclassBaconIpsum:""" Python API to baconipsum.com. """_base_url="https://baconipsum.com/api/"# Return "meat-and-filler" if "mixed-meat" or "mixed" passed like:# _meat_and_filler["mixed-meat"] or _meat_and_filler.get("mixed")_meat_and_filler=_restclient.Alias("meat-and-filler",["mixed-meat","mixed"])_all_meat=_restclient.Alias("all-meat",["all-meat","all"])# Treat multiple alias as one alias_meat_options=_restclient.AliasGroup([_meat_and_filler,_all_meat])def__init__(self):self._restclient=_restclient.RestClient(self._base_url,cache_filename="bacon_ipsum_cache",cache_expire_after=43200,# in secondsn_retries=5,)@classmethoddefget(cls,type:str):"""Get either 'mixed-meat' / 'mixed' or 'all-meat' / 'all'Mixed contains filler 'lorem ipsum' text. All is just the meats!"""inst=cls()params={"type":inst._meat_options[type],"sentences":1,"format":"json"}response=inst._restclient.get(parameters=params)returnresponse.json()if__name__=="__main__":print(BaconIpsum.get("all-meat"))print(BaconIpsum.get("mixed"))Output['Short loin tongue drumstick chuck beef meatball brisket ball tip pork loin doner kielbasa flank.']['Andouille excepteur in, shankle irure laboris voluptate et ad incididunt.']
Package: hydrotrack
HydroTrack - Python Framework for Tracking and Forecasting ClustersdocsstatuspackageinfoOverviewhydrotrackHydroTrack is a Python package designed to identify, track, analyze and forecast hydrological phenomena across a diverse range of datasets.
It offers a robust modular framework that seamlessly integrates various cutting-edge algorithms for feature identification, tracking, and comprehensive analyses.
One of HydroTrack’s paramount advantages lies in its remarkable versatility, which liberates it from any reliance on specific input variables or grid structures.DocumentationFor a more detailed information ofhydrotrackpackage please read the user guide availableclick here.Download packageDownload package direct from github:git clone https://github.com/hydrotrack-project/hydrotrack/InstalationCreate a Python env using Anaconda3, Miniconda, Mamba, or etc.Install env from environment.yml:cd hydrotrack
conda env create -n hydrotrack --file environment.yml
conda activate hydrotrackor install package from local file:cd hydrotrack
pip3 install -r requirements.txtor just type:pip3 install hydrotrackExample of usageOpen In Colab.Support and ContactFor support, emailhelvecio.neto@inpe.br,alan.calheiros@inpe.br
Package: hydrozincite
Hydrozincite - Zn₅(CO₃)₂(OH)₆A simple static page generator for Git repositories.InstallationPyPIYou can find onPyPIand install via your favorite package installer for Python, likepip.python3-mpipinstallhydrozinciteSourceFrom source, needbuild&pdm-backendas build dependencies.python3-mbuild--wheelAnd install via your favorite package installer.python3 -m pip install dist/*.whlOr directly viapdm.pdminstallArchlinuxOn Archlinux, via the PKGBUILD file.cdPKGBUILD
makepkg-scriUsageYou can show help viahydrozincite --help.# Genereate HTML pagehydrozincite--repositories"<path>"--output"<path>"--clone-url"<url>"# Show info on your repositorieshydrozinciteinfo# Show info on repository hookshydrozincitehook# Write hooks on your repositorieshydrozincitehook--writeIt's possible use a config file from./hydrozincite.toml,$XDG_CONFIG_HOME/hydrozincite/config.toml,~/.config/hydrozincite/config.toml,/etc/hydrozincite/config.toml"or via--conf <path>parameter.title="Awesome git repositories !"clone_url="https://git.foo.bar"[bare]path="<path>"[render]output="<path>"template="<path>"theme="default"ContributingSeeCONTRIBUTING.mdLicenseBSD 2-clause "Simplified" LicenseMirrorsforge.dotslashplay.it(source)git.lett.reFeedbackI take any constructive feedback to improves the project. You can find my emailhere.
Package: hydrus
hydrushydrus is a set ofPythonbased tools for easier and efficient creation of Hypermedia driven REST-APIs. hydrus utilises the power ofLinked Datato create a powerful REST APIs to serve data.
hydrus uses theHydra(W3C)standard for creation and documentation of it's APIs.Start-up the demowithDockeranddocker-composeinstalled, rundocker-compose up --buildopen the browser athttp://localhost:8080/api/vocabYou should be displaying the example API as served by the server.Add your own Hydra documentation fileTo serve your own Hydra-RDF documentation file:create adoc.pyfile as the ones inexamples/directory containing your ownApiDocset theAPIDOC_REL_PATHvariable indocker-compose.yml. This should the relative path from the project rootstart-up the demo as above.You should be displaying your API as served by the server.Table of contentsFeaturesRequirementsDemoUsageFeatureshydrus supports the following features:A client that can understand Hydra vocabulary and interacts with a Hydra supporting server to basicCRUDoperations on data.A generic server that can serve required data and metadata(in the form of API documentation) to a client over HTTP.A middleware that allows users to use the client to interact with the server using Natural Language which is processed machine consumable language.(under development)RequirementsThe system is built over the following standards and tools:Flaska Python based micro-framework for handling server requests and responses.JSON-LDas the preferred data format.Hydraas the API standard.SQLAlchemyas the backend database connector for storage and related operations.Apart from this, there are also various Python packages that hydrus uses. Usingpython setup.py installinstalls all the required dependencies.NOTE:You'll need to usepython3notpython2. Hydrus does not support python < 3.6DemoTo run a demo for hydrus using the sample API, just do the following:Clone hydrus:gitclonehttps://github.com/HTTP-APIs/hydruscdhydrusInstall aPython virtual environmentusing:python3.7-mvenv.venvor:virtualenv-ppython3.7.venvInstall hydrus using:source.venv/bin/activate
pipinstall-rrequirements.txt
pythonsetup.pyinstallNOTE: there is an alternative way to install dependencies withpoetry:pip3installpoetry
poetryinstallThis is mostly used to check dependencies conflicts among packages and to release toPyPi.After installation is successful, torun the server:hydrusserveThe demo should be up and running onhttp://localhost:8080/serverapi/.UsageFor more info, head to theUsagesection of thewiki.DevelopmentFrom thehydrusdirectory:To run formatter:pip install black && black *.pyTo test for formatting:flake8 *.py
Package: hydrusvideodeduplicator
Hydrus Video DeduplicatorHydrus Video Deduplicator finds potential duplicate videos through the Hydrus APIHow It Works:The deduplicator works by comparing videos similarity by theirperceptual hash.Potential duplicates can be processed through the Hydrus duplicates processing page just like images.You can choose to process only a subset of videos with--queryusing Hydrus tags, e.g.--query="character:edward"will only process videos with the tagcharacter:edward.For more information check out thewikiand theFAQInstallation:Dependencies:Python>=3.10python3-mpipinstallhydrusvideodeduplicatorUsage:python3-mhydrusvideodeduplicator--api-key="<your key>"For full list of options see--helpor theusage page.TODO:Option to rollback and remove potential duplicatesOR predicates for --queryParallelize hashing and duplicate searchAutomatically generate access key with Hydrus APIDocker containerUpload Docker container to Docker Hub (GitHub Action)Pure Python port of vpdqWindows compatibility without WSL or DockerContact:Create an issue on GitHub for any problems/concerns. Provide as much detail as possible in your issue.Message @applenanner on theHydrus Discordfor other general questions/concernsAttribution:Hydrus Network(DWTFYWTPL)Hydrus API Library(GNU AGPLv3) by cryzedpdq(BSD) by Metavpdq(BSD) by MetaBig Buck Bunny,Sintel(CC BY 3.0) clips by Blender Foundation
Package: hyena-api.py
Hyena API Wrapper❓| Hyena APIThe Hyena API is an API made by Donut#4427 for stuff like:ChatbotNSFWTo use the Hyena API you can visitthis page. And to get more info on it you can visit theDocs!And to see how the hyena-bot works and test it out visit the officialDiscord server!InstallingPython 3.8 or higher is requiredTo install the library use the following commands:pip install hyena-api.py
# or
pip install hyena-api.py==versionTo install from the master branch do this:$ git clone https://github.com/AHiddenDonut/hyena-api.py.py hyena-api
$ cd hyena-api
$ python3 -m pip install -U .ExamplesSome quick examples to show how you can use the apiSync:importhyena.Synchyena=hyena.Sync.Client("MY SUPER SECRET API KEY")# Chatbot responseresp=hyena.chatbot("Hello!",name="My bot's name",owner="My name")print(resp)importhyena.Synchyena=hyena.Sync.Client("MY SUPER SECRET API KEY")# NSFW imagesresp=hyena.nsfw("endpoint",format="json")# format will be a class by defaultprint(resp)"""How to use the response class [Default]resp.title : Title of responseresp.description : Description of responseresp.image_url : Image URL of responseresp.url : url of the original post"""Async:importhyena.Async,asyncioasyncdefmain():asyncwithhyena.Async.Client("KGgGT#FnFE_z2BdcERAqeZvYmU6D0Q")asclient:asyncwith(awaitclient.nsfw("random",format="image"))asresp:pass# stuffimporthyena.Async,asyncioasyncdefmain():asyncwithhyena.Async.Client("KGgGT#FnFE_z2BdcERAqeZvYmU6D0Q")asclient:asyncwith(awaitclient.chatbot("hello world"))asresp:pass# stuff# with discord.py:importhyena.Asynchyena=hyena.Async.Client("MY SUPER SECRET API KEY")@client.command()asyncdefchatbot(ctx,*,message):my_reply=awaithyena.chatbot(message,language="en",owner="myname",name="my bot's name")awaitctx.reply(my_reply)## Links-[Documentation](https://docs.hyenabot.xyz/)-[OfficialServer](https://discord.gg/QePftyb2kN)-[APILink](https://www.hyenabot.xyz/api)
Package: hyfi-absa
HyFI-ABSAHyFI-ABSA is a Python package developed as a plugin for the Hydra Fast Interface (HyFI).Documentation:https://hyfi-absa.entelecheia.aiGitHub:https://github.com/entelecheia/hyfi-absaPyPI:https://pypi.org/project/hyfi-absaThe main function of HyFI-ABSA is to facilitate Aspect-Based Sentiment Analysis (ABSA), a specialized form of sentiment analysis that allows you to evaluate specific components or aspects of a given text. It also performs general sentiment analysis tasks. One feature of the HyFI-ABSA package is its capacity to employ generative language models for creating training datasets. This feature not only eliminates the extensive manual labor typically associated with data collection and preparation, but also ensures a more diverse and balanced dataset, which subsequently improves the overall performance and accuracy of sentiment analysis. In addition to these primary features, HyFI-ABSA boasts seamless integration with the existing HyFI architecture, thus allowing you to effortlessly combine it with other HyFI plugins and tools.ChangelogSee theCHANGELOGfor more information.ContributingContributions are welcome! Please see thecontributing guidelinesfor more information.LicenseThis project is released under theMIT License.
Package: hyfi-template
HyFI TemplateA GitHub Template Repository for HyFI-based ProjectsDocumentation:https://hyfi-template.entelecheia.aiGitHub:https://github.com/entelecheia/hyfi-templatePyPI:https://pypi.org/project/hyfi-templateHyFI-Template is a ready-to-use GitHub template repository designed to streamline the process of initializing new projects based on HyFI, a robust framework for building interfaces for Python applications. HyFI leverages the power of the Hydra configuration system and the Pydantic data validation library, offering a comprehensive set of tools to create flexible and adaptable interfaces for various Python projects.ChangelogSee theCHANGELOGfor more information.ContributingContributions are welcome! Please see thecontributing guidelinesfor more information.LicenseThis project is released under theMIT License.