Saravanan666/pypi-org huggingface dataset:
Package: bchelpers
bchelpers=========Handy helpers to make botocore easier to use.I find myself wanting to use botocore quite often because its very simpleand pared down and it's also always current with AWS functionality.  Andbecause Python 3.x.That's great.  But it is pretty low-level and kind of awkward to use.The long-term fix for that is boto3 but in the mean time I've createda few helpers to make things a bit easier.Using bchelpers---------------A simple use would be like this:from bchelpers.region import Regionregion = Region(region_name='us-west-2', profile='dev')ec2 = region.get_service_endpoint('ec2')instances = ec2.call('describe-instances',query='Reservations[*].Instances[*]')The variable `instances` would now be a list of dictionaries, each containingthe full data for each instance running in this account in this region.Note the ability to pass a[jmespath](https://github.com/boto/jmespath) query in the call.  Thisquery will be run against the raw data from the response so you caneasily tailor the output to include exactly what you want and onlywhat you want.  Check out the `jmespath` link for full details on all ofthe awesome things you can do with it.I'll probably add more stuff to this over time but I fully expect this tojust go away eventually and be replaced with boto3.
Package: bchess
BChess“I give 98 percent of my mental energy to BChess; others
give only 2 percent.” —Bobby FischerBChess is a beginner-friendly offline chess in a console, with
batteries included. It contains opponents of various skill levels,
from a beginner to an expert, all to keep you from getting bored.How to play“To play for a draw, at any rate with white, is to some
degree a crime against BChess.” —Mikhail TalStart BChess, select your opponent, click on the piece you want
to move or enter your move inalgebraic notation. Try to win.
Or at least to have fun.How to install“BChess, like other arts, must be practiced to be appreciated.”
—Alexander AlekhineBChess runs on most Unix-like machines withPython3.6 or newer.
You can install or upgrade it to the latest release fromPyPIby first optionally upgrading your PIP:python3 -m pip install --user 'pip>=20.1'and then running:python3 -m pip install --user --upgrade bchessThis will install thebchessprogram into~/.local/binfolder, and if that folder is in your$PATH, then you will be
able to play by just typingbchessin your terminal. If not,
usepython3 -m bchess.Q&A“If a ruler does not understand BChess, how can he rule over
a kingdom?” —Khosrow II ParvizIs BChess any good?It’s getting there.Will playing BChess improve my skill?It won’t.To improve, one needs to practice deliberately, study theory,
review games, and be guided by a teacher. BChess is about having fun.Why the text art?Maximizing the board size on the screen makes playing easier.
Practice shows that textual piece names (i.e. KQRBN) are hard to
recognize when the square size is 3x6 or larger: they get lost
in the white space. Unicode symbols (i.e. ♚♛♜♝♞) are similarly
small, and suffer from poor rendering on systems where fonts
were not tuned well enough. This leaves us with only textual
art.Can I run this on Windows?You can’t. Unless you know about “Windows Subsystem for
Linux”. Then maybe.
Package: bchlib
python-bchlibThis is a python module for encoding, decoding, and correcting data usingBCH codes.RequirementsPython 3.6 or greater required.Installing the latest release:$ pip install bchlibInstalling from source:On Linux, you will need python-dev or equivalent package.  Windows, you need Microsoft Visual C++.  I've tested this manually usingVisual Studio 2015.$ pip install .Usage ExampleSee Python's built-in helpimport bchlib; help(bchlib)and the module'stestsfor usage examples.
Package: bchmemo
A python package for Memo. Memo is  an on-chain social network built on Bitcoin Cash.Based onbitcash.Installation$pipinstallbchmemoV0.0.3Action of “reply to memo” supported>>>user=MemoUser('bitcoincash:qqplzy4l2uxzwa5k3zc2mftkw3q6340a4cfy4kd3nf')>>>user.private_key='PRIVATEKEY'>>>user.reply_to_memo('b2d072076b4eb462341775792e769a1eecf085091567ad15f18c3ec7fd3a3401','reply test')Successfullysent!txid=21083c0730b25f8cb0831e76db1cf099d30ea811079e05a2bcb8b63fe3770913Checkiton:https://explorer.bitcoin.com/bch/tx/21083c0730b25f8cb0831e76db1cf099d30ea811079e05a2bcb8b63fe3770913>>>user.get_memos()>>>user.list_replies()bchmemoV0.0.3(aPythonpackage)@...4kd3nfrepliedtomemo(b2d072076b4eb462341775792e769a1eecf085091567ad15f18c3ec7fd3a3401)at2018-04-2614:12:00replytestHow to use>>>frombchmemoimportMemoUser>>>user=MemoUser('bitcoincash:qqplzy4l2uxzwa5k3zc2mftkw3q6340a4cfy4kd3nf')>>>user.private_key='PRIVATEKEY'>>>user.set_name('Test User For Python Package')Successfullysent!txid=d8c9d05ffefd5211353b3ef7b47ede424d3b78f7f1a4911d5d8792ae35072c04Checkiton:https://explorer.bitcoin.com/bch/tx/txid=d8c9d05ffefd5211353b3ef7b47ede424d3b78f7f1a4911d5d8792ae35072c04>>>user.post_memo('First memo from python package!')Successfullysent!txid=1f84551778197aae7ea82dced737d4ef644b9517e91cc867a354a1aad55f9b09Checkiton:https://explorer.bitcoin.com/bch/tx/1f84551778197aae7ea82dced737d4ef644b9517e91cc867a354a1aad55f9b09>>>user.follow('bitcoincash:qzdxp2z5yuxzlskafh2d8wsq7grg7rt46csg3qcn80')Successfullysent!txid=8d95e27eb1df5c4d97446fda69baab3fa7df568131252d2685d7c3203c366fe1Checkiton:https://explorer.bitcoin.com/bch/tx/8d95e27eb1df5c4d97446fda69baab3fa7df568131252d2685d7c3203c366fe1>>>user.like_memo('f0e24cbeb7d5cebc1577d76385892c0f28d50d34f24729981617c64441329de5')Successfullysent!txid=262fd91e68381e1d32a6b5d93b8d672d12117588efadaa02e603aaf6253728eeCheckiton:https://explorer.bitcoin.com/bch/tx/262fd91e68381e1d32a6b5d93b8d672d12117588efadaa02e603aaf6253728ee>>>user.get_memos()>>>user.list_memos()TestUserForPythonPackage@...4kd3nfpostedat2018-04-2502:31:49Firstmemofrompythonpackage!Liked(f0e24cbeb7d5cebc1577d76385892c0f28d50d34f24729981617c64441329de5)at2018-04-2502:31:49following:3qcn80bitcoincash:qzdxp2z5yuxzlskafh2d8wsq7grg7rt46csg3qcn80If you want to generate private keys, you can usebitcashfrom sporestackCreditssporestackfor the bitcash
Package: bchmnn.poodle
Poodle - Python MoodlePoodle is an SDK intended for providing typed api interfacing with any Moodle instance.It is part of a Bachelor Thesis and therefore only meant for educational purposes.It is still work in progress.InstallationPoodle can be installed by running:pipinstallbchmnn.poodleBasic Usageasyncdefmain(moodle:Poodle):course=awaitmoodle.get_user_course("<some_course_name>")groups=awaitmoodle.core_group_get_course_groups(course.id)assignment=awaitmoodle.get_assignment(course.id,index=-1)participants=awaitmoodle.mod_assign_list_participants(assignment.id)asyncdefsetup():url="https://your.moodle.instance"poodle=Poodle(url)asyncwithpoodleasmoodle:awaitmoodle.auth()awaitmain(moodle)if__name__=="__main__":asyncio.run(setup())Advanced UsageCredential ProviderThe default credential provider isCLICredentialProviderthat retrieves credentials through the command line.If you want to define your own credential provider:classCustomCredentialProvider(GenericCredentialProviderInterface):def__init__(self,id_provider_url="*",priority=3):super().__init__(id_provider_url,priority)defretrieve(self)->Tuple[str,str]:# get credentialsreturn(username,password)@propertydefname(self)->str:return"PassCredentialProvider"Authentication ProviderThe default authentication provider isBrowserSSOHandlerthat opens thepublic_config.launchurlin a browser and retrieves the token by registering a custom url schema. The OS will handle the callback and propagate the token.If you want to define your own authentication provider:classCustomAuthHandler(AbstractSSOHandler):_login_providers:List[GenericCredentialProviderInterface]def__init__(self,id_provider_urls:List[str],priority:int=0,login_providers:List[GenericCredentialProviderInterface]=[],):super().__init__(priority,id_provider_urls)self.init_logger(LOGGER_NAME,level=LOGGING_LEVEL)self._login_providers=login_providersasyncdefauthenticate(self,public_config:CoreSitePublicConfig,session:aiohttp.ClientSession,)->Tokens:# fetch tokensreturntokensUse Custom ProvidersIf you have defined your custom credential and authentication provider you can apply them:asyncdefsetup():url="https://your.moodle.instance"login_provider=CustomCredentialProvider()auth_handler=CustomAuthHandler([url],priority=1,login_providers=[login_provider])poodle=Poodle(url,auth_handlers=[auth_handler])asyncwithpoodleasmoodle:awaitmoodle.auth()# do some stuffFeaturesauthentication using os browsercachingtypescore_webservice_get_site_infocore_enrol_get_users_coursescore_course_get_contentscore_group_get_course_groupsmod_assign_get_assignmentsmod_assign_get_submissionsmod_assign_list_participantsmore to come
Package: bchydro
BCHydro APIBCHydro Python API for extracting electricity usage statistics from your personal account.InstallationViaPyPi:pipinstallbchydroVia Github:# Fetch the codegitclonehttps://github.com/emcniece/bchydro.gitcdbchydro# Set up environmentpipinstall-rrequirements.txtUsageRunning the example script:pipinstallbchydroexportBCH_USER=your.email@domain.comexportBCH_PASS=your-bch-password

pythontest.pyUsing in a project:importasynciofrombchydroimportBCHydroApiasyncdefmain():bch=BCHydroApi("username","password")# Asynchronous usage:print(awaitbch.get_usage())print(awaitbch.get_latest_point())print(awaitbch.get_latest_usage())print(awaitbch.get_latest_interval())print(awaitbch.get_latest_cost())# Mostly synchronous usage:awaitbch.refresh()print(bch.usage)print(bch.latest_point)print(bch.latest_usage)print(bch.latest_interval)print(bch.latest_cost)asyncio.run(main())⚠ Read-Only Account SharingThis project accesses your BCHydro account as would a human in a browser. It is recommended that a read-only account is set up for use with this project for more secure operation. Using this secondary account also enables backup access in the event of account lockout.Instructions for adding read-only accounts can befound here.Read-only accounts can beconfigured hereafter logging in.Version PublishingThis repo is automatically published toPyPiby means of aGithub Workflowwhen a newreleaseis created on Github.MaintenanceDependencies can be updated withpip-tools:# Install pip-compile and pip-syncpipinstallpip-tools# Upgrade requirementspip-compile--upgradeTodoPublish on release, not tagHandle account locking (looks for HTML alert dialogs)Unit testsAutomatic initial and re-authenticationRate limiting, auth retriesException documentationDisclaimerThis package has been developed without the express permission of BC Hydro. It accesses data by submitting forms that end-users would typically use in a browser. I'd love to work with BC Hydro to find a better way to access this data, perhaps through an official API... if you know anyone that works there, pass this along!
Package: bci4als
BCI - 4 - ALSA complete EEG Motor Imagery Classification pipelineExplore the docs »View Demo·Report Bug·Request FeatureTable of ContentsAbout The ProjectBuilt WithGetting StartedPrerequisitesInstallationUsageRoadmapContributingLicenseContactAcknowledgementsAbout The ProjectBCI's measure brain activity, process it, and produce control signals that reflect
the user's intent. We aim to detect and classify patterns of activity in the
ongoing brain signals that are associated with specific tasks or events.A common mental strategy is called motor imagery. In our implementation,
we aim to tell if the users are thinking of moving your left hand, right hand, or not moving (idle).Built WithpsychopyMNE-featuresKerasscikit-learnGetting StartedTo get a local copy up and running follow these simple example steps.PrerequisitesLabRecorderOpenBCI GUI (recommended)InstallationInstall python modulespipinstall-ihttps://test.pypi.org/simple/bci4alsUsageFor more examples, please refer to the [examples][examples-url]RoadmapSee theopen issuesfor a list of proposed features (and known issues).ContributingContributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make aregreatly appreciated.Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull RequestLicenseDistributed under the MIT License. SeeLICENSEfor more information.ContactEvyatar Luvaton -luvaton@post.bgu.ac.ilNoam Siegel -noamsi@post.bgu.ac.ilProject Link:https://github.com/evyatarluv/BCI-4-ALSAcknowledgementsComputational Psychiatry Lab @ BGULab Streaming LayerMIT License[examples-url]: recycle bin/semester A report/Report.ipynb
Package: bci-dataset
bci-datasetPython library for organizing multiple EEG datasets using HDF.Support EEGLAB Data!*For do deep learning, this library was created as a tool to combine datasets for the major BCI paradigms.Installationpip install bci-datasetHow to UseAdd EEG DataSupported FormatsEEGLAB(.set)Epoching (epoch splitting) on EEGLAB is required.numpy(ndarray)Commonalityimportbci_datasetfpath="./dataset.hdf"fs=500# sampling rateupdater=DatasetUpdater(fpath,fs=fs)updater.remove_hdf()# delete hdf file that already existAdd EEGLAB Dataimportnumpyasnplabels=["left","right"]eeglab_list=["./sample.set"]# path list of eeglab files# add eeglab(.set) filesforfpineeglab_list:updater.add_eeglab(fp,labels)Add NumPy Data#dummydummy_data=np.ones((12,6000))# channel × signaldummy_indexes=[0,1000,2000,3000,4000,5000]#Index of trial startdummy_labels=["left","right"]*3#Label of trialsdummy_size=500#Size of 1 trialupdater.add_numpy(dummy_data,dummy_indexes,dummy_labels,dummy_size)Apply PreprocessingIf the "preprocess" method is executed again with the same group name, the already created group with the specified name is deleted once before preprocessing."""preprocessing examplebx : ch × samples"""defprepro_func(bx:np.ndarray):x=bx[12:15,:]returnStandardScaler().fit_transform(x.T).Tupdater.preprocess("custom",prepro_func)Contents of HDFNote that "dataset" in the figure below refers to the HDF dataset (class).hdf file
├ origin : group / raw data
│ ├ 1 : dataset
│ ├ 2 : dataset
│ ├ 3 : dataset
│ ├ 4 : dataset
│ ├ 5 : dataset
│ └ …
└ prepro : group / data after preprocessing
　 ├ custom : group / "custom" is any group name
　 │ ├ 1 : dataset
　 │ ├ 2 : dataset
　 │ ├ 3 : dataset
　 │ ├ 4 : dataset
　 │ ├ 5 : dataset
　 │ └ …
　 └ custom2 : group
　 　 └ ...omit (1,2,3,4,…)Check the contents with software such as HDFView.Use "h5py" or similar to read the HDF file.importh5pywithh5py.File(fpath)ash5:fs=h5["prepro/custom"].attrs["fs"]dataset_size=h5["prepro/custom"].attrs["count"]dataset79=h5["prepro/custom/79"][()]#ch × samplesdataset79_label=h5["prepro/custom/79"].attrs["label"]Merge DatasetIn order to merge, "dataset_name" must be set.If the order of channels is different for each dataset, the order can be aligned by specifying ch_indexes.Source's preprocessing group is not inherited. In other words, preprocess() must be executed after the merge.Example: Merge source1 and source2 datasetstarget=DatasetUpdater("new_dataset.h5",fs=fs)target.remove_hdf()# reset hdfs1=DatasetUpdater("source1.h5",fs=fs,dataset_name="source1")s2=DatasetUpdater("source2.h5",fs=fs,dataset_name="source2")s1_ch_indexes=[1,60,10,5]# channel indexes to usetarget.merge_hdf(s1,ch_indexes=s1_ch_indexes)target.merge_hdf(s2)Pull requests / IssuesIf you need anything...
Package: bci-framework
Developed byYeison Nolberto Cardona ÁlvarezAndrés Marino Álvarez Meza, PhD.César Germán Castellanos Dominguez, PhD.Digital Signal Processing and Control Group|Grupo de Control y Procesamiento Digital de Señales (GCPDS)Universidad Nacional de Colombia sede ManizalesBCI-FrameworkA distributed processing tool, stimuli delivery, psychophysiological experiments designer and real-time data visualizations for OpenBCI.BCI-Framework is an open-source tool for the acquisition of EEG/EMG/ECG signals, developed to work withOpenBCI's Cyton board, the main core of this software lies onOpenBCI-Stream, a library designed to handle all thelow-level hardware featuresand extend the hardware capabilities with high-level programming libraries.An optionally distributed paradigm for data acquisition and streaming is available to be implemented, this approach stabilizes the sampling rate on non-real-time acquisition systems and consists on delegate the board handle to a dedicated environ and stream out the data in real-time.Write custom visualizationfor raw or processed time series anddesign custom neurophysiological experimentsare the major features available in this application.BCI-Framework comprises a graphical user interface (GUI) with a set of individual computational processes (distributed or in a single machine), that feed a visualization, serve a stimuli delivery, handle an acquisition, storage data, or stream a previous one (offline analysis). It has a built-in development environment and a set of libraries that the user can implement to create their specific functionality.
Package: bcikit
Brain-Computer Interface ToolkitThe toolkit consists of datasets, model architectures, and common signal processing functions for BCI before applying on PyTorch.Installationgit clone https://github.com/ntubci/bcikit.git
pip install -r requirements.txtContributingIf you plan to contribute new dataset, features, utility functions, or extensions to the core, please first open anissueand discuss the feature with us. Sending a PR without discussion might end up resulting in a rejected PR because we might be taking the core in a different direction than you might be aware of.To learn more about making a contribution to Pytorch, please see ourContributionpage.CommunicationGitHub Issues: If you want to report a bug, feature requests, install issues, thoughts, or suggest an enhancement, we'd love for you to open anissueat this github repositoryThe TeamLicenseBCI Toolkit is licensed under the Apache License 2.0, as found in theLICENSEfile.
Package: bci-ml
Basic informationImplementation code for BCI models. Thesource codepresented on the github.All methods were implemented based onpytorchfor simple parallelization by usingcuda.All information about this project can be found in thedocumentation.Requirements and InstallationA simple instruction of installation using pip is provided near thesource code.More information about installation can be found in documentationinstallationpage.Example of useA simple examples of module usage can be found in documentationexamplepage.
Package: bcirc
Boolean circuits packageThis Python 3 package implements Boolean circuits for my complexity theory class.Example:frombcircimportInputGates,AndGate,MultiOrGate,BooleanCircuita,b,c=InputGates(3)d,e,f=AndGate(a,b),AndGate(a,c),AndGate(b,c)g=MultiOrGate(d,e,f)bc=BooleanCircuit((a,b,c),g)print(bc(True,True,False))h=a&~(b|c)a.set(True)b.set(False)c.set(False)print(h.value())
Package: bcitflex
BCIT Flexwww.bcitflex.techA website for easily viewing BCIT course offerings.
Features a course filter to aid in course selection.Please feel free to report any issues, bugs or suggestions. Pull requests are welcome.Installationpip install bcitflexPrerequisitesAPostgreSQL instanceis required for the database.Dependencypsycopg2requireslibpq-devto be installed for Ubuntu/Debian systems.sudoaptinstalllibpq-devpython3-devFor more details see thepycopg2 documentationandStackOverflow.DB SetupPostgreSQL is used as the DBMS.
To create and initialize the database:Create a database using the cli command. Pass--helpfor more information.flask--appbcitflexcreate-dbBuild schema using alembic:flask--appbcitflexupgrade-dbPopulate the subject table with the list of subjects to scrape courses for:flask--appbcitflexload-subjectsBy default, subjects COMP, MATH, COMM AND BLAW are loaded. To load all subjects pass the--all-subjectsflag.UsageTo run the webscraper and populate the database with the latest course offerings:flask--appbcitflexload-dbTo run the dev webserver:flask--appbcitflexrunRoadmapFilter by prerequisitesIndividual Course OfferingsRate My ProfessorsWeb appGUIReturn RMP ratingIndication of data freshnessProgram informationUser relevant course viewUser course wishlistUser course schedule plannerContributorsSam -0x53616D75656CJonathan -jonbiemond
Package: bcitoolbox
BCI Toolbox is a Python implementation of the hierarchical Bayesian Causal Inference (BCI) model for multisensory research. BCI model is a statistical framework for understanding the causal relationships between sensory inputs and prior expectations of a common cause, which can account for human perception in a number of tasks, including temporal numerosity judgment (Shams et al., 2005; Wozny et al., 2008), spatial localization judgment (Körding et al., 2007; Wozny & Shams, 2011), size-weight illusion paradigm (Peters et al., 2016), rubber-hand illusion paradigm (Chancel et al., 2022; Chancel & Ehrsson, 2023).
Package: bcj-cffi
In data compression, BCJ, short for Branch-Call-Jump, refers to a technique that improves the compression of
machine code of executable binaries by replacing relative branch addresses with absolute ones.
This allows a LZMA compressor to identify duplicate targets and archive higher compression rate.BCJ is used in 7-zip compression utility as default filter for executable binaries.bcj-cffi is a python bindings with BCJ implementation by C language.
The C codes are derived from p7zip, portable 7-zip implementation.
bcj-cffi support Intel/Amd x86/x86_64, Arm/Arm64, ArmThumb, Sparc, PPC, and IA64.Development statusA development status is considered asBetastate.InstallationAs usual, you can install bcj-cffi using python standard pip command.pip install bcj-cffiLicenseCopyright (C) 2020,2021 Hiroshi Miura7-Zip Copyright (C) 1999-2010 Igor PavlovLZMA SDK Copyright (C) 1999-2010 Igor PavlovThis library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
Lesser General Public License for more details.You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
02110-1301  USA
Package: bcl2fq-local
bcl2fq-localA bcl2fastq and qc wrapperInstallpipinstallbcl2fq-localorgitcloneURLcdbcl2fq-local
pythonsetup.pyinstallUsageFor Bcl2fastqusage: bcl2fq-local [-h] [-i DirPath] [-o DirPath] [--sample-sheet File]
                    [--mismatch N] [--process N] [--io-process Num]
                    [--binpath File] [--dry-run] [--mask MASK]
                    [--mask-reads N] [--mask-index N] [-v]

A bcl2fastq wrapper

optional arguments:
  -h, --help           show this help message and exit
  -i DirPath           Sequence run dir
  -o DirPath           Output dir for fastq files
  --sample-sheet File  Using custom sample sheet file
  --mismatch N         Mismatch for barcode, default: 1
  --process N          Process number for demultiplexing and processing
  --io-process Num     Process number for reading and writing
  --binpath File       bcl2fastq binary file path
  --dry-run            Only print the cmd without running it
  --mask MASK          using self defined mask
  --mask-reads N       set reads mask base count, default is: 0,0
  --mask-index N       set index mask base count, default is: 0,0
  -v, --version        show version infoFor QCusage: qc-local [-h] [-r RAW_DIR] [-c CLEAN_DIR] [-q QC_DIR] [-2 ADAPTER_DIR]
                [-t THREADS] [--paired-end] [-v]

A qc wrapper

optional arguments:
  -h, --help      show this help message and exit
  -r RAW_DIR      Raw reads dir input
  -c CLEAN_DIR    Dir to write output clean reads
  -q QC_DIR       Dir to write qc info file, default: <clean_dir>/QC
  -2 ADAPTER_DIR  Dir to write removed reads, default: <clean_dir>/002
  -t THREADS      Threads number for parallel, default:8
  --paired-end    Use qc for paired end reads
  -v, --version   Show version info