Saravanan666/pypi-org huggingface dataset:
Package: opinionated
opinionatedprovides simple, clean stylesheets for plotting withmatplotlibandseaborn.It’s modeled and named after hrbrthemes in R, by hrbrmstr, which are
“Opinionated, typographic-centric ggplot2 themes”. It’s not meant to be
an exact clone though, I have made a few different choices.The main application I had in mind was to increase the quality of plots
in colab-environments, where there is a very small range of preinstalled
fonts. The package therefore automatically downloads fonts from
GoogleFonts. But I think everything should also work on your local
machine. Be aware though, that it’s not super well-tested, and might e.
g. fail with facets.Installationpip install opinionatedUsageThe package is very simple to use, you just import it and set the style
you want:import opinionated
plt.style.use("opinionated_rc")Then you do your plotting:f, ax = plt.subplots(figsize=(10, 7))
sns.scatterplot(x="bill_length_mm", y="flipper_length_mm", hue='species', data=penguins, s=100, alpha=0.9)And finally, you can slap on some additional information, using some
convenience functions with reasonable defaults. Of course, the usual
ways of setting titles, legends, etc. still work.opinionated.add_legend(title='Species')
opinionated.add_attribution('by Maximilian Noichl')
opinionated.set_title_and_suptitle('Penguins!','They are an excellent type of bird!')Here’s the result:This certainly does look better than what the defaults would give you,
right? –You can find more options, stylesheets, etc. in the github-repository!
Package: opinionx
Opinion Analysis ToolkitA toolkit to extract opinions and useful information from textInstallationpip install opinionxExample UsageFind opinionsfromopinionx.textimportget_opiniontext=open("test.txt",'r',encoding='utf-8').read()opinion_words=['表示','认为','说','介绍','提出','透露','指出','强调','：']list_opinion,_,_=get_opinion(text,lang='zh',opinion_words=opinion_words)foropinioninlist_opinion:print(opinion)Find Leader's Opinionsfromopinionx.textimportget_leader_opinionstext=open("test.txt",'r',encoding='utf-8').read()list_opinion=get_leader_opinions(text,save_path="",search_keywords_path="data/search_keywords.csv",leader_path="data/g20_leaders.csv")print()foropinioninlist_opinion:print(opinion)print(opinion["opinion"])print(opinion["first_found_keyword"])print(opinion["first_found_leader"])print()run tf-idf and tf models for massive text filesfromopinionx.tfidf_shellimport*run_tfidf_shell(input_folder="tfidf_folder/raw_data",# a list of text filesoutput_folder="tfidf_folder/output",# output folderuser_dict_path="tfidf_folder/user_dictionaries",# the folder contains csv files with each line as a wordfont_path="utils/fonts/SimHei.ttf",# use it when analysis Chinese textis_html=True)Credits & ReferencesStanzajiebaLicenseTheopinionxproject is provided byDonghua Chen.
Package: opinum-api-connector
This package simplifies the Opinum API calls.No magic. You need to follow theOpinum Swagger Documentationfor correct formatting of your requestsBe aware that this is a new package, certainly missing a lot of features. Feel free to contribute.You first need to create an instance of the ApiConnector class with following parameters:environmenta dictionary of environment variablesifNone, ApiConnector uses your environment variables (os.environ)Mandatory environment variables are:OPINUM_USERNAME: the Datahub user.TAKE CARE: if this user has access to multiple tenants and if you do not specify a tenant id,
ApiConnector will use the last tenant used.OPINUM_PASSWORD: the password for the userOPINUM_CLIENT_ID: the client id for accessing the APIOPINUM_SECRETthe corresponding secretOptional environment variables are:OPINUM_API_URL: another API URL than the Europe SaaS one (https://api.opinum.com)OPINUM_AUTH_URL: another authentication URL than the Europe SaaS one (https://identity.opinum.com)DEFAULT_PUSH_URL: another push URL than the Europe SaaS one (https://push.opinum.com)OPINUM_SCOPE: the scope of you session (default: "datahub-api")if you want to push data, the scope should be "datahub-api push-data"account_idone of the tenant ids available for the Datahub user (default:None)retries_when_connection_failurenumber of extra attempts when no 200 or 204 return code (default: 0, maximum: 5)Once you have your ApiConnector instance, you may use the class methodsgetpostputdeleteAll keyword arguments will be converted to path parameters in the API call with one important exception,
thedatakeyword referring to the body of your call.There are two other class methods for data pushing because we have another API for thispush_datapush_dataframe_dataThere is a little bit of magic with the method multi_thread_request_on_path that splits a list of parameters
Allowing to make parallel calls.
Package: opium
opiumOpenShift Pod Independent Usage Metrics (OPIUM) pulling & aggregating metrics from multiple pods made easy!Free software: MIT licenseDocumentation:https://opium.readthedocs.io.Aggregating metrics from pods through OKD’s router can be a challenge. OPIUM offers the next best thing.
While it doesn’t (yet) aggregate the varius metrics it gathers the metrics from the defined Deployment Configs
on a specific OKD project and serves them to a single /metrics endpoint. This way it allows for easier gathering
of per-pod metrics.ConfigurationOPIUM is configured through environment variables and specifically the following:OPIUM_OKD_URL => The url of the OKD instance’s master, including the scheme and without a trailing/OPIUM_OKD_TOKEN => An access token for a service account withviewandeditpermissions on the desired projectOPIUM_PROJECT => Project to be exportedOPIUM_DEPLOYMENT_CONFIGS => Comma separated (no spaces) list of Deployment Configs to exportOKD PreparationIn your OKD admin CLI you will need to run the following# create a service accountoccreateserviceaccount<account># Retrieve the service account's access token (set this to OPIUM_OKD_TOKEN)ocserviceaccountsget-token<account># Give the service account the required permissions to the desired projectocpolicyadd-role-to-userviewsystem:serviceaccount:<project>:<account>ocpolicyadd-role-to-usereditsystem:serviceaccount:<project>:<account>ExecutionTo start OPIUM (after you’ve set the configuration environment variables appropriately) simly run:opiumThis will spawn an HTTP server listening on your system’s public interface and on port 8080.Configuration for the listening interface, as well as a containerized version of OPIUM will follow in later versions.FeaturesGather the response of/metricsfrom all the pods of the specifieddeployment_configServe them as one responseCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-10-25)First release on PyPI.
Package: opject-client
opjectObject Passing LibraryContentsAboutInstallUsagePackagesCodeophonAboutopjectis a specification and implementation for passingobjects through the network.Anobjectis a self-contained piece of code.The passing of the object through the network is obtained in 2 steps:a registered object is requested by theopject clientfrom theopject server;theopject clientinstantiates or runs in a virtual machine the received object.opjecthas clients forNodeJSPythonTheopject servercan serve any kind of object. However, depending on the preferred language, a specificopject servercan be used forNodeJSPythonTheopject registrygrants extended functionality through a web interface. The registry can be self-hosted orcloud-hosted.InstallInstall by runningpipinstallopject-clientUsageTheopjectclient requires a server. The server can be self-hosted orcloud-hosted.The simplest use-case implies registering anopject, requiring, and running it.fromopject_clientimportClientasOpjectClientendpoint='http://server.address'token='secret_token_obtained_from_server'opject_id='some-opject-python'# Optional name.opject_name='SomeOpject'opject_data='class SomeOpject:\n\tdef __init__(self):\n\t\tself.internal = 12\n\tdef read(self):\n\t\treturn self.internal\n'## The data can also be a multiline string.# opject_data_multiline = """# class SomeOpject:#     def __init__(self):#         self.internal = 12#     def read(self):#         return self.internal# """opject_client=OpjectClient(endpoint=endpoint,token=token,)opject_client.register(opject_id,opject_data,)some_opject=opject_client.require(id=opject_id,name=opject_name,)value=someOpject.read()# 12Packages@plurid/opject-client-javascript•JavaScriptopject client@plurid/opject-server-javascript•JavaScriptopject server@plurid/opject-client-python•Pythonopject client@plurid/opject-server-python•Pythonopject serverCodeophonlicensing:delicenseversioning:αver
Package: opject-server
opjectObject Passing LibraryContentsAboutInstallUsagePackagesCodeophonAboutopjectis a specification and implementation for passingobjects through the network.Anobjectis a self-contained piece of code.The passing of the object through the network is obtained in 2 steps:a registered object is requested by theopject clientfrom theopject server;theopject clientinstantiates or runs in a virtual machine the received object.opjecthas clients forNodeJSPythonTheopject servercan serve any kind of object. However, depending on the preferred language, a specificopject servercan be used forNodeJSPythonTheopject registrygrants extended functionality through a web interface. The registry can be self-hosted orcloud-hosted.InstallInstall by runningpipinstallopject-serverUsageA simpleopject serverwill only require passing averify_tokenfunction andstarting the server.fromopject_serverimportServerasOpjectServertest_token='__TESTS__'port=7766defverify_token(token,):returntoken==test_tokenserver=OpjectServer(verify_token=verify_token)server.start(port=port,)Theopjectserver will use the local filesystem for storing data.Custom functions can be passed to theopjectserver to implement any kind of logic handling following the interfacesdefverify_token(token:str,):passdefget_object(id:str,):passdefget_metadata(id:str,):passdefregister_object(id:str,data:str,):passdefregister_metadata(id:str,data:dict,# { dependencies: str }):passdefremove_object(id:str,):passPackages@plurid/opject-client-javascript•JavaScriptopject client@plurid/opject-server-javascript•JavaScriptopject server@plurid/opject-client-python•Pythonopject client@plurid/opject-server-python•Pythonopject serverCodeophonlicensing:delicenseversioning:αver
Package: opk
OPKOPK is a spherical top keycap profile developed inCadQueryand released under the very permissive Apache License 2.0.It's especially suited for creating high/medium profile spherical top keycaps.The profile is still highly experimental and very alpha stage.Please contribute!I'm new to CadQuery (CQ), Ptyhon and code based CAD in general. There's a lot of guesswork and fumbling around to get to this point, any help is very much appreciated.UsageIf you are new to CQ, the easiest is to download aCQ-Editorpre-built package (I use the latest development release), load thekeycap.pyfile and hitRender!The mainkeycapfunction in theopk.pyfile has sane defaults and parameters are sufficiently commented.Theexportdirectory includes a few examples in both STL and STEP format.Some examples:Create the default 1u keycapcap = keycap()
show_object(cap)Create a 1.5U keycapcap = keycap(unitX=1.5)
show_object(cap)Create a flat top (not angled) 2U keycapcap = keycap(unitX=2, angle=0)
show_object(cap)Create a 4U spacebarcap = keycap(unitX=4, angle=1, convex=True)
show_object(cap)A 2.25U Return key in system-default-serif fontcap = keycap(unitX=2.25, legend="Return", font="serif", fontsize=8)
show_object(cap)Save and Export resultsCQ can export in many formats including STEP and STL. The easiest way to export an STL in CQ-Editor is by selecting the model in the 3D view and then clicking onTools > Export as STL.Of course it can be done programmatically and that can make exporting an entire keyboard very easy. The following code exports an STL and a STEP file.cap = keycap()
show_object(cap)

exporters.export(cap, 'keycap.stl', tolerance=0.001, angularTolerance=0.05)
exporters.export(cap, 'keycap.step')Notes / RecommendationsDefault angle is 7deg that should be a good starting point for Row 3.For spacebars a small angle (0-4deg) is recommended.Not all fonts will be centered correctly, choose carefully. I'll add some way to better center the legends.The code includes an example to use a DXF drawing as legend (check the comments at the end of the keycap function).TODOadd support for vertical keyadd stepped keyadd ISO Enteradd 2U pos-like stabilizersadd support for logos and graphical legendsadd reinforcement for big keysbetter legend centeringsecondary, tertiary, quaternary legendsside printed legendsadd alternative stemsallow reduced height of keycap for non 19.xx spacingclean up the codeautomatic export script of all needed keycap for a full keyboardoutput files already supported for 3D printingonline editoradd secondary/tertiary legendsadd support for stems other than cherryclean up the codefix negative angle top
Package: opkode.rateablediscussion
IntroductionThis package provides the ability for users to rate the comments in Plone.
They can do this by clicking the up or down arrows shown left of the comment
author.DependenciesThis product works only with plone.app.discussion, which as of Plone 4.1 is the default commenting
framework for Plone.Changelog0.1a2 (2012-03-02)Fixed compatibility with plone.app.discussion 2.3.1 [jcbrand]0.1a1 (2011-03-24)Initial release
Package: oplab-client-python
oplab-client-pythonDescriptionThis pip package's intent is to provide a safe and easy way to interact withOplab's public API. With it, you should be able to use the latest feature additions to the API as we publish them.Installationpip install oplab-client-pythonUsage// TODOSupportYou can go to our Discord server and ask for some help over the#devtalkchannel. You can also just open an issue asking for help. Use thehelplabel.RoadmapIf you have ideas for releases in the future, it is a good idea to list them in the README.ContributingWe accept contributions. Just send us a pull request if you have some improvement to contribute!Authors and acknowledgmentMade with care by the oplab-team.LicenseMITProject statusThis project is still @ it's dawn. If you feel like contributing, or participating in any forms, just send us an issue.
Package: oplab-pipeline
oplab_pipelineoplab_pipeline is a python toolchain to process AUV dives from raw data into navigation and imaging products. The software is capable of:Process navigation: fuses AUV or ROV sensor data using state of the art filters and geolocalises recorded imagery.Camera and laser calibration: performs automatic calibration pattern detection to calibrate monocular or stereo cameras. Also calibrates laser sheets with respect to the cameras.Image correction: performs pixel-wise image corrections to enhance colour and contrast in underwater images.Please review the latest changes in theCHANGELOG.md.InstallationForproduction, to install this package run:pipinstall-Ugit+https://github.com/ocean-perception/oplab_pipeline.gitThis will make the commandsauv_nav,auv_calandcorrect_imagesavailable in the terminal. For more details refer to the documentation.Fordevelopment, clone the repository, navigate to the oplab-pipeline folder and runpipinstall-U--user-e.DocumentationThe documentation is hosted inread the docs.CitationIf you use this software, please cite the following article:Yamada, T, Prügel‐Bennett, A, Thornton, B. Learning features from georeferenced seafloor imagery with location guided autoencoders. J Field Robotics. 2020; 1– 16.https://doi.org/10.1002/rob.21961LicenseCopyright (c) 2020-2022, University of Southampton. All rights reserved.
This work is licensed under aCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.See LICENSE.md file in the project root for full license information.ContributingPlease document the code usingNumpy Docstrings.
If you are using VSCode, there is a useful extension that helps namedPython Docstring Generator. Once installed, make sure you select Numpy documentation in the settings.Runpre-commit installto installpre-commitinto your git hooks. pre-commit will now run on every commit. If you don't havepre-commitinstalled, runpip install pre-commit.
Package: ople
Ople ClientTable of ContentsOverviewInstallation and SetupPython ClientTerminal ClientOverviewOple has created two clients to simplify the integration with your codebase and help you be more
productive. You can use the python client to connect to your Ople models  with your python code.
You can also leverage the ople-client command to make predictions directly from the terminal.Installation and SetupInstall python3Run pip3 install ople (If it doesnt work run python3 -m pip install ople)Setup ENV variables to make the workflow easierRunexport OPLE_KEY="<YOUR OPLE_KEY>"Runexport OPLE_SECRET="<YOUR OPLE_SECRET>"Runexport OPLE_MODEL_ID="<YOUR OPLE_MODEL_ID>"If you want to use S3 to get data then you need to setup awsRunexport AWS_KEY="<YOUR AWS_KEY>"Runexport AWS_SECRET="<YOUR AWS_SECRET>"Python ClientUse the python code snippet below. All you need to update is the code for getting the input.
The client handles the following inputs:Dictionary with S3 information: '{"bucket": "", "path": ""}'`Path to Local CSV FileString in CSV formatDictioanry in Ople Format:'{"columns": [<YOUR COLUMNS>], "rows": [[<YOUR ROW>]]}'Pandas DataFrameHere is the code snippet:fromople.clientimportOpleClientimportos# ****************************************************input="<YOUR CODE TO GET INPUT>"# ****************************************************# Specify what model to runople_key=os.environ['OPLE_KEY']ople_secret=os.environ['OPLE_SECRET']ople_model_id=os.environ['OPLE_MODEL_ID']# OPTIONAL Parameters to use S3 to uploadaws_key=os.environ['AWS_KEY']aws_secret=os.environ['AWS_SECRET']# Connect to Specified Modelifaws_keyoraws_key:model=OpleClient(ople_key,ople_secret,ople_model_id)else:model=OpleClient(ople_key,ople_secret,ople_model_id,aws_key,aws_secret)# Specify Prediction Parametersshap=Falseglobal_shap=Falseallow_unknown_categories=True# Make a Prediction with modelresult=model.predict(input,shap=shap,global_shap=global_shap,allow_unknown_categories=allow_unknown_categories)Terminal ClientTo run the terminal client you need to follow the installation and setup steps above. After that
you will be able to run theople-clientcommand. Here are the parameters you can specify:'-s', '--shap': This flag returns the shap values for each predictions'-g', '--global_shap': This flag returns the Global Shap values for each predictions'-u', '--allow_unknown_categories': This flag'-K', '--key': This will set the ople key to whatever comes after it'-S', '--secret': : This will set the ople secret to whatever comes after it'-ID', '--model_id': : This will set the model_id to whatever comes after it'-AS', '--aws_secret': : This will set the AWS secret to whatever comes after it'-AK', '--aws_key': : This will set the AWS key to whatever comes after itHere are some examples of how to run the command:ople-client -u -s -g '<YOUR FILE PATH>/<FILENAME>'ople-client -u -s -g '{"bucket": "<YOUR BUCKET>", "path": "<YOUR S3 FILEPATH>"}'ople-client -u -s -g '<YOUR CSV STRING>'ople-client -u -s -g '{"columns": [<YOUR COLUMNS>], "rows": [[<YOUR ROW>]]}'
Package: oplogutils
# oplogutilsUtilities for inspecting and manipulating the MongoDB oplog.By Dan McKinley - dan@etsy.com - [http://mcfunley.com](http://mcfunley.com)## OverviewThis package contains utilities for manipulating a MongoDB oplog, which can benecessary in recovery scenarios. The tools are:<pre>oplog-count - counts the number of events in the oplog after a certain dateand time.oplog-trim  - deletes events from the oplog after a certain date and time.</pre>## InstallationThe easiest way to install is using [setuptools](http://pypi.python.org/pypi/setuptools).<pre>$ easy_install oplogutils</pre>To install from source, extract the tarball and use the following commands.<pre>$ python setup.py build$ sudo python setup.py install</pre>## Examples<pre>oplog-trim --host=myhost.domain.com --port=27017 --remove-after="2010-05-22 03:42:00"</pre>## Unit Testsoplogutils comes with a fairly complete unit test suite. The suite requires themongod program to be somewhere in the $PATH. To run the tests, use:<pre>python setup.py test</pre>## See Also* [The MongoDB website](http://www.mongodb.org/)* See [articles tagged MongoDB on the Etsy developer blog](http://codeascraft.etsy.com/tag/mongodb/) for some use cases.## Version History### Version 0.1.2* 05-30-2010 - Added --version switch to commands. Test fixes.### Version 0.1.1* 05-30-2010 - Added oplogutils to cheeseshop.### Version 0.1*  05-21-2010 - Initial release.
Package: oplusclient
# Oplusclient![GitHub](https://img.shields.io/github/license/openergy/oplusclient?color=brightgreen)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/oplusclient)
![PyPI](https://img.shields.io/pypi/v/oplusclient)Oplusclient is a Python package providing a client for working with Openergy’s Oplus platform.## DocumentationThe official documentation is hosted on readthedocs.io :https://oplusclient.readthedocs.io## License[Mozilla Public License Version 2.0](./LICENSE.txt)
Package: opmatch
The package provides the following matching methods:optimal matchone-to-one matchone-to-many matchvariable ratio matchfull match (not implemented yet)greedy matchnot implemented yetAll matching methods support covariate and propensity score matching.To obtain matches you can simply run:fromopmatch.matcherimportMatchercase_control_dict=Matcher(df,matching_ratio,min_mr,max_mr,n_controls,metric,matching_type,case_col,var_cols,ps_col,).match()Parametersmatching_rationumber of controls per case if matching ratio is constantmin_mr: minimum number of controls per casemax_mr: maximum number of controls per casen_controls: number of controls to matchmetric: PS or one of the metrics accepted by scipy.spatial.distances.cdistmatching_type: constant or variable matching ratiocase_col: boolean column where cases are 1s and controls 0svar_cols: columns containing relevatn patient variables
if metric!=PS: var_cols used for matching
if metric==PS but ps_col is not specified: var_cols used to compute PS using logistic regressionps_col: column containing the propensity scorecase_col: column name of case column, should contain 1s and 0s
Package: opmcpg
The code in folder src\opm was taken fromhttps://opm-project.org/in particularhttps://github.com/OPM/opm-grid/tree/master/opm/gridand upgraded to C++ using std::vector instead of pointers ans mallocto be able to expose them to python. Added pybind11.
This wheel contains only dynamic library which could be imported in python
Package: op_mewtwo
# op_mewtwoThis package contains a python class called Risk Dataframe which contains methods to perform a missing not at random analysis and to automate a risk-based segmentation process by analyzing all the features in the dataset and identifying the best split for segmenting each feature.In order to effectively use the methods in this class, some degree of data cleaning and preparation will be required. A demo file using a Car Loans Portfolio Dataset is provided.To access the Risk Dataframe class within the Mewtwo package, please do the following,from op_mewtwo.RiskDataframe import RiskDataframe