Saravanan666/pypi-org huggingface dataset:
Package: thiticanfunctions
ฟังก์ชันของ Can Thitipongเพื่อทดสอบการใช้งาน upload libraryAll functioncsv_read(path): read 'titanic_dataset.csv' file and convert to pandas dataframeeasy_plot(df): Plotageof passenger in Titanic inhistogramgender_pie_plot(df): Plotgenderof passenger inpie chartgender_bar_plot(df): Plotgenderof passenger inbar chartsurvived_plot(df): PlotNumber of suvived passengerinpie chartclass_plot(df): Plot number of passenger in each ticket class in bar chartclass_survived(df): return table of survived number in eash classbar_plot(df,variable): pass in dataframe and variable you want to plot in bar chartplot_hist(df,variable): pass in dataframe and variable you want to plot in histogramChange Log0.0.1 (30/08/2022)First Release
Package: thk-target-mysql
target-mysqltarget-mysqlis a Singer target for MySQL.
(forked fromradbrt/target-oracle)Build with theMeltano Target SDK.ConfigurationAccepted Config OptionsA full list of supported settings and capabilities for this
target is available by running:target-mysql--aboutConfigure using environment variablesThis Singer target will automatically import any environment variables within the working directory's.envif the--config=ENVis provided, such that config values will be considered if a matching
environment variable is set either in the terminal context or in the.envfile.Source Authentication and AuthorizationUsageYou can easily runtarget-mysqlby itself or in a pipeline usingMeltano.Executing the Target Directlytarget-mysql--version
target-mysql--help# Test using the "Carbon Intensity" sample:tap-carbon-intensity|target-mysql--config/path/to/target-mysql-config.jsonDeveloper ResourcesFollow these instructions to contribute to this project.Initialize your Development Environmentpipxinstallpoetry
poetryinstallCreate and Run TestsCreate tests within thetarget_mysql/testssubfolder and
then run:poetryrunpytestYou can also test thetarget-mysqlCLI interface directly usingpoetry run:poetryruntarget-mysql--helpTesting withMeltanoNote:This target will work in any Singer environment and does not require Meltano.
Examples here are for convenience and to streamline end-to-end orchestration scenarios.Next, install Meltano (if you haven't already) and any needed plugins:# Install meltanopipxinstallmeltano# Initialize meltano within this directorycdtarget-mysql
meltanoinstallNow you can test and orchestrate using Meltano:# Test invocation:meltanoinvoketarget-mysql--version# OR run a test `elt` pipeline with the Carbon Intensity sample tap:meltanoelttap-carbon-intensitytarget-mysqlSDK Dev GuideSee thedev guidefor more instructions on how to use the Meltano Singer SDK to
develop your own Singer taps and targets.
Package: thLib
=====thLib=====*thLib* contains functions for working with sound, and for fitting circles,lines, sine-waves, and exponential decays. For signal processing, aSavitzky-Golay filter is included, as well as a demonstration of thecalculation of a power spectrum. UI utilities, and a few useful vectorfunctions (e.g. an implementation of the Savitzky-Golay algorithm) round off*thLib*.**Note:** All functions for working with 3D kinematics have been moved intothe new package "scikit-kinematics"!(http://work.thaslwanter.at/skinematics/html)Compatible with Python 2 and 3.Dependencies------------numpy, scipy, matplotlib, pandas, statsmodels, skimage, sympyHomepage--------http://work.thaslwanter.at/thLib/html/Author:  Thomas HaslwanterDate:    09-02-2018Ver:     0.12.2Licence: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)Copyright (c) 2018, Thomas HaslwanterAll rights reserved.Installation------------You can install thlib withpip install thLiband upgrade to a new version withpip install thLib -UFits====Functions---------- fits.demo_ransac ... RANSAC fit of best circle in image- fits.fit_circle ... basic circle fit- fits.fit_exp ... exponential fit- fits.fit_line ... linear regression fit, complete with confidence intervals for mean and values, and with plotting- fits.fit_sin ... sine fit- fits.fit_ellipse ... ellipse fit (Taubin's method)- fits.regress ... multilinear regression fit, similar to MATLABSignal Processing Utilities===========================- signals.pSpect ... simple power spectrum from FFT- signals.savgol ... Savitzky-Golay filterGUI Utilities=============- ui.getfile ... GUI for selecting an existing file- ui.getdir ... GUI for selecting a directory- ui.listbox ... GUI for item selection- ui.progressbar ... Show a progressbar, for longer loops- ui.savefile ... GUI for saving a file- ui.get_screensize ... width and height of screen
Package: thlock
thlockOverviewTangledHub library for etcd_lock with a focus on asynchronous functionsLicencingthlock is licensed under the BSD license. Check theLICENSEfor detailsInstallationpipinstallthlockTestingdocker-composebuildthlock-test;docker-composerun--rmthlock-testBuildingdocker-composebuildthlock-build;docker-composerun--rmthlock-buildPublishdocker-composebuildthcrypto-lock;docker-composerun--rm-ePYPI_USERNAME=__token__-ePYPI_PASSWORD=__SECRET__thlock-publishUsagesetupCreate instance of EtcdLockHOST='etcd-test'PORT=2379# create instance of EtcdLocklock=EtcdLock(host=HOST,port=PORT,name='lock-0')Acquire lockHOST='etcd-test'PORT=2379# create instance of EtcdLocklock=EtcdLock(host=HOST,port=PORT,name='lock-0')# acquire lockawaitlock.acquire()
Package: thmapi
API PyPython wrapper for TryHackMe public APIOfficially sponsored and affiliated withTryHackMeInstallationpipinstallthmapiUsagefromthmapiimportTHMcreds={'username':'<USERNAME>','password':'<PASSWORD'}thm=THM(credentials=creds)# Logging in is optionalthm.get_stats()# {'publicRooms': 203, 'totalUsers': 88017, 'cloneableRooms': 967}ContributingYou're welcome to create Issues/Pull Requests with features you'd want to seeLicenseMIT LICENSE
Package: thmbooom
![](https://imgur.com/DhH1S3P.png)# Thmbooomthmbooom is a simple script to download lecture slide from Technical University of Applied Science Gießen [THM Moodle](https://moodle.thm.de/login/index.php)# DependenciesIt's important you have Firefox installed on you're local machine to make thmbooom happy, because thmboom is using the Firefox driver.As well thmbooom is usign some awesome dependecies like selenium to access all functionalities of Selenium WebDriver in an intuitive way and beautifulsoup for easy parsing.Make sure to install those dependecies* `pip install selenium`* `pip install beautifulsoup4`* `pip install lxml`* `pip install requests`# GeckodriverInstall geckodriver on your local machine to interact with Gecko-based browsers.* macOS <br>`brew install geckodriver`* Linux```wget https://github.com/mozilla/geckodriver/releases/download/v0.16.1/geckodriver-v0.16.1-linux64.tar.gzsudo sh -c 'tar -x geckodriver -zf geckodriver-v0.16.1-linux64.tar.gz -O > /usr/bin/geckodriver'sudo chmod +x /usr/bin/geckodriverrm geckodriver-v0.16.1-linux64.tar.gz```# InstallationYou can use pip to install the module```pip install thmbooom```# Requirement'sPython 3 +### Author[@reffas_bilal](https://twitter.com/Reffas_Bilal)[bilalreffas@googlemail.com]()### Thank youIf you like this project please leave a star here on Github and share it.### License```MIT LicenseCopyright (c) 2018 Bilal ReffasPermission is hereby granted, free of charge, to any person obtaining a copyof this software and associated documentation files (the "Software"), to dealin the Software without restriction, including without limitation the rightsto use, copy, modify, merge, publish, distribute, sublicense, and/or sellcopies of the Software, and to permit persons to whom the Software isfurnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in allcopies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS ORIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THEAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHERLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THESOFTWARE.```
Package: thnkrSDK
DescriptionWelcome to thnkrAI’s Pricing Model and SDK documentation.thnkrAI allows you to conduct detailed pricing analysis on any kind of product or product datasets you want in an easy and accessible way. thnkrAI’s pricing model can bring enabling price predictions, discovery, and insights to your data.Use the thnkrAI SDK to quickly read product information and predict prices using the model’s price prediction endpoint.Model OverviewthnkrAI’s General Pricing ModelModel Description:Text-to-Price Model takes in product data from everyday consumer products and predicts an estimated price based on that information.Model Type:Natural Language Processing (NLP); Regression ModelModel Input:String of product data (title/description)Model Output:Numerical float as the predicted price ($USD)Key Features:Natural Language Processing, Character vectorization, Regression.Model Evaluation:Evaluated using Mean Absolute Percentage Error (MAPE), R-Squared (R2), and Mean Squared Error (MSE).Installation!pipinstallthnkrSDKthnkrSDK IntegrationLICENSE: MITimportthnkrSDKasthnkrusername="YOUR_USERNAME"password="YOUR_PASSWORD"model="general_v1"product_title="EXAMPLE_TEXT"prediction=thnkr.predict(model,product_title,username,password)Example Output:55.49Contact InformationContactthe thnkrAI team atcontact@thnkrai.comwith any questions or concernsModel LimitationsModel is currently trained on limited text data from products listed online. The products come from one online ecommerce marketplace, meaning products found on other marketplaces may be interpreted differently by the model. We are currently working on collecting more product data from a multitude of various online sources to combat this issue.If the product data input is not descriptive enough or lacks substantial detail, the model may return inaccurate price predictions compared to those with substantial detail. To combat this, we are working on training the model with more diverse lengths of input strings.SupportIf you have any issues or questions please contact us atcontact@thnkrai.comVersion HistoryTrained November 15, 2023: thnkrAI general_v1Base URLhttps://api.thnkrai.comParametersthnkr.predict('model','data_input','username','password')ErrorsThis API uses the following error codes:200 Successful Request: The request was malformed or missing required parameters.400 Bad Request: The request was malformed or missing required parameters.401 Unauthorized: The API key provided was invalid or missing.404 Not Found: The requested resource was not found.500 Internal Server Error: An unexpected error occurred on the server.
Package: thoaded
ThoadedLibrary for simple and fast design pattern that extends model, view and controller for asynchronous events in supported modern programming languages.InstallationUse pip to install these utilities:pipinstallthoadedDocumentationThoaded applications are created by composing a series of simple inheritances. By convention, this components extends Modify-based Asynchronous Pattern.fromthoadedimportModeled,Viewed,ControlledimportasyncioclassSampleMoldeled(Modeled):asyncdefadded(self,value):returnvalueasyncdefupdated(self,value):returnvalueasyncdefdeleted(self,value):returnvalueasyncdefreaded(self,value):returnvalueasyncdefqueried(self,value=None):returnvalueasyncdeflistened(self,value=None):returnvalueasyncdefunlistened(self,value=None):returnvalueclassSampleViewed(Viewed):asyncdefadded(self,value):returnvalueasyncdefupdated(self,value):returnvalueasyncdefdeleted(self,value):returnvalueasyncdefreaded(self,value):returnvalueasyncdefqueried(self,value=None):returnvalueasyncdeflistened(self,value=None):returnvalueasyncdefunlistened(self,value=None):returnvalueclassSampleControlled(Controlled):def__init__(self):super().__init__(SampleMoldeled(),SampleViewed())text=asyncio.run(self.modeled.readed("Hello, World!"))text=asyncio.run(self.viewed.readed(text))print("Print "+text+" successfully.")CompatibilityThoaded supports all environments that arePython 3.
Package: thomas
Thomas======Thomas is many things, among them:* A framework to build a streaming platform by inputting, processing and outputting files.* A segmented downloader that makes it possible to play file while downloading.Install-------From pypi (stable):::pip install thomasFrom GitHub (develop):::virtualenv thomas-envthomas-env/bin/pip install git+https://github.com/JohnDoee/thomas.git#developUpgrade from previous version-----------------------------Upgrading from Github (develop)::thomas-env/bin/pip install git+https://github.com/JohnDoee/thomas.git#develop --upgrade --force-reinstallInstructions------------Start by installing.Then get downloading::thomas http://rbx.proof.ovh.net/files/100Mio.datSee more commands by looking at ``thomas -h``License-------MIT, see LICENSE
Package: thomas-core
ThomasVery simple (almost naive ;-) bayesian network implementation.Contains examples (thomas.core.examples) from the book "Probabilistic
Graphical Models: Principles and Techniques" from Koller and Friedman (PGM
Stanford) and from the lecture byAdnan
Darwicheon YouTube:6a. Inference by
Variable Elimination I (Chapter
6).6b. Inference by Variable
Elimination II (Chapter 6).Table of contentsRegular InstallationAlternativesUsageDevelopment installationRegular installationTo install the latest version from PyPI:pipinstallthomas-coreAdditional packagesIf you're using JupyterLab, I recommend also installing the Widget that can display Bayesian Networks.pipinstallthomas-jupyter-widgetSee therepository(or the screenshot above) for details on how to use it.AlternativesIf you don't feel like installing thomas locally, there are two alternative ways to give the platform a try.DockerIf you have docker available, the following command will start a JupyterLab server listening onhttp://localhost:8888:dockerrun--rm-it-p8888:8888mellesies/thomas-coreBindermybinder.orgcourteously provides an environment where you can try out Thomas without any local installation. Just clickhere(or the badge above) and after some waiting, JupyterLab should open in your browser.You can also directly open one of the example notebooks:notebooks/1. Factors.ipynbnotebooks/2. Bags of factors.ipynbnotebooks/3. Conditional probability tables.ipynbnotebooks/4. Bayesian Networks.ipynbUsageTo get started with querying a network, launchpython3and try the following:fromthomas.coreimportexamples# Load an example networkGs=examples.get_student_network()# This should output the prior probability of random variable 'S' (SAT score).print(Gs.P('S'))print()# Expected output:# P(S)# S# s0    0.725# s1    0.275# dtype: float64# Query for the conditional probability of S given the student is intelligent.print(Gs.P('S|I=i1'))# Expected output:# P(S)# S# s0    0.2# s1    0.8# dtype: float64Development installationTo do a development install:gitclonehttps://github.com/mellesies/thomas-corecdthomas-core
pipinstall-e.
Package: thomasfermi
ThomasFermiPerform Thomas Fermi calculation to find the lowest energy density
configuration of two dimensional electronic systems. See theref paperfor detail setup of the calculation.Example usageA detailed example of FQHE is given in aJupyter notebookfromthomasfermi.FQHEimportFQHE_TFdatafromthomasfermi.phiimport*fromthomasfermi.algoimportTF_enginepars={'L':20.,'l_B':7.,'d_t':30,'d_b':30,'dx':0.5,'dy':0.5,'Ec':5.85/40.}data=FQHE_TFdata(pars)pars={'TFdata':data,'Phi_EW_ext':-0.045,'Phi_NS_ext':-0.015,'Phi_back':-0.055}Phi=make_QPC_Phi(pars)pars={'TFdata':data,'Phi_ext':Phi.Phi_ext,'n_iter':8}engine=TF_engine(pars)n=engine.find_local_opt(engine.init_n(eta=0.3))n=engine.find_opt(n0=n,n_iter=40)Installationthomasfermican be installed viapip install thomasfermi.CitationIf this code has benefited your research, please support us by citing:arXiv:2204.10296 [cond-mat.mes-hall]
Package: thomas-jupyter-widget
thomas-jupyter-widgetWidget to display and interact with Bayesian Networks in JupyterLab. This version only supports JupyterLab >= 3.Regular InstallationThe package can be installed throughpip:pipinstallthomas-jupyter-widgetDevelopment InstallationFirst ensure thatnodejs(>=12) andyarnare installed. In debian, you can use the following if you havesudoprivileges:curl-sLhttps://deb.nodesource.com/setup_12.x|sudobash-# Running apt-get update is not necessary, since it's alread called in the# above setup script.sudoapt-getinstall-ynodejs# Install yarn properly. The version of yarn that's available by default is# just weird.sudoapt-getremovecmdtest
sudonpminstall--globalyarnSecond, depending on your situation, you may want to create a virtual environment. For example, this might be useful if the commandjupyter labextension develop --overwrite .fails because it cannot write to the system-wide extensions folder (e.g./usr/local). Under debian/ubuntu, a virtual environment can be created as follows:# Install the virtualenv package to make life easierpipinstallvirtualenv# Create a virtual environment in the subdirectory '.venv'virtualenv.venv# Activate the virtual environment.source.venv/bin/activateAfter the preliminaries, install the package as follows:# Clone the repository and move into the directorygitclonehttps://github.com/mellesies/thomas-jupyter-widget.gitcdthomas-jupyter-widget# Checkout branch jupyter3gitcheckoutjupyter3# Install the package in editable mode.pipinstall-e.# We need the latest version of JupyterLab, otherwise this will failpipinstallgit+https://github.com/jupyterlab/jupyterlab# link your development version of the extension with JupyterLabjupyterlabextensiondevelop--overwrite.# watch the source directory, automatically rebuilding when neededyarn--cwd./jsrunwatchThen, start JupyterLab like you normally would in a separate terminal (and don't forget to activate the virtual environment if you're using one ;-).
Package: thomas-server
ThomasThomas' RESTful API and webinterface.InstallationNormalTo install from PyPI usepip:pipinstallthomas-serverDevelopmentTo do a development install:gitclonehttps://github.com/mellesies/thomas-servercdthomas-serverpipinstall-e.DockerA Docker image is available for easy deployment. The following command will
start a server, listening onlocalhost, port5000:dockerrun--rm-it-p5000:5000mellesies/thomas-serverUsageStart the server as follows:thomasstartThen point your browser towardslocalhost.
Package: thomasthechatbot
Thomas the ChatbotInstallationPython 3.9+ is requiredThis package can be installed fromPyPiwith:pip install thomasthechatbotCLITypettcto begin talking to Thomas.How does Thomas work?I wrote amedium articleto explain how Thomas works.UsageBasic UsagefromttcimportChatbot,Context,download_nltk_data# Only needs to be run once (can be removed after first run)download_nltk_data()# Creating the contextctx=Context()# Initializing the chatbotchatbot=Chatbot()talk=Truewhiletalk:msg=input("You: ")ifmsg=="s":talk=Falseelse:# Getting the responseresp=chatbot.respond(ctx,msg)# Saving the response to the contextctx.save_resp(resp)print(f"Thomas:{resp}")# Saving the chatbot datachatbot.save_data()Configurationschatbot=Chatbot(path="brain",learn=False,min_score=0.5,score_threshold=0.5,mesh_association=0.5,)ContributingOpen to contributions, please create an issue if you want to do so.FormattingBlack,isortandPrettierare used for formatting
Package: thompson-sampling
thompson-samplingThompson Sampling Multi-Armed Bandit for PythonThis project is an implementation of a Thompson Sampling approach to a Multi-Armed Bandit. The goal of this project is to easily create and maintain Thompson Sampling experiments.Currently this project supports experiments where the response follows a Bernoulli or Poisson distribution. Further work will be done to allow for experiments that follow other distributions, with recommendations/collaboration welcome.UsageSetting up the experiment:The following method will instantiate the experiment with default priors.fromthompson_sampling.bernoulliimportBernoulliExperimentexperiment=BernoulliExperiment(arms=2)If you want set your own priors using the Priors module:fromthompson_sampling.bernoulliimportBernoulliExperimentfromthompson_sampling.priorsimportBetaPriorpr=BetaPrior()pr.add_one(mean=0.5,variance=0.2,effective_size=10,label="option1")pr.add_one(mean=0.6,variance=0.3,effective_size=30,label="option2")experiment=BernoulliExperiment(priors=pr)Getting an action:Randomly chooses which arm to "pull" in the multi-armed bandit:experiment.choose_arm()Updating reward:Updating the information about the different arms by adding reward information:rewards=[{"label":"option1","reward":1},{"label":"option2","reward":0}]experiment.add_rewards(rewards)InstallationPippip install thompson-sampling
Package: thonny-astro-pi-replay
thonny-astro-pi-replayA plugin to run the Astro Pi Replay tool inside Thonny.Instead of running your program with the Thonny's Python interpreter, this plugin installs an Astro Pi Replay button in Thonny's Run menu which makes it easy to run your program using the Astro Pi Replay tool - just click the button to simulate your program on the ISS!InstallationOpen Thonny and navigate to Tools > Manage plug-ins...In the window that appears, type 'thonny-astro-pi-replay' and hit the search button.You should get a 'thonny-astro-pi-replay' result in the list. Click on the blue text (a URL) saying 'thonny-astro-pi-replay' and then click the install button.Restart ThonnyIf installation is successful, you should see the "Astro Pi Replay" button in the "Run" menu:Selecting this option Use this to run the current open program using the Astro Pi Replay tool!
Package: thonny-autosave
A sample Python projectA sample project that exists as an aid to thePython Packaging User
Guide'sTutorial on Packaging and Distributing
Projects.This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.The source for this project is available here.The metadata for a Python project is defined in thepyproject.tomlfile,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.This is the README file for the project.The file should use UTF-8 encoding and can be written usingreStructuredTextormarkdownwith the appropriatekey set. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, and should be
written for that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not a
good idea, although a simple “What's New” section for the most recent version
may be appropriate.
Package: thonny-black-format
Thonny black code format pluginAutomatically format your python code with Black!InstallationThis can be easily installed via Thonny. Go toTools -> Manage plug-ins..., typethonny-black-formatand click onFind package from PyPI. Install the plugin and restart Thonny.Alternatively, you can install via pip withpip install --user thonny-black-formatUsageThere are two ways to run the plugin:Go toTools -> Format with BlackPressCtrl+Alt+cAll changes will be automatically saved. If you prefer, you can undo them.ContributingContributions are welcome! Feel free to sumbit an issue and/or a pull request.Useful links:PyPIThonnyBlack