Saravanan666/pypi-org huggingface dataset:
Package: junc-utils
junc_utilsUtility functions for analyzing splicing junction (generated by STAR, .SJ.out.tab files)DependencyPythonpysam,annot_utils>=0.3.0packages.Softwarehtslib,bedtoolsInstallpip install junc_utilsAlternatively, you can install from the source codewget https://github.com/friend1ws/junc_utils/archive/v0.5.0.tar.gz
tar zxvf v0.5.0.tar.gz
cd junc_utils-0.5.0
python setup.py build installThis package has been tested on Python 3.6, 3.7, 3.8, 3.9Commandsfitlerfilter out splicing junctions outside specified conditionsjunc_utils filter [-h] [--read_num_thres READ_NUM_THRES]
                         [--overhang_thres OVERHANG_THRES] [--keep_annotated]
                         [--pooled_control_file POOLED_CONTROL_FILE]
                         sample.SJ.out.tab output.txtannotateannotate splicing junctionsjunc_utils annotate [-h] [--grc] [--genome_id {hg19,hg38,mm10}]
                           [--junction_margin JUNCTION_MARGIN]
                           [--exon_margin EXON_MARGIN]
                           sample.SJ.out.tab output.txtmerge_controlmerge, compress and index the splicing junction listjunc_utils merge_control [-h] [--read_num_thres READ_NUM_THRES]
                                [--overhang_thres OVERHANG_THRES]
                                [--keep_annotated]
                                [--sample_num_thres SAMPLE_NUM_THRES]
                                junc_list.txt output_pathassociateassociate junctions with mutations or SVsjunc_utils associate [-h] [--grc] [--genome_id {hg19,hg38,mm10}]
                            [--donor_size donor_size]
                            [--acceptor_size acceptor_size]
                            [--reference reference.fa] [--debug]
                            [--mutation_format {vcf,anno}] [--only_dist]
                            [--only_dist_search_margin only_dist_search_margin]
                            [--sv] [--branchpoint]
                            [--branchpoint_size branchpoint_size]
                            annotated_junction.SJ.out.tab mutation.vcf.gz
                            output_file
Package: jundo
jundoA general purpose library to help implement undo for existing objects.The main idea is that if one limits oneself to objects that can be viewed as
JSON-like data structures (ie. are composed of strings, numbers, lists and
dictionaries) it is possible to record changes in a completely generic way,
and use these recordings to reconstruct objects or to roll back changes, for
example to implement undo.A key requirement was that the model objects must be completely decoupled from
the recording mechanism, and will not need any awareness of it.The way this is implemented here is through proxy objects: instead of modifying
the original model objects directly, one uses proxy objects that mimic the
model object, allowing the proxy to pass on change information (deltas) to an
undo manager.The undo manager collects change deltas (and their inverses), and can apply
them to the original model object when undo or redo is requested.The model object is passed to the undo manager with theundoManager.setModel(model), which returns a proxy for the model. Client code
must use the proxy object instead of the model to modify the model. Here is an
example:>>> model = [1, 2, 3, {"a": 123}]
>>> um = UndoManager()
>>> proxy = um.setModel(model)
>>> # Modifications must be done within a change set context:
>>> with um.changeSet(title="replace list item"):
...     proxy[1] = 2000
...
>>> model[1]
2000
>>> um.undo()
>>> model[1]
2
>>> um.redo()
>>> model[1]
2000
>>> with um.changeSet(title="replace nested dict item"):
...     proxy[3]["a"] = 456
...
>>> model[3]["a"]
456
>>> um.undo()
>>> model[3]["a"]
123In this example, only Python list and dict objects are used as containers, but
any type of Mapping or Sequence (in the collections.abc-sense) can be used, or
any object type that uses attribute access to modify its model data. See the
Examples folder for more elaborate examples.Sets are also supported.To support model objects that are not JSON-like, custom proxy classes can be
registered via theregisterUndoProxy()function.AcknowledgmentsThe approach implemented here is inspired by Raph Levien's ideas, as he wrote
them downhere.
It also draws inspiration fromjsonpatch.
Package: juneau
The Juneau ProjectThe past decade has brought a sea change in the availability of data. Instead of a world in which we have small number
of carefully curated data sources in a centralized database -- instead we have a plethora of datasets, data versions,
and data representations that span users, groups, and organizations. Devices and data acquisition tools make it easy to
acquire new data, cloud hosting makes it easy to centralize and share files, and cloud data analytics and machine learning
tools have driven a desire to integrate and extract value from that data.We have been missing management tools to centralize and capture such data resources. Data scientists often end up doing
redundant work because they have no effective way of finding appropriate resources to reuse and retarget to new applications.The Juneau Project develops holistic data management tools to find, standardize, and benefit from the existing resources
in the data lake.  This extension to Jupyter Notebook is a point of access for our dataset management tools.For more on the project, please see the projecthome, as well as our research papers:Finding Related Tables in Data Lakes for Interactive Data Science. Yi Zhang and Zachary G. Ives. SIGMOD 2020.Dataset Relationship Management. Yi Zhang, Soonbo Han, Nan Zheng. CIDR 2019.SetupPrerequisites: relational and graph databasesSimple VersionInstall Docker, including docker-compose, for your preferred operating system.Downloadthis filefor Docker-ComposeRundocker-compose upfrom the directory.Copyjuneau/config-default.pytojuneau/config.pyThese will use the default user IDs and passwords that exist inconfig.yaml.Custom VersionFirst, be sure you have installed:PostgreSQL, version 10 or laterNeo4J 3.3, version or laterThen set up a default user ID and password for each:Runsudo -u postgres psqland then enter\password.  Set a password for the account (by default this is assumed to behabitat1).Open your browser tolocalhost:7474and change the password on theneo4jpassword, by default tohabitat1.Copyjuneau/config-default.pytojuneau/config.pyNow either edit the YAML file injuneau/config/config.yamlto match your password and account info or
change the environment variables in your terminal.Sample data lake corpusNext, downloadjuneau_start.zipand unzip it.For the Docker container, you can import as follows:Run./neo4j-update.shOtherwise, you can use:neo4j-admin load --database=data.db --from=juneauG.dump --forcepsql -h localhost -U postgres < juneauD.pgsqlAnd finally you need to edit theneo4j.conffile to set the database
todata.db.Install Jupyter Notebook extensionsSee theDeveloper's Guidefor details.sudo -H python setup.py installsudo -H jupyter serverextension enable --py juneaujupyter nbextension install dataset_inspector / --userjupyter nbextension enable dataset_inspector/main --user
Package: juneberry
juneberryjuneberry — Advanced Logging in PythonInstallationYou can installyakuiwith Python Installer of Python (pip)pipinstalljuneberryExamplesexamples/basic.pyfromjuneberryimportLoggerlogger=Logger(name="basic")logger.info("Hello, world!")logger.warn("Hello, world!")logger.debug("Hello, world!")logger.error("Hello, world!")logger.fatal("Hello, world!")
Package: junevis
JUNE-visA dashboard to visualize the JUNE simulation results for epidemic modeling and interventionInstallpip install junevisServe the Example ProjectThis code ships with an example project (mask_wearing) for the interface. From the command line, run:junevis_serveAdd new simulation results to JUNE-visTheJUNEsimulation logs events as they occur into hdf5 files calledrecords. These are transformed into csv files calledsummariesthat are automatically placed into a folder the frontend interface can serve. To create a new visualization from a simulation,junevis_create path/to/folder/containing/recordsFor more details on the expected contents of this folder, seethis documentation.Changing the homepageYou will need to clone the source code if you want to change the landing page to describe the set of simulation results deployed. The file to change is injunevis/client/src/views/Intro.vue(this is a.vuefile that contains all the needed HTML, CSS, and javascript).After cloning and making your desired changes, from this project's root:pip install -e .
cd junevis/client
npm install
npm run build
junevis_serveDeployingYou can easily host this for others to see by runningjunevis_serveand exposing (default port)8000.WalkthroughThe Explore PageThe parameter selection gridChoose which runs to compare across a single dimension:The parameter grid (left) is designed so you can view a single simulation by clicking on a valid combination of hyperparameter values. Alternatively, you can highlight all the values of the hyperparameters by clicking on the name of the hyperparameter to compare simulations accross all values of that dimension.A card and SIR cruve for every simulationEvery run is associated with a set of hyperparameters. Its effect can be summarized with an SIR curve, shown as the thumbnail:The Detailed Comparison PageSometimes we want to compare simulations across more subtle effects, such as the difference in where the infections occurred, shown below:You can add any additional plot here to compare between two simulations. Included are SIR curve, ages of the infected, the locations of infection, and a detailed geography overview:The slider will color the background of each region according to the selected dimension at that point in time.The Geography OverviewTo see the geographical effects of all simulations at once, we include a special page to sort and observe the peak value at each location for the course of a simulation:Known BugsRendering the geojson associated with the map of England does not workCreating a new project using the PyPI package will overwrite existing projects. We need to move processed projects into a configuration folder in the home directory and symbolically link them to the client
Package: junglead
匠岭-通用组件-Python版本简述一些常用的组件功能，打包发布至Pypi，从而实现快速迁移及版本同步。项目尽可能只包含一些不依赖外部组件的基础功能，或者仅依赖部分常见的组件。对于一些少用组件，暂时不会依赖。安装pipinstalljunglead# 或者python-mpipinstalljunglead使用方法安装好组件之后，可以导入对应的类进行使用。方法分为静态方法和属性方法，具体将在每个组件的使用方法中单独说明。组件列表针对一些常用的底层功能，所做的二次封装，提高使用的便利性。注意！注意！注意！本组件仅依赖于原生代码，不依赖于任何第三方组件，因此一些需要第三方组件支持的功能，暂不包含。日志组件Python自带了记录日志的logger组件，只是不便使用，封装的日志组件提供了更加简单的使用方法，只需要设置日志级别以及可选的前缀，即可针对不同场景和目的将日志记录在不同的文件中，详细介绍。序列化组件Python自带的json组件，仅支持序列化最基础的数字、字符串类型，对于稍微复杂点的类型均不提供支持。同时由于Python是弱语言类型，导致也无法像其他强类型语言一样根据反射机制来获取属性配置，从而对于过于复杂的对象类型，也无法做到通用的配置。因此只能封装几个比较简单的特殊类型，详细介绍。日期时间组件Python中的日期时间对象，也是一个比较难用的组件，因此提供一个封装组件，提供一些计算、比较等方面的便利操作，详细介绍正则表达式组件正则表达式也是语言内置的功能，封装组件仅是封装了一些常见的正则判断语句，用来进行简单的类型判断，详细介绍邮件组件封装了邮件操作的组件，支持原生SMTP方式发送带有附件的邮件。但是由于不想引入额外的组件依赖，因此不支持添加网络URL的文件，仅支持服务器本地文件。同时，支持原生的POP收取邮件。详细介绍雪花算法对于大型的分布式应用，或者海量并且有关联数据的写入操作来说，对于任何数据库来说，用自增列做为唯一标识都不是一个好的选择。雪花算法正是为解决这一问题而生。详细介绍版本差异2.4邮件发送工具，增加了读取本地图片并发送的功能。
Package: jungle-chess
Jungle ChessJungle Chessis a network board game for two players. It is written in Python 3 usingPygamelibrary.ScreenshotsOverviewJungle Chess(akaDou Shou Qi) is a modern Chinese strategy board game for two players.The Jungle gameboard represents a jungle terrain withdens,traps"set" around dens, andrivers. Each player controls eight game pieces representing different animals of various rank. Stronger-ranked animals can capture ("eat") animals of weaker or equal rank. The player who is first to maneuver any one of their pieces into the opponent's den wins the game. An alternative way to win is to capture all the opponent's pieces.Please review the rules and detailed description of the Dou Shou Qi onWikipedia.Getting StartedInstallationpip install jungle-chessRunningjungle-chessorpython -m jungle-chessPlayingTo start a game server choose "Start new game" in the main menu.To connect to the game server choose "Connect to game server" and enter the server IP address or hostname.Total game time is 15 minutes per player, plus 5 second per-move free time.Time constants can be changed inconstants.pyfile.You can surrender any time by clicking on the white flag button and confirming you intention by pressingYafter that.LicenseThis project is licensed under the MIT License — see theLICENSEfile for details.CreditsVecteezyfor SVG vectorsPablo Pizzaro R.forPygame-menulibraryAlex-ProductionsforChinese New Year Is Comingmusic theme
Package: junifer
junifer - JUelich NeuroImaging FEature extractoRAboutjunifer is a data handling and feature extraction library targeted towards neuroimaging data specifically functional MRI data.It is currently being developed and maintained at theApplied Machine Learninggroup atForschungszentrum Juelich, Germany. Although the library is designed for people working atInstitute of Neuroscience and Medicine - Brain and Behaviour (INM-7), it is designed to be as modular as possible thus enabling others to extend it easily.The documentation is available athttps://juaml.github.io/junifer.Repository Organizationdocs: Documentation, built using sphinx.examples: Examples, using sphinx-gallery. File names of examples that create visual output must start withplot_, otherwise, withrun_.junifer: Main library directory.api: User API module.configs: Module for pre-defined configs for most used computing clusters.data: Module that handles data required for the library to work (e.g. parcels, coordinates).datagrabber: DataGrabber module.datareader: DataReader module.external: Module for external libraries and tools.markers: Markers module.onthefly: Transformation components (on-the-fly) module.pipeline: Pipeline module.preprocess: Preprocessing module.storage: Storage module.testing: Testing components module.utils: Utilities module (e.g. logging)InstallationUsepipto install from PyPI like so:pip install juniferYou can also install viaconda, like so:conda install -c conda-forge juniferCitationIf you use junifer in a scientific publication, we would appreciate if you cite our work. Currently, we do not have a publication, so feel free to use the projectURL.FundingWe thank theHelmholtz Imaging PlatformandSMHBfor supporting development of Junifer.
(The funding sources had no role in the design, implementation and evaluation of the pipeline.)ContributionContributions are welcome and greatly appreciated. Please read theguidelinesto get started.Licensejunifer is released under the AGPL v3 license:junifer, FZJuelich AML neuroimaging feature extraction library.
Copyright (C) 2022, authors of junifer.This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or any later version.This program 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 Affero General Public License for more details.You should have received a copy of the GNU Affero General Public License
along with this program.  If not, seehttp://www.gnu.org/licenses/.
Package: junior-ssl
Junior SSL (Omniwheel soccer setup)PresentationYouTube demonstration videoThe goal of this project is to provide an open-source material to build and operate a setup featuring
omniwheel robots and a soccer field monitoring them with a camera.With this setup, it is possible for two teams to code a program and play against each other.DocumentationThis is a WORK IN PROGRESS, you can't (yet) build the whole setup by following instructions from
this repository, if any question, do not hesitate to open an issueRobotsCoordinates, field and markersGame Controller and Python APILicenseThis work is licensed under aCreative Commons Attribution-NonCommercial 2.0 Generic License.
Package: juniper-nxpy
nxpy: Network XML Python Proxynxpymaps exported XML configuration (from Juniper network devices) to python
classes. It is compatible only with Junipers' xml configuration and is developed
to work alongsidencclient.nxpy allows for retrieving the device configuration in xml format (using either
netconf or "show configuration | display xml" via an expect script), transform
the configuration to python classes in order to manipulate them (view, edit,
delete).  After editing, the configuration can be applied back to the device via
netconf or cli expect.Furthermore, it allows for building configuration via python classes, and apply
it to the device(s) via netconf or cli expect.  For the time, it supports
limited configuration changes.InstallingRequirements:Python 2.6 <= version < 3.0lxml (tested with 2.2.6)To install:python setup.py installExamplesGrab the configuration in xml formatLet's say that you have grabbed your Juniper device configuration in xml format
(This is STRICT!!!). You can use either "show configuration | display xml" (and
copy paste the output to a file), or use an automated cli excpect script or
invoke netconf.  nxpy is developed as a companion toncclient.Feed the configuration to nxpyimport nxpy as np
conf = np.Parser(<configuration_file_OR_configuration_text>)
conf = conf.export()To check if it worked:conf.interfaces(...you should get the list of device interfaces)Changelogv0.4.3:Python packaging changes (README,setup.py)Change license to GPLv3v0.4.2:Preliminary support for L2VPNSEthernet OAM supportv0.4.1:Fix version in setup.pyv0.4:Added full support for bgp flowspec (routing-options flow) configurationv0.3:Support for basic interface configuration (name, description, vlan)Support for basic vlan configurationLicenseThis project is licensed under the GPL License - see theLICENSEfile
for details.
Package: juniperSNMPv3crypt
juniper SNMPv3 cryptThis is python library to deal with hashing (md5 or sha1) and then encrypting SNMPv3 user profiles for Juniper devices. The hashing follows RFC 3414 for snmpv3 profiles.The $9$ encryption is a port from Perl fromhttps://metacpan.org/pod/Crypt::JuniperThe encrypt function supports a seed in order to have idempotent encryption.InstallThis package works in both python 2 and 3pip install juniperSNMPv3crypt
Package: junison
The ultimate JSON 3-way merger.Free software: BSD licenseInstallationpip install junisonDocumentationhttps://junison.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.1.0 (…)Development starts
Package: junit2influx
This library provide some utilities to manipulate test data from junit files
and sendthem to a influxdb server.Installationpipinstalljunit2influxUsageAjunit2influxbinary is available, as shown below:$junit2influx--helpUsage:junit2influx[OPTIONS]JUNIT_FILEExtracttestdatafromajunitfileandsendittoinfluxdb:junit2influxtest.xml--influxdb-urlurlEachtestissendasasingledatapointwithitsresultanddurationunderthe"tests"measurement,andoveralldata(numberoftests,totalduration,etc.)issentunderthe"builds"measurement.Youcanprovideadditionaltagsandfieldstobesentwitheachdatapointtoinfluxdbbyusingthe--fieldand--tagflags:junit2influxtest.xml--influxdb-urlurl--taghost=myhost--fieldcommit=dh876d0Alladditionaltagsandfieldsaresentasstringbydefault,butyoucancastthemtospecificjsontypes(bool,floatandint)ifneeded:--tagint:stage=1# add an integer "stage" tag with a value of 1--tagbool:fake=true# add a boolean "fake" tag with a value of True--fieldfloat:dur=3.5# add a float "dur" field with a value of 3.5Multiple--fieldand--tagflagscanbeprovided.If you want to send data to influx but don’t have a Junit file available, you
can use thepush2influxwrapper:$push2influx--help✘Usage:push2influx[OPTIONS]MEASUREMENTPushdatadirectlytoinfluxdb:push2influxmeasurement_name--influxdb-urlurl--tagresult=successTagsandfieldsworkthesamewaythedoforjunit2influxOptions:--fieldTEXT--tagTEXT--influxdb-urlTEXT[required]--helpShowthismessageandexit.
Package: junit-report
Junit-ReportThis Python package adds more control to your tests by decorating your functions and pytest fixtures and exporting them as JUnit xml.Table of contentsJunit-ReportInstallationUsageOS parameters used for configurationInstallationpipinstalljunit-reportUsageimportpytestfromjunit_reportimportJunitTestSuite,JunitTestCase,JunitFixtureTestCaseclassTestSomeThing:@pytest.fixture@JunitFixtureTestCase()defmy_fixture(self):# do stuff ..@JunitTestCase()defnested_case():passyieldnested_case@JunitTestCase()defmy_first_test_case(self):pass@JunitTestCase()defmy_second_test_case(self,name:str):raiseValueError(f"Invalid name{name}")@JunitTestSuite()deftest_suite(self,my_fixture):my_fixture()self.my_first_test_case()self.my_second_test_case("John")Output<?xml version="1.0" ?><testsuitesdisabled="0"errors="0"failures="1"tests="4"time="0.000301361083984375"><testsuitedisabled="0"errors="0"failures="1"name="TestSomeThing_test_suite"skipped="0"tests="4"time="0.000301361083984375"><testcasename="my_fixture"time="0.000163"classname="TestSomeThing"class="fixture"/><testcasename="nested_case"time="0.000034"classname="module_name.test"class="function"/><testcasename="my_first_test_case"time="0.000017"classname="TestSomeThing"class="function"/><testcasename="my_second_test_case"time="0.000087"classname="TestSomeThing"class="function"><failuretype="ValueError"message="Invalid name John">Traceback(mostrecentcalllast):...ValueError:InvalidnameJohn</failure></testcase></testsuite></testsuites>OS parameters used for configurationVariableDescriptionJUNIT_REPORT_DIRReports directory where the reports will be extracted. If it does not exist - create it.
Package: junit-reporter
junit-reporterA Python3 package that generates test results in the standard JUnit XML format for use with Jenkins and other build integration servers.InstallationUse the following command to installjunit-reporter:$ pip install junit-reporterQuickstartCreate a test report:fromjunit_reporterimportTestCase,TestSuite,JUnitReportertest_case=TestCase('Test #1',classname='some.class.name',stdout='I am stdout!',stderr='I am stderr!')test_suite=TestSuite('Test Suite #1',[test_case])xml=JUnitReporter.report_to_string([test_suite])It produces the following output:<?xml version="1.0" ?><testsuitesdisabled="0"errors="0"failures="0"tests="1"time="0"><testsuitename="Test Suite #1"tests="1"assertions="0"disabled="0"errors="0"failures="0"skipped="0"time="0"><testcasename="Test #1"classname="some.class.name"><system-out>Iamstdout!</system-out><system-err>Iamstderr!</system-err></testcase></testsuite></testsuites>Running the tests$ pytest testsLicenseThis project is licensed under theGNU General Public License v3.0.