Saravanan666/pypi-org huggingface dataset:
Package: kikuchipy
kikuchipy [ki-ko-chi-pai] is a library for processing, simulating and indexing of
electron backscatter diffraction (EBSD) patterns in Python, built on the tools for
multi-dimensional data analysis provided by the HyperSpy library.DeploymentBuild statusDocumentationMetricsActivityCitationCommunityLicenseDocumentationRefer to thedocumentationfor detailed installation
instructions, a user guide, and thechangelog.Installationkikuchipy can be installed withpip:pipinstallkikuchipyorconda:condainstallkikuchipy-cconda-forgeYou can also visitPyPI,Anaconda, orGitHubto download the source.Further details are available in theinstallation guide.Citing kikuchipyIf you are using kikuchipy in your scientific research, please help our scientific
visibility by citing the Zenodo DOI:https://doi.org/10.5281/zenodo.3597646.Contributors ✨Thanks go to these wonderful people (emoji key):AndreasBugten📖Carter Francis📖Eric Prestat💻🚧🐛Håkon Wiik ÅnesLars Lervik🐛💻📖💬👀⚠️Magnus Nord📖Ole Natlandsmyr💻📖💬👀⚠️Tina Bergh💻👀Zhou Xu🐛💻📖⚠️erlenmos🐛This project follows theall-contributorsspecification.
Contributions of any kind welcome! Please see ourcontributing guidefor information
on how best to contribute (or just explain what you want to doin an issue).
Package: killdupes
Python version support: CPython 2.6, 2.7, 3.2, 3.3, 3.4 and PyPy.How it workskilldupesscans your filesystem to find duplicate files, partial files
and empty files.It performs n:n comparison of files through md5 hashing and heavy use of
dictionaries.
Execute with wildcard, or input file containing file names to check.The method:Scan all files, find the smallest.Readread sizeamount of bytes (equal to the remaining size of the
smallest file, or at mostCHUNKsize) from all files intorecords.Hash all records, use hashes as keys intooffsets[current_offset]dict.Files in the same bucket are known to be equal up to this offset.Continue until at least two files remain that are still equal at all
offsets.Equal files are either a duplicate case (if they are the same size), or
one is partial relative to the other (if not the same size).Memory consumption should not exceedfiles_in_bucket * read_size.The algorithm adapts to file changes; it will read all files until eof regardless
of the filesize as recorded at startup.Installation$pipinstallkilldupesUsage$killdupes.py'tests/samples/*'Emptyfiles:X0.0B14.03.1417:39:48tests/samples/emptyIncompletes:=2.0B14.03.1418:17:43tests/samples/fullX1.0B14.03.1418:17:26tests/samples/partialDuplicates:=2.0B14.03.1418:17:43tests/samples/fullX2.0B14.03.1418:17:37tests/samples/full2Killfiles?(all/empty/incompletes/duplicates)[a/e/i/d/N]If there are many files to scan it will display a progress dashboard while
working:176.1KB|Offs0.0B|Buck1/1|File193868/600084|Rs1.0BThe dashboard fields:Total bytes readCurrent offset of readingCurrent number of bucketsFile/files in this bucketReadsize at this offset
Package: killeengeo
KilleenGeo2D and 3D geometry in Python. This library was originally a part of theDeepDRRpackage, but has been split out into its own package
for easier use in other projects. It contains a lot of custom implementations with no overarching
design philosophy, other than getting me through my PhD.InstallationpipinstallkilleengeoUsageimportkilleengeoasgeoDocumentationDocumentation is (maybe) available atkilleengeo.readthedocs.io.
Package: killercoda-cli
killercoda-cliTable of ContentsInstallationIntroductionDocumentationRequirementsExample usageDisclaimerLicenseInstallationpip install killercoda-cli[!NOTE]If there is an installation permissions error [Error 13], consider installing
with--userflag or adding./local/binto thePATHvariable.IntroductionThe interactivekillercodascenarios are a great way learn new technologies in
anhands-onapproach. However, creating the scenarios and managing can be
tedious and time consuming.Thekillercoda-clisolves some of those problems by:Adding a new step after the existing last step and creating a directory for
including foreground and background scripts placeholders.Renaming and re indexing step files and directories allowing for inserting a
step in between existing steps and moving contentdownUpdating theindex.jsonfile to reflect changes in step order and titles.DocumentationAutogenerated API documentation generated inpdocavailable
at:https://piotr1215.github.io/killercoda-cli/killercoda_cli/cli.html.RequirementsThe tool must be run in a directory containing step files or directories (e.g. step1.md, step2/).Anindex.jsonfile must be present in the directory, which contains metadata about the steps.Example usageSuppose you have a scenario directory with the following structure:.
├── index.json
├── step1.md
└── step2
    └── step2.mdAnd you want to insert a new step betweenstep1.mdandstep2/, titled "New Step".Runkillercoda-cli.Enter the title for the new step: "New Step".Enter the step number to insert the new step at: 2.After running the tool, your directory structure will be updated to:.
├── index.json
├── step1.md
├── step2
    └── step2.md (previously step1.md content)
└── step3
    └── step3.md (previously step2.md content)Theindex.jsonfile will also be updated to include the new step and renumber existing steps accordingly.Before:{"steps":[{"title":"Step 1","text":"step1.md"},{"title":"Step 2","text":"step2/step2.md"}]}After:{"steps":[{"title":"Step 1","text":"step1.md"},{"title":"New Step","text":"step2/step2.md"},{"title":"Step 2","text":"step3/step3.md"}]}DisclaimerThis is an my personal project to easier create and manage killercoda scenarios.
Check outkillercodainteractive scenarios to learn more about
the service.Licensekillercoda-cliis distributed under the terms of theMITlicense.
Package: kill-hogs
Kill hogsWe at team HPC of the university of Groningen run job schedueling clusters.
Some users are running their programs on the login node of these clusters instead of submitting jobs. This leads to a high load on the login node, which leads to unhappy users who are unable to submit jobs.
"kill hogs" is a crude attempt at mittigating these problems. It is a cronjob that checks the resources (cpu and ram) that are used by each user. If they reach a certain treshold, all the user's processes above a minimum treshold are killed.
The user is informed of this via a message in the terminal.
A message is also send to slack to inform us.this program might kill processes you don't want killed and lock you outInstallation.We install the cronjob via ansible on a centos7 host instead of installing the requirements with pip, we opt for the following yum packages.python36-requestspython36-psutilThe cronjob looks like this:*/2 * * * * root /usr/bin/python36 /opt/kill_hoggs/kill_hoggs.py --slackRun testspython-munittestunittests.test_kill_hogs# Or if you want coverage information.coveragerun-munittestunittests.test_kill_hogscoveragereport-mkill_hogs.py
Package: killport
killportKill processes using a given port on your laptop.Installationpip install killportUsagekillport 1234kills any process using the 1234 port on your laptopkillport 1234 2345kills any process using either the 1234 or 2345 ports on your laptopkillport 1234 --view-onlyDisplays the processes that would be deleted, but doesn't actually delete them.How does this differ from...freeport-- Windows support, since this usespsutilrather thanlsofkill-port-- Very similar, but this is available in PyPI vs NPM
Package: killprocess-cli
killprocess-cliIntroductionkillprocess-cliis a Python module designed to simplify the process of terminating specific processes by name on Linux operating systems. The module uses theps auxandgrepcommands to identify the process ID of all associated processes, and then uses thekillcommand to terminate them.InstallationTo installkillprocess-cli, simply run the following command:pip3 install killprocess-cliUsageOnce installed, you can usekillprocess-clito terminate all instances of a specific process by typing the following command:killprocess <process-name>FeedbackIf you have any feedback or suggestions for improvement, please don't hesitate to reach out to me. My name is Tushar Pandey, and I'm the developer ofkillprocess-cli. You can contact me attushark39@gmail.com.Licensekillprocess-cliis licensed under the MIT License. See theLICENSEfile for more information.
Package: kill-thread
Kill Thread只有一个功能，杀死线程！安装pip install kill-thread示例importtimeimportdatetimeimportthreadingfromkill_threadimportkill_threaddefprint_time():whileTrue:time.sleep(1)print(datetime.datetime.now())# 生成启动一个每隔一秒打印当前时间的线程thread=threading.Thread(target=print_time)thread.start()# 十秒后杀死线程time.sleep(10)kill_thread(thread)# 阻塞主线程观察打印时间的线程是否被杀死input('按任意键终止主线程')
Package: kill-timeout
Limit the function execution timeThis module provides a decorator which runs the function in a separate
multiprocessing.Process and sends SIGKILL after the specified timeout if the
function didn't complete.RequirementsPython 3.7+ (needed for multiprocessing.Process.kill)Installpipinstallkill-timeoutUsagefromkill_timeoutimportkill_timeoutlimit_in_seconds=5@kill_timeout(limit_in_seconds)deflong_running_function(**parameters):"""Function which makes some computationsIt could take too long for some parameters."""...try:result=long_running_function(iterations=9001)print("Function returned:%r"%result)exceptTimeoutError:print("Function didn't finished in%dseconds"%limit_in_seconds)exceptException:print("Function failed with its internal error! Its original traceback:")raise
Package: killtimer
Killtimer - do more working less!This utility helps limit the amount of time devoted to work by keeping track of clearly defined work periods.
It's like a timer which cannot be stopped but will kill the passed command forcing the user to finish the job.This may sound counterintuitive, but a lot of studies show that limiting time for a task forces the person to use the time more effectively.FeaturesCLI interfaceThree configurable effort levels:MinimalWorkOvertimeKeep track of user-provided command and kill it if it has been running too long (after overtime)Utilizes desktop notifications to inform users about the finished periodOptionally can play a sound when minimal effort/work period is doneAllow storing amount of work done as CSV file for further analysisInstall$pipinstall[--user]killtimerUsageusage: killtimer [-h] [-m duration] [-w duration] [-o duration] [-l log_file] [-t title] [-s sound_file] [command [command ...]]

Close application when time runs out

positional arguments:
  command               Executable (with arguments) to run (default: None)

optional arguments:
  -h, --help            show this help message and exit
  -m duration, --minimal-effort duration
                        Minimal work duration (default: 0:10:00)
  -w duration, --work duration
                        Proper work duration (default: 1:00:00)
  -o duration, --overtime duration
                        Overtime duration (default: 0:15:00)
  -l log_file, --log log_file
                        Log file where to store amount of work done (default: None)
  -t title, --title title
                        Title to display above progress bars and configuration (default: None)
  -s sound_file, --sound sound_file
                        Sound file to play when minimal effort or work period is reached (default: None)Usually you would want to create alias in your*rcfile like:aliasblender-work="killtimer -m 10m -w 1h -o 10m -t 'Creative work' -l /path/to/worklog.csv blender"
Package: kilojoule
kilojouleConvenience functions for solving thermodynamic and heat transfer problemsInstallationpip install kilojouleDescriptionkilojoule is a Python module/package to provide convenience functions
for performing thermodynamic and heat transfer calculations.  The
primary use case for these functions is in scripts written to solve
specific problems.  To that end, simplicity of syntax for variables
and readability of output are prioritized over speed.  Wrapper
functions are provided to pull thermodynamic properties from multiple
sources (CoolProp for real fluids and PYroMat for ideal gases) using
a consistent syntax regardless of the source and including units
(supplied by the Pint library) as an integral part of all calculations.Variable names are chosen to be consistent with those commonly used in
the mechanical engineering texts.
Package: kilonova-heating-rate
kilonova-heating-rateThis is a Python package to calculate kilonova light curves using the
Hotokezaka & Nakar (2019) model, which assumes radioactive heating, a power-law
velocity profile, and gray opacities that are a piecewise-constant function of
velocity.This Python package is based on the original source code release from the 2019
paper (https://github.com/hotokezaka/HeatingRate), but includes the following
enhancements:Easy to installwithPip, the Python package manager.Physical unitsare integrated withAstropy, the community Python
package for astronomy.Flexiblespecification of opacities: either constant, or piecewise
constant as a function of ejecta velocity.Fastdue to the use ofNumpyto evaluate the right-hand side of the
system of ordinary differential equations that is solved to evalute the light
curve.To citeIf you use this work to produce a peer-reviewed journal article, please cite
the following papers:Korobkin, O., Rosswog, S., Arcones, A., & Winteler, C. 2012, "On the
astrophysical robustness of the neutron star merger r-process,"Monthly
Notices of the Royal Astronomical Society, 426, 1940.https://doi.org/10.1111/j.1365-2966.2012.21859.xHotokezaka, K. & Nakar, E. 2020, "Radioactive Heating Rate ofr-process
Elements and Macronova Light Curve,"Astrophysical Journal, 891, 152.https://doi.org/10.3847/1538-4357/ab6a98To installInstallation is easy withPip:$ pip install kilonova-heating-rateTo useSee example code inexample.py.
Package: kilonovanet
KilonovaNet: Kilonova Surrogate ModellingA conditional variational autoencoder (cVAE) framework for producing continuous
surrogate spectra for kilonova models.This package provides the interface to predict spectra. It does not provide an
interface to do the data prep for training and training itself. The currently
trained and provided models are:M. Bulla BNS ModelsM. Bulla BHNS ModelsD.Kasen BNS ModelsThis work requires the use ofpyphot; pyphot requires hdf5.
See their installation intructions to see how to install that for your system. Then, if you install
KilonovaNet via pip, dependencies (including pyphot) should install properly.InstallationInstall via pip:pip install kilonovanetUsageIn order to produce surrogate spectra, you will need to specify the model and torch files.These are not included in this package, you must download them separately fromthe KilonovaNet githubfrom data and model folders.After you have the files in your system, you can produce spectra with the following:importkilonovanetimportnumpyasnpmetadata_file="data/metadata_bulla_bns.json"torch_file="models/bulla-bns-latent-20-hidden-1000-CV-4-2021-04-21-epoch-200.pt"times=np.array([1.2,2.2])physical_parameters=np.array([1.0e-2,9.0e-2,3.0e1,3.0e-1])model=kilonovanet.Model(metadata_file,torch_file)spectra=model.predict_spectra(physical_parameters,times)In order to produce some photometric observations, the following have to be specified:the modelthe corresponding parameters of the model (see their papers, repositories, etc.)the times post-merger to produce the observationsthe filters in which to produce the observationsI have specified some filters in the github folder filter_data, but any filter transmission curves should work properly.After you have filter profiles, use the following to produce synthetic photometric observations:importkilonovanetimportnumpyasnpmetadata_file="data/metadata_bulla_bns.json"torch_file="models/bulla-bns-latent-20-hidden-1000-CV-4-2021-04-21-epoch-200.pt"filter_lib="data/filter_data"times=np.array([1.2,1.2,1.2,2.2,2.2,2.2,2.2])filters=np.array(["LSST_u","LSST_z","LSST_y","LSST_u","LSST_z","LSST_y"])distance=40.0*10**6*3.086e18# 40 Mpc in cmphysical_parameters=np.array([1.0e-2,9.0e-2,3.0e1,3.0e-1])model=kilonovanet.Model(metadata_file,torch_file,filter_library_path=filter_lib)mags=model.predict_magnitudes(physical_parameters,times=times,filters=filters,distance=distance)If you intend to use the same set of observations often, e.g. when doing an
MCMC-based fit, you can specify all of them in anObservationsobject and
then simply callmodel.predict_magnitudes(physical_parameters).
Package: kilovolt
Kilovolt clientPython client for Kilovolt servers, supports Kilovolt Protocol v9+Getting startedpip install kilovolt(onPyPI)Example usageimportasyncioimportosfromkilovoltimportKilovoltClientasyncdefconnect():kv=KilovoltClient("ws://localhost:4337/ws",os.getenv("KILOVOLT_PASSWORD"))awaitkv.connect()# Subscribe to keyawaitkv.subscribe("twitch/ev/message",lambdakey,val:print(val))# List all keys that begin with "twitch"print(awaitkv.list("twitch"))asyncio.run(connect())LICENSEKilovolt client is licensed under ISC, seeLICENSEfor more details.
Package: kilroy-face-client-py-sdk
kilroy-face-client-py-sdkSDK for kilroy face clients in Python 🧰InstallingUsingpip:pipinstallkilroy-face-client-py-sdkUsagefromkilroy_face_client_py_sdkimportFaceServiceservice=FaceService(host="localhost",port=10000)awaitservice.get_metadata()
Package: kilroy-face-py-shared
kilroy-face-py-sharedshared code for kilroy face SDKs in Python 🤝OverviewThis package contains code shared by SDKs related to faces.
Mostly it's just a bunch of utilities and dataclasses.InstallingUsingpip:pipinstallkilroy-face-py-sharedMessagesMessages are dataclasses that are used in the APIs.
They are automatically generated from the protobuf definitions.PostsPosts arepydanticmodels that are used to represent various types of posts.
There are definitions for:TextOnlyPostImageOnlyPostTextAndImagePostTextOrImagePostTextWithOptionalImagePostImageWithOptionalTextPostModelsOne useful thing this package provides is aSerializableModelclass.
It's a base class forpydanticmodels
that can be serialized to and from JSON
with a proper case convention.
Package: kilroy-face-server-py-sdk
kilroy-face-server-py-sdkSDK for kilroy face servers in Python 🧰InstallingUsingpip:pipinstallkilroy-face-server-py-sdkUsagefromkilroy_face_server_py_sdkimportFace,FaceServerclassMyFace(Face):...# Implement all necessary methods hereface=awaitMyFace.build()server=FaceServer(face)awaitserver.run(host="0.0.0.0",port=10000)
Package: kilroy-module-client-py-sdk
kilroy-module-client-py-sdkSDK for kilroy module clients in Python 🧰InstallingUsingpip:pipinstallkilroy-module-client-py-sdkUsagefromkilroy_module_client_py_sdkimportModuleServiceservice=ModuleService(host="localhost",port=11000)awaitservice.get_metadata()
Package: kilroy-module-huggingface
kilroy-module-huggingfacekilroy module using Hugging Face models 🤗InstallingUsingpip:pipinstallkilroy-module-huggingfaceUsageTo run the module server, install the package and run the following command:kilroy-module-huggingfaceThis will start the face server on port 11000 by default.
Then you can communicate with the server, for example by usingthis package.
Package: kilroy-module-py-shared
kilroy-module-py-sharedshared code for kilroy module SDKs in Python 🤝OverviewThis package contains code shared by SDKs related to modules.
Mostly it's just a bunch of utilities and dataclasses.InstallingUsingpip:pipinstallkilroy-module-py-sharedMessagesMessages are dataclasses that are used in the APIs.
They are automatically generated from the protobuf definitions.PostsPosts arepydanticmodels that are used to represent various types of posts.
There are definitions for:TextOnlyPostImageOnlyPostTextAndImagePostTextOrImagePostTextWithOptionalImagePostImageWithOptionalTextPostModelsOne useful thing this package provides is aSerializableModelclass.
It's a base class forpydanticmodels
that can be serialized to and from JSON
with a proper case convention.
Package: kilroy-module-pytorch-py-sdk
kilroy-module-pytorch-py-sdkSDK for kilroy modules using PyTorch 🧰InstallingUsingpip:pipinstallkilroy-module-pytorch-py-sdkUsagefromkilroy_module_pytorch_py_sdkimportBasicModule,ModuleServerclassMyModule(BasicModule):...# Implement all necessary methods heremodule=awaitMyModule.build()server=ModuleServer(module)awaitserver.run(host="0.0.0.0",port=11000)
Package: kilroy-module-server-py-sdk
kilroy-module-server-py-sdkSDK for kilroy module servers in Python 🧰InstallingUsingpip:pipinstallkilroy-module-server-py-sdkUsagefromkilroy_module_server_py_sdkimportModule,ModuleServerclassMyModule(Module):...# Implement all necessary methods heremodule=awaitMyModule.build()server=ModuleServer(module)awaitserver.run(host="0.0.0.0",port=11000)
Package: kilter.protocol
Kilter ProtocolKilter is a framework for writingmail filters(known as "milters")
compatible with Sendmail and Postfix MTAs.  Unlike many previous milter implementations in
Python it is not simply bindings to thelibmilterlibrary (originally from the Sendmail
project).  The framework aims to provide Pythonic interfaces for implementing filters,
including leveraging coroutines instead oflibmilter's callback-style interface.Thekilter.protocolpackage contains the parsers and filter state machine for the
communications protocol used between the Mail Transfer Agents (MTA) and filters.Users looking for something as simple to use aslibmiltershould take a look
atkilter.service.What is understood about the wire protocol is documented inWire Protocol.Sendmail FiltersThe Sendmail filter (milter) API facilitates communication between a Mail Transfer Agent
(MTA) and arbitrary filters running as external services.  These filters can perform
a number of operations on received and outgoing mail, such as: virus scanning; checking
senders' reputations; signing outgoing mail; and verifying signatures of incoming mail.While the protocol was originally for filtering mail through a Sendmail MTA, Postfix has
also reverse engineered the protocol and supports most filters made for Sendmail.libmilterHistorically filters used thelibmilterlibrary supplied by the Sendmail project to handle
all aspects of communication with an MTA.  Filters simply registered callbacks for various
events then started the library's main loop. This approach makes implementing simple filters
in C easy for users, but makes writing "Pythonic" filters difficult, especially when a user
wishes to make use of async/await features.Use oflibmilterto implement filters is almost universal as it is a black-box; the
on-the-wire protocol used is undocumented and subject to change between versions, which
makes writing a third-party parser difficult.UsageMost users will be looking for an asynchronous API using Python coroutines: this is
available with thekilter.servicepackage.  The protocol handlers provided by this
package support asynchronous operation but does not handle any IO, thus have no awaitable
entrypoints. Instead a service that handles socket communication with MTAs passes bytes from
a socket to a protocol handler, which returns event objects.  Some of these events instruct
the service to pass bytes back to the socket, others are actionable in ways that are
implementation specific.  When an implementation wants to communicate with an MTA, it
registers messages with the protocol handler, which again returns event objects.