Saravanan666/pypi-org huggingface dataset:
Package: os-bedtime
os_bedtimeos_bedtime is a python modulemade for windows (for now)to turn off the computer with simple functions.Currently support for Linux is unplanned but kept in mind, but for me there is no way to test OSX :'(So that will come much later.Early accessCurrently this module is yet in the major version0.x.xand with that in mind the interface might change at any time.It is kept in mind to make changes as little as possible, but it is not out of question that things might brake during early access.If you feel like contributing. (OSX or Linux or improvements for Windows even) Let me know in an issue.How to installpip install os-bedtimeIssue:Sleep issuesYour computer might be waking from sleep mode because certain peripheral devices, such as a mouse, a keyboard, or headphones are plugged into a USB port or connected via Bluetooth. It might also be caused by an app or a wake timer.Windows:Turn off wake-timersWake timers are timers that may wake your computer on a set time. Disabling this make this impossible until you re-enable it.If you use wake-timers to automatically wake your computer at 7 am or something, this will not work anymore.Right-click Windows button. SelectEnergy-managementScroll down and click the link forExtra energy-settingsClick theChange power-planto the right of the currently selected power-planClick theChange advanced energy-settingslinkScroll and findSleep->Allow activation timersSet it toNoorDisabledWindows:Turn off peripheral-devices access to wake your pcPeripheral-devices (like for example your keyboard) have access to wake your computer out of sleep-mode.
For something like a keyboard this makes sense. You press the space-bar and the computer wakes up, but there are other devices where this might make less sense. For example a network device may wake your computer?left-click the windows button. TypeCMDand chooseRun as administratorIt will prompt you if you did this and allow this... AllowType in the consolepowercfg -devicequery wake_armedand press enterIt gives you a list of all your peripheral devices that may wake up your computer out of sleep modeRight-click Windows button. SelectDevice-managementFind all the devices (or the onces you want to disable), in the list and double-click themFind the tab called:Energy-managementand uncheck the checkbox forThis device may wake the computer out of sleepmodeNote: Some devices like mice and keyboards show up multiple times(that is normal), but not all have this tab(that is also normal). Find the onces that do and disable those
Package: os-benchmark
OS-Benchmarkis a simple tool to measure object storage operations.FeaturesBenchmarksUpload TimingDownload TimingApache BenchmarkInstallpip install os-benchmarkUsageConfigurationYou need to specify a YAML config file such as following:---
my_exoscale:                                  # Configuration ID
    driver: exoscale                          # Driver key
    aws_access_key_id: mykey                  # Drivers params
    aws_secret_access_key: mysecrect
    endpoint_url: https://sos-ch-dk-2.exo.ioIn command line,--config-fileand--config-rawcan be used to make the
choice, else~/.osb.yml, then/etc/osb.ymlwill be used.Command lineos-benchmark --help
usage: os-benchmark [--config-file CONFIG_FILE] [--config-raw CONFIG_RAW]
                  [--config-name CONFIG_NAME] [-v {0,1,2}] [-i]
                  {help,create-bucket,list-buckets,delete-bucket,list-objects,upload,delete-object,clean-bucket,clean,time-upload,time-download}
                  ...

positional arguments:
  {help,create-bucket,list-buckets,delete-bucket,list-objects,upload,delete-object,clean-bucket,clean,time-upload,time-download}
                      Sub-command

optional arguments:
  --config-file CONFIG_FILE
                      Specify a configuration file to use.
  --config-raw CONFIG_RAW
                      Provide a raw configuration as JSON instead of a
                      stored file.
  --config-name CONFIG_NAME
                      Select a driver configuration to use.
  -v {0,1,2}, --verbosity {0,1,2}
                      Verbosity level; 0=minimal output, 1=normal output
                      2=verbose output
  -i, --noinput       Disable any promptTest example: File uploading$ os-benchmark time-upload --object-size 1 --object-number 5
version         0.1
operation       upload
ops             5
time            2.701468
rate            1.850845
bw              0.000002
object_size     1
total_size      5
avg             0.539478
stddev          0.126987
med             0.537267
min             0.415063
max             0.744637ContributeThis project is created with ❤️ for free byCloud Mercatounder BSD License. Feel free to contribute by submitting a pull request or an issue.
Package: osbnr
OSBNR: One Side Behavioral Noise ReductionOSBNR (One Side Behavioral Noise Reduction) is an approach developed to deal with the problem of class imbalance in the presence of behavioral noise. This method is based on two stages: clustering and reduction. In the clustering stage, k-means algorithm is used to group similar instances of the minority class into multiple behavior clusters, each with distinct characteristics in the high-dimensional feature space. In the reduction stage, instances of the majority class, considered as behavioral noise, which overlap with the behavior clusters of the minority class are selected and eliminated.The main steps of OSBNR approach are:Separate: Separate the majority class and minority class from the original training dataset.Clustering: Apply k-means clustering to group similar minority class instances into behavior clusters.Reduction: Eliminate the noisy majority class instances that overlap with the behavior clusters of the minority class.Combination: Combine the reduced majority class instances with the minority class instances to form a new training dataset.Accurate identification and elimination of these instances maximize the visibility of the minority class instances and at the same time minimize excessive elimination of data.This approach has been applied to various domains, including fraud detection, anomaly detection, and imbalanced datasets.UsageTo use OSBNR, import the OSBNR class from the osbnr module and create an instance of the class with the desired number of clusters (k). Then, call the fit_resample method of the OSBNR instance, passing in the feature matrix (X) and label vector (y) of the original dataset. The method will return the balanced feature matrix and label vector.from osbnr import OSBNR

osbnr = OSBNR(k=5)  # Create an instance of the OSBNR class with k=5 clusters
X_resampled, y_resampled = osbnr.fit_resample(X, y)  # Resample the dataset using OSBNRInstallationTo install OSBNR, use pip:pip install osbnrLicenseThis project is licensed under the GNU License - see the LICENSE file for details.PublicationsEl Hajjami, S., Malki, J., Bouju, A., & Berrada, M. (2020, October). A machine learning based approach to reduce behavioral noise problem in an imbalanced data: application to a fraud detection. In 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) (pp. 11-20). IEEE.El Hajjami, S., Malki, J., Berrada, M., & Fourka, B. (2020, November). Machine learning for anomaly detection. performance study considering anomaly distribution in an imbalanced dataset. In 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) (pp. 1-8). IEEE.El Hajjami, S., Malki, J., Bouju, A., & Berrada, M. (2021). Machine Learning Facing Behavioral Noise Problem in an Imbalanced Data Using One Side Behavioral Noise Reduction: Application to a Fraud Detection. International Journal of Computer and Information Engineering, 15(3), 194-205.
Package: osbpy
osbpyosbpy is a simple library for creating storyboards for the game osu!. It can help you automate the process of creating a high number of storyboard objects, but even to make very advanced effects.What can it do?It supports all functions of storyboarding that osu! does, including triggers and loops. The only exception are the storyboard hitsounds, because it has no use for people who create storyboards.Additionally, you can generate an audio spectrum data from an audio file and use that with your objects to create a visual spectrum very easily.Am I allowed to change the library?Of course. The library is using MIT license. As long as you follow its simple conditions, I don’t mind you using or distributing this library in any way.If you want to modify other libraries that this library is using, please follow their license. This license only applies to files that are included on this repository.RequirementsThis library requires:Python (3.7+), I will always update osbpy to the newest version.numpy 1.15.4+matplotlib 3.0.2+scipy 1.1.0+
All these libraries can be easily installed with pip.Installationpip install osbpy
Package: osbrowser
See Moments.path for updated versionOSBrowser is a python module that provides an object oriented interface to your computer’s local filesystem.  It leverages python’s native os and os.path libraries to give a higher level interface to the system.  Starting at the level of Nodes on a filesystem, it works up to higher level objects such as Images and Playlists.I like to think of it as a python based file system explorer/browser/finder without a graphical user interface.OSBrowser can utilize the Moments module for logging and meta data generation.  This ensures metadata will not get locked into proprietary formats.
Package: osc2mqtt
AnOSCtoMQTTbridge based onpylibloandpaho-mqtt.Quick Startpip install osc2mqttGetosc2mqtt.iniand edit theoptionssection and set your MQTT broker
host and port and, optionally, an OSC host and port as a reveiver.Runosc2mqtt -vand start publishing MQTT messages or sending OSC
messages toudp://localhost:9001/.Watch debugging output for the MQTT topics, OSC addresses and the kind of
MQTT message payload and OSC arguments the messages have.Add conversion rules toosc2mqtt.inias needed. TheDEFAULTsection has
helpful comments. Also change thesubscriptionsoption to only receive the
MQTT messages you’re interested in.Quitosc2mqttwith Control-C and restart it to try out your new
configuration. Repeat from step 4, if necessary.
Package: osc4py3
Author:Laurent Pointal <laurent.pointal@limsi.fr> <laurent.pointal@laposte.net>Organization:CNRS - LIMSICopyright:CNRS - 2013-2018license:CeCILL-2.1Version:1.0.8Module documentationSubversion repository & bug tracking(on french academic SourceSup site).Developer pageNoteTesters feedback welcome. This development was finally not tested in its
initial planning, any problem / bug / info are welcome.What is it?This module is an implementation ofOpen Sound Control (OSC)message
transport protocol within a Python3 package.Manage different sides of OSC in possibly different contexts:encoding/decoding of OSC message packets (including bundles)routing of incoming messages based on selector regexps or globbingtimed messages with possible delay periodnamed client/server for sending/subscribingdifferent scheduling models (single process, totally multithread, only multithread for communications)extra processing of packets (hack points to encrypt/decrypt, sign/verify…)Note: routing, timed messages, named client/server, scheduling models make a complex system
(see the “big picture” in doc). The oscbuildparse module of osc4py3 package can be used as
is and provides nice OSC packets encoding/decoding functions usable in your own message
transmission scheme.InstallationUnless someone built a package for your OS distro, the simplest procedure
is to usepipto install the module:pip install osc4py3If you have no admin access to install things on you computer, you may install
a virtualenv and run pip inside this virtual env, or you can do a local user
installation:pip install –user osc4py3
Package: oscal-pydantic
OSCAL PydanticDescriptionA simple module that contains pydantic datamodels representing the OSCAL standard. They are built from the OSCAL models published by NIST athttps://github.com/usnistgov/OSCALSeveral Python projects include data models, but importing a large project just to get access to the datamodel represents a significant overhead. This module simply provides the models.Installationpip install oscal-pydanticUsageTo import a specific model, include it in your python file:e.g.: from oscal_pydantic import catalogAlternatively, you can import the complete OSCAL schema:from oscal_pydantic import completeAfter importing, you should be able to define OSCAL objects that support pydantic's rich validation rules.LicenseThis code is released under the [CC0 1.0 Universal Public Domain Dedication] (https://creativecommons.org/publicdomain/zero/1.0/).
Package: oscal-pydantic-v2
OSCAL PydanticDescriptionA simple module that contains pydantic datamodels representing the OSCAL standard. They are built from the OSCAL models published by NIST athttps://github.com/usnistgov/OSCALSeveral Python projects include data models, but importing a large project just to get access to the datamodel represents a significant overhead. This module simply provides the models.Installationpip install oscal-pydantic-v2UsageTo import a specific model, include it in your python file:e.g.: from oscal_pydantic import catalogLicenseThis code is released under the [CC0 1.0 Universal Public Domain Dedication] (https://creativecommons.org/publicdomain/zero/1.0/).
Package: oscar
Python interface for OSCAR dataThis is a convenience library to access World of Code data
(WoC; it was referred internally as oscar while development, hence the name).
Since everything is stored in local files it won't work unless you have access
to one of the WoC servers.InstallationNormally it is preinstalled on WoC servers. To install manually,
e.g. to a virtual environment not using system packages, just use:python3setup.pybuild_ext
python3setup.pyinstall--userInstalling from sources requires extra tools to compile (cython,
manylinux docker image etc), but still possible. Refer to theBuild pagein the reference.ReferencePlease seehttps://ssc-oscar.github.io/oscar.pyfor the full reference.
Package: oscar-ai
Steps to build packagepython3 -m pip install --upgrade buildpython3 -m buildCreate test pypi accoundhttps://test.pypi.org/account/register/Create and save private tokenhttps://test.pypi.org/manage/account/#api-tokenspython3 -m pip install --upgrade twinepython3 -m twine upload --repository testpypi dist/*Installation from test.pypi.org$ python3 -m venv venv
$ source venv/bin/activate
$ python -m pip install --index-url https://test.pypi.org/simple/ --no-deps oscar-aiTest it$ python

>>> from oscar_ai.actions.speak import Speaker
>>> oscar = Speaker()
>>> oscar.say_something()Unistall$ pip uninstall oscar-ai
Package: oscar-corpus-downloader
OSCAR Corpus DownloaderSimple tool to download the OSCAR corpus.1. InstallationInstallation can be done usingpypi:$pipinstalloscar-corpus-downloader2. UsageSubmit an OSCAR access request following the procedure described on theproject page.Once you have received your credentials, you can use the command line interface to download an OSCAR corpus part.$exportOSCAR_USERNAME=username
$exportOSCAR_PASSWORD=password
$oscardownload--help
Usage:oscardownload[OPTIONS]Options:-u,--urlTEXTOSCARcorpusurl[required]-o,--output-dirTEXTOutputdirectory[required]--resumeResumedownload--helpShowthismessageandexit.

$oscardownload\--urlhttps://oscar-prive.huma-num.fr/2301/fr_meta\-o./oscar-fr
Package: oscar.flag
oscar.flagprovides extensible, namespaced flags which can be
parsed from environment variables, command-line arguments and config
files.Flags are declared where they are used in an application or library,
and they are accessed through a namespace matching their fully
qualified module path.Documentation lives atRead the Docs, the code onGitHub.ExampleApplication entry-point:import sys
from oscar import flag

import other_module


FLAGS = flag.namespace(__name__)
FLAGS.some_int = flag.Int('some integer value', default=1)


if __name__ == '__main__':
    flag.parse_commandline(sys.argv[1:])
    flag.die_on_missing_required()

    print 'other_module.multiply_by(%d) = %d' % (
        FLAGS.some_int,
        other_module.multiply_by(FLAGS.some_int))other_module.py:from oscar import flag

FLAGS = flag.namespace(__name__)
FLAGS.multiplier = flag.Int('some integer', default=flag.REQUIRED)

def multiply_by(i):
    return i * FLAGS.multipliershell:$ python example.py
Missing required flags:
      [other_module.]multiplier
Usage of example.py:
__main__:
      [__main__.]some_int=None: some integer value

other_module:
      [other_module.]multiplier=<required>: some integer

# Note the namespaced reference --other_module.multiplier.
$ python example.py --other_module.multiplier=2 --some_int=3
other_module.multiply_by(3) = 6LicenseCopyright 2015 Mulberry Health Inc.Licensed under theApache License, Version
2.0.
Package: oscars-toolbox
oscars-toolboxA package for helpful general algorithms I've developed. See also my wesbite,https://oscars47.github.io/.Current functions as of version 0.0.3:trabbitcustom gradient descent algorithm to determine optimal params to minimize loss function.How to use: specify loss function, function to generate random parameters. Optionally, you can define bounds for the parameters, an initial list of parameters, the total number of iterations you want, the learning rate, the temperature (how often we seek a random solution), the tolerance for convergence (default is 1e-5), the size of the gradient step (default is 1e-5), and a boolean option verbose for whether to print out progress or not (default = True).Updates0.0.4trabbit:renamed the folder 'gradient_descent' -> 'oscars_toolbox' so now you can actually import the package as 'oscars_toolbox'0.0.3trabbit:renamedfrac->temperatureadded option forboundsof inputsadded parameter forgrad_stepsetverbose = Trueby default.0.0.2initial release
Package: oscar-vehicle-api
OSCAR vehicle APIРепозиторий содержит API низкоуровневой системы управления автомобилемAlpha, разрабатываемой в рамках проектаOSCAR, для беспилотных транспортных средств.Установка с PyPIpip3 install --user oscar_vehicle_apiУстановка из исходниковgit clone https://gitlab.com/starline/oscar_vehicle_api.git && cd oscar_vehicle_api
pip3 install --user -e .Использованиеimport oscar_vehicle_api as oscar
vehicle = oscar.Vehicle(“/dev/ttyACM0”)
vehicle.drive()
vehicle.steer(20)
vehicle.move(10)
vehicle.manual()
vehicle.led_blink()
vehicle.emergency_stop()
vehicle.recover()
vehicle.left_turn_signal()
vehicle.right_turn_signal()
vehicle.emergency_signals()
vehicle.turn_off_signals()
vehicle.get_vehicle_speed()
Package: osc-client
OSC ClientAn OpenStreetCam.org client in Python for ease in requesting publicly available resources from the platform.Originally created as part of the following volunteer AI challenge:Omdena + iRAP: Preventing Road Crashes and Saving Lives(https://omdena.com/projects/ai-road-safety/).Installationpipinstallgit+https://github.com/d33bs/osc_client@main#egg=osc_clientUsageSimple Request Examplefromosc_clientimportOSCosc=OSC()photos_list=osc.get_photos_from_point(lat=34.94083565649461,lng=-82.8662492514243)photos_list[0]TestingVarious tests may be found under the ./tests directory within this repo. See below for a straightforward example on running those tests.pipinstallpytest
pytesttests
Package: oscclip
osccliposcclipis a little, zero dependency python utility which utilizes the system clipboard via OSC52 escape sequences.
Using these sequences, the system clipboard is accessible via SSH as well.
Terminal multiplexers, such astmuxandscreenare supported.ExamplesSetting the clipboard$ echo "Foo" | osc-copySetting the clipboard and bypass terminal multiplexers$ echo "Foo" | osc-copy --bypassReading the clipboard$ osc-paste
FooTested TerminalsalacrittyfootFor a list of terminals that support OSC52, seethis table.CaveatstmuxThere is abugintmuxDue to thisosc-pastedoes not work withtmux < 3.3running infoot.In order to make--bypasswork,allow-passthroughmust be enabled.
Check the manpage oftmux.osc-copyissues a warning tostderrwhen this option is not set and--bypassis present.InstallationArch Linux$ paru -S oscclipRun via poetryCheck if your distribution providespoetryvia its package management system!
It might be calledpython-poetry,python3-poetryor similar!Otherwise:https://python-poetry.org/docs/#installation$ poetry install [--no-dev]
$ poetry run ocs-copy--no-devomits the development dependencies, such as static code checkers.
Package: oscer
oscerAverysimple command line OSC message sender.InstallationYou will need Python>=3.7 and pythonosc.The simplest way to install is using pip:$ pip install oscerExample$ oscer
usage: oscer [-h] [--ip IP] [--port PORT] OSC_ADDR [OSC_VALUE ...]

positional arguments:
  OSC_ADDR     OSC address to send
  OSC_VALUE    OSC values

optional arguments:
  -h, --help   show this help message and exit
  --ip IP      The ip of the OSC server
  --port PORT  The port the OSC server is listening onSo:$ oscer --ip localhost --port 57120 /hello argument1 "another argument" 3It will try to guess the data type correctly, otherwise send as a string argument.You can use the environment variablesOSCER_HOSTandOSCER_PORTalso, so you don't need to specify the hostname and port every time.
Package: osc-gen
osc_gen is a Python library for creating and managing oscillator wavetables.Functionality includes:Generating common waveforms (sine, saw, square, etc.)Oscillator effects (waveshaping, distortion, downsampling, etc.)Resynthesising or slicing audio from wav files or other sourcesSaving wavetables to a wav file for use in samplersSaving wavetables in .h2p format for use in the u-he Zebra2 synthesiser
Package: oschameleon
OS Fingerprint Obfuscation for modern Linux  Kernels.Author: Anton Hinterleitner is111012@fhstp.ac.atDescription: Fools the probes of nmap scannerPrerequisites:LinuxPython 2.6+python-nfqueue (apt-get install python-nfqueue)scapy (pip install -r requirements.txt)Note: This script flushes iptables before and after usage!
Package: oscillo
oscillo记录命令执行过程中对系统产生的负载，以图片的形式展现出来Record the system load at the execution of the command line and display it graphically安装方式 / InstallationInstall from pypipipinstalloscilloInstall from localpythonsetup.pyinstall使用方式 / Usage通过命令行参数启动 / Boot start by command line parameter命令行参数的格式是"<name>: <command [args]>"：name: 命令行的别名/id (任意字符串)，当--commands/-c参数指定多个命令时，该值将作为命令的唯一标识，不可重复command [args]: 需要测试资源消耗的命令，比如gzip file.ext示例如下，监控gzip压缩一个文件时耗费的cpu、memory和时间：oscillo -c 'gzip: gzip file.ext' -o output-file-c 代表将执行一个linux cmd 命令。参数后面可以跟以空格隔开的多个参数-o 结果输出文件:命令执行完成后，会在当前目录下生成一个<output-file>.log文件。文本结构是json 格式. 数据结构如下{
  "test": {
            "elapsed": 0.022143125534057617,  //总执行时间
            "cpu": [], 
            "memory": []
          }
}同时会产生一个<output-file>.png文件，<output-file>由-o参数指定，默认值为metrix-c/--commands可以接受多个参数，以空格隔开，任务会线性执行。使用场景：对比多个命令对资源的消耗e.g.:
对比gzip和tar命令对资源的消耗：oscillo-c't1: gzip file.ext''t2: tar czf target.tar.gz file1'-ooutput在控制台上，oscillo会打印summary信息，其中包含命令的耗时、最大内存使用、最大cpu使用、退出码等，效果如下：通过配置文件启动 / Boot start by config file当命令很长或者很多时，可以使用配置文件启动
Whe the commands is too many, you can boot start by config fileoscillo--config</path/to/config-file.yml>[-g]配置文件模板 / Config fileThe command line in commands will be executed in order# Demo
commands:
  -
    name: gzip
    cmd: gzip big-file

  -
    name: tar
    cmd: tar -zcf tmp.tar.gz big-file

output: gzip-and-tar-gz