Saravanan666/pypi-org huggingface dataset:
Package: realreq
realreq: real requirements for real projectsrealreqisthelightweight tool that provides you with the actual
dependencies used by your project.Why use realreq?Better dependency managementIt is not a secret that python package management is not easy.
Separating package dependencies from tool dependencies is enough to make you pull
your hair out. Determining what packages are actual being used by your code, and
what their dependencies are is a hassle.realreqfixes this for you by
examining your source files and parsing out the dependencies for you.Lightweightrealreqdoes not depend on any third party package, except forpip. It
does not make any assumptions about the presence or absence of a virtual
environment, or what tool you used to make your virtual environment. This
means it will work with whatever tool set you are already using, without
getting in the way of your workflowInstallrealreqis available for install via pip.pip install realreqUsageShallow DependenciesBy default realreq delivers a "shallow" dependency specification, giving you a list of the packages
directly used by your code:realreq -s ./path/to/mypackage > requirements.txtThis is useful for Library maintainers who want to make sure the minimal essentials are there,
without over specifying the actual requirements for their library.Deep DependenciesAlternatively, you can get a "deep" dependency list by using the-dflagrealreq -d -s ./path/to/mypackage > requirements.txtThis is beneficial for maintaining executable/standalone programs, by defining the exact requirements under which
the program will function.Adding aliasesDue to the way python packaging works, it is possible for a python package to have a differentinstallname thanimportname. Realreq works by inspecting yourimportsand then checking the versions
they were installed with. If the names mismatch, there will be an issue with realreq being unable to
find them.This can be resolved by adding an--alias/-aflag, mapping theimportname to theinstallname.realreq -d -s ./path/to/mypackage -a bs4=beautifulsoup4 > requirements.txtIf you have a lot of these aliases you can also specify them in an alias-file:cat << EOF > realreq-aliases.txt
bs4=beautifulsoup4
airflow=apache-airflow
dateutil=python-dateutil
EOF
realreq -d -s ./path/to/mypackage --alias-file realreq-aliases.txt > requirements.txtAdditional toolsInverted TreeSometimes you may want to explore the reasons for certain dependencies. Realreq lets you see an
inverted tree view that displays all your dependencies and what libraries they are used by. For
example the inverted tree for realreq's test suite looks like this:- attrs
  |- pytest
    |- pytest-mock
- iniconfig
  |- pytest
    |- pytest-mock
- pluggy
  |- pytest
    |- pytest-mock
- py
  |- pytest
    |- pytest-mock
- pyparsing
  |- packaging
    |- pytest
      |- pytest-mock
- tomli
  |- pytest
    |- pytest-mockCurrently realreq does not support a regular tree view, though that feature is one that I want to
implement in the future.
Package: real-robots
real-robotsRobots that learn to interact with the environment autonomouslyInstallationpipinstall-Ureal_robotsIf everything went well, then you should be able to run :real-robots-demoand it should (eventually) open up a small window with a little robotic arm
doing random stuff.Usageimportgymimportnumpyasnpimporttimeimportreal_robotsfromreal_robots.policyimportBasePolicyclassRandomPolicy(BasePolicy):def__init__(self,action_space):self.action_space=action_spaceself.action=action_space.sample()defstep(self,observation,reward,done):ifnp.random.rand()<0.05:self.action=self.action_space.sample()returnself.actionenv=gym.make("REALRobot2020-R2J3-v0")pi=RandomPolicy(env.action_space)env.render("human")observation=env.reset()reward,done=0,Falsefortinrange(40):action=pi.step(observation,reward,done)observation,reward,done,info=env.step(action)Local Evaluationimportgymimportnumpyasnpimportreal_robotsfromreal_robots.policyimportBasePolicyclassRandomPolicy(BasePolicy):def__init__(self,action_space):self.action_space=action_spaceself.action=action_space.sample()defstep(self,observation,reward,done):ifnp.random.rand()<0.05:self.action=self.action_space.sample()returnself.actionresult,detailed_scores=real_robots.evaluate(RandomPolicy,environment='R1',action_type='macro_action',n_objects=1,intrinsic_timesteps=1e3,extrinsic_timesteps=1e3,extrinsic_trials=3,visualize=False,goals_dataset_path='goals-REAL2020-s2020-50-1.npy.npz')# NOTE : You can find goals-REAL2020-s2020-50-1.npy.npz file in the REAL2020 Starter Kit repository# or you can generate one using the real-robots-generate-goals command.#print(result)# {'score_REAL2020': 0.06529471503519801, 'score_total': 0.06529471503519801}print(detailed_scores)# {'REAL2020': [0.00024387094790936833, 0.19553060745741896, 0.00010966670026571288]}See also ourFAQ.Free software: MIT licenseFeaturesThe REALRobot environment is a standard gym environment.It includes a 7DoF kuka arm with a 2DoF gripper, a table with 3 objects on it and a camera looking at the table from the top.
For more info on the environment seeenvironment.md.AuthorsFrancesco MannellaEmilio CartoniSharada Mohanty
Package: realsense-recorder
RealsenseRecorderA set of Python scripts to operate Intel Realsense Cameras. By using multi-thread techniques, this script can record color + depth from up to 4 Realsense Cameras (2 x L515 and 2 x D435)InstallationFrom Sourcegitclonehttps://github.com/mvig-robotflow/rfimu-realsense-recordercdrfimu-realsense-recorder
pythonsetup.pyFrom PyPipython-mpipinstallrealsense-recorderUsageScript Usageimportioimportcv2importnumpyasnpimportyamlfromrealsense_recorder.commonimportnew_realsense_camera_system_from_config,RealsenseSystemModelcfg_str="""realsense:cameras:- color:- exposure: -1format: rs.format.bgra8fps: 30height: 1080width: 1920depth: [] # Do not user depthendpoint:{}imu: []product_id: 0B64 # 0B64 for L515product_line: L500 # Currently supported models are L500(L515) and D400(D435)ref: 1sn: f0220485 # SN of target Camera, can get from RealSenseViewersystem:base_dir: ./realsense_dataframe_queue_size: 100interactive: falseinterval_ms: 0use_bag: false"""defmain():cfg=yaml.load(io.StringIO(cfg_str),yaml.SafeLoader)sys=new_realsense_camera_system_from_config(RealsenseSystemModel,cfg['realsense'],None)print(sys.cameras)cam=sys.cameras[0]cam.open()cam.start()mtx=np.array(cam.intrinsics_matrix)whileTrue:color_image,depth_image,ts,sys_ts,frame_counter=cam.get_frames()cv2.imshow("frame",color_image)key=cv2.waitKey(1)ifkey==27:print('esc break...')cv2.destroyAllWindows()breakmain()Command Line UsageTo Create and persist record configuration:python-mrealsense-recorderconfigureTo launch a remote record station that supports REST APIpython-mrealsense-recorderrun--app=remote_record_seq
Package: realsr-ncnn-vulkan-python
RealSR ncnn Vulkan PythonIntroductionrealsr-ncnn-vulkanis nihui's ncnn implementation of Real-World Super-Resolution via Kernel Estimation and Noise Injection super resolution.realsr-ncnn-vulkan-python wrapsrealsr-ncnn-vulkan projectby SWIG to make it easier to integrate realsr-ncnn-vulkan with existing python projects.DownloadsLinux/Windos/Mac X86_64 build releases are available now.However, for Linux distro with GLIBC < 2.29 (like Ubuntu 18.04), the ubuntu-1804 pre-built should be used.BuildFirst, you have to install python, python development package (Python native development libs in Visual Studio), vulkan SDK and SWIG on your platform. And then:Linuxgitclonehttps://github.com/ArchieMeng/realsr-ncnn-vulkan-python.gitcdrealsr-ncnn-vulkan-python
gitsubmoduleupdate--init--recursive
cmake-Bbuildsrccdbuild
makeWindowsI used Visual Studio 2019 and msvc v142 to build this project for Windows.Install visual studio and open the project directory, and build. Job done.The only problem on Windows is that, you cannot useCMake for Windowsto generate the Visual Studio solution file and build it. This will make the lib crash on loading.The only way isuse Visual Studio to open the project as directory, and build it from Visual Studio.About RealSRReal-World Super-Resolution via Kernel Estimation and Noise Injection (CVPRW 2020)https://github.com/jixiaozhong/RealSRXiaozhong Ji, Yun Cao, Ying Tai, Chengjie Wang, Jilin Li, and Feiyue HuangTencent YouTu LabOur solution is thewinner of CVPR NTIRE 2020 Challenge on Real-World Super-Resolutionin both tracks.https://arxiv.org/abs/2005.01996UsagesExample ProgramfromPILimportImagefromrealsr_ncnn_vulkanimportRealSR# if installed from pypi or binary wheels,# from realsr_ncnn_vulkan_python import RealSRim=Image.open("0.png")upscaler=RealSR(0,scale=4)out_im=upscaler.process(im)out_im.save("temp.png")If you encounter crash or error, try to upgrade your GPU driverIntel:https://downloadcenter.intel.com/product/80939/Graphics-DriversAMD:https://www.amd.com/en/supportNVIDIA:https://www.nvidia.com/Download/index.aspxOriginal RealSR NCNN Vulkan Projecthttps://github.com/nihui/realsr-ncnn-vulkanOriginal RealSR Projecthttps://github.com/jixiaozhong/RealSROther Open-Source Code Usedhttps://github.com/Tencent/ncnnfor fast neural network inference on ALL PLATFORMShttps://github.com/webmproject/libwebpfor encoding and decoding Webp images on ALL PLATFORMShttps://github.com/nothings/stbfor decoding and encoding image on Linux / MacOShttps://github.com/tronkko/direntfor listing files in directory on Windows
Package: realtime
realtime-pyPython Client Library to interface with the Phoenix Realtime ServerRequirementsPython 3 higherInstallationpip3installrealtime==1.0.2Installation from sourcepip3install-rrequirements.txt
python3usage.pyQuick Startfromrealtime.connectionimportSocketdefcallback1(payload):print("Callback 1: ",payload)defcallback2(payload):print("Callback 2: ",payload)if__name__=="__main__":URL="ws://localhost:4000/socket/websocket"s=Socket(URL)s.connect()channel_1=s.set_channel("realtime:public:todos")channel_1.join().on("UPDATE",callback1)channel_2=s.set_channel("realtime:public:users")channel_2.join().on("*",callback2)s.listen()Sample usage with SupabaseHere's how you could connect to your realtime endpoint using Supabase endpoint. Correct as of 5th June 2021. Please replaceSUPABASE_IDandAPI_KEYwith your ownSUPABASE_IDandAPI_KEY. The variables shown below are fake and they will not work if you try to run the snippet.fromrealtime.connectionimportSocketSUPABASE_ID="dlzlllxhaakqdmaapvji"API_KEY="eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJyb2xlIjoiYW5vbiIsImlhdCI6MT"defcallback1(payload):print("Callback 1: ",payload)if__name__=="__main__":URL=f"wss://{SUPABASE_ID}.supabase.co/realtime/v1/websocket?apikey={API_KEY}&vsn=1.0.0"s=Socket(URL)s.connect()channel_1=s.set_channel("realtime:*")channel_1.join().on("UPDATE",callback1)s.listen()Then, go to the Supabase interface and toggle a row in a table. You should see a corresponding payload show up in your console/terminal.
Package: real-time-analog-monitoring-tool
# rtamtSpecification-based real-time monitoring library## Install### Install back-ends`bash sudo apt installlibboost-all-devsudo apt installpython-devsudo apt installpython-pip`If your work for TL development, following package is needed in addition.`bash sudo apt install antlr4 `### Build the tool#### clone the repository`bash git clonehttps://github.com/nickovic/rtamt`#### build cpp libs (required only if you want to use CPP backend)`bash cd rtamt/rtamt mkdir build cd build cmake ../ make `#### install rtamt tool`bash cd rtamt/ sudo pip install . `## Run examples### online-STL example`bash cd rtamt/examples/basic $ python monitor_basic.py time=0 rob=122.0 time=1 rob=3.0 time=2rob=-10.0`### offline-STL example`bash cd rtamt/examples/basic $ python monitor_offline.py Robustness:-98.0`### Io-STL example`bash cd rtamt/examples/offline_monitors $ python offline_monitor_dt.py Example (a) - standard robustness: 3.0 Example (a) - output robustness: 3.0 Example (a) - input vacuity: 0 Example (b) - standard robustness: 1.0 Example (b) - output robustness: inf Example (b) - input vacuity: 1.0 Example (c) - standard robustness:-2.0Example (c) - output robustness:-2.0Example (c) - input vacuity: 0 Example (d) - standard robustness:-1.0Example (d) - output robustness:-2.0Example (d) - input vacuity: 0 `
Package: realtime-earthquake-indonesia
latest-earthquake-report-indonesiaThis package inform the latest earthquake in Indonesia from BMKG | Meteorological, Climatological, and Geophysical Agency.How it workThis package informs earthquake that just occurred in Indonesia, sourced from BMKG.How to useimport gempaterkini

if __name__ == '__main__':
    print("Aplikasi Utama")
    result = gempaterkini.ekstraksi_data()
    gempaterkini.tampilkan_data(result)AuthorPanji Perdana
Package: realtimefunc
realtimefuncA decorator is used to update a function at runtime to easy correct code in development.installpip install realtimefuncusagefrom realtimefunc import realtimefunc

@coroutine
@realtimefunc
def test():
    # function bodyNOTEsuper()is not allowed whilesuper(kls, obj)is ok.WARNINGCode that below the decorated function could not map to the correct line anymore.TODObeautify stack infosupport logcachesupportsuper()solve warning mentioned
Package: realtimekql
Real-Time KQL for PythonTo process data with Kusto Query Language (KQL) queries today, users generally have to upload their data to storage first and then query it. The Kql Tools eliminate this need by processing event streams with KQL queriesas events arrive, in real-time.UsageReal-Time KQL is broken up into three parts: the output, the query, and the input.The OutputReal-Time KQL for Python has aPythonOutputclass that allows you to customize what happens to events when they are outputted. The simplest usage of thePythonOutputclass is to instantiate it with no parameters. This will print events to console in JSON format:>>> from realtimekql import *
>>> o = PythonOutput()To customize the output, you can pass in any Python function that takes a dictionary as the only parameter to thePythonOutputclass. For example, this function stores events in a list to use them later:>>> events = []
>>> def storeEvents(event):
...		events.append(event)
...
>>> from realtimekql import *
>>> o = PythonOutput(storeEvents)ThePythonAdxOutputclass allows you to ingest data to an Azure Data Explorer (Kusto) table through queued ingestion. The class can be instantiated as follows:>>> from realtimekql import *
>>> o = PythonAdxOutput("YourCluster.kusto.windows.net", "YourDatabase", "YourTable", "YourClientId", "YourClientSecret", "YourAuthorityId", resetTable=True)The QueryYou can optionally pass a .kql query into Real-Time KQL to filter, transform, and enrich your events before they even reach the output stage.The InputReal-Time KQL supports various real-time and file input sources. Each input class takes a unique set of arguments, an instance of one of the output classes, as well as an optional path to a query file. This prints real-time Etw TCP events to console in JSON format:>>> from realtimekql import *
>>> o = PythonOutput()
>>> e = EtwSession("tcp", o)
>>> e.Start()Here are all the supported input options and how to use them:EtwSession(sessionName, o, q)
EtlFileReader(filePath, o, q)
WinlogRealTime(logName, o, q)
EvtxFileReader(filePath, o, q)
CsvFileReader(filePath, o, q)The variablesoandqrepresent the output part and the query part respectively. The query part is optional and can be left out.
Package: realtimeplt
Installationpipinstallrealtimepltrealtimeplt is a Python library made by VisionBrain which foucuses on getting the Live training loss plot in Jupyter Notebook for Keras, PyTorch and TensorFlow.Our main objective is to bring the ability to  visualize the data hyperparameters in real time.Most Noticeable changes:-Runs on the latest version of python
-Runs on the latest version of keras
-Runs on the latest version of pytorch
-Runs on the latest version of tensorflowUseThe major dependency of realtimeplt is livelossplotfromlivelossplotimportPlotLossesKerasmodel.fit(X_train,Y_train,epochs=10,validation_data=(X_test,Y_test),callbacks=[PlotLossesKeras()],verbose=0)Supported DataScience LibrariesNote*** Better results when used with Jupyter Notebook.MatplotlibSeabornSciPyBokehCreated byLead Developer& the VisionBrain team, MNNIT Allahabad. Thanks for using our library.Note*** realtimeplt is a faster and accurate version ofliveplot& in no manner VisionBrain is trying to coppy liveplot.
Package: real-time-screen-gaze
This package is designed to allow users of the Pupil Labs eyetracking hardware, especiallyNeon, to acquire screen-based gaze coordinates in realtime without relying onPupil Core software.This works by identifying the image of the display as it appears in the scene camera. We accomplish this withAprilTags, 2D barcodes similar to QR codes. This package provides amarker_generatormodule to create AprilTag image data.frompupil_labs.real_time_screen_gazeimportmarker_generator...marker_pixels=marker_generator.generate_marker(marker_id=0)More markers will yield higher accuracy, and we recommend a minimum of four. Each marker must be unique, and themarker_idparameter is provided for this purpose.Once you’ve drawn the markers to the screen using your GUI toolkit of choice, you’ll next need to setup aGazeMapperobject. This requires calibration data for the scene camera.frompupil_labs.realtime_api.simpleimportdiscover_one_devicefrompupil_labs.real_time_screen_gaze.gaze_mapperimportGazeMapper...device=discover_one_device()calibration=device.get_calibration()gaze_mapper=GazeMapper(calibration)Now that we have aGazeMapperobject, we need to specify which AprilTag markers we’re using and where they appear on the screen.marker_verts={0:[# marker id 0(32,32),# Top left marker corner(96,32),# Top right(96,96),# Bottom right(32,96),# Bottom left],...}screen_size=(1920,1080)screen_surface=gaze_mapper.add_surface(marker_verts,screen_size)Here,marker_vertsis a dictionary whose keys are the IDs of the markers we’ll be drawing to the screen. The value for each key is a list of the 2D coordinates of the four corners of the marker, starting with the top left and going clockwise.With that, setup is complete and we’re ready to start mapping gaze to the screen! On each iteration of our main loop we’ll grab a video frame from the scene camera and gaze data from the Realtime API. We pass those along to ourGazeMapperinstance for processing, and it returns our gaze positions mapped to screen coordinates.frompupil_labs.realtime_api.simpleimportdiscover_one_device...device=discover_one_device(max_search_duration_seconds=10)whileTrue:frame,gaze=device.receive_matched_scene_video_frame_and_gaze()result=gaze_mapper.process_frame(frame,gaze)forsurface_gazeinresult.mapped_gaze[screen_surface.uid]:printf(f"Gaze at{surface_gaze.x},{surface_gaze.y}")
Package: realtime-sge-wdroz
realtime_sgeThis project aims to provide real-time usage of SGE for user-defined tasks.Install using pippip install realtime_sge-wdrozBuilding instructionFirst create a virtualenv and install the dependencies fromrequirements.txtTo compile the protobuf files, typepython3 -m grpc_tools.protoc -I . --python_out=. --grpc_python_out=. realtime_sge/protos/*.proto
Package: realtime-stock
Realtime StockRealtime Stock is a Python package to gather realtime stock quotes from Yahoo Finance. The package enables you to handle single stocks or portfolios, optimizing the nunber of requests necessary to gather quotes for a large number of stocks.Documentation:https://realtime-stock.readthedocs.io.Free software: MIT licenseFeaturesStock classMethod to get stock’s latest price.Method to get all stock’s information provided by Yahoo Finance.Method get stock’s daily historical information.Method download stock’s historical data from Yahoo Finance.Utility functionsFunction to request recent quotes about a list of tickers.Function to get stock’s daily historical information.Function to download historical data about a list of tickers.InstallationTo install Realtime Stock, run this command in your terminal:$pipinstallrealtime-stockCheckherefor further information on installation.Basic UsageFor single stocks:>>>fromrtstock.stockimportStock>>>stock=Stock('AAPL')>>>stock.get_latest_price(){'LastTradePriceOnly':'95.89','LastTradeTime':'4:00pm'}CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History1.0.0 (2016-07-27)First release on PyPI.Stock class implemented.Function to request recent quotes about a list of tickers.Function to get stock’s daily historical information.Function to download historical data about a list of tickers..
Package: real-time-tf
Realtime Time-Frequency VisualizationRealtime Time Frequency Plotting of EEG data from Muse headsethttps://github.com/dxganta/real-time-tf/assets/47485188/a0484f7d-1aea-43df-84ad-d772f191bb85RequirementsCompatible with Python 3.xCompatible with all muse headsets supported by muselsl libraryGetting StartedFirst installmuselsl, connect to your muse headset and start a muse stream usingmuselsl streamThen install the realtime_tf package usingpip install real-time-tfKeep the muselsl stream running and in a separate terminal runrealtime_tfto visualize the realtime time frequency plot of the streamed eeg data from your muse headset.The time-frequency plot is shown of 1 second EEG data and the plot is updated every 0.2 seconds by default. But you can update these parameters if required usingrealtime_tf --show_time_window NEW_VALUE --update_time_window NEW_VALUEThe muse headset generally has 4 EEG electrodes/channels ('TP9', 'AF7', 'AF8', 'TP10'). By default the time-frequency plot average across all 4 channels is shown. But you can output only the time-frequency plot for a specific channel usingrealtime_tf --channel 0This will output the tf plot for channel 0 which is 'TP9'.Referenceshttps://www.udemy.com/course/solved-challenges-ants/
Package: realtime-trains
IntroductionThis is an unofficial Python library for theRealtime Trains API.The library allows you to make calls to the Realtime Trains API, and receive full response as JSON, or as custom objects. Some basic helper filtering of data is also included.RequirementsThis library requires you to use your own API key, which (at the time of writing) can be obtained for free from Realtime Trainsdeveloper website.Once registered and logged in, please make note of theAPI Auth Credentialsthat have automatically been generated for your account.InstallationUse pip to install this library. We recommend you use it within a virtual environment (either withvenvorpoetry).pipinstallrealtime-trainsUsageTo be able to use the library, you need to authenticate yourself. The result will be an object which you can then use to query the Realtime Trains data.fromrealtime_trains.realtime_trains.apiimportRealtimeTrainsApi,SearchOptionsrtt=RealtimeTrainsApi(username="<your_rtt_username>",password="<your_rtt_password>")Request station informationTo specify the search options, use theSearchOptionsclass.options=SearchOptions(from_station="CLJ",# requiredto_station="WAT",# optionaldate="25 Jan 2024",# optionaltime="13:45",# optionalplatform="2",# optionalnum_results=5,# optionalpassenger_only=True,# optionalshow_arrivals=False,# optionalshow_trains_only=True# optional)NOTE:Not all options apply to all calls.After you have created your authenticated object and you specified the search options, you can start requesting data.Get raw json responsejson=rtt.get_search_json(options)Get resulting data as an objectdata=rtt.get_search(options)Get data as a list of train servicesservices=rtt.get_search_services(options)Reporting issuesPlease report all the issues and feature requests viaGitHub.Enjoy!
Package: realtime-web-logs
realtime_web_logs文件日志实时显示到web页面。附带全系统硬盘的文件浏览下载功能。gunicorn -w 9 -k gevent --bind 0.0.0.0:8888 log_to_web:app看日志希望带有彩色，希望从浏览器上看到，不用连到机器上看。

 浏览系统的文件夹，scan + 系统文件夹的层级名字当做url路由，可以深层次看到机器上任何层级的文件夹，
 实现系统文件夹浏览下载。

如果是点击文件夹进入子目录。

如果是点击文件，尝试以文本格式读取文件，并以实时更新的方式显示到浏览器日志控制台并加彩。 
主要是要做到不遗漏推送日志和不重复推送日志，采用的是python 操作文件的seek和tell。浏览系统目录和下载文件的页面查看实时日志更新的页面，提供了暂停功能和自动下拉功能。把日志根据级别加了彩色，更容易观察哪些是严重的，哪些是debug的。