Saravanan666/pypi-org huggingface dataset:
Package: contrastive-inverse-regression
contrastive-inverse-regressioncir is a python package provided the algorithm for contrastive inverse regression (CIR) for dimension reduction used in a supervised setting to find a low-dimensional representation by solving a nonconvex optimization problem on the Stiefel manifold.InstallationMake sure you have numpy, pandas, and scipy install beforehand and the version of these packages compatible with cir. The easy way to install is usingpip:pipinstallcontrastive-inverse-regressionAlternatively, you can also install by cloning this repository:pipinstallgit+https://github.com/myueen/cir.gitDependenciesPython (>= 3.10.9)numpy (>= 1.24.3)pandas (>= 2.1.4)scipy (>= 1.9.3)To run exmaple, matplotlib (>= 3.8.2) is requiredExampleThe dataset for the following example is included in the example/dataset folder.fromcontrastive_inverse_regressionimportCIRimportpandasaspdd=2alpha=0.0001# download the dataset and set it to the absolute path in your computerfg=pd.read_csv('../foregroundX.csv')bg=pd.read_csv('../backgroundX.csv')Y=pd.read_csv('../foregroundY.csv')Yt=pd.read_csv('../backgroundY.csv')fg=fg.iloc[0:,1:]bg=bg.iloc[0:,1:]Y=Y.iloc[0:,1:]Yt=Yt.iloc[0:,1:]V=CIR(fg,Y,bg,Yt,alpha,d)Other detailed examples for employing cir are provided.For the case of discrete foreground Y values, the mouse protein dataset  Data_Cortex_Nuclear.csv is used and the corresponding visualization in mp_regression.py and regression testing in mp_regression.py.For the case of continuous foreground Y values, cir is applied on the retinol dataset Retinol.txt and the corresponding regression is in plasma_regression.py. Continuous values are not usually for classification, hence visualization is not provided.Citing contrastive-inverse-regressionIf you find this algorithm helpful in your research, please add the following bibtex citation in references.@phdthesis{hawke2023contrastive,title={Contrastiveinverseregressionfordimensionreduction},author={Hawke,SamandLuo,HengruiandLi,Didong},journal={arXivpreprintarXiv:2305.12287},year={2023}}References.. [1] : Hawke, S., Luo, H., & Li, D. (2023)
"Contrastive Inverse Regression for Dimension Reduction",
Retrieved fromhttps://arxiv.org/abs/2305.12287.. [2] Harry Oviedo (2024).
SGPM for minimization over the Stiefel Manifold (https://www.mathworks.com/matlabcentral/fileexchange/73505-sgpm-for-minimization-over-the-stiefel-manifold), MATLAB Central File Exchange. Retrieved January 12, 2024.
Package: contrasttransferfunction
contrasttransferfunctioncontrasttransferfunctionis a python library to model and estimate the contrast transfer function for transmission electron microscopy data. The library is designed to:Follow CTFFIND conventions and algorithms as much as possibleBe highly compatible by using pure python, no compiled codeStay fast by using numpy vectorizationInstallationpip install contrasttransferfunctionExamplesPlot 1D powerspectrumfromcontrasttransferfunctionimportContrastTransferFunctionimportmatplotlib.pyplotaspltmyctf=ContrastTransferFunction(defocus1_angstroms=8000,pixel_size_angstroms=2.3)plt.plot(myctf.frequency_angstroms_1d,myctf.powerspectrum_1d)plt.gca().xaxis.set_major_formatter(lambdax,pos:f"{1/x:.1f}Å")plt.xlabel("Spatial resultion (A)")Plot 2D powerspectrummyctf=ContrastTransferFunction(defocus1_angstroms=8000,defocus2_angstroms=6000,defocus_angle_degrees=25.3,pixel_size_angstroms=2.3)plt.imshow(myctf.powerspectrum_2d,cmap="Greys")Licensecontrasttransferfunctionis distributed under the terms of theMITlicense.
Package: contrat
contratSimple python module to keep function signature introspection compatibility between
python versionsInstallpipinstallcontratUsageThe following example work for python 2.7 and python 3.0+:#!/usr/bin/pythonfromcontratimportgetargspecdefsample(arg1,arg2=True,arg3=1):passprint(str(getargspec(sample)))# will display# ArgSpec(args=['arg1', 'arg2', 'arg3'], varargs=None, keywords=None, defaults=(True, 1))
Package: contres
# Contres
Contres command line interface - WIPContres is the command line interface for deploying a research
project in a Gitlab environment following the CR guidelines.
CR is short for continuous research (like contres), an outline
of how to utilize gitlab CI runners to make research scripts
reproducible and cloneable with one click.A paper or guide will be distributed with this CLI once it left
experimental stage.## InstallationPlease not that this tool might heavily change as it is still work in progress.Install contres using pip`bash pip install contres `Then, there should be a cli available on your path calledcontres.`bash contres-h`This guide will be extended with the next release.
Package: contrib-colormaps
contrib_colormaps: User-contributed colormapsBuild StatusLatest dev releaseLatest releaseDocsWhat is it?contrib_colormaps is a collection of user-contributed colormaps
for use with Python plotting programs such asBokeh,Matplotlib,HoloViews, andDatashader.Installationcontrib_colormaps supports Python 2.7, 3.5, 3.6 and 3.7 on Linux, Windows,
or Mac and can be installed with conda from the pyviz channel:conda install -c pyviz contrib_colormapsor with pip:pip install contrib_colormapsContributingTo add a colormap, open a pull request on this repository adding the following files:comma-separated file of RGB values to the contrib_colormaps/colormaps
directory. This file should look like:0, 0.20755, 0.97632
0, 0.22113, 0.96201A Jupyter notebook inexamples/colormapsmeeting the following criteria:a name that matches the name of the csv
e.g. for a new colormap calledrainforestwith a csvrainforest.csvthere should be a correspondingrainforest.ipynban explanation of the colormap - what is it? and when/why would someone use it?a swatch of the colormap - we recommend using ourswatch function, but it's not requiredat least one example plot using the colormap - it can be exclusively Bokeh, Matplotlib, or HoloviewsThe notebook should be cleared of all outputs. To use the UI, click Cell -> All Outputs -> ClearOR clear them automatically on commit using the predefined git hook.
From within the cloned repository, run:gitconfigcore.hooksPath.githooksA pytest-mpl baseline image for tests. To create this image first install pytest-mpl:pipinstallpytest-mplThen generate the figurefrom within the tests directoryrun:pytest--mpl-generate-path=baselineSeeexamples/colormapsfor more details.Sample Pull RequestYou can use this sample pull request as a model:#3About PyVizcontrib_colormaps is part of the PyViz initiative for making Python-based
visualization tools work well together. Seepyviz.org.
Package: contrib-extension
contrib extensionIntroduction자신이 직접 라이브러리를 구현할때 contrib 형태의 라이브러리 관리를 사용한다면 contrib extension 라이브러리는 자신의 라이브러리의 root에서 하위 라이브러리를 접근하는 데에 도움을 줄 수 있습니다.If you use library management in the form of a content library when you implement your own library, the content extension library can help you access sub libraries from the root of your library.Installationpipinstallcontrib-extensionUse"""- test- __init__.py- test.py # main- post.py- __category__ = "board"- __id__ = "post"- set(self) -> return "ok"- contrib- __init__.py- download.py- __category__ = "board_ext"- __id__ = "download"- do(self) -> return "what?"- upload- __init__.py- __category__ = "board_ext"- __id__ = "upload"- upload.py- setting.py- __category__ = "board"- __id__ = "upload""""fromcontrib_extensionimportExtensionclasstest:def__init__(self):self._ext_=Extension(use_lib_name=test)print(self._ext_)"""{"board_ext":{"download":<module>,"upload":<module>},"board":{"upload":<module>}}"""self._ext_.importlib("post",False)print(self._ext_.get_info())"""{"board_ext":{"download":<module>,"upload":<module>},"board":{"upload":<module>,"post":<module>}}"""print(self._ext_)"""{"board_ext":{"download":<module>,"upload":<module>},"board":{"upload":<module>,"post":<module>}}"""defpost(self,text):module=self._ext_.get_class("board","post")obj=module()print(obj.set())# okdefupload(self,data):module=self._ext_.get_class("board_ext","download")obj=module()print(obj.do())# what?About이 프로젝트는 다른 프로젝트에 도움을 주기 위해 진행되었습니다. 지속적으로 업데이트를 통해 좀 더 나은 형태로 나아가겠습니다. 더 좋은 아이디어가 있다면 언제든지 연락바랍니다.This project was undertaken to help other projects. We'll continue to update and move on to a better shape. Feel free to contact me if you have a better idea.Versionv0.0.1.1909230047-rc미작동 오류 해결(Fix Error)v0.0.1.1908252311-rcPrivate 프로젝트에서 분리(Detach from Private project)프로젝트 정리(Project theorem)LicenseMIT 라이선스로 공개되었습니다.Released under the MIT license.
Package: contributics
Stackalytics============Application Features--------------------Stackalytics is a service that automatically analyzes OpenStackdevelopment activities and displays statistics on contribution. The features are:* Extraction of author information from git log, store it in the database;* Calculate metrics on number of lines changed (LOC) and commits;* Mapping authors to companies and launchpad ids;* Filter statistics by time, modules, companies, authors;* Extract blueprint and bug ids from commit messages;* Auto-update of database.Quickstart----------To run Stackalytics, you first need to create two kind of configuration files.The one is default_data.json which shows which sources(git repo, ml, etc.) needto be analyzed. Another is stackalytics.conf which shows basic configuration likeHTTP listening host and port, etc. Stackalytics repository contains the defaultfiles of these configuration under etc/ directory. It would be useful to copy andchange them as you like.#. You need to install Stackalytics. This is done with pip after you check outStackalytics repository::$ git clone https://git.openstack.org/openstack/stackalytics$ cd stackalytics$ sudo pip install -r requirements.txt$ sudo python setup.py install#. Install and run memcached DB::$ sudo apt-get install memcached$ memcached -u memcache -d#. Analyze data which are specifed on default_data.json and store the data into memcached DB::$ stackalytics-processor#. Start HTTP server of Stackalytics::$ stackalytics-dashboard#. Users can access Stackalytics site on http://127.0.0.1:8080 as the default.Project Info------------* Web-site: http://stackalytics.com/* Source Code: https://git.openstack.org/cgit/openstack/stackalytics* Wiki: https://wiki.openstack.org/wiki/Stackalytics* Launchpad: https://launchpad.net/stackalytics* Blueprints: https://blueprints.launchpad.net/stackalytics* Bugs: https://bugs.launchpad.net/stackalytics* Code Reviews: https://review.openstack.org/#/q/status:open+project:openstack/stackalytics,n,z* IRC: #openstack-stackalytics at freenode
Package: contributing
✨ ContributingSimpleCONTRIBUTING.mdgenerator.InstallationBefore you installcontributingyou should have Python 3.x and PIP installed.Then enter in your terminal:pipinstallcontributingUsageTo generate CONTRIBUTING.md in the current working directory enter in your terminal:contributingIf you want to specify path to which the CONTRIBUTING.md content should be saved enter in your terminal:contributing-o<path>AuthorIlya Vouk(voilalex) - initial work.LicenseMIT
Package: contributions
Contributions/!\ Fresh Paint! Still in development /!\Generate personalized github timelines from input textInstall$pipinstall-UcontributionsUsageFirst, create a fakegithuborgitlabrepoObtain a github personal access tokenor use your passwordExecute thecontribtionscommandExample:$contributions--help
usage:contributions[-h][-f{arial,KeepCalm-Medium}][-sSPACE][-pPIXEL][-lLEFT_SPACE][-tTOP_SPACE][--simulate][--no-push]textprojectuserpasswordgitnamegitemail

Generatethemostcoolestgithub/gitlabtimelines

positionalarguments:textthetexttodisplayprojectthefakeprojecttocommittouserthegithub/gitlabusernametoauthenticatetopasswordthegithub/gitlabpasswordtoauthenticatetogitnametheusernametocommittogitemailtheuseremailtocommitto

optionalarguments:-h,--helpshowthishelpmessageandexit-f{arial,KeepCalm-Medium},--font{arial,KeepCalm-Medium}thefonttouse-sSPACE,--spaceSPACEthedefaultspacecharactertouse-pPIXEL,--pixelPIXELthedefaultpixelcharactertouse-lLEFT_SPACE,--left-spaceLEFT_SPACEspaceatleft-tTOP_SPACE,--top-spaceTOP_SPACEspaceattop--simulatesimulatdisplay--no-pushrunwithoutpushtorepo

CreditsHervéBeraud(4383)$# /!\ The following command need to be run 4 or 5 times /!\$# execute on your project by replacing by yours data$contributions\BADASS\https://github.com/4383/badass\<yourgithub/gitlabusername>\<yourgithub/gitlabtoken>\"Hervé Beraud"\herveberaud.pro@gmail.com\-l0Others examples
Package: contributor-ci
Contributor CIContributor CI provides tools and visualizations for analyzing contributions. You
can read more about the library in thedocumentationand releases are available onpypi.ThanksThe graphQL queries come fromllnl.github.iowhich is released
under anMIT license.😁️ Contributors 😁️We use theall-contributorstool to generate a contributors graphic below.Vanessasaurus💻
Package: contributor-network
Contributor Network GraphCreate a graph visualization from a code repository.Installingpip install contributor-networkRunningGiven a file calleddependencies.csv, like:name,repository_type,repository_url,depended_by
contributor-network,git,https://github.com/PythonicCafe/contributor-network,
lxml,git,https://github.com/lxml/lxml,contributor-network
tqdm,git,https://github.com/tqdm/tqdm,contributor-networkExecute:python-mcontributor_network.cli\--temp-dir=/tmp/repositories/\dependencies.csv\network/
Package: contributors
ContributorsA command-line script to get all the contributors for one or more GitHub projects.Free software: Apache Software License 2.0Documentation:https://contributors.readthedocs.io.UsageAdd aGITHUB_API_SECRETto your environment variablespip install contributorsRun the command:$contributorsaudreyr/cookiecutter,pydanny/cached-propertyWait while it processesOpenoutput.rstin the same directory you ran the script.Warning:This may eat up a lot of GitHub bandwidth. If you are worred about that, do the following to limit the range of search:$contributorsaudreyr/cookiecutter,pydanny/cached-property-s20160602-u20160610ExamplesPyCon 2016 Cookiecutter sprint contributorsCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.HistoryAdd support for generating output in either markdown or rst with-foption.0.3.0 (2016-07-04)Markdown supportFix input equality check when rate-limitedAllow unauthenticated GitHub API callsSupport configurable output filenames0.2.0 (2016-06-09)Added pull requests and issuesMade datetime not niave0.1.0 (2016-06-09)First release on PyPI.
Package: contributter-ranking-bot
contributter-ranking-botContributterRanking Bot for TwitterForked fromshuntatakemoto/contributter-ranking-botDeployment on TwitterOriginal:→ Forked:Installationpipinstallcontributter-ranking-bot# or:pipinstallgit+https://github.com/eggplants/contributter-ranking-botTweet Format✨Contribution Ranking - {YYYY/MM/DD}✨
{prefix} {num}🟩: @{mention_interrupt}{name}
...
{prefix} {num}🟩: @{mention_interrupt}{name}
📊<[ppl: {contrib_n}👤, sum: {contrib_sum}🟩, avg: {avg:.2f}🟩]
#contributter_rankingCLI Usage$ crb -h
usage: crb [-h] [-k PATH] [-d DAY] [-w SEC] [-n N] [-q] [-D] [-V]

This command makes Contributter Ranking Bot easier to run.

optional arguments:
  -h, --help                show this help message and exit
  -k PATH, --key PATH       key file (default: None)
  -d DAY, --day-before DAY  n days before (default: 1)
  -w SEC, --wait-sec SEC    interval of retrieving tweets (default: 10)
  -n N, --top-n N           top n to tweet (default: 3)
  -q, --quiet               suppress log print (default: False)
  -D, --dry-run             tweet without mentions (default: False)
  -V, --version             show program's version number and exit

$ cat .twitter.key
CONSUMER_KEY="***"
CONSUMER_SECRET="***"
ACCESS_TOKEN="***"
ACCESS_TOKEN_SECRET="***"

$ crb -k .twitter.key
# Running Bot was successful!
# See at: https://twitter.com/satoch_bot/status/1517223447868448768
{
    "created_at": "Thu Apr 21 19:27:13 +0000 2022",
...
}

# 5 days before, tweet top-five ranking
$ crb -d 5 -n 5 -k .twitter.keyLicenseMIT
Package: controku
Controku is a library and GTK3 application that allows you to control
Roku devices from the comfort of your own desktop.DependenciesPyGObject (GUI only)appdirs(GUI only)requestsSSDPyNote for Windows usersSSDPy, the library used to discover Roku devices on the local network,
hasproblems on Windowsthat make device discovery impossible on the platform. Windows users
will need to use the Roku device's IP address as a command line
argument, or manually add it to the cache atC:\Users\username\AppData\Local\benthetechguy\controku\Cache\devices.jsonwith the syntax[{'name': 'device name here', 'ip': 'ip address here'}].Keyboard ControlInstead of just using the mouse, you also can press the following keys:Key on keyboardKey sent to Roku deviceBackspace or BBackIInfoEscape or HHomeWASD or arrow keysUp, Down, Left, RightEnter, Space, or OSelect/OK, or RRewind. or FFast Forward/ or PPlay/Pause[ or -Volume Down] or +Volume Up\ or MMuteNote: Using the arrow keys unintentionally highlights different
buttons in the GUI, and pressing enter or space can activate one of
them. To prevent the activation of an unintended button like power or
home, it's recommended to instead use WASD for direction buttons and/or
use the O key for Select/OK.
Package: control-block-diagram
Control Block DiagramOverview|Quickstart|Examples|InstallationOverviewThe control-block-diagram package is a Python Toolbox for drawing block diagrams. It is build
uponPylatex, and therefore, can generate Latex
files and also PDF files. It allows you to construct typical control block diagrams with the
usual building blocks, i.e., PI-Controllers, Adders, Multiplier. It is also possible to
define own blocks.Getting StartedThe easiest way to get started with the ControlBlockDiagram Toolbox is to play around with the Hands On Jupyter Notebook. There the most important functions are explained and demonstrated with simple examples.Hands On Jupyter NotebookA basic routine is as simple as:fromcontrol_block_diagramimportControllerDiagramfromcontrol_block_diagramimportPoint,Box,Connectionif__name__=='__main__':doc=ControllerDiagram()box_control=Box(Point(0,0),text='Control')box_block=Box(box_control.position.add_x(3),text='Block')box_diagram=Box(box_block.position.add_x(3),text='Diagram')Connection.connect(box_control.output,box_block.input)Connection.connect(box_block.output,box_diagram.input)doc.save('pdf')doc.show()The output of this code is:ExamplesThere are some examples in the examples folder:Flux ObserverFPGA BoardInduction Motor ControllerModel Predictive ControllerInstallationInstall from GitHub source:git clone git@github.com:upb-lea/control-block-diagram.git 
cd control-block-diagram
# Then either
python setup.py install
# or alternatively
pip install -e .

# or alternatively
pip install git+https://github.com/upb-lea/control-block-diagramYou also need a latex compiler such as pdfLaTex to create a PDF file.  For example, you can get this from the latex distributionMiKTeX.
Package: control-broker
Control BrokerGive everyone in your organization subsecond security and compliance decisions based on the organization's latest policies.ContributingPlease seeCONTRIBUTING.md.FeaturesRuns a Policy as Code service as a serverless AWS application - you bring the policies, and Control Broker helps you store, organize, and use them - plus it helps you monitor, and analyze their usage.Defined in the AWS Python CDK for push-button, repeatable deployment.Can be invoked from anywhere in your environment that can invoke an API Gateway API.Supports policies written for Open Policy Agent (CloudFormation Guard planned).Also helps with notifications, auditing, and analysis of discovered compliance issues.Example use casesUsing the Control Broker from a CodePipeline application pipeline to block deployment of non-compliant CDK resourcesUsing the Control Broker to detect non-compliant changes to deployed resources with AWS ConfigUsing the Control Broker from a development machine to evaluate IaC against the organization's latest security policies as it is being writtenDeploying Your Own Control BrokerNote: You can change the name of the secret that Control Broker uses by changing the value of the "control-broker/secret-config/secrets-manager-secret-id" context variable.-->Deploy the CDK appInstall theAWS CDK Toolkit
v2CLI tool.If you encounter issues running thecdkcommands below, check the version ofaws-cdk-libfrom./requirements.txtfor the exact
version of the CDK library used in this repo. The latest v2 version of the CDK
Toolkit should be compatible, but try installing the CDK Toolkit version
matchingrequirements.txtbefore trying other things to resolve your issues.Clone this repo to your machine before proceeding.Follow the setup steps below to properly configure the environment and first
deployment of the infrastructure.To manually create a virtualenv on MacOS and Linux:$ python3 -m venv .venvAfter the init process completes and the virtualenv is created, you can use the
following step to activate your virtualenv.$ source .venv/bin/activateIf you are on a Windows platform, you would activate the virtualenv like this:% .venv\Scripts\activate.batOnce the virtualenv is activated, you can install the required dependencies.$ pip install -r requirements.txtBootstrapthe
cdk app:cdk bootstrapAt this point you candeploythe CDK
app for this blueprint:$ cdk deployAfter runningcdk deploy, the Control Broker will be set up.Next StepsTry launching one of theExample use cases!
Package: control-characters
Installation$[sudo]pipinstallcontrol-charactersFeaturesfixesplistlibproblem with control characters>>>plistlib.dump(data,open(path,'wb'))...ValueError:stringscan't contains control characters; use bytes insteadExamplesenvironment variables without control characters:env=dict((k,control_characters.remove(v))fork,vinos.environ.items())readme42.com
Package: control-error-handler
Error handler for flask with sending exceptions to control service.InstallingFrom pypi:$ pip install control_error_handleror from source:$ python setup.py installQuick startAdd to flask app.configs:CONTROL_EXCEPTION_URL = 'http://localhost:5000/api/monitoring/send-exception/'
SEND_EXCEPTION_TO_CONTROL = TrueInit control handler in__init__.pyon flask project:from flask import Flask
from control_error_handler.error_handler import ControlHandler

app = Flask(__name__)

ControlHandler(app)Use ES54Exception:from control_error_handler.exceptions import ES54Exception

raise ES54Exception('Some error...', ext_data={'any_action': True})
Package: control-f
Control-F Python clientpipenvinstallcontrol-fUsage>>>importcontrol_fasfind>>>find.food('This morning, I ate an apple with some peanut butter.').json(){'matches':['peanut butter','apple'],'result':'This morning, I ate an apple with some peanut butter.'}Dependenciesrequests (2.23.0)DevelopmentPackaging#Generatingdistributionarchives$pythonsetup.pysdistbdist_wheel#Uploadingthedistributionarchives$twineupload--skip-existingdist/*