Saravanan666/pypi-org huggingface dataset:
Package: yasim-sctcr
yasim_sctcr-- Yet Another SIMulator for Single-Cell T-Cell Receptor Sequencing (scTCR-Seq)Markdown compatibility guideThis file is written inMyst-flavored Markdown, and may show errors on the default landing page of PYPI or Git Hostings. You can correctly preview it on generated Sphinx documentation orVisual Studio CodewithExecutableBookProject.myst-highlightplugin.IntroductionSingle-Cel T-Cell Receptor (TCR) Sequencing (scTCR-Seq) is an important method in studying the diversity and dynamics of T-cell populations in organisms. However, since number of publically available scTCR-Seq datasets are limited, researchers often needs to reconstruct TCR contigs from scRNA-Seq data, and a benchmark of such tools is required.This software provides an easy way to simulated Next-Generation Sequencing (NGS)-based scTCR-Seq using Illumina sequencer simulator. With realistic TCR contig constructed from statistics of1.08 millionhuman TCR V/J CDR3 sequences fromhUARdb, it supports simulation of TCR contigs from arbitrary cell number, sequencing depth, read length with Paired/Single End support. It also supports mixing scTCR-Seq data with simulated scRNA-Seq data, which allows calculation of both precision and sensitivity.InstallationUsing the Pre-Built Version from PYPIYou need a workingPythoninterpreter (CPython implementation) >= 3.7 (recommended 3.8) and the latestpipto install this software fromPYPI. Command:pipinstallyasim-sctcr==0.1.0You are recommended to use this application inside a virtual environment likevenv,virtualenv,pipenv,conda, orpoetry.Build from SourceBefore building from the source, get a copy of the latest source code fromhttps://github.com/WanluLiuLab/yasimusingGit:gitclonehttps://github.com/WanluLiuLab/yasim-sctcrOr, if you prefer to useGNU Wget.wget-oyasim-master.ziphttps://github.com/WanluLiuLab/yasim-sctcr/archive/refs/heads/master.zip
unzipyasim-master.zipYou need Python interpreter (CPython implementation) >= 3.7, latest PYPAbuild, andsetuptoolsto build this software. You are recommended to build the software in a virtual environment provided byvirtualenv, etc.Build and install the simulator using:cdyasim-sctcr
python3-mbuild
pipinstalldist/yasim-sctcr-0.1.0-py3-none-any.whlApart from above instructions, you should also installARTwhich is a general-purpose NGS DNA-Seq simulator and is available fromCondaandAPT. Tested versions are2.5.8 (June 6, 2016).
Package: yasirpedia-api
YasirPediaApi-PyOfficial Python Wrapper for YasirPedia API.How to install:pip install yasirpedia-apiUpdate to latest:pip install -U yasirpedia-apiFeatures From API:Google Search (google)TextPro (textpro)KBBI (kbbi)Layarkaca21 Scraper (lk21)Terbit21 Scraper (terbit21)IMDB Search (imdb_search)Facebook Downloader (fbdl)Examples:For examples go toexamples dir.Copyright:Copyright 2022 byYasirArisM.
Package: yasiu-image
Readme ofyasiu-imageModule with useful math functions that are missing in numpy or scipy.Installationpipinstallyasiu-imageSubmodulesimage- General functionalityfeatures- Transform images into features and backfilters- Functions that modify imageconvolve- 2D convolutionSequence reader Generatorsread_webp_frames- generatorread_gif_frames- generatorsave_image_list_to_gif- save sequence using PillowUse example:fromyasiu_image.imageimportread_gif_framesframes=list(read_gif_frames(path))All packages1. Native Package2. Math Package3. Image Package4. Pyplot visualisation Package
Package: yasiu-math
Readme ofyasiu-mathModule with useful math functions that are missing in numpy or scipy.Installationpipinstallyasiu-mathMoving averageImport:fromyasiu_math.mathimportmoving_averageUse example:moving_average(Union[list,"1d np array"],radius=1,padding="try",kernel_type="avg",kernel_exp=2)All packages1. Native Package2. Math Package3. Image Package4. Pyplot visualisation Package
Package: yasiu-native
Readme ofyasiu-nativeModule with useful measure time decorators.Installationpipinstallyasiu-nativeTime decoratorsmeasure_perf_time_decoratordecorator that measures time usingtime.perf_countermeasure_real_time_decoratordecorator that measures time usingtime.timeMeasuring timefromyasiu_native.timeimportmeasure_perf_time_decorator@measure_perf_time_decorator()deffunc():...@measure_perf_time_decorator(">4.1f")deffunc():...@measure_perf_time_decorator(fmt=">4.1f")deffunc():...Print buffering will impact your performance!Use with caution for multiple function callsFlexible decoratorDecorator that checks if decorated function was passed with()or notfromyasiu_native.decoratorsimportflexible_decorator@flexible_decoratordefcustom_decorator(func,decor_variable):defwrapper(*a,**kw):print(f"Decorated with{decor_variable}")returnfunc(*a,**kw)returnwrapper@custom_decoratordeftest_1():pass@custom_decorator()deftest_2():pass@custom_decorator(a=1)deftest_3(a=0):passConsole execution timernot here yet.All packages1. Native Package2. Math Package3. Image Package4. Pyplot visualisation Package
Package: yasiu-time
Readme ofyasiu.timeModule with useful measure time decorators.Installationpipinstallyasiu-timeTime decoratorsmeasure_perf_time_decoratordecorator that measures time usingtime.perf_countermeasure_real_time_decoratordecorator that measures time usingtime.timeImport:fromyasiu_time.timeimportmeasure_perf_time_decoratorPrint buffering will impact your performance!Use with cauction for multiple function callsUse examples@measure_perf_time_decorator()deffunc():...@measure_perf_time_decorator(">4.1f")deffunc():...@measure_perf_time_decorator(fmt=">4.1f")deffunc():...Console execution timernot here yet.All packages1. Time Package2. Math Package3. Image Package4. Pyplot visualisation Package
Package: yasiu-vis
Readme ofyasiu-visHigh level functions, to quickly visualise data frames.Installationpipinstallyasiu-visSequence reader Generatorssummary_plot- plot dataframe, possible grouping by columnsUse example:fromyasiu_vis.visualisationimportsummary_plot# df: pandas.DataFramesummary_plot(df)summary_plot(df,group="column-name")summary_plot(df,group="column-name",split_widnow="column")All packages1. Native Package2. Math Package3. Image Package4. Pyplot visualisation Package
Package: yasiu.visualisation
Readme ofyasiu.visualisationHigh level functions, to quickly visualise data frames.Installationpipinstallyasiu.visualisationSequence reader Generatorssummary_plot- plot dataframe, possible grouping by columnsImport:fromyasiu.visualisationimportsummary_plotUse example:summary_plot(df)summary_plot(df,group="column-name")summary_plot(df,group="column-name",split_widnow="column")
Package: yasjenkins
Yas Jenkins [Handler]A handler foryasto interact with a Jenkins instanceSetupTo simply run a an instance of yas with this handler,docker runmay be executed directly, albeit with a number of requisite environment variables:docker run --rm --tty \
    --env YAS_SLACK_TOKEN=xoxb-XXXXXXXXXXXX-XXXXXXXXXXXXXXXXXXXXXXXX \
    --env YAS_BOT_NAME=yasjenkins \
    --env YAS_JENKINS_URL=https://jenkins.example.com \
    --env YAS_JENKINS_USERNAME=yasjenkins \
    --env YAS_JENKINS_PASSWORD=superdupersecret \
    --env YAS_JENKINS_JOBS='{"MyJob": "do ci (?P<branch>\w+)"}' \
    --env YAS_HANDLER_LIST=yas.handlers.ignored_types_handler.,yas.handlers.not_talking_to_bot_handler.,yas.handlers.help_handler.,yas.handlers.identify_handler.,yasjenkins.,yas.handlers.rules_handler.,yas.handlers.default_handler. \
    schlueter/yasjenkins:latestThat handler list should be made DRYer sometime; the important bit for this module is theyasjenkins., but the
rest is mostly necessary for YAS to operate in a reasonable way.With yas installed manually, this module may be installed from pip withpython -m pip install yasjenkinsand the handler entry
"yasjenkins." added to theYAS_HANDLER_LISTenvironment variable in the execution environment. This handler may work
with pre 2.0 versions of yas by adding it to the handler list in the configuration file, but that is neither tested nor supported.ConfigurationIn addition to the configuration of yas itself, this handler is configured via the environment variablesYAS_JENKINS_URL,YAS_JENKINS_USERNAME,YAS_JENKINS_PASSWORD, andYAS_JENKINS_JOBS. The url, username, and password refer to the url
of a Jenkins instance, and the user credentials for a user on that instance. A service account should be preferred. The
jobs variable is used to expose jobs (surprise) via the indicated command. The command is a python regex string with any
parameters to the job present as named group like(?P<my_parameter>\w+)where my_parameter matches anexistingparameter defined in the job and\w+will match any value which should be expected from a slack request.ArchitectureThis plugin uses thepython-jenkinsmodule to interact with the
configured Jenkins instance. At present (version 1.0) only triggering builds of jobs is exposed. The configured
jobs' command regexes are looped over to determine if any of them match a previously unmatched message to the host
YAS instance and a request to build the associated job is sent. At present (version 1.0) there is meek acknowledgement
that the request was made (it doesn't indicate which job or anything else), and nothing more. Improving that initial
response should be an expected feature in an upcoming version, but acknowledgement of failure or other state of
completion of the job will be left to the Jenkins job.
Package: yasl
Yet Another Sparkline (yasl)Why? Well, it is actually really easy to do this and I wanted some different
controls that the other programs didn't have. Also, I like the MIT license.UsageYou can run it from the command line:kevin@dalekpsl$sparkbar.py123E+04▂▅█kevin@dalekpsl$sparkbar.py-z123E+04▎▋█kevin@dalekpsl$sparkbar.py1234321035▁▃▄▆▄▃▁▄█Or call it from a python program:#!/usr/bin/env pythonfrommathimportsin,pifromyaslimportSparkif__name__=="__main__":sp=Spark()data=[0,3,6,8.5,7,5,2,8,-8,1]print(u'max:{:.2f}min:{:.2f}[{}]'.format(max(data),min(data),sp.vbar(data)))data=[]foriinrange(36):data.append((sin(4*pi*i/36)))print(u'max:{:.2f}min:{:.2f}[{}]'.format(max(data),min(data),sp.hbar(data)))sp.dump()kevin@dalekpsl$python3example.pymax:8.50min:-8.00[▃▅▆█▇▆▄▇▄]max:0.98min:-0.98[▌▋▊▉██▉▊▋▌▎▏▏▎▍▋▊▉▉█▉▊▋▌▎▏▏▎]MIT LicenseCopyright (c) 2017 Kevin J. WalchkoPermission 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: yaslha
yaslha: Yet Another SLHA module for Python3A Python3 package to manipulateSLHAfiles and convert them to other file formats (JSON and YAML).Quick Start(TBW)IntroductionTheSUSY Les Houches Accordis a data format widely used in particle physics phenomenology.
It is originally defined inarXiv:hep-ph/0311123and extended to SLHA2 inarXiv:0801.0045.Because of its birth in FORTRAN era it is a fixed length format such asBLOCK SMINPUTS                  # Standard Model input parameters
     1     1.27934000e+02   # alpha_em^-1(M_Z)^MSbar
     2     1.16637000e-05   # G_F [GeV^-2]
     3     1.17200000e-01   # alpha_S(M_Z)^MSbar
     4     9.11876000e+01   # mZ (pole)
     5     4.18000000e+00   # mb(mb)^MSbar
     6     1.73300000e+02   # mt (pole)
     7     1.77682000e+00   # mtau (pole)
#
BLOCK ALPHA                    #
          -2.68630018e-02   # Higgs mixing parameter
#
BLOCK HMIX Q= 2.00000000e+02   # Higgs parameters (DRbar)
     1     5.40000000e+02   # mu(Q)
     2     4.00000000e+01   # tanbeta(Q)
     3     2.46220569e+02   # vev(Q)
     4     2.30400000e+05   # mA^2(Q)
#
...and extended in many program codes.Python has two famous SLHA parser:PySLHAby Andy Buckley andpylhaby David M. Straub.yaslhais "yet another" SLHA parser, influenced much by these two parsers.Python regrettably experienced a terrible era due to the transition from Python2 to Python3.
To reduce code complexity, this package supports only Python3.4 and later versions.Usage(TBW)AuthorSho Iwamoto / Misho, under much influence fromPySLHAby Andy Buckley andpylhaby David M. Straub.
Package: yasm
state machine for humansThere are two types of developers in this world: those who love state
machines and those whowilleventually.I fall in the first camp. I think it is really important to have a
declarative way to define the states of an object. That’s why I
developedyasm.InstallpipinstallyasmBasic Usagefromcollectionsimportdequeimportoperatorimportsixfromstringimportwhitespace,digitsfromyasmimportEvent,state_machinefromyasm.utilsimportdispatch@state_machine('calculator')classCalculator(object):operators={'+':operator.add,'-':operator.sub,'*':operator.mul,}ifsix.PY3:operators['/']=operator.truedivelse:operators['/']=operator.divdef__init__(self):self.stack=deque()self.result=Nonedefreset(self):self.stack.clear()self.result=Noneself.machine.reinit_instance(self)defcalculate(self,string):self.reset()forcharinstring:dispatch(self,Event('parse',input=char))returnself.resultdefstart_building_number(self,state,event,instance):digit=event.inputself.stack.append(int(digit))defbuild_number(self,state,event,instance):digit=event.inputnumber=str(self.stack.pop())number+=digitself.stack.append(int(number))defdo_operation(self,state,event,instance):operation=event.inputy=self.stack.pop()x=self.stack.pop()self.stack.append(self.operators[operation](float(x),float(y)))defdo_equal(self,state,event,instance):number=self.stack.pop()self.result=numberdefis_digit(state,event,instance):returnevent.inputindigitssm=Calculator.machinesm.add_states(['initial','number','result'],initial='initial')sm.add_transitions([{'from_state':'initial','to_state':'number','event':'parse','conditions':[is_digit],'before':'start_building_number'},{'from_state':'number','to_state':'number','event':'parse','conditions':[is_digit],'before':'build_number'},{'from_state':'number','to_state':'initial','event':'parse','conditions':[lambdastate,evt,ins:evt.inputinwhitespace]},{'from_state':'initial','to_state':'initial','event':'parse','conditions':[lambdastate,evt,ins:evt.inputin'+-*/'],'before':'do_operation'},{'from_state':'initial','to_state':'result','event':'parse','conditions':[lambdastate,evt,ins:evt.input=='='],'before':'do_equal'},])calc=Calculator()forsyntax,valuein((' 167 3 2 2 * * * 1 - =',2003),('    167 3 2 2 * * * 1 - 2 / =',1001.5),('    3   5 6 +  * =',33),('        3    4       +     =',7),('2 4 / 5 6 - * =',-0.5),):result=calc.calculate(syntax)assertresult==value,(syntax,result,value)calc.reset()Thank youtoaasmand ruby’sstate_machineand jtushman’sjtushman/state_machineand
all other state machines that I loved before
Package: yasod
yasodVersion 0.1.1Yet another simple object detectorInstallingpipinstallyasodGetting startedAn example for a config and models could be found intests/data.Here is a simple example how to detect the objects of a giveninput-image.jpgand draw an output image accordingly:fromyasodimportYasodmodel=Yasod("simple-yasod-config.yml").get_model("yolov4-tiny")img,detections=model.detect("input-image.jpg")model.draw_results(img,detections,"output-image.jpg")
Package: yason
yason: Jupyter Notebook Scheduler for ArgoJupyterHub deployed on Kubernetes allows teams to do data analysis in the browser and efficiently share computational resources. But when it comes to Jupyter Notebooks, it is not easy to run them remotely and non-interactively or schedule them. Fortunately, with Papermill and Argo Workflows it is now possible to do just that. Yason is Python package and CLI for scheduling the remote execution of Jupyter Notebooks.Free software: MIT licenseDocumentation:https://yason.readthedocs.io.FeaturesCheck the status of all scheduled notebook jobsSchedule a notebook for executionGet resulting notebook after executionDelete scheduled notebook jobComing soonIncluding multiple files/folders together with Notebook for executionIntegration with Argo events for schedulingPrerequisitesYason is intended to run on JupyterLab pods spawned by JupyterHub deployed on Kubernetes. Yason also requires Argo Workflows to be deployed on the same cluster in the namespaceargo. S3 bucket is required for intermediate storage of Notebooks before and after their execution.See more details and instructions in the full documentationhttps://yason.readthedocs.ioUsageTo use Yason in Python project:from yason import yasonCLI ToolTo use Yason as CLI tool, type:yason COMMAND [ARGS]To see the list of all scheduled Notebooks for your JupyterHub username, type:yason listTo schedule and execute Notebook immediately, type:yason run <Notebook Name>.ipynbTo get the resulting Notebook after execution, type:yason get <Workflow ID> <Destination>i.e.:yason get 25fe9753bc854148aac26ff7d97ba128 My_Notebook_result.ipynbTo delete the scheduled Notebook from your list, type:yason delete <Workflow ID>i.e.:yason delete 25fe9753bc854148aac26ff7d97ba128NameYason (or Jason) in Greek mythology is the leader of Argonauts. Yason brings your Jupyter Notebooks on board of Argo (Workflows).CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-05-22)First release on PyPI.
Package: yasp
YASPYet another Apertium stream parserUsage>>> from pprint import pprint
>>> import yasp
>>> pprint(yasp.stream.parse('^prpers<prn><subj><p1><mf><sg>$ ^saw<vblex><pres>$ ^a<det><ind><sg>$ ^cat<n><sg>$'))
[('LEXICAL-UNIT',
  [{'FLAG': '',
    'INVARIABLE-PART0': [],
    'INVARIABLE-PART1': [],
    'LING-FORM': 'prpers',
    'TAGS': ['prn', 'subj', 'p1', 'mf', 'sg']}]),
 ('UNPARSED', ' '),
 ('LEXICAL-UNIT',
  [{'FLAG': '',
    'INVARIABLE-PART0': [],
    'INVARIABLE-PART1': [],
    'LING-FORM': 'saw',
    'TAGS': ['vblex', 'pres']}]),
 ('UNPARSED', ' '),
 ('LEXICAL-UNIT',
  [{'FLAG': '',
    'INVARIABLE-PART0': [],
    'INVARIABLE-PART1': [],
    'LING-FORM': 'a',
    'TAGS': ['det', 'ind', 'sg']}]),
 ('UNPARSED', ' '),
 ('LEXICAL-UNIT',
  [{'FLAG': '',
    'INVARIABLE-PART0': [],
    'INVARIABLE-PART1': [],
    'LING-FORM': 'cat',
    'TAGS': ['n', 'sg']}])]
>>>
Package: ya-speech-key
Extracts Yandex Speech API key from translate pageThis API key can be used for Yandex SpeechKit Cloud API. No guarantees, Yandex can break it at any time.Installpip install ya-speech-keyUsagefromya_speech_keyimportAPIKeykey=APIKey()print(key.key)lifetimeset key update interval in seconds. Default3600.proxiessets proxies inrequests format. DefaultNone.key=APIKey(lifetime=3600,proxies=None)
Package: yaspop
YASPOPyaspop is yet another simple python option parser. use it and make pop proud.Free software: GNU General Public License v3Documentation:https://yaspop.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-07-01)First release on PyPI.
Package: yass-algorithm
Note: YASS is in an early stage of development. Although it is stable, it has only been tested
with the data in our lab, but we are working to make it more flexible. Feel free to send
feedback throughGitter. Expect a lot of API changes in the near future.ReferenceLee, J. et al. (2017). YASS: Yet another spike sorter. Neural Information Processing Systems. Available in biorxiv:https://www.biorxiv.org/content/early/2017/06/19/151928InstallationInstalling the last stable version:pipinstallyass-algorithmIf you are feeling adventurous, you can install from the master branch:pipinstallgit+git://github.com/paninski-lab/yass@masterExampleQuick example of YASS using a sample of the neuropixel data fromNick
Steinmetz:# install last stable versionpipinstallyass-algorithm# clone repo to get the sample datagitclonehttps://github.com/paninski-lab/yass# move to the examples/ folder and run yass in the sample datacdyass/examplesyasssortconfig_sample.yaml# see the spike traincatdata/spike_train.csvYou can also use YASS in Python scripts. See the documentation for details.DocumentationDocumentation hosted athttps://yass.readthedocs.ioRunning testsTo run tests and flake8 checks (from the root folder):pipinstall-rrequirements.txtmaketestBuilding documentationYou need to install graphviz to build the graphs included in the
documentation. On macOS:brewinstallgraphvizTo build the docs (from the root folder):pipinstall-rrequirements.txtmakedocsContributorsPeter Lee,Eduardo Blancas,Nishchal Dethe,Shenghao Wu,Hooshmand Shokri,Calvin Tong,Catalin Mitelut
Package: yassh
yasshYassh is a python library used to run
asynchronous, dependent remote and local tasks.UsageimportloggingfromyasshimportReactor,RemoteRun,RemoteConfiguration,LocalRunlogging.basicConfig(level=logging.DEBUG)# Create a remote ; only host parameter is mandatoryremote=RemoteConfiguration(host='localhost',username='user')# ssh_config(5) options can be set in RemoteConfiguration objectremote.set('Compression','yes')remote.set('CompressionLevel','9')r=Reactor()c1=RemoteRun(r,remote,'sleep 5')c2=LocalRun(r,'echo ok')c3=RemoteRun(r,remote,'echo "finished" && sleep 1')defstart_c2(run):# Start cmd2 when cmd1 complete successfullyifrun.result==0:c2.start()c1.register_exit_monitor(start_c2)defstart_c3(run):# Start cmd3 when cmd2 completec3.start()c2.register_monitor(u'ok',start_c3)defon_c3_finished(run):# Print dummy message when c3 is near terminaisonprint('c3 almost finished')c3.register_monitor(u'finished',on_c3_finished)# Start first taskc1.start()# Infinitetimeout=-1whiler.run(timeout)>0:passInstallationTo install latest release:pip install yasshTo install latest code:pip install git+https://github.com/enyx-opensource/yassh.gitTestingRun from project root:behaveLicenseMIT License (see LICENSE)