Saravanan666/pypi-org huggingface dataset:
Package: pykillerx
pykillerx LibraryUserbot PyrogramDisclaimer️                       ⚠️ WARNING FOR YOU ️ ️⚠️
   pykillerx is used to help your account activities on Telegram
   We are not responsible for what you misuse in this repository
   !  Be careful when using this repository!
   If one of the members misuses this repository, we are forced to ban you
   Never ever abuse this repositoryInstallingpip3 install --upgrade pykillerx
Package: pykilosort
Python port of KiloSort2This is a Python port of the original MATLAB version ofKilosort 2.5, written by Marius Pachitariu, with Neuropixel specific improvements and software engineering enhancements.
The modifications are described inthis white paper.InstallationSystem RequirementsThe code makes extensive use of the GPU via the CUDA framework. A high-end NVIDIA GPU with at least 8GB of memory is required.Doing the install using Anaconda (Linux)Only on Linux, first install fftw by running the followingsudo apt-get install -y libfftw3-devNavigate to the desired location for the repository and clone itgit clone https://github.com/int-brain-lab/pykilosort.git
cd pykilosortCreate a conda environmentconda env create -f pyks2.yml
conda activate pyks2
pip install -e .UsageExampleWe provide a few datasets to explore parametrization and test on several brain regions.
The smallest dataset is a 100 seconds excerpt to test the installation. Here is the minimal working example:importshutilfrompathlibimportPathfrompykilosort.iblimportrun_spike_sorting_ibl,ibl_pykilosort_params,download_test_datadata_path=Path("/mnt/s0/spike_sorting/integration_tests")# path on which the raw data will be downloadedscratch_dir=Path.home().joinpath("scratch",'pykilosort')# temporary path on which intermediate raw data will be written, we highly recommend a SSD driveks_output_dir=Path("/mnt/s0/spike_sorting/outputs")# path containing the kilosort output unprocessedalf_path=ks_output_dir.joinpath('alf')# this is the output standardized as per IBL standards (SI units, ALF convention)# download the integration test data from amazon s3 bucketbin_file,meta_file=download_test_data(data_path)# prepare and mop up folder architecture for consecutive runsDELETE=True# delete the intermediate run products, if False they'll be copied over to the output directory for debuggingshutil.rmtree(scratch_dir,ignore_errors=True)scratch_dir.mkdir(exist_ok=True)ks_output_dir.mkdir(parents=True,exist_ok=True)# loads parameters and runparams=ibl_pykilosort_params(bin_file)params['Th']=[6,3]run_spike_sorting_ibl(bin_file,delete=DELETE,scratch_dir=scratch_dir,ks_output_dir=ks_output_dir,alf_path=alf_path,log_level='INFO',params=params)TroubleshootingManaging CUDA ErrorsErrors with the CUDA installation can sometimes be fixed by downgrading
the version of cudatoolkit installed. Currently tested versions are 9.2,
10.0, 10.2, 11.0 and 11.5To check the current version run the following:conda activate pyks2
conda list cudatoolkitTo install version 10.0 for example run the followingconda activate pyks2
conda remove cupy, cudatoolkit
conda install -c conda-forge cupy cudatoolkit=10.0
Package: py-kim
Introducing Kim:.. code-block:: python>>> mapper = UserMapper(data=response.json())
>>> mapper.marshal()
User(id='one', name='Bruce Wayne', 'title'='CEO/Super Hero')
>>> user_two = User.query.get('two')
>>> mapper = UserMapper(obj=user_two)
>>> mapper.serialize()
{u'id': 'two', u'name': 'Martha Wayne', 'title': 'Mother of Batman'}Kim FeaturesKim is a feature packed framework for handling even the most complex
marshaling and serialization requirements.Web framework agnostic - Flask, Django, Framework-XXX supported!Highly customisable field processing systemSecurity focusedControl included fields with powerful roles systemHandle mixed data types with polymorphic mappersMarshal and Serialize nested objectsKim officially supports Python 2.7 & 3.3–3.5InstallationInstall Kim using pip:.. code-block:: bash$ pip install py-kimDocumentationLearn all of Kim’s features with these simple step-by-step instructions or check out the
quickstart guide for a rapid overview to get going quickly.http://kim.readthedocs.io/en/latest/
Package: pykin
pykinPython Interface for the robot Kinematics library pykinThis library has been created simply by referring toikpy.FeaturesPure python librarySupport only URDF fileCompute forward, inverse kinematics and jacobian, referred to theIntroduction to Humanoid Robotics book.Check robot self-collision and collision between objectsPlot robot kinematic chain and meshInstallationRequirementsYou need apython-fclpackage to do object collision checking.For Ubuntu, usingaptsudo apt install liboctomap-devsudo apt install libfcl-devFor Mac, First, Download the source and build it.octomapgit clone https://github.com/OctoMap/octomap.git$ cd octomap
$ mkdir build
$ cd build
$ cmake ..
$ sudo make
$ sudo make installfclgit clone https://github.com/flexible-collision-library/fcl.gitSince python-fcl uses version 0.5.0 of fcl, checkout with tag 0.5.0$ cd fcl
$ git checkout 0.5.0
$ mkdir build
$ cd build
$ cmake ..
$ sudo make
$ sudo make installInstall Pykinpip3 install pykinQuick StartYou can see various examples in examples directoryRobot InfoYou can see 7 robot info.baxter, sawyer, iiwa14, iiwa7, panda, ur5e, doosan$cdexamples
$pythonrobot_info.py$(robot_name)# baxter$pythonrobot_info.pybaxter# saywer$pythonrobot_info.pysawyerForward kinematicsYou can compute the forward kinematics as well as visualize the visual or collision geometry.$cdexamples/forward_kinematics
$pythonrobot_fk_baxter_test.pyvisualcollisionInverse KinematicsYou can compute the inverse kinematics using levenberg marquardt(LM) or newton raphson(NR) method$cdexamples/inverse_kinematics
$pythonrobot_ik_baxter_test.pySampling based Inverse KinematicsYou can compute the inverse kinematics using geometric-aware bayesian optimization(GaBO) methodFor more detailed information, checkGaBO module$cdexamples/inverse_kinematics
$pythonrobot_ik_gabo_test.pyCollision checkThe below images show the collision result as well as visualize robot using trimesh.Scene class$cdexamples/trimesh_renders
$pythonsawyer_render.pytrimesh.SceneResultVisualizationYou can see visualization using matplotlib library or trimesh.Scene class.Visualizesimple urdfusingmatplotlibur5esawyeriiwa14pandaVisualizevisual geometryusingmatplotlibur5esawyeriiwa14pandaVisualizecollision geometryusingmatplotlibur5esawyeriiwa14pandaVisualize mesh aboutvisual/collision geometryusingtrimesh.Scene class
Package: pykindle
# pykindleA library to make book.mobi files.Kindlegen packaged all the functions in a file,and here we'd like to develop it's python API.# Install`pip install pykindle`Please get kindlegen from amazon website and# Documentation[documentation](https://pykindle.readthedocs.io)# Usage```pythonimport pykindlebook = pykindle.MagazineBook()article = pykindle.MarkdownArticleItem()article.href = 'page1.html'article.source = 'This is a page.'book.append(article)book_bytes = book.create()with open('output.mobi', mode='wb') as file:file.write(book_bytes)```For example, click [here][magazine_example].# Not finished yetThere are so many functions that haven't been implemented yet.Currently, PyKindle only support to generate a magazine format book without photos.[magazine_example]: https://github.com/panhaoyu/pykindle/blob/master/tests/test_magazine.py# Need helpI'm new in open source, and many people help me,so if you want to join my project, tell me,and I'll try my best to help you.As a previous version,you can change the APIs,you can add anything you like.I hope to make pykindle the best kindle library.If you are also new here,help me to create documentation, create testcases, or translate the documentation.
Package: pykindler
Command line tool that automatically detects and converts downloaded e-books to mobi format (and auto-uploads to Kindle, on a schedule)Where to get itThe source code is currently hosted on GitHub at:https://github.com/VivekBits2210/pykindlerPackage is deployed atPython Package Index (PyPI)pip3installpykindlerEnsure Calibre is installed (Guide)sudo-v&&wget-nv-O-https://download.calibre-ebook.com/linux-installer.sh|sudosh/dev/stdinKnow your Kindle’s e-mail addresshereDependenciesUNIX based system needed (Linux, MacOS)Calibrepyenchant - Offers an English Dictionary, helpful in detecting if a file is a bookpython-crontab - Allows scheduling of pykindler jobs that auto-convert and email your books in the backgroundpgi - Auto-detects your downloads folderblack - Code formatterargparse - Neater interface with command line argumentskeyring - Safe storage of email credentialsUsageIn default mode, pykindler will auto-detect your downloads folder and populate ‘Converted_Books’ and ‘Processed_Books’ folders
.. code-block:: shpykindler-runIn custom mode, specify your downloads folder and setup a twice-a-day conversion jobpykindler-run--folder/home/some-user/Desktop--jobIf you just want to convert one file, specify it after –filepykindler-run--file/home/some-user/Desktop/my-book.epubIf you want to not convert to mobi, choose your own extension with –extpykindler-run--file/home/some-user/Desktop/my-book.mobi--extepubFor more helppykindler-run-hLicenseMITGetting HelpFor usage questions, the best place to go to isStackOverflow.Contributing to pykindlerAll contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
Here is what Iplan to do nextPlease feel free to mail ideas to the maintainer: [viveknayak2210@gmail.com]
Package: pykinect-recorder
pykinect-recorderDescriptionThe pykinect-recorder is an educational/industrial library that provides sensor recording (including audio), playback, and computer vision soultions through a python wrapper of the Azure Kinect Sensor SDK.Recording and playback example below.DocumentationYou can find the API documentation on our website:https://pykinect-recorder.readthedocs.io/en/latest/index.html.For details about API for Azure Kinect SDK please see Azure Kinect Sensor SDK github:https://github.com/microsoft/Azure-Kinect-Sensor-SDK.FeaturesSee RGB, IR, Depth, IMU and Audio data when recoding.Control recording option (FPS, brightness, ...).Change layout with drag and drop.Playback recorded video.3D reconstruction viewer with streaming/recorded video.Sync devicesscreen zoom in-outimu, microphone panel redesignRecording audio.Deep learning inference (mediapipe and native) with streaming/recorded video.Intel RealSenseZenmuse SDK for PythonPrerequisitesEnvironmentWindows 10 (Recommended)Windows 11Install Azure Kinect SDKMake sure you download Azure Kinect SDK before using this repo.SDK version '1.4.1' supported in release 0.9.3.You can download Azure Kinect SDKhere.InstallationUsing pippython-mvenv.venv
.venv/Scripts/activate.ps1
pipinstallpykinect-recorder
pykinectUsing .exeMIT LicenseCopyright (c) 2023 teamvisualPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, 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 THE
SOFTWARE.
Package: pykinematics
pykinematicspykinematicsis an open-source Python package for estimating hip kinematics using both novel Magnetic and Inertial
Measurement Unit (MIMU) wearable sensors and existing Optical Motion Capture (OMC) algorithms. The novel MIMU algorithms
have been validated against OMC, and include novel methods for estimating sensor-to-sensor relative orientation and
sensor-to-segment alignment.DocumentationDocumentation including the below examples, and the API reference can be found atpykinematics documentationRequirementsPython >=3.6NumpyScipyh5py*pip should automatically collect any uninstalled dependencies.* h5py is required to run the example code in/scripts/example_code.py, as the sample data
provided (seeExample Usage) is stored in the.hdfformat. Pip will not catch and installh5pyas it is not used bypykinematics, and must be installed manually to run the example code.pipinstallh5pyor if using Anacondacondainstall-canacondah5pyInstallationpykinematicscan be installed using pip:pipinstallpykinematicsAlternatively, you can clone this repository and install from source.pykinematicscan be uninstalled by runningpipuninstallpykinematicsRunning testsTests are implemented withpytest, and can be automatically run with:pytest--pyargspykinematics.testsOptionally add-vto increase verbosity.If you don't want to run the integration tests (methods tests), use the following:python-mpykinematics.tests--no-integrationIf you want to see coverage, the following can be run (assumingcoverageis installed):coveragerun-mpytest--pyargspykinematics.tests# generate the reportcoveragereport# generate a HTML report under ./build/index.htmlcoveragehtmlExample UsageA full example script can be found in/scripts/example_code.py. This requires a sample
data file, which can be downloaded fromSample Dataexample_code.pycontains a helper function to load the data into Python.
Once the data is imported, the bulk of the processing is simple:importpykinematicsaspkstatic_calibration_data,star_calibration_data,walk_fast_data=<loadedsampledata># define some additional keyword arguments for optimizations and orientation estimationfilt_vals={'Angular acceleration':(2,12)}ka_kwargs={'opt_kwargs':{'method':'trf','loss':'arctan'}}jc_kwargs=dict(method='SAC',mask_input=True,min_samples=1500,opt_kwargs=dict(loss='arctan'),mask_data='gyr')orient_kwargs=dict(error_factor=5e-8,c=0.003,N=64,sigma_g=1e-3,sigma_a=6e-3)mimu_estimator=pk.ImuAngles(gravity_value=9.8404,filter_values=filt_vals,joint_center_kwargs=jc_kwargs,orientation_kwargs=orient_kwargs,knee_axis_kwargs=ka_kwargs)# calibrate the estimator based on Static and Star Calibration tasksmimu_estimator.calibrate(static_calibration_data,star_calibration_data)# compute the hip joint angles for the Fast Walking on a treadmillleft_hip_angles,right_hip_angles=mimu_estimator.estimate(walk_fast_data,return_orientation=False)Right hip angles from the sample data for walking fast:
Package: py-kingbot
py-kingbotA stable userbot base library , based on PyrogramDeploying To Heroku ⚙Installation'pip install py-kingbot'Usage=> Create folders namedownerr,utilss,assistantandadmins.=> Add your plugins in theownerrfolder and others accordingly.=> Create a.envfile withAPI_ID,API_HASH,SESSION,BOT_TOKEN,BOT_USERNAME,DBI_URL,OWNER_ID,REDIS_URI,REDIS_PASSWORD&LOG_CHANNELas mandatory environment variables.
=> Runpython -m kingbotto start the bot.Made with 💕 by@TeamDevilCoder.CreditsDanforPyrogramAryan-: A newbie but full of talentSammy -: Source of ideaAbhishek -: A humble thanks to you for the graphics
Package: py-kinguserbot
py-kinguserbotA stable userbot base library , based on PyrogramDeploying To Heroku ⚙Installation'pip install py-kingbot'Usage=> Create folders namedownerr,utilss,assistantandadmins.=> Add your plugins in theownerrfolder and others accordingly.=> Create a.envfile withAPI_ID,API_HASH,SESSION,BOT_TOKEN,BOT_USERNAME,DBI_URL,OWNER_ID,REDIS_URI,REDIS_PASSWORD&LOG_CHANNELas mandatory environment variables.
=> Runpython -m kingbotto start the bot.Made with 💕 by@TeamDevilCoder.CreditsDanforPyrogramAbhishek -: A humble thanks to you for the graphics
Package: pykinisi
pykinisiPython package for kinisi motor controller. This package is used to control the kinisi motor controller via serial interface.Description of the commands can be found inKinisi Motion Controller framework documentationInstallationInstall pykinisi with pip:pipinstallpykinisiControlling Single MotorThis example shows how to connect to the motor controller and control a single motor.\importtimefrompykinisiimport*port="COM3"controller=KinisiController()ifnotcontroller.connect(port):print(f"Can't open serial connection. Port:{port}")exit()# Motor testmotor_index=MotorIndex.Motor0speed=40# Speed in percentage# Is motor reversedis_reversed=Falsecontroller.initialize_motor(motor_index,is_reversed)# Set motor speed to speedcontroller.set_motor_speed(motor_index,speed)time.sleep(5)# Stop motorcontroller.stop_motor(motor_index)More ExamplesThere are several examples in the examples folder which show how to use the pykinisi package to control the kinisi motor controller.Run the examples with:cdexamples
python<example_file><serial_port>Package DevelopmentInstall pykinisi for development:pipinstall-e.Update KinisiCommands.py file:cdtools
pipinstall-rrequirements.txt
pythonupdate-commands.py--branch=mainLinksKinisi Motion Controller firmwareKinisi Motion Controller hardwareJavaScipt package for kinisi motor controllerPython package for kinisi motor controller
Package: pykinml
pyKinML: Package for training Neural Net Potential Energy SurfacesDescriptionThis repository contains the code to train NNPESs and use those models with an ASE calculator.How to installThis package can be installed with pip or by cloning this repo and installing it locally.Install with pip:pip install pykinmlClone from repo:git clone git@github.com:sandialabs/pykinml.gitThis package relies on PyTorch_scatter to sum the atomic contributions to energy. Ensure you have the proper version. Instructions for installation can be found at:https://github.com/rusty1s/pytorch_scatterWe also highly recomend (required for force training) using the aevmod package for calculation of the aevs and their jacobians:https://github.com/sandialabs/aevmod.gitFor transition state optimization, we recomend Sella:https://github.com/zadorlab/sella
Package: pykins
Python API and CLI for Jenkins in one package :)InstallationA virtual environment is recommended for development.
To install the latest version ofpykins, run the following commands:virtualenv .venv && source .venv/bin/activate
pip install .To install from PyPi (not necessarily latest version!):pip install pykinsPython API Examples`python >>> import pykins >>> jenkins =pykins.Jenkins('http://my_jenkins')>>>print(jenkins.get_jobs())`
Package: pykinsol
pykinsolprovides aPythonbinding to theNonlinear system of equationsolver algorithms fromkinsolin theSUNDIALS suite.pykinsolallows a user to numerically solve
(systems of) non-linear equations. Note that not all functionality of KINSOL are currently exposed in the python wrapper. Additional functionality is however easy to add if needed.You may also want to know that you can usepykinsolfrompyneqsyswhich can e.g. derive the Jacobian analytically for you.DocumentationAutogenerated API documentation is foundhere.InstallationSimplest way to install pykinsol is to use theconda package manager:$ conda install -c bjodah pykinsol pytest
$ python -m pytest --pyargs pykinsoltests should pass.Binary distribution is available here:https://anaconda.org/bjodah/pykinsolSource distribution is available here:https://pypi.python.org/pypi/pykinsolTo compile from source you may proceed e.g. as follows:$ LLAPACK=openblas python3.4 setup.py install --userwhere we specified specifically what LAPACK implementation we used for building SUNDIALS.ExamplesSeeexamples/, and rendered jupyter notebooks here:http://hera.physchem.kth.se/~pykinsol/branches/master/examplesHere is a simple example:>>>deff(x,fout):...fout[0]=x[0]+(x[0]-x[1])**3/2-1...fout[1]=x[1]+(x[1]-x[0])**3/2...>>>defj(x,Jout,fx):...Jout[0,0]=1+3/2*(x[0]-x[1])**2...Jout[0,1]=-3/2*(x[0]-x[1])**2...Jout[1,0]=-3/2*(x[1]-x[0])**2...Jout[1,1]=1+3/2*(x[1]-x[0])**2...>>>frompykinsolimportsolve>>>result=solve(f,j,[0,0])>>>assertresult['success']>>>print(result['x'])# doctest: +SKIP[0.841163890.15883611]LicenseThe source code is Open Source and is released under the simplified 2-clause BSD license. SeeLICENSEfor further details.Contributors are welcome to suggest improvements athttps://github.com/bjodah/pykinsolAuthorBjörn I. Dahlgren, contact:gmail address: bjodah
Package: pykiq
PykiqAsidekiqclient library for python 3. It uses a simple redis connection to store jobs in one of sidekiqs redis queues.UsageThe same redis connection, i.e. server, port and database, must be used as it is already done for sidekiq. You can store it in a pykiq connector object:importpykiqimportrediserror_handler=pykiq.error.NullErrorHandler()conn=redis.Redis(name,port,db)connector=pykiq.connector.RedisConnector.from_existing_connection(error_handler,conn)This connection can be used to create a pykiq connection:kiq=pykiq.Sidekiq(connector)Unfortunately, you cannot use this connection directly, since each job has arguments to process and a queue, in which it is enlisted.Therefore, an object-oriented approach should be used:classMyJobs(pykiq.Sidekiq):def__init__(self,connector:Connector)->None:super().__init__(connector)...Queues can be declared directly in the constructor. You must re-use the names of the sidekiq queues.classMyJobs(pykiq.Sidekiq):def__init__(self,connector:Connector)->None:super().__init__(connector)urgent_queue=pykiq.SidekiqQueue("urgent",self)A job can now easily be added:classMyJobs(pykiq.Sidekiq):def__init__(self,connector:Connector)->None:super().__init__(connector)urgent_queue=pykiq.SidekiqQueue("urgent",self)self.__clean_up=CleanUpJob(urgent_queue)@propertydefclean_up(self):returnself.__clean_upclassCleanUpJob(pykiq.Job):def__init__(self,queue:pykiq.SidekiqQueue)->None:super().__init__(queue,"full::name::of::CleanUpJob")defclean_up_at(self,time_span:datetime.timedelta,amount:int):returnsuper()._perform_in(time_span,amount)When declaring a new job, the full name of the ruby class including the module namespaces must be added. Each job declares to methods:_perform_inand_perform_async, where the first takes a timedelta to specify a delay. Please note, that the delay must be positive and should respect network latencies.Any other argument can be added using the variadic argument list and hence a job should declare its own function with arguments.The usage now is quite simple:jobs=MyJobs(connector)jobs.clean_up.clean_up_at(timedelta(minutes=30),50)That's it.LicenseThis is merely a re-write of sidekiq's ruby code in python. Hence the same license for sidekiq and pykiq are used and the library can be freely distributed according to the LGPLv3.VersioningThis library follows semantic versioning 2.0. Any breaking change will produce a new major release. Versions below 1.0 are considered to have a unstable interface.
Package: pyKirara
pyKirarapyKirara is a Python library for the starlight.kirara REST APIBasic DocumentationAsync PortUsageimportpyKiraraclient=pyKirara.Kirara()uzuki=client.get_idol(101)print(f"HI! MY NAME IS{uzuki.conventional}")print("I'll do my best!")print(f"I'am{uzuki.age}years old!")# Returns:# HI! MY NAME IS Shimamura Uzuki# I'll do my best!# I'am 17 years old!RequirementsPython 3.5+RequestslibraryLicenseMIT
Package: pykismet
PyKismet is a simple, thread safe, Python client for the Akismet API, based on
urllib3.Example usage:frompykismet.apiimportAkismetapi=Akismet('yourapikey','http://myblog.com')api.check_comment(user_ip='192.168.2.1',user_agent='Mozilla',comment_content="Hello there Akismet!")