Saravanan666/pypi-org huggingface dataset:
Package: risu
IntroductionRisu is a framework to help with system configuration validation on
either live system or any sort of snapshot of the filesystem.Via execution of ‘plugins’ it reports status on each one execution
against the system that gives you an idea on health status, actual
problems or problems that will reveal themselves if no preventive action
is taken.Please if you have any idea on any improvements please do not hesitate
to open an issue.HighlightsPlugins written in your language of choice.Allows to dump output to json file to be processed by other tools.Allow to visualize html from json output.Check our sample at:Risu-wwwAnsible playbook support.Save / restore default settingsCheck latest changes onChangelog.mdCheck for plugins listing onrisuclient/plugins/Framework contains additional readmes for the tools included:Risu READMEMagui READMEInstallationCheckREADMEabove for detailed instructions outside os simple ones:
- Just clone the git repository and execute it from there ‘or’ - use
‘pipsi’ or create a python virtual env to install package ‘risu’Give it a try running as ‘root’ on your system with:./risu.py-lso it will showlive executionand onlyFailedtests.How does it looks like?Check how does it look in an execution at:asciicastAdditional resourcesCheckhttps://risuorg.github.iofor more details and information
Package: rita-dsl
RITA DSLThis is a language, loosely based on languageApache UIMA RUTA, focused on writing manual language rules, which compiles into eitherspaCycompatible patterns, or pure regex. These patterns can be used for doingmanual NERas well as used in other processes, like retokenizing and pure matchingAn Introduction VideoLinksWebsiteLive DemoSimple Chat bot exampleDocumentationQuickStartLanguage Syntax Plugin for IntelijJ based IDEsSupportInstallpip install rita-dslSimple Rules examplerules="""cuts = {"fitted", "wide-cut"}lengths = {"short", "long", "calf-length", "knee-length"}fabric_types = {"soft", "airy", "crinkled"}fabrics = {"velour", "chiffon", "knit", "woven", "stretch"}{IN_LIST(cuts)?, IN_LIST(lengths), WORD("dress")}->MARK("DRESS_TYPE"){IN_LIST(lengths), IN_LIST(cuts), WORD("dress")}->MARK("DRESS_TYPE"){IN_LIST(fabric_types)?, IN_LIST(fabrics)}->MARK("DRESS_FABRIC")"""Loading in spaCyimportspacyfromrita.shortcutsimportsetup_spacynlp=spacy.load("en")setup_spacy(nlp,rules_string=rules)And using it:>>>r=nlp("She was wearing a short wide-cut dress")>>>[{"label":e.label_,"text":e.text}foreinr.ents][{'label':'DRESS_TYPE','text':'short wide-cut dress'}]Loading using Regex (standalone)importritapatterns=rita.compile_string(rules,use_engine="standalone")And using it:>>>list(patterns.execute("She was wearing a short wide-cut dress"))[{'end':38,'label':'DRESS_TYPE','start':18,'text':'short wide-cut dress'}]
Package: ritc
A Python library for interactions with Rotman Interactive Trader Market
Simulator Client Application via REST exchange APIThe official REST API for Rotman Interactive Trader can be found in theirAPI documentation.FeaturesThis library aims to be as lean and efficient as possible while automatically
handling all of the repetitive and frustrating parts of interacting with the
Rotman Interactive Trader. Some of the features include:Automatic handling ofrate limit exceeded.error responses.Programmatic interface for all available RIT REST API.Strictmypytype-checking compatibility.Futureproof design compatible with every RIT REST API versions…InstallationThis library is available onPyPI.You can install it by executing the following command:pipinstallritcDocumentationYou can read thelatestandstabledocumentation online. Or, you can also
generate the documentation for offline viewing with Sphinx.
Package: rite
Python Utility PackageReport a Bug|Request a Feature|Ask a Question|Make a Sugestion|Start a DiscussionTable of ContentsAboutQuick StartUsageAuthorsLicenseContributingDisclaimerAboutriteis a Python Utility Package.Quick StartInstallationpip install ritepip install rite==0.0.0PyPiUsage...Authorsriteis an open-source project byScape Agency.Scape AgencyScape Agency is a spatial innovation collective that dreams, discovers and designs the everyday of tomorrow. We blend design thinking with emerging technologies to create a brighter perspective for people and planet. Our products and services naturalise technology in liveable and sustainable –scapes that spark the imagination and inspire future generations.website:scape.agencygithub:github.com/scape-agencyLicenseExcept where otherwise noted,riteis licensed under the terms of theMIT License.ContributingWe'd love for you to contribute and to makeriteeven better than it is today!
Please refer to thecontribution guidelinesfor information.DisclaimerTHIS SOFTWARE IS PROVIDED AS IS WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING ANY IMPLIED WARRANTIES OF FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR NON-INFRINGEMENT.
Package: rith
Arithmetic helps you create production-ready APIs so that your team can build faster, smarter, and more secure.pip install rithBest PracticesWhen working with Python applications, it’s always advisable to work within
the constraints of a Virtual Environment.Arithemtic and therithtools require Python 3+. To create a virtual environment
that uses Python 3 execute the following command in your project directory.virtualenv -p python3 venvStart Your Virtual Environmentsource venv/bin/activate
Package: rit-housing-data-schema
rit-housing-data-schemaNormalized data schema for the output of the data-ingest pipeline.InstallationInstall this library usingpip:pipinstallrit-housing-data-schemaUsageImport this package then use it to construct normalized objects with type
enforcement.fromrit_housing_data_schemaimportlocationlocation.NormalizedLocation(id="vaccinebot:uuid-for-site",source=location.Source(source="vaccinebot",id="uuid-for-site",fetched_from_uri="https://vaccinateTheStates.com",published_at="2021-01-13T00:00:00-08:00",data={},))For more details on the schema, read the inline comments or thedata-ingestwiki.DevelopmentTo contribute to this library, first checkout the code. Then create a new
virtual environment:cdrit-housing-data-schema
python-mvenvvenvsourcevenv/bin/activateOr if you are usingpipenv:pipenvshellNow install the dependencies, linters, and tests:pipinstall-e'.[lint,test]'To run code formatters:isort.
black.To run linters:mypy.
flake8rit_housing_data_schemaTo run the tests:pytestReleaseideally from within a virtual env as described above...pip install build twine
python -m build
twine check dist/*
twine upload dist/*
Package: ritl
# ritl
Relatives Import Tool : How many time I was blocked by relative import problems. This time is over now…# Installation` pip install ritl `# UsageWe just need to put :` from ritl import relative_import relative_import.add_rel_imp() `and we can import easily our modules in the same directory of our file. Wherever you execute it.You can also add more directories, relative to your file directory :`relative_import.add_rel_imp("../utils")`This will allow you to relatively import your modules from other directory in utilsAfter this, you just have to import your modules
Package: ritm-annotation
ritm_annotationTool to do dataset annotation for semantic segmentation datasets.Based onSamsungLabs/ritm_interactive_segmentationWork in progressInstallationPippipinstallritm_annotationNix flakesRunning it using Nix flakesnix run github:lucasew/ritm_annotation -- --helpDockerdocker run -ti ghcr.io/lucasew/ritm_annotation:latest bashUsage$python-mritm_annotation--help# or$ritm_annotation--helpPretrained model weightsHere you can download pretrained weightsIn my tests I usedcoco_lvis_h18_itermask.pth(SHA256 5f69cfce354d1507e3850bfc39ee7057c8dd27b6a4910d1d2dc724916b9ee32b)DevelopmentRead theCONTRIBUTING.mdfile.
Package: ritprofile
PyPi:https://pypi.org/project/ritprofile/สวัสดี แพ็กเกจนี้คือแพ็กเกจที่อธิบายโปรไฟล์ของคุณริด
สามารถนำไปใช้กับผู้อื่นได้วิธีติดตั้งเปิด CMD / Terminalpipinstallritprofileวิธีใช้งานแพ็คเพจนี้เปิด IDLE ขึ้นมาแล้วพิมพ์…my=Profile('Loong')my.company='uncle-engineer'my.hobby=['Youtuber','Reading','Sleeping']print(my.name)my.show_email()my.show_myart()my.show_hobby()my.show_cat()พัฒนาโดย: Super rit FB:https://www.facebook.com/UncleEngineerYouTube:https://www.youtube.com/UncleEngineer
Package: ritremixerator
Hi,This program is a web application which enables users to build a Fedora
Spin/Remix. It is a Free Software liscenced under the GNU Affero General
Public License and written using Django/Python.The project is hosted athttps://fedorahosted.org/dorrie/and its wiki is
located athttps://fedoraproject.org/wiki/Remixes_Web_InterfaceFor installation related information see INSTALL
Package: ritsis
RIT SIS \nPackage Containing Some Of the Features of SISFunctions Availabledict getAttendance(usn,dob)This returns the attendance of the student with ResultCode as ** Success ** and ResultCode will be ** Fail ** if the credentials are incorrect.bool isStudent(usn,dob)This returns ** true ** if credentials are correct and ** false ** otherwisedict getDetails(usn,dob)This returns the details of the student
Package: ritual
Visual scripting for PytonGetting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes.InstallingYou can install the method by typing:pip install ritualBasic usageCreate a new folder, and start the local editor using the command:ritual--editorThen, go tohttp://0.0.0.0:5000/.
On the local website, right click to add functions or variables (represented as boxes), and link them to create a graph.
You can save the graph as well as running it by clicking on the top-right buttons.You can add different pre-existing packages to the editor, as well as your own (documentation soon), by adding the--packagesparameter:ritual--editor--packagesritual.lib_requestsritual.lib_pandasThese packages add a new set of boxes that you can place and call, in this case http requests and Pandas DataFrame basic I/O functions.When saved, the graph and the packages references are stored in a single .json file (in the graphs/ folder) that you can call later on by omitting the--editorparameter:ritual-fgraphs/XXXX.jsonOr by loading directly the graph in Python:fromritualimportload_graphritual=load_graph("graphs/XXXX.json")state=ritual.run()Thestatevariable is a dict that contains the boxes output values, accessible by box name.AuthorsMaixent Chenebaux
Package: rituals
Theritualspackage provides PyInvoke tasks that work for any
project, based on its project metadata, to automate common
developer chores like ‘clean’, ‘build’, ‘dist’, ‘test’, ‘check’,
and ‘release-prep’ (for the moment).The guiding principle for these tasks is to strictly separate
low-level tasks for building and installing (viasetup.py) from
high-level convenience tasks a developer uses (viatasks.py).
Invoke tasks can use Setuptools ones as building blocks, but
never the other way ‘round – this avoids bootstrapping head-
aches during package installations usingpip.The easiest way to get a working project based onritualsis
thepy-generic-projectcookiecutter template. That way you have
a working project skeleton within minutes that is fully equipped,
with all aspects of bootstrapping, building, testing, quality
checks, continuous integration, documentation, and releasing
covered. See here for more:https://github.com/Springerle/py-generic-projectCopyright ⓒ  2015 - 2019 Jürgen HermannThis program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License version 2 as
published by the Free Software Foundation.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.The full LICENSE file and source are available athttps://github.com/jhermann/rituals
Package: ritzfizz
INSTALL YOUR THEMEThese instructions assume your documentation is hosted on Github, and already working on ReadTheDocs. First let’s get the proper files in place, then we will show you the proper structure.Add requirements.txt to the project root
Open your newly created requirements.txt and add:THEMENAME==versionFor example, the RitzFizz theme would read:ritzfizz==0.9These package names grab the packages we have setup for you on Pypi. You can find the latest version on our front page or by searching the package in PyPi.Modify config.py
You will need to add the following values, or modify them to match the following:# imports go up top
import THEMENAME# then call the package
html_theme = ‘THEMENAME-theme’
html_theme_path = THEMENAME.get_html_theme_path()Configure ReadTheDocsRTD’s must now be configured to use your new requirements file. It can already find configy.py, so we just need to point it to requirement.txt. In your project admin, go to Advanced Settings, click “Install your project inside a virtualenv using setup.py install”. Type in “requirements.txt” in the next field. That’s it! Your documents can now be rebuilt and your new theme will appear.If you have any trouble please email us atadamw@xepler.com
Package: rival-regions-calc
Rival Regions CalcUnofficial calculator for Rival Regions. Easily calculate common known formulas from the game.Currently supportedWork productivityInstall$pipinstallrival-regions-calcDemoProductionfromrival_regions_calcimportItem,WorkProductionRESOURCE=Item("oil")WP=WorkProduction(RESOURCE)WP.user_level=86WP.work_exp=142000WP.factory_level=185WP.resource_max=371WP.department_bonus=1.9WP.wage_percentage=100WP.tax_rate=2WP.profit_share=75WP.calculate()print(WP.wage())Constructionfromrival_regions_calcimportConstructionCosts,BuildingBUILDING=Building("hospital")CC=ConstructionCosts(BUILDING,1805)CC.calculate(50)print(CC.cash)print(CC.gold)print(CC.oil)print(CC.ore)Deep explorationfromrival_regions_calcimportItem,DeepExplorationresource=Item("oil")DE=DeepExploration(resource,223)DE.calculate_max()print(DE.cash)print(DE.gold)print(DE.diamond)Resource coefficientfromrival_regions_calcimportResourceCoefficient,Itemresource=Item("oil")RC=ResourceCoefficient(resource,266)print(RC.calculate())RC=ResourceCoefficient(resource,371)print(RC.calculate())DevelopmentI'll add formulas if I think they are important.
If you'd like to see a more improvements you can open an issue or make pull request.
Package: rivals-top8-results
rivals-top8-resultsA small package built usingpillow, useful in quickly creating top 8 results views for Rivals of Aether tournaments.Also comes with averyexperimental GUI built with qt-designer, which has got an also experimental support for Challonge brackets.Installpip install rivals-top8-resultsUsageYou can just grab the release here and run the executable.
Alternatively, using this is as simple as importing this library and calling thedraw_top8anddraw_resultsfunctions, which you can import as follows:from rivals_top8_results.draw_results import draw_top8, draw_resultsthentop8 = draw_top8(
    nicknames,
    characters,
    skins,
    secondaries,
    tertiaries,
    layout_rgb=(255, 138, 132),
    bg_opacity=100,
    resize_factor=1.3,
)anddraw_results(
    top8,
    title="EU RCS Season 6 Finals",
    attendees_num=89,
    date="24-01-2022",
    stage="Aethereal Gates",
    stage_variant=2,
    layout_rgb=(255, 138, 132),
)TodosAdd support for layoutsAdd support for Smash.gg bracketsAdd (proper) support for mains and aliasesCreditsGraphics by@KiirochiiResource folder filled using@Readeku's wonderful tool's assets.
Package: rivector
rivector - risknu vectorsThis Python library introduces a set of fast vector classes, designed for convenient and rapid vector operations, similar to those in Unity. It is well-suited for game development using Pygame and also useful for general mathematical applications. The library is open source and available on GitHub.[!NOTE]
It's worth noting that while you can add your own methods to the module, the library may not be as stable as other vector libraries. Please exercise caution and consider testing thoroughly, especially when compared to more established alternatives.How it Works and Why It's FastThe core mathematical logic and vector class are entirely written in C++. They are then utilized through CPython in Python, transforming it into a wrapper class that can be fully utilized in Python. Speaking of its power, here are some speed-related details, including a comparison with the popular numpy library in mathematics:+ Vector2Wrapper 0.8779744120547548- NumPy 1.6510654900339432Installation and ExamplesTo install this library on Python, you need Python version 3.12 or higher. Here are installation examples for different operating systems:Windows$pipinstallrivectorLinux/MacOS$pip3installrivectorUsage Examples and Creating a Simple Vector:fromrivectorimportVector2vector=Vector2(10,10)print(vector.magnitude)# Magnitude of the vectorprint(vector+10)# Vector(20, 20)print(vector+vector)# Vector(20, 20)# Other operations...[!NOTE]
The library allows you to create and manipulate 2D vectors easily. You can find more details and methods in thewikior inofficial website.Feel free to contribute and enhance the functionality of the library as needed.LicenseThis library is distributed under the Apache License 2.0.
Package: rivendellaudio
This is a Python 3 interface for the Rivendell Radio
Automation System. It contains the following modules:PyPAD. A system for processing Program Associated Data (PAD) generated
by Rivendell.Example plug-ins for processing PAD updates can be found in theRivendell sourcesin 'apis/pypad/tests/'.Web API. Classes for remotely interacting with Rivendell objects,
includingCartsCutsGroupsLogsPodcastsScheduler CodesExample scripts for testing and exercising the API can be found in theRivendell sourcesin 'apis/rivwebpyapi/tests/'.
Package: riverapi
River API ClientThis is an API client created fordjango-river-mlthat is intended to make it easy to interact with an online ML server providing river models (learning, predicting, etc.).
It currently does not provide a terminal  or command line client and is intended to be used
from Python, but if there is a good use case for a command line set of interactions
this can be added.Quick StartGiven that you have a server running that implements the same space as django-river-ml, you can do
the following. Note that if your server requires authentication, you can generate a token and export to:exportRIVER_ML_USER=dinosaurexportRIVER_ML_TOKEN=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxAnd then do the following example.fromriverimportdatasetsfromriverimportlinear_modelfromriverimportpreprocessingfromriverapi.mainimportClientdefmain():# This is the default, just to show how to customizecli=Client("http://localhost:8000")# Basic server info (usually no auth required)cli.info()# Upload a modelmodel=preprocessing.StandardScaler()|linear_model.LinearRegression()# Save the model name for other endpoint interactionmodel_name=cli.upload_model(model,"regression")print("Created model%s"%model_name)# Train on some dataforx,yindatasets.TrumpApproval().take(100):cli.learn(model_name,x=x,y=y)# Get the model (this is a json representation)model_json=cli.get_model_json(model_name)model_json# Saves to model-name>.pkl in pwd unless you provide a second arg, destcli.download_model(model_name)# Make predictionsforx,yindatasets.TrumpApproval().take(10):res=cli.predict(model_name,x=x)print(res)# By default the server will generate an identifier on predict that you can# later use to label it. Let's do that for the last predict call!identifier=res['identifier']# Let's pretend we now have a label Y for the data we didn't before.# The identifier is going to allow the server to find the features,# x, and we just need to do:res=cli.label(label=y,identifier=identifier,model_name=model_name)print(res)# Note that model_name is cached too, and we provide it here just# to ensure the identifier is correctly associated.# Get stats and metrics for the modelcli.stats(model_name)cli.metrics(model_name)# Get all modelsprint(cli.models())# Stream eventsforeventincli.stream_events():print(event)# Stream metricsforeventincli.stream_metrics():print(event)# Delete the modelcli.delete_model(model_name)if__name__=="__main__":main()ContributorsWe use theall-contributorstool to generate a contributors graphic below.Vanessasaurus💻LicenseThis code is licensed under the MPL 2.0LICENSE.
Package: riverreliability
River reliabilityInstallInstall the package with:pip install riverreliabilityHow to useBelow, we show some basic funtionality of the package. Please look at the notebooks for more examples and documentation.np.random.seed(42)We start of by generating a fake dataset for classification and splitting it in a train and test set.X,y=sklearn.datasets.make_classification(n_samples=5000,n_features=12,n_informative=3,n_classes=3)X_train,X_test,y_train,y_test=sklearn.model_selection.train_test_split(X,y,test_size=0.2,shuffle=True)For this example we use an SVM. We fit it on the training data and generate probabilities for the test set.logreg=sklearn.svm.SVC(probability=True)logreg.fit(X_train,y_train)y_probs=logreg.predict_proba(X_test)As a sanity check we compute some performance metrics.print(f"Accuracy:{sklearn.metrics.accuracy_score(y_test,y_probs.argmax(axis=1))}")print(f"Balanced accuracy:{sklearn.metrics.balanced_accuracy_score(y_test,y_probs.argmax(axis=1))}")Accuracy: 0.808
Balanced accuracy: 0.8084048918146675To get an insight into calibration we can look at the posterior reliability diagrams and the PEACE metric.We can plot the diagrams aggregated over all classes:ax=riverreliability.plots.river_reliability_diagram(y_probs.max(axis=1),y_probs.argmax(axis=1),y_test,bins="fd")peace_metric=riverreliability.metrics.peace(y_probs.max(axis=1),y_probs.argmax(axis=1),y_test)ax.set_title(f"PEACE:{peace_metric:.4f}")_=ax.legend()Or class-wise to spot miscalibrations for particular classes:importmatplotlib.pyplotaspltaxes=riverreliability.plots.class_wise_river_reliability_diagram(y_probs,y_probs.argmax(axis=1),y_test,bins=15)peace_metric=riverreliability.metrics.class_wise_error(y_probs,y_probs.argmax(axis=1),y_test,base_error=riverreliability.metrics.peace)_=plt.suptitle(f"PEACE:{peace_metric:.4f}")In this particular example we can see that the classifier is well calibrated.See the notebooks directory for more examples.