Saravanan666/pypi-org huggingface dataset:
Package: overviewpy
overviewpyoverviewpy aims to make it easy to get an overview of a data set by displaying relevant sample information.Installation$pipinstalloverviewpyUsageImplemented FunctionsThe goal ofoverviewpyis to make it easy to get an overview of a data set by displaying relevant sample information. At the moment, there are the following functions:overview_tabgenerates a tabular overview of the sample (and returns a data frame). The general sample plots a two-column table that provides information on an id in the left column and a the time frame on the right column.overview_naplots an overview of missing values by variable (both by row and by column)overview_tabGenerate some general overview of the data set using the time and scope
conditions withoverview_tab. The resulting data frame collapses the time condition for each id by
taking into account potential gaps in the time frame.fromoverviewpy.overviewpyimportoverview_tabimportpandasaspddata={'id':['RWA','RWA','RWA','GAB','GAB','FRA',\'FRA','BEL','BEL','ARG'],'year':[2022,2023,2021,2023,2020,2019,2015,\2014,2013,2002]}df=pd.DataFrame(data)df_overview=overview_tab(df=df,id='id',time='year')overview_naoverview_nais a simple function that provides information about the
content of all variables in your data, not only the time and scope
conditions. It returns a horizontal ggplot bar plot that indicates the
amount of missing data (NAs) for each variable (on the y-axis). You can
choose whether to display the relative amount of NAs for each variable
in percentage (the default) or the total number of NAs.fromoverviewpy.overviewpyimportoverview_naimportpandasaspdimportnumpyasnpdata_na={'id':['RWA','RWA','RWA',np.nan,'GAB','GAB',\'FRA','FRA','BEL','BEL','ARG',np.nan,np.nan],'year':[2022,2001,2000,2023,2021,2023,2020,\2019,np.nan,2015,2014,2013,2002]}df_na=pd.DataFrame(data_na)overview_na(df_na)Roadmapoverviewpyseeks to mirror the functionality ofoverviewRand will extend its features with the following functionality in the future:overview_crosstabgenerates a cross table. The conditional column allows to disaggregate the overview table by specifying two conditions, hence resulting a 2x2 table. This way, it is easy to visualize the time and scope conditions as well as theoretical assumptions with examples from the data set.overview_latexconverts the output of bothoverview_tabandoverview_crosstabinto LaTeX code and/or directly into a .tex file.overview_plotis an alternative to visualize the sample (a way to present results fromoverview_tab)overview_crossplotis an alternative to visualize a cross table (a way to present results fromoverview_crosstab)overview_heatplots a heat map of your time lineoverview_overlapplots comparison plots (bar graph and Venn diagram) to compare to data framesContributingInterested in contributing? Check out thecontributing guidelines. Please note that this project is released with aCode of Conduct. By contributing to this project, you agree to abide by its terms.Licenseoverviewpyis licensed under the terms of the BSD 3-Clause license.Creditsoverviewpywas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: overwatch
Version:1.3.2Introductionoverwatchis easily integrable two-in-one package built to simplify local
dev & debug processes. This package contains both client and server with GUI.
It includes special logger and GUI written in GTK2 that allows you
to perform real-time logs monitoring from a bunch of applications.You need PyGTK 2.0 installed in order to run this properly.App idea inspired bylogglyservice which is great but not free.InstallationYou can installoverwatcheither via the Python Package Index (PyPI)
or from source.To install usingpip:$ pip install overwatchTo install usingeasy_install:$ easy_install overwatchUsageIntegrating into scriptYou should simply add these lines within the head of your Python application:import overwatch
overwatch.init('My app friendly name')Server handles all clients independantly, so you can patch as many scripts as you want.
All their logs will be captured in parallel.Browsing logsJust execute this command on the same machine:$ python2 -m overwatchEnjoy!ContributingDevelopment ofoverwatchresides at:http://bitbucket.org/AndrewDunai/overwatchYou are highly encouraged to participate in the development.LicenseThis software is licensed under theGNU GPL v2.
Package: overwatch-ci-helper
overwatch-ci-helperThis python package is built to help overwatch during ci builds/tests. It utilizes the docker-py package to manage deploying any number of overwatch microservices during CI testing. It requires you to have a config defined which has information about the image to use, the config for the overwatch microservices and the such.InstallationTo install the package, install it in the same way anyone install python packages:python3 -m pip install overwatch-ci-helperUsageTo use the package, simply use it in the following manner:needs model-registry overwatch-server input-server -c PATH/TO/config.yaml --keystores PATH/TO/.dcp
...
...
...

needs model-registry overwatch-server input-server -c PATH/TO/config.yaml --keystores PATH/TO/.dcp --cleanupThe first command spins up a model-registry, overwatch-server and an input-server using the config specified and with the keystores given. The second cleans up the spun up docker containers, kills them and removes them from docker.
Package: overwatch-client
Overwatch ClientThis is a python client for the Overwatch REST API. This implementation is a work in progress but is still able to communicate with the overwatch server and register/infer models.fromoverwatch_clientimportHTTPClientclient=HTTPClient(overwatch_server_url="https://overwatch.distributive.network",model_registry_url="https://models.overwatch.distributive.network")print(f"Can we connect?{client.check_overwatch_server_connection()}")#### Register a Modelresponse=client.register_model(model_name="my_model",model_path="./my_model.onnx",preprocess_path="./preprocess.py",postprocess_path="./postprocess.py",password="my_password",language="python",packages=["numpy","pandas","opencv-python"])print(response.text)### Model registered successfully!#### Infer a Modelresponse=client.infer(inputs=files,# a list of the bytes of each inputmodel_name="my_model",slice_batch=1,# number of inputs per sliceinference_id="my_inference_id",# An identifier for the inferencecompute_group_info="joinKey/joinSecret"# The compute group info for the inference)
Package: overwatcher
OverwatcherA simple app for sending telegram notification on completion of a long-running command.Setting upInstall package:pipinstalloverwatcherSetup environment variables (at least two required):exportOVERWATCHER_TOKEN=...exportOVERWATCHER_CHAT=...The former should contain token of a telegram bot which could be obtained from@BotFather, the latter can be requested from@chatid_echo_bot.
Other env variables are not necessary:exportOVERWATCHER_BASE_FILE_PATH=...# base filepath from which to start searching mentioned files (default value is /home)exportOVERWATCHER_N_FILES=...# the number of last files from the log to push (default value is 3)exportOVERWATCHER_N_LINES=...# the number of lines from command output to forward (default value is 20)exportOVERWATCHER_MAX_LINE_LENGTH=...# max number of characters on a single line (default value is 100)UsageExample:python-moverwatchernotify"ls -alh && sleep 10"Response is sent to a telegram chat:Commandechoniceiscompletedin0.003seconds.Result:
nice
Package: overwatchpy
OverwatchPyA Python wrapper forhttps://overfast-api.tekrop.fr/I wrote this very fast so it's not very good, but it works.Please open a PR if you want to improve it or add more features.RequirementsPython 3.11 (3.9+ should work)PoetryInstallationpoetryinstallUsagefromoverwatchpyimportOverwatchsearch:Overwatch.player_search=Overwatch.player_search("twizy","quickplay","pc","public")forplayerinsearch:print(player.name)client=Overwatch()heros=client.heroes(role="tank")forheroinheros:print(hero.name)game_modes=client.gamemodes()forgame_modeingame_modes:print(game_mode.name)print(game_mode.description)LicenseMIT
Package: overwatch-stats
Overwatch StatsA Python library to query a player's overwatch stats from Battle.netRequirementsPython 3.2+requestslxmlinflectInstallationpip install overwatch-statsUsageimportoverwatch.statsstats=overwatch.stats.query('pc','현종환#3104')APIoverwatch.stats.query(platform, battle_tag)Queries a player's stats from the Battle.net.Parametersplatform:pc,xblorpsnbattle_tag: the player's battle tag
Package: ovex-py
OVEX-pyOVEX Python API.Installationpip install ovex-pyHow to use itMost functions should have useful help text, but essentially:API object creationfrom ovex_python.client import Client
c = Client(api_key_id='key_id', base_url='', timeout=0)Where optional parameters can be passed as follows:ParameterDescriptionDefaultapi_key_idYour API key created on the OVEX websiteRequired for authenticated callsbase_urlThe API host URLhttps://www.ovex.io/api/v2timeoutThe maximum time to wait for requests10 (s)API callsGet accounts for all currenciesc.get_accounts()Returns: list of dictionaries containing balances for all currenciesGet currency infoc.get_currency_info('btc')Returns: dictionary containing information about the currency requestedRequest a quoteo.get_quote_rfq(to_amount=1, market='btczar', side='buy')Returns: dictionary containing quote information including a tokenKnown IssuesNot all error handling has been handledNot all endpoints work as expected, sometimes parameters get ignoredExceptions have not been handled and might not even output a warningAcknowledgementsThis repo was largely based on the work done on pyluno, thanks to Grant Stephenshttps://github.com/grantstephens/pylunoTo DoAdd in a rate limiterAdd in processing layer to make results more user-friendlyContributeFork itContributeGive feedbackStart again
Package: ovh_api_cli
OVH API CLI is a command line tool that allows usage of OVH’s API fromwithin a terminal.Installationpython setup.py install
source /etc/bash_completion.d/ovhcliUsagePut credentials inside~/.ovhcli$ echo '{\
    "AK": "Account_KEY",\
    "AS": "Account_SECRET",\
    "CK": "Consumer_KEY"\
} > ~/.ovhcliUse tab to trigger the completion$ ovhcli [TAB]
 $ ovhcli /[TAB][TAB]
 allDom                housing               freefax               directadmin           overTheBox            support
 auth                  installationTemplate  horizonView           office                database              telephony
 ...
 $ ovhcli /me
 me                              bill                            history                         subAccount
 backupCode                      changeEmail                     order                           subscription
 ...
 $ ovhcli /me/contact [TAB][TAB]
 get                             put
 $ ovhcli /me/contact PUT [TAB][TAB]
 --city=                         --line2=                        --province=
 --country=                      --line3=                        --zip=
 --line1=                        --otherDetails=

# some parameters can be automagically listed:
$ ovhcli /me/contact/\{contactId\} GET --contactId=[TAB][TAB]
contact1                         contact2                        contact3
Package: ovh-dynhost
ovh-dynhostThis script sets the DynHost service (that must be created before, checkhttps://docs.ovh.com/gb/en/domains/hosting_dynhost/) with your current public IP that is retrieved from ipify.org API (default, can be overridden).InstallationInstall the script using pip (I strongly suggest to use a virtualenv)pip install .(The script will be now available in thePATH. Reactivate the virtualenv if necessary)PS: The script is also available directly on PyPI.pip install ovh_dynhostUsageSets thehome.mydomain.comhost to your current public IP retrieved from (SSL)ipify.orgusingmyusernameandmypasswordas credentials.ovh-dynhost home.mydomain.com myusername mypasswordSets the0.0.0.0IP instead of the current public IPovh-dynhost --ip=0.0.0.0 home.mydomain.com myusername mypasswordUse the (unencrypted, check thehttp://instead ofhttps://) API fromwhatismyipaddress.comovh-dynhost --pub-ip-source=http://bot.whatismyipaddress.com home.mydomain.com myusername mypasswordSame as first but logging also intoovh.logfileovh-dynhost --log-file=ovh.log home.mydomain.com myusername mypasswordIf your prefer to not show up your username or password in the shell, you can provide a separate JSON configuration file. By default the script will lookup into$HOME/.ovh-dyndns.conf, but you can also provide a custom configuration fileovh-dynhost home.mydomain.com --conf-file=/etc/ovh-dyndns/config.json
Package: ovhspams
Getting StartedChange directory into your newly created project:cd ovhspamsCreate a Python virtual environment:python3 -m venv envUpgrade packaging tools:env/bin/pip install --upgrade pip setuptoolsInstall the project in editable mode with its testing requirements:env/bin/pip install -e ".[testing]"Run your project’s tests:env/bin/pytestDownload ini files:wget http://static.frkb.fr/ovhspams/development.ini
wget http://static.frkb.fr/ovhspams/production.iniAdaptdomainesentry in[app:main]section from ini filesee onhttps://api.ovh.com/how to obtain keys.Or go directly tohttps://api.ovh.com/createToken/index.cgiRun your project:env/bin/pserve development.ini
or
env/bin/pserve production.iniAcces your project:http://localhost:6543Change log0.0.52017/11/01Déplacement de la configuration des domaines et de leurs clés chez OVH
dans les fichier .ini
Package: ovipositor
ovipositorFlask link-shortening service and database systemcreditsname by Ellis KayintroductionOvipositor is a link-shortener web program. A long URL is entered,
together with an optional shortlink and an optional comment. When
creating a shortlink, the long URL and the shortlink are saved to a
database. When an attempt is made to use a shortlink, the specified
shortlink is searched for in the database and, if the shortlink is in
the database, there is a redirect to its corresponding long URL.setupsudoaptinstallsqlitesudopipinstallovipositorFlaskwhiletrue;doovipositor--home="index.html"doneGunicornsudogunicorn--workers=1"ovipositor.__init__:WSGI(argv=['--home=index.html'])"--bind=0.0.0.0:443--certfile=/home/user/certificates/fullchain.pem--keyfile=/home/user/certificates/privkey.pemovipositor database structureThere is one table in an ovipositor database called “shortlinks”. This
table has 6 fields:fielddescriptioncommentshortlink descriptive commentcountshortlink usage countIPIP address that created the shortlinkURLlong URL to which the shortlink correspondsshortlinkshortlink texttimestampshortlink creation timestampchanging from YOURLS to ovipositorAn export dump should be made of the YOURLS MySQL database and the dump
should be converted to an SQLite3 database. This can be done usingmysql2sqlite.The YOURLS SQLite database then can be converted to an ovipositor
database using the scriptconvert_YOURLS_SQLite_database_to_ovipositor_database.py.convert_YOURLS_SQLite_database_to_ovipositor_database.py--helpconvert_YOURLS_SQLite_database_to_ovipositor_database.py\--database_YOURLD=linkdb.db\--database_ovipositor=ovipositor.dbThe YOURLS database contains three tables, “yourls_url”,
“sqlite_sequence” and “yourls_options”. These tables have the
following fields:yourls_urlclicksipkeywordtimestamptitleurlsqlite_sequencenameseqyourls_optionsoption_idoption_nameoption_valueIn changing from YOURLS to ovipositor, the following database table and
field conversions are made:YOURLSovipositoryourls_urlshortlinksclickscountipIPkeywordshortlinkurlURLtimestamp (datetime.datetime)timestamp (datetime.datetime)titlecomment
Package: ovirt-imageio
ovirt-imageioovirt-imageio enables uploading and downloading of disks and snapshots using HTTPS.The project provides ovirt-imageio service and imageio client library.ovirt-imageio service can act in two different roles:as a service exposing images over HTTPS, allowing clients to read and write images.as a proxy service, allowing clients without access to the host network to read and write images.imageio client library provides wrapper around REST API provided by ovirt-imageio.
Besides making REST API easy to use, it also provides other features like on-the-fly image
format conversion or support for incremental backup out of the box. It can also be used
as a reference implementation of imageio client.How this project is related to other oVirt projects?vdsm - control and monitor imageio service, prepare and finalize
upload and download operations.engine - manage upload and download operations, communicating with
vdsm and imageio service running on the engine. Engine does not communicate with imageio
service on the hosts.Documentationhttp://ovirt.github.io/ovirt-imageio/InstallationOn engine, the imageio service is installed as part ofoVirt engine installationShould be enabled during engine-setup.On host, the imageio service is installed as part ofoVirt node installationContributingPatches are welcome!Refer toDevelopment Sectionfor build and commit instructions.Send a pull request onGitHubGetting HelpPlease send mail to one of the following lists for help.For discussion about usage and general help:http://lists.ovirt.org/mailman/listinfo/usersFor technical discussion about the project and its code:http://lists.ovirt.org/mailman/listinfo/develLicensingThe project is provided under the terms of the GPL-2.0-or-later License.Please see theLICENSES/GPL-2.0-or-later.txtfile for complete license terms.
Package: ovisbot
OvisBotOpen source Discord bot for CTF teamsOverview•Installation•Documentation•Contribution•LicenseOverviewOvisBot is a modular, feature-extensive Discord bot for managing CTF teams through discord. It facilitates collaboration and organisation by providing well defined commands to create/delete/update discord category/channels in order to structure CTF problems and provide more efficient team commmunication. In addition the bot provides basic utility functions to assist the solving process of CTF challenges (encoding schemes, etc.. ). Finally, promotes competitiveness amongst team members by providing a aut-synchronised leaderboard to common cybersecurity training platforms such asCryptoHackandHack The Box,Note that the majority of the features are provided by isolated plugins and thus they can be enabled/disabled on demand.This is a self-hosted bot, therefore it requires to be hosted on a private server in order to be used. Further instructions to do so are provided below. It also required a running instance of MongoDB on the server but still, the docker-based installation instructions take care of that.InstallationThere are couple ways to install the bot but generally the installing using docker-compose is the most convenient way to do it. Nevertheless, don't hesitate to use any other methods that suits you.Installing using pipTo install using pip run the following commandpip install ovisbotThe above will installovisbotin your python environment and will introduce theovisbotcli. The cli provides commands to launch and interact with ovisbot.At runtime, the bot requires a running MongoDB server. An easy way to run a local mongodb server is using docker. You skip this step if you already have one runningdocker run -d -p 27017-27019:27017-27019 --name mongodb mongoSince OvisBot requires some predifined configuration before launch, it is necessary the you set your environment variables accordingly. Alternatively you can create a.envfile that defined the required variables. Refer to.env.examplefor an example.OvisBot cli provides thesetupenvcommand which assists the creation of a .env file. Therefore to contrinue run and fill in the variables.ovisbot setupenvAt the end of the process a new.envfile will be create in your current directory.Finally to launch the bot, run:ovisbot runInstalling using dockerInstallation using docker takes care of running mongo db automatically without requiring any extra steps. To achieve this,docker-composeis utilised therefore make sure that you havedockeranddocker-composeinstalled on your system.Firstly clone this repository:git clone https://github.com/cybermouflons/ovisbot ovisbot && ovisbotFor the next step make sure that you have your environment variables configured properly and run:docker-compose -f docker-compose.yml -f docker-compose.prod.yml upVersioningWe useSemVerfor versioning. For the versions available, see thetags on this repository.ContributionHave a feature request? Make a GitHub issue and feel free to contribute. To get started with contributing refer toCONTRIBUTE.md.Current Contributors:apogiatziskgeorgioucondiomnpitsillosSikkisLicenseReleased under the GNU GPL v3 license.
Package: ovito
OVITO (Open Visualization Tool) is a scientific visualization and analysis package for atomistic and
particle-based simulation data. Visit the official website for more information:https://www.ovito.org/This PyPI package provides theovitomodule, which allows you to employ OVITO's
data I/O, analysis and rendering capabilities in standalone Python scripts
and integrate them into automated workflows.The OVITO desktop application is available as a separate download
fromwww.ovito.org. It provides a graphical user interface
for interactive analysis and visualization of simulation data.DocumentationDocumentation for the most recent version of theovitomodule:https://docs.ovito.org/python/InstallationTo download and install theovitomodule in your Python interpreter, runpython -m pip install -U ovitoTheovitomodule is available for the following target platforms:Operating systemCPU architecturePython versionsWindows64-bit (x86_64)3.8 - 3.12Linux64-bit (x86_64)3.8 - 3.12macOS 10.15+64-bit (x86_64)3.8 - 3.11macOS 12.0+64-bit (arm64)3.9 - 3.12
Package: ovitoclient
Ovito ClientOvito Client is a command-line utility with some basic rendering capabilities for trajectory files using Ovito.Free software: MIT licenseDocumentation:https://ovitoclient.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-09-10)First release on PyPI.
Package: ovl
OVL - Object Vision LibraryPython Module for Computer Vision Object Tracking and Detection mainly for the FIRSTֲ® Robotics Competition ProgramOvl support complex yet modular computer vision pipelines that are easy to create and modify.Easy to create and setup for beginners and flexible for prosYou can follow up on changes in for the current version in
thechangelog folderDocumentationThere are multiple code exampleshereDocumentation is availablehereDependencies:The following python module dependencies are needed:OpenCVnumpyThe following python modules are optional for certain features:NetworkTables (pyNetworkTables) forNetworkTableConnection(installed automatically)OVL is officially supported for python 3.7+ only!Installation:Usingpip:python -m pip install ovl[cv]For the full installation of all features use:python -m pip install ovl[full]For the frc related features use the frc option:python -m pip install ovl[frc]Note that ovl doesn't come with the precompiled version of
opencv for python automatically. If you wish to compile opencv for yourself -
simply refrain from using the cv flag during installation.Usage:The library uses simple yet highly customizable syntax to create a vision pipeline using theVisionobjectA pipeline that detects a yellow circle:import ovl

target_filters = [ovl.percent_area_filter(min_area=0.005),
                  ovl.circle_filter(min_area_ratio=0.7),
                  ovl.area_sort()]

threshold = ovl.Color([20, 100, 100], [55, 255, 255])

yellow_circle = ovl.Vision(threshold=threshold,
                           target_filters=target_filters,
                           camera=0,  # open the first connected camera
                           image_filters=[ovl.gaussian_blur()])

while True:
    image = yellow_circle.get_image()
    targets, filtered_image = yellow_circle.detect(image)
    directions = yellow_circle.get_directions(targets, filtered_image)

    print(directions)  # prints out the (x, y) coordinates of the largest targetThere are more code examples and usageshere