Saravanan666/pypi-org huggingface dataset:
Package: tmdb-python
TMDb Pythontmdb-python, an async Python library for TMDb API.OverviewGetting startedConfigurationOverviewThetmdb-pythonis an asynchronous wrapper, written in Python, for The
Movie Database (TMDb) API v3.The Movie Database (TMDB)is a
community built movie and TV database.TheTMDB APIservice
is for those of you interested in using our movie, TV show or actor
images and/or data in your application.ATMDB user accountis
required to request an API key.Getting startedRequirementspython(Python >=3.9)pip(Python package manager)InstallThe easiest way to install tmdb-python is viapip.pip install tmdb-pythonAPI KeyYou will need an API key to The Movie Database to access the API. To
obtain a key, follow these steps:Registerfor and
verify an account.Loginto your account.Select theAPI sectionon left side of your account page.Click on the link to generate a new API key and follow the
instructions.UsageThe first step is to initialize a TMDB object and set your API Key.importasynciofromtmdbimportroute,schemaasyncdefmain():base=route.Base()base.key="YOUR_API_KEY"movies=awaitroute.Movie().search("fight club")formovieinmovies:print(movie["name"])movies=movies.to(schema.Movies)# convert `dict` to `schema.Movies`formovieinmovies:print(movie.name)asyncio.run(main())Alternatively, you can export your API key as an environment variable.$exportTMDB_KEY="YOUR_API_KEY"And then you will no longer need to set your API key.importasynciofromtmdbimportrouteasyncdefmain():# implicit env var: TMDB_KEY="YOUR_API_KEY"movies=awaitroute.Movie().popular()formovieinmovies:print(movie["name"])asyncio.run(main())For more information, see thedocs.ConfigurationInitialize a TMDB object and set your API Key, language and region.fromtmdbimportrouteasyncdefmain():base=route.Base()base.key="YOUR_API_KEY"base.language="pt-BR"base.region="BR"providers=awaitroute.Movie().providers_list()Alternatively, you can export your API key, language and region
logger as an environment variable.$exportTMDB_KEY="YOUR_API_KEY"$exportTMDB_LANGUAGE="pt-BR"# ISO 639-1$exportTMDB_REGION="BR"# ISO-3166-1And then you will no longer need to set your API key, language and region.asyncdefmain():# implicit env vars: TMDB_KEY="YOUR_API_KEY" TMDB_LANGUAGE="pt-BR" TMDB_REGION="BR"providers=awaitroute.Movie().providers_list()You also can set language and region on object instantiation.asyncdefmain():# implicit env vars: TMDB_KEY="YOUR_API_KEY" TMDB_LANGUAGE="pt-BR" TMDB_REGION="BR"movies=awaitroute.Movie().discover()# discover with the BR regional release datemovies=awaitroute.Movie(language="en-US",region="US").discover()# discover with the US regional release date
Package: tmdsclient
tmdsclient.pyThis repository contains the packagetmdsclient.
It is an async, fully typed Python client for the Technical Master Data Service (TMDS).Its features are handwritten and extended as we need them.
So it isnotautogenerated from the TMDS OpenAPI.InstallationInstall itfrom PyPI:pipinstalltmdsclientUsagefromyarlimportURLfromtmdsclientimportTmdsClient,TmdsConfigtmds_config=TmdsConfig(server_url=URL("https://my-tmds.xtk-test.org/"),usr="my-usr",pwd="my-pwd",)client=TmdsClient(tmds_config)netzvertrage=awaitclient.get_netzvertraege_for_melo("DE1234567890123456789012345678901")Even though we did not fully replicate the TMDS data model (mainlyBO4E.net+ some wrapper classes),
we tried to make the client as flexible as possible.
You can use any unmapped field returned by TMDS by using themodel_extraproperty of pydantic.DevelopmentFor development of this library, follow the instructions in ourPython Template Repository.
tl;dr:tox -e devwill set up a development environment for you.Release (CI/CD)To release a new version of this library,create a new releasein GitHub.
Make sure its tag starts withvand the version number, e.g.v1.2.3.
Releases are not restricted to the main branch, but we prefer them to happen there.ContributeYou are very welcome to contribute to this template repository by opening a pull request against the main branch.
Package: tmd-top
🌈 介绍|🌐[中文说明]  |🌐 [English   Introduction] |😈TMD🖕TOP  是一款Linux终端专用的网络流量监控工具，能实时展示服务器与各客户端IP连接数、上传下载速度及带宽使用情况。它深度分析各类服务和脚本对网络资源的影响，帮助运维人员快速识别并优化高带宽消耗的服务，以确保系统性能高效稳定。凭借简洁易读的表格视图和灵活筛选功能，TMD-TOP是管理员进行网络管理与性能调优的理想助手。顺手点个star⭐🌈 版本要求👊 注意：python >= 3.8🌈 环境要求安装pidstat命令(apt install sysstat)
安装netstat命令(apt install net-tools)🌈 安装教程sudopipinstalltmd-top🌈 使用说明在终端上执行运行tmd-top命令root@davin-system:~/tmd-top#tmd-top或root@davin-system:~/tmd-top#/usr/local/bin/tmd-top或（家目录.local/bin下）root@davin-system:~/tmd-top#~/.local/bin/tmd-top👊 **注意：**只展示有流量活动的服务🌈 快捷指令快捷指令"q"---quit    #退出
'v'---InputPID		#输入pid进行查询
't'---LowSpeedRefresh		#慢速刷新数据
'y'---RestoreRefresh		#恢复刷新数据速度
'c'---ConnectionNumberSort	#连接数排序
'i'---IpNumberSort		#ip数量排序
'u'---UploadSort			#上传排序
'd'---DownloadSort 		#下载排序
'z'---CpuSort				#cpu排序
'x'---MemorySort			#内存排序
'n'---IoReadSort			#io读排序
'm'---IoWriteSort			#io写排序🌈 IP识别数据来源:ip识别是使用geoip来实现的，目前geoip的更新时间是：  ⌚️ 2024-02-16中文显示地区:将操作系统的语言改成zh_CN，locale命令查看系统语言。🌈 数据展示图服务器监听流量服务器运行程序产生的流量服务器网卡流量详细客户端流量🌈 创作想法作为一名Linux运维人员，我发现FinalShell的流量视图功能在排查网络问题时极为高效实用。尽管尝试过iftop和nload等工具，但其易用性和可视化效果均不及FinalShell。因此，鉴于对FinalShell流量监控功能的依赖，我决定借鉴其设计思路，自行开发一款直观精确的网络流量监控工具以满足工作需求。🌈 软件架构这里是列表文本数据收集模块：通过Python的subprocess或os库执行Linux底层命令（如netstat），实时获取服务器内部TCP连接的状态信息。这些信息包括但不限于客户端IP、端口、连接状态、传输速率等关键指标。这里是列表文本数据库存储模块：利用SQLite作为本地持久化存储解决方案，将收集到的TCP连接数据以结构化形式写入数据库表中。此模块负责创建和管理数据库连接，以及执行SQL插入操作。数据处理模块：对原始数据进行预处理和格式化，例如转换为适合存储在SQLite中的格式，并可能进一步进行数据分析，计算出如每秒上传下载速度、并发连接数等高级统计指标。用户界面展示模块：基于textual库构建终端界面，动态显示从数据库中读取的TCP连接统计数据。该请作者喝杯咖啡如果该项目对你有帮助，帮您解决了问题，可以请作者喝杯咖啡！！!
Package: tmdybinding
TMDybinding<img src=https://github.com/BertJorissen/kite/blob/master/docs/assets/images/logo.png?raw=truewidth="100">TMDybindingis an open-source Python module forPybinding.Capabilitiesgenerate accurate lattices for most used TB models for TMDseasily add hoppingautomatic generation of lattice cell with 90-degree cornersautomatic inclusion of SOC termsautomatic generation of hopping terms taking into account the symmetry of the orbitallabel all the different obrital contributionsfully compatible withpybindingandKITEInstallationThat's easy, just runpip install tmdybinding
Package: tme
Taylor moment expansion (TME) in PythonPlease see the documentation of the package inhttps://tme.readthedocs.io.InstallInstall viapip install tmeorpython setup.py install(Please note that if you would like to use JaX, please
installjaxby yourself beforehand).Examplesimporttme.base_jaxastmeimportjax.numpyasjnpfromjaximportjit,vmap# Define SDE coefficients.alp=1.defdrift(x):returnjnp.array([x[1],x[0]*(alp-x[0]**2)-x[1]])defdispersion(x):returnjnp.array([0,x[0]])# Jit the 3-order TME mean and cov approximation functions@jitdeftme_m_cov(x,dt):returntme.mean_and_cov(x,dt,drift,dispersion,order=3)# Compute E[X(t) | X(0)=x0]x0=jnp.array([0.,-1])ts=jnp.array([0.25,0.5,1.])m_t,cov_t=vmap(tme_m_cov,in_axes=[None,0])(x0,ts)Inside folderexamples, there are a few Jupyter notebooks showing how to use the TME method (in SymPy and JaX).LicenseThe GNU General Public License v3 or later
Package: tmeasures
✴ Transformational Measures 📏The Transformational Measures (tmeasures) library allows neural network designers to evaluate the invariance, equivariance and other properties of their models with respect to a set of transformations. Support for Pytorch (current) and Tensorflow/Keras (coming).🔎 Visualizationstmeasuresallows computing invariance, same-equivariance and other transformational measures, and contains helpful functions to visualize these. The following are some examples of the results you can obtain with the library:🔥 Invariance heatmapEach column shows the invariance to rotation of a layer of a Neural Network. Each row/block inside each column indicates the invariance of a feature map or single neuron, depending on the layer.📉 Average Invariance vs layer, same modelPlot the transformational and sample invariance to rotations of a simple neural network trained on MNIST, with and without data augmentation. The X axis indicates the layer, while the Y axis shows the average invariance of the layer.📈 Average invariance by layer, different models:Plot of the invariance to rotations of several well-known models trained on CIFAR10. The number of layers of each model is streched on a percentage scale, so that different models can be compared.💻 PyTorch APIThese notebooks contain step-by-step code and explanations to measure invariance in both custom and pretrained model, both using predefined and custom transformations. They can be executed in google colab directly. Alternatively, you can download them for local execution, but be aware you will have to provide your own virtualenv withtorchandtorchvisioninstalled.Measuring invariance to brightness changes on a pretrained ResNet18 on TinyImageNetMeasuring invariance to rotations on a custom CNN on MNISTOther examples with multiple measures and pretrained models can be found in thedocfolder of this repository.💻 TensorFlow APIWe are still developing the Tensorflow API.📋 ExamplesYou can find many uses of this library in therepository with the codefor the articleMeasuring (in)variances in Convolutional Networks, where this library was first presented. Also, in the code for the experiments of the PhD Thesis"Invariance and Same-Equivariance Measures for Convolutional Neural Networks" (spanish).🤙🏽 CitingIf you use this library in your research, we kindly ask you to citeInvariance and Same-Equivariance Measures for Convolutional Neural Networks.@article{quiroga20,
  author    = {Facundo Quiroga and
               Laura Lanzarini},
  title     = {Invariance and Same-Equivariance Measures for Convolutional Neural Networks},
  journal   = {J. Comput. Sci. Technol.},
  volume    = {20},
  number    = {1},
  pages     = {06},
  year      = {2020},
  url       = {https://doi.org/10.24215/16666038.20.e06},
  doi       = {10.24215/16666038.20.e06},
}
Package: tmengine
tmengine: A TextMate grammar engine in Python.Installationpython-mpipinstalltmengine# orpython-mpipinstallgit+https://github.com/AaravMalani/tmengineUsagefromtmengineimportTextMateEngineimportjsonengine=TextMateEngine.load_folder('grammars/')# Load all grammars in the grammars/ folderprint(engine.languages)# Returns a tuple of scopeNamesprint(engine.parse('source.akbs','compile($FILES)'))# Parse 'compile($FILES)' using language with `scopeName` as `source.akbs`# [TextMateExpression(name='punctuation.paren.open', range=(7, 8)), TextMateExpression(name='expression.group', range=(7, 15)), TextMateExpression(name='keyword.function', range=(0, 7)), TextMateExpression(name='expression.inner', range=(8, 14)), TextMateExpression(name='punctuation.paren.close', range=(14, 15)), TextMateExpression(name='variable.other', range=(8, 14))]withopen('grammars/akbs.json','r')asf:# Found at https://github.com/akbs-org/akbs-vscode/blob/main/syntaxes/akbs.tmGrammar.jsonengine2=TextMateEngine(json.load(f))# or tmengine.TextMateEngine([json.load(f)])TasklistAccept YAMLAccept XMLMake code ignore errorsChangelogVersion 1.0.1Fixed bug which returned indices/a range less than expected when there were multiplematchexpressions satisfied on separate linesFixed only a singlebegin/endexpression of a type being selected
Package: tmesh
tmeshFast operators for triangular meshes.Getting Startedtmeshis available onPyPI:pipinstalltmeshSee theexamplesdirectory to see how to use the package.The following script demonstrates how to use the package to generate an
STL or OBJ file from a simple mesh:importmathfromtmeshimportregular_polygon_mesh,Affine2D,linear_extrude# Create a cuboid.a=regular_polygon_mesh(1.0,n=4)b=regular_polygon_mesh(1.0,n=4)# Move the second cuboid around.rot=Affine2D(rot=math.pi/4)trans=Affine2D(trans=(0.0,0.5))b<<=rot@trans# Gets the union of A and B.c=a|b# Extrudes the 2D mesh to a 3D mesh with height 1.0.mesh=linear_extrude(c,1.0)# Save the resulting mesh.fromtmeshimportsave_stl,save_stl_text,save_obj,save_plysave_stl_text("simple_example_ascii.stl",mesh)save_stl("simple_example.stl",mesh)save_obj("simple_example.obj",mesh)save_ply("simple_example.ply",mesh)MotivationAt various times I have wished there were something likeOpenSCADfor Python. Packages liketrimeshexist, but they come with a number of clunky dependencies (including, interestingly enough, OpenSCAD itself). This package is a standalone implementation of the core functionality of OpenSCAD, but with a Pythonic API, making it easy to integrate into existing Python code. It is also written in pure C++, so it's hopefully relatively fast.
Package: tmessage
tmessageThis is a lightweight and low bandwidth CLI tool which can be used for group communication right from your terminal (isn't this cool ; ) )Contributions are welcome forHacktoberfest 2019(Presented by DigitalOcean and DEV). Search for issues labeledHacktoberfest.Our Contributing GuidelinesOur Code of ConductInstallation instructionsClone this project using the commandgit clone https://github.com/Haider8/tmessage.gitSetup your python3 virtual environment using this commandvirtualenv -p python3 venvSwitch to your venv usingsource venv/bin/activateNow install the required dependencies by runningpip install .Now to run tmessage usetmessage --user [YOUR USERNAME] --port [PORT (optional)] --server [YOUR BROKER IP OR URL (optional)]To test this you can open different terminal tabs.Some optional instructions for PROs ;)If you don't want to create your own broker it's perfectly fine as tmessage is using a default brokertest.mosquitto.org. Therefore, you can then run tmessage usingpython msg.py --user [YOUR USERNAME].If you happen to use this on a different port, it can be specified as -tmessage --user [USERNAME] --port [PORT]. If omitted, it defaults to 1883.This project uses anAPIfor the authentication of users. Default API Url isthis. If you want to use your own authentication API you can deploy ourtmessage-apilocally(for testing) or on some cloud (the default API is hosted on Heroku). Then, you can set that Url in.envfile like thisTMESSAGE_API_URL=http://localhost:8080orTMESSAGE_API_URL=<your-api-url>.Development InstructionsYou can first fork tmessage and can then clone using commandgit clone https://github.com/[YOUR USERNAME]/tmessage.gitPlease send PRs using new branch and not masterCommit message should perfectly describe the code changes done by youWould love to hear any suggestions, feedback, feature requests or any other issue
Package: tm-eval
Topic Modeling EvaluationA toolkit to quickly evaluate model goodness over number of topicsMetricsCoherence measure to be used.Fastest method - 'u_mass', 'c_uci' also known asc_pmi.For 'u_mass' corpus should be provided, if texts is provided, it will be converted to corpus using the dictionary.For 'c_v', 'c_uci' and 'c_npmi'textsshould be provided (corpusisn't needed)ExamplesExample 1: estimate metrics for one topic model with specific number of topicsfromtm_evalimport*# load a dictionary with document key and its term list split by ','.input_file="datasets/covid19_symptoms.pickle"output_folder="outputs"model_name="symptom"num_topics=10# runresults=evaluate_all_metrics_from_lda_model(input_file=input_file,output_folder=output_folder,model_name=model_name,num_topics=num_topics)print(results)Example 2: find model goodness change over number of topicsfromtm_evalimport*if__name__=="__main__":# start configure# load a dictionary (key,value) with document id as key and its term list combined by ',' as value.input_file="datasets/covid19_symptoms.pickle"output_folder="outputs"model_name="symptom"start=2end=5# end configure# run and explorelist_results=explore_topic_model_metrics(input_file=input_file,output_folder=output_folder,model_name=model_name,start=start,end=end)# summarize resultsshow_topic_model_metric_change(list_results,save=True,save_path=f"{output_folder}/metrics.csv")# plot metric changesplot_tm_metric_change(csv_path=f"{output_folder}/metrics.csv",save=True,save_folder=output_folder)Output resultsLicenseThetm-evaltoolkit is provided byDonghua Chenwith MIT License.ReferencesTopic Modeling in Python: Latent Dirichlet Allocation (LDA)Evaluate Topic Models: Latent Dirichlet Allocation (LDA)
Package: tmf882x-driver
tmf882x-driverEasy-to-use python driver for the AMSTMF8820andTMF8821ToF sensorsInstallationThe package is available onPyPI. Installation is can be done with your favourite package manager. For example:pipinstalltmf882x-driverUsageIn order to initialise the device we need an openSMBusobject.
Depending on the machine that you are running on you may need to provide another bus number or path:fromsmbus2importSMBusfromtmf882ximportTMF882xwithSMBus(1)asbus:device=TMF882x(bus=bus)The address of theTMF882xdefaults to0x41. If your device's address is different, you can provide it
likeTMF882x(bus=bus, address=0x59).See thedatasheetfor more details.
Package: tmflow
Taylor map flow is a package for a 'flowly' construction and learning of polynomial neural networks (PNN) for time-evolving process prediction.Based on the input time-series data, it provides:(construct) a module to construct ordinary differential equations (ODEs) in the polynomial form(map) a module to construct a matrix Taylor map for ODEs(learn) a TensorFlow-based module to build and train a polynomial neural network (PNN).
Taylor map matrices can be used as PNN initial weights.PNN built in this flow way is strongly connected with ordinary differential equations.
This combination reveals the data-underlying deterministic process without manual equation derivation
and allows treating cases even when only small datasets or partial measurements are available.
The proposed hybrid models provide explainable and interpretable results to leverage optimal control applications.'Construct', 'map', and 'learn' modules can be used sequentially or independently from each other.MapModule for Taylor Mapping construction.
It gives a tool to construct Taylor mapping for the given system of  Ordinary Differential Equations with polynomial right-hand side.FeaturesMatrices of Taylor mapping completely describe the given ODEs' dynamics up to the required degree of accuracy for any initial conditions.It is possible to compute Taylor mapping matrices in a symbolic form.If the right-hand side of the ODE system depends on parameters, it is possible to construct parameter-dependent TM matrices.InterfaceThe list of inputs for the module:NumPy array of matrices P in the ODEs right-hand sideorder of nonlinearity for Taylor mapping (order)Taylor mapping step (h)The module output:NumPy array of Taylor mapping matrices RInstallationBase library installpipinstalltmflowInstallation with additional dependencies for GitHub examplespipinstalltmflow[examples]Library development installation for GitHub sourcesThe library can be installed in development mode,
which enables library loading from the disk with all saved code changes.
You may also need to uninstall the public version of the library.Commands should be executed from the library project root folder.pipuninstalltmflow
pipinstall-rrequirements.txt
pipinstall-e.
Package: tmg-biomechanics
TMGUtilities for time-series analysis of tensiomyography (TMG) measurement data.
TMG is used to non-invasively measure muscle belly displacement with respect to time during electrically induced isometric contraction, and is used in both sports diagnostics and medical contexts.
To quickly visualize a TMG measurement,see this ~90 second animation.
For more information about TMG measurements, seethe TMG website's description of the TMG measurement device.The Python scriptsrc/tmg/tmg.pydoes the following:Compute the standard TMG signal parametersDm,Td,Tc,Ts,Tr.Compute the time derivative of a TMG signal.Compute parameters describing the derivative of the TMG signal:rdd_max,rdd_min,rdd_peak_to_peak,rdd_max_time, andrdd_min_time.
Package: tmg-data
TMG data library has the functionalities to interact with Google Cloud services allowing to develop more reliable and standard data pipelines.Client Library DocumentationQuick StartInstallationInstall this library in avirtualenvusing pip.Supported Python VersionsPython >= 3.5Mac/Linuxpip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install tmg-dataExample UsageTransform from MySQL to BigQueryfromtmg.dataimporttransfertransfer_client=transfer.Client(project='your-project-id')transfer_client.bq_to_mysql(connection_string='root:password@host:port/your-database',bq_table='your-project-id.your-dataset.your-table',mysql_table='your-database.your-table')TestingDownload environment variables required for unit tests from GCS and set in your local environment:gs://tmg-datalake-admin/tmg_data_testing_env_vars.txt
Package: tmg-etl-pipeline
TMG ETL pipeline is the base structure of our ETL pipelines.
Any pipeline should inherit from TMGETLPipeline class and implement the necessary methods.Quick StartInstallationInstall this library in avirtualenvusing pip.virtualenvis a tool to
create isolated Python environments. The basic problem it addresses is one of
dependencies and versions, and indirectly permissions.Supported Python VersionsPython >= 3.5Mac/Linuxpip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install tmg-etl-pipelineExample Usagefromtmg_etl_pipeline.etlimportTMGETLPipelineclassMyETL(TMGETLPipeline):defrun(self):# your ETL logic goes here# access the configs# self.config['some-config-variable']# access the secrets# self.secrets['some-secret-variable']defcleanup(self):# your clean up code goes hereetl=MyETL(app_name='your-app-name',config_path='path/to-the-config-file')etl.execute()
Package: tmi
What is TMI?TMI(Task Manage Interface) is open source console software for managing user's
tasks.  It provides some method and text-based user interface.  This software is
developed for the various Linux OS and by python.Bugs and feature requestsThis software may have many bugs or few features. Have a bug or a feature request?
Please leave your idea as an issue or pull request, but provide as much detail and context as possible.InstallationThis software recommends you to use PyPI.(In fact, this is only way)
Supported OS are Linux distributions like Devian, Ubuntu. We plan to don't care any other
OS yet.Install TMI from PyPI (recommended):sudopipinstalltmiIf you are using a virtualenv, you may want to avoid using sudo:pipinstalltmiAlternatively: install TMI from the GitHub source:First, clone TMI usinggit:gitclonehttps://github.com/onnoo/TMI.gitThen,cdto the TMI folder and run the install command:cdTMI
sudopythonsetup.pyinstallLicenseCode released under theMIT License.