Saravanan666/pypi-org huggingface dataset:
Package: jafgen
🥪 Jaffle Shop Generator 🏭The Jaffle Shop Generator orjafgenis a python package with a simple command line tool for generating a synthetic data set suitable for analytics engineering practice or demonstrations.InstallationpipinstalljafgenUsejafgentakes one argument, years, which sets the length of time the simulation generates synthetic data for.# generate a simulation spanning 3 years from 2016-2019jafgen--years3PurposeFinding a good data set to practice, learn, or teach analytics engineering with can be difficult. Most open datasets are great for machine learning -- they offer single wide tables that you can manipulate and analyze. Full, real relational databases on the other hand are generally protected by private companies. Not only that, but they're a bittooreal. To get to a state that a beginner or intermediate person can understand, there needs to be an advanced amount of analytics engineering transformation applied.ApproachComing soon.ContributionComing soon.
Package: jageocoder-converter
jageocoder-converterThis package is a CUI application for creating an address dictionary for use with the address geocoderjageocoder.
It can be used when you want to create a dictionary that includes only some prefectures.For more detailed information, please refer tothe Japanese document.PrerequisitesRequires Python 3.7 or later.InstallingInstall the package usingpip install jageocoder-converter.UninstallingRemove the directory containing the database.
Then, dopip uninstall jageocoder-converter.RunShow help with the following command.python-mjageocoder_converter-hTo create a dictionary, run the following command.python-mjageocoder_converterDuring the process, it downloads the location reference information
needed to create the dictionary from the web. Before downloading,
you will be prompted with a link to the terms of use.
Be sure to read them, and enter a capital 'Y'.More examplesTo create a dictionary, including only 東京都,
without records from 住居表示住所, run the following command.python-mjageocoder_converter--no-jusho13You may create a dictionary in the specified directory.python-mjageocoder_converter--db-dir=oazadb--no-gaiku--no-jushoIf you already understand the terms of use, etc., and want to process it
in the background, you can specify the quiet option. It may be useful
to add a debug option to check the progress in the log file.nohuppython-mjageocoder_converter--quiet--debug&AuthorsTakeshi SAGARA -Info-proto Co.,Ltd.LicenseThis project is licensed underthe MIT License.This is not the scope of the dictionary data license.
Please follow the terms of use of the respective dictionary data.
Package: jagger
jagger-pythonPython binding for Jagger(C++ implementation of Pattern-based Japanese Morphological Analyzer) :https://www.tkl.iis.u-tokyo.ac.jp/~ynaga/jagger/index.en.htmlInstall$ python -m pip install jaggerThis does not install model files.You can download precompiled KWDLC model fromhttps://github.com/lighttransport/jagger-python/releases/download/v0.1.0/model_kwdlc.tar.gz(Note that KWDLC has unclear license/TermOfUse. Use it at your own risk)Exampleimportjaggermodel_path="model/kwdlc/patterns"tokenizer=jagger.Jagger()tokenizer.load_model(model_path)text="吾輩は猫である。名前はまだない。"toks=tokenizer.tokenize(text)fortokintoks:print(tok.surface(),tok.feature())print("EOS")"""吾輩    名詞,普通名詞,*,*,吾輩,わがはい,代表表記:我が輩/わがはい カテゴリ:人は      助詞,副助詞,*,*,は,は,*猫      名詞,普通名詞,*,*,猫,ねこ,*である  判定詞,*,判定詞,デアル列基本形,だ,である,*。      特殊,句点,*,*,。,。,*名前    名詞,普通名詞,*,*,名前,なまえ,*は      助詞,副助詞,*,*,は,は,*まだ    副詞,*,*,*,まだ,まだ,*ない    形容詞,*,イ形容詞アウオ段,基本形,ない,ない,*。      特殊,句点,*,*,。,。,*EOS"""# print tagsfortokintoks:# print tag(split feature() by comma)print(tok.surface())foriinrange(tok.n_tags()):print("  tag[{}] ={}".format(i,tok.tag(i)))print("EOS")Batch processing(experimental)tokenize_batchtokenizes multiple lines(delimited by newline('\n', '\r', or '\r\n')) at once.
Splitting lines is done in C++ side.importjaggermodel_path="model/kwdlc/patterns"tokenizer=jagger.Jagger()tokenizer.load_model(model_path)text="""吾輩は猫である。名前はまだない。明日の天気は晴れです。"""# optional: set C++ threads(CPU cores) to use# default: Use all CPU cores.# tokenizer.set_threads(4)toks_list=tokenizer.tokenize_batch(text)fortoksintoks_list:fortokintoks:print(tok.surface(),tok.feature())Train a model.Pyhthon interface for training a model is not provided yet.
For a while, you can build C++ trainer cli using CMake(Windows supported).
Seetrain/for details.LimitationSingle line string must be less than 262,144 bytes(~= 87,000 UTF-8 Japanese chars).Jagger versionJagger version used in this Python binding is2023-02-18For developerEditdev_mode=Truein to enable asan + debug buildRun python script with$ LD_PRELOAD=$(gcc -print-file-name=libasan.so) python FILE.py

or

$ LD_PRELOAD=$(clang -print-file-name=libclang_rt.asan-x86_64.so) python FILE.pyReleasingVersion is created automatically usingsetuptools_scm.tag it:git tag vX.Y.Zpush tag:git push --tagsTODOProvide a model file trained from Wikipedia, UniDic, etc(clearer & permissive licencing&TermOfUse).Use GiNZA for morphological analysis.Split feature vector(CSV) considering quote char when extracting tags.e.g. 'a,b,"c,d",e' => ["a", "b", "c,d", "e"]Optimize C++ <-> Python interfacestring_view(or read-only string literal) for tag str.pickle support(for exchanging Python object when using multiprocessing)https://pybind11.readthedocs.io/en/latest/advanced/classes.html#pickling-supportLicensePython binding is available under 2-clause BSD licence.Jagger andccedar_core.his licensed under GPLv2/LGPLv2.1/BSD triple licenses.Third party licencesstack_container.h: BSD like license.nanocsv.h MIT license.
Package: jaglion
JaglionJaglion is a Modern Command Line Tool for Apache Kafka. It is easy to install and use.To use jaglion, follow the following steps:Create a python virtual environment or use system wide environment$ python3 -m venv venv
$ source venv/bin/activateInstall jaglion package with pip.$ pip install jaglionGet jaglion command line help$ jaglion --help
Package: jagular
Out-of-core pre-processing of big-ish electrophysiology data, including spike detection and alignment.InstallationThe easiest way to install jagular is to usepip. From the terminal, run:pipinstalljagularAlternatively, you can install the latest version of jagular by running the following commands:gitclonehttps://github.com/kemerelab/jagular.gitcdjagularpythonsetup.py[install,develop]where thedevelopargument should be used if you want to modify the code.What is Jagular used for?We perform long (multiple days to multiple week-long) chronic in-vivo electrophysiology recordings, resulting in many terabytes of data per experiment. These long recording periods pose significant challenges to almost every part of a typical analysis pipeline, including filtering, spike detection, and alignment.For spike detection, for example, we need to filter the wideband data prior to doing some form of threshold crossing detection. But if we have terabytes worth of data, even a simple step such as filtering can become tricky, since we have to do the filtering out-of-core (since the data does not fit into memory). In addition, there can be substantial drift on the electrodes over such a long period of time, so an adaptive threshold crossing aproach would be more appropriate.Jagular makes doing these out-of-core tasks easier, by providing a simple interface to read in well-defined chunks from multiple files, in a seamless manner. These chunks can then be used to process the data in a more manageable way. Jagular also has complete support built in for the full (filtering)-(spike-detection)-(waveform-allignment) part of the analysis process, which works out-of-core, and deals elegantly with electrode drift.Wheredownloadhttps://pypi.python.org/pypi/jagularinspirationhttps://www.youtube.com/watch?v=WLCDAPNTpaMdocscoming soon!codehttps://github.com/kemerelab/jagularLicenseJagular is distributed under the MIT license. See theLICENSEfile for details.
Package: jahs-bench
JAHS-Bench-201The first collection of surrogate benchmarks for Joint Architecture and Hyperparameter Search (JAHS), built to also support and
facilitate research on multi-objective, cost-aware and (multi) multi-fidelity optimization algorithms.Please see ourdocumentation here. Precise details about the data collection and surrogate creation process, as well as our experiments, can be found in the assosciatedpublication.InstallationUsing pippipinstalljahs-benchOptionally, you can download the data required to use the surrogate benchmark ahead of time withpython-mjahs_bench.download--targetsurrogatesTo test if the installation was successful, you can, e.g, run a minimal example withpython-mjahs_bench_examples.minimalThis should randomly sample a configuration, and display both the sampled configuration and the result of querying the
surrogate for that configuration. Note: We have recently discovered that XGBoost - the library used for our surrogate models - can suffer from some incompatibility issues with MacOS. Users who run into such an issue may consultthisdiscussion for details.Using the BenchmarkCreating ConfigurationsConfigurations in our Joint Architecture and Hyperparameter (JAHS) space are represented as dictionaries, e.g.,:config={'Optimizer':'SGD','LearningRate':0.1,'WeightDecay':5e-05,'Activation':'Mish','TrivialAugment':False,'Op1':4,'Op2':1,'Op3':2,'Op4':0,'Op5':2,'Op6':1,'N':5,'W':16,'Resolution':1.0,}For a full description on the search space and configurations see ourdocumentation.Evaluating Configurationsimportjahs_benchbenchmark=jahs_bench.Benchmark(task="cifar10",download=True)# Query a random configurationconfig=benchmark.sample_config()results=benchmark(config,nepochs=200)# Display the outputsprint(f"Config:{config}")# A dictprint(f"Result:{results}")# A dictMore Evaluation OptionsThe API of our benchmark enables users to either query a surrogate model (the default) or the tables of performance data, or train a
configuration from our search space from scratch using the same pipeline as was used by our benchmark.
However, users should note that the latter functionality requires the installation ofjahs_bench_201with the
optionaldata_creationcomponent and its relevant dependencies. The relevant data can be automatically downloaded by
our API. See ourdocumentationfor details.Benchmark DataWe providedocumentation for the performance datasetused to train our surrogate models andfurther information on our surrogate models.Experiments and Evaluation ProtocolSeeour experiments repositoryand ourdocumentation.LeaderboardsWe maintainleaderboardsfor several optimization tasks and algorithmic frameworks.
Package: jahs-bench-201
JAHS-Bench-201The first collection of surrogate benchmarks for Joint Architecture and Hyperparameter Search (JAHS), built to also support and
facilitate research on multi-objective, cost-aware and (multi) multi-fidelity optimization algorithms.Please see ourdocumentation here.InstallationUsing pippipinstallgit+https://github.com/automl/jahs_bench_201.gitOptionally, you can download the data required to use the surrogate benchmark ahead of time withpython-mjahs_bench.download--targetsurrogatesTo test if the installation was successful, you can, e.g, run a minimal example withpython-mjahs_bench_examples.minimalThis should randomly sample a configuration, and display both the sampled configuration and the result of querying the
surrogate for that configuration.Using the BenchmarkCreating ConfigurationsConfigurations in our Joint Architecture and Hyperparameter (JAHS) space are represented as dictionaries, e.g.,:config={'Optimizer':'SGD','LearningRate':0.1,'WeightDecay':5e-05,'Activation':'Mish','TrivialAugment':False,'Op1':4,'Op2':1,'Op3':2,'Op4':0,'Op5':2,'Op6':1,'N':5,'W':16,'Resolution':1.0,}For a full description on the search space and configurations see ourdocumentation.Evaluating Configurationsimportjahs_benchbenchmark=jahs_bench.Benchmark(task="cifar10",download=True)# Query a random configurationconfig=benchmark.sample_config()results=benchmark(config,nepochs=200)# Display the outputsprint(f"Config:{config}")# A dictprint(f"Result:{results}")# A dictMore Evaluation OptionsThe API of our benchmark enables users to either query a surrogate model (the default) or the tables of performance data, or train a
configuration from our search space from scratch using the same pipeline as was used by our benchmark.
However, users should note that the latter functionality requires the installation ofjahs_bench_201with the
optionaldata_creationcomponent and its relevant dependencies. The relevant data can be automatically downloaded by
our API. See ourdocumentationfor details.Benchmark DataWe providedocumentation for the performance datasetused to train our surrogate models andfurther information on our surrogate models.Experiments and Evaluation ProtocolSeeour experiments repositoryand ourdocumentation.LeaderboardsWe maintainleaderboardsfor several optimization tasks and algorithmic frameworks.
Package: ja-icd10
ja-ICD10ICD-10 国際疾病分類の日本語情報を扱うためのパッケージインストール使い方まず、ICDクラスのインスタンスを作成します。fromicd10importicdicd=icd.ICD()ICD-10のカテゴリー情報を取得するICD-10のカテゴリー名から情報を取得します。ICD-10のカテゴリー表記は、A000,A00.0どちらも可能です。In[]:print(icd["A000"])<ICDCategory:[A00.0]コレラ菌によるコレラ>In[]:icd["A000"].nameOut[]:'コレラ菌によるコレラ'In[]:icd["A000"].codeOut[]:'A00.0'また、登録されているカテゴリーの中分類であれば、範囲指定も可能です。In[]:icd["A00-A09"]Out[]:<ICDCategory:[A00-A09]腸管感染症>In[]:icd["A00-A09"].is_blockOut[]:TrueIn[]:icd["A00-B99"]Out[]:<ICDCategory:[A00-B99]感染症及び寄生虫症>In[]:icd["A00-B99"].is_chapterOut[]:True名称からICD-10カテゴリーを探す索引を元にカテゴリーを検索します。In[]:icd.find_categories_by_name("頭痛")Out[]:[<ICDCategory:[R51]頭痛>]In[]:icd.find_categories_by_name("吐き気")Out[]:[<ICDCategory:[R11]悪心及び嘔吐>]partial_match=Trueを指定することで、すべてのカテゴリー名からの部分検索ができます。In[]:icd.find_categories_by_name("頭痛",partial_match=True)Out[]:[<ICDCategory:[G43]片頭痛>,<ICDCategory:[G43.0]前兆＜アウラ＞を伴わない片頭痛［普通型片頭痛］>,<ICDCategory:[G43.1]前兆＜アウラ＞を伴う片頭痛［古典型片頭痛］>,<ICDCategory:[G43.2]片頭痛発作重積状態>,<ICDCategory:[G43.3]合併症を伴う片頭痛>,<ICDCategory:[G43.8]その他の片頭痛>,<ICDCategory:[G43.9]片頭痛，詳細不明>,<ICDCategory:[G44]その他の頭痛症候群>,<ICDCategory:[G44.0]群発頭痛症候群>,<ICDCategory:[G44.1]血管性頭痛，他に分類されないもの>,<ICDCategory:[G44.2]緊張性頭痛>,<ICDCategory:[G44.3]慢性外傷後頭痛>,<ICDCategory:[G44.4]薬物誘発性頭痛，他に分類されないもの>,<ICDCategory:[G44.8]その他の明示された頭痛症候群>,<ICDCategory:[O29.4]妊娠中の脊髄又は硬膜外麻酔誘発性頭痛>,<ICDCategory:[O74.5]分娩における脊髄麻酔及び硬膜外麻酔誘発性頭痛>,<ICDCategory:[O89.4]産じょく＜褥＞における脊髄麻酔及び硬膜外麻酔誘発性頭痛>,<ICDCategory:[R51]頭痛>]傷病情報を取得する病名管理番号から傷病名を検索します。In[]:icd.get_disease_by_byomei_id("20088330").nameOut[]:'外傷性横隔膜ヘルニア・胸腔に達する開放創合併あり'In[]:icd.get_disease_by_byomei_id("20088330").codeOut[]:'S2781'In[]:icd.get_disease_by_byomei_id("20088330").name_kanaOut[]:'ガイショウセイオウカクマクヘルニア・キョウクウニタッスルカイホウソウガッペイアリ'In[]:icd.get_disease_by_byomei_id("20088330").name_abbrevOut[]:'外傷性横隔膜ヘルニア・胸腔開放創あり'カテゴリーの下の階層の傷病を取得する指定したICD-10のカテゴリーの階層下にある傷病をすべて取得します。In[]:print(icd.get_diseases_by_code("A000"))[<Disease:[A00.0][20050788]アジアコレラ>,<Disease:[A00.0][20065915]真性コレラ>]In[]:print(icd.get_diseases_by_code("A00"))[<Disease:[A00.0][20050788]アジアコレラ>,<Disease:[A00.0][20065915]真性コレラ>,<Disease:[A00.1][20051356]エルトールコレラ>,<Disease:[A00.9][20051879]コレラ>,<Disease:[A00.9][20058027]偽性コレラ>]カテゴリー以下の階層のカテゴリーと傷病を取得する指定したICD-10のカテゴリーの階層下にあるカテゴリーと傷病をすべて取得します。In[]:icd.get_diseases_and_categories_by_code("A000")Out[]:[<ICDCategory:[A00.0]コレラ菌によるコレラ>,<Disease:[A00.0][20050788]アジアコレラ>,<Disease:[A00.0][20065915]真性コレラ>]In[]:icd.get_diseases_and_categories_by_code("A00")Out[]:[<ICDCategory:[A00]コレラ>,<ICDCategory:[A00.0]コレラ菌によるコレラ>,<Disease:[A00.0][20050788]アジアコレラ>,<Disease:[A00.0][20065915]真性コレラ>,<ICDCategory:[A00.1]エルトールコレラ菌によるコレラ>,<Disease:[A00.1][20051356]エルトールコレラ>,<ICDCategory:[A00.9]コレラ，詳細不明>,<Disease:[A00.9][20051879]コレラ>,<Disease:[A00.9][20058027]偽性コレラ>]情報元本レポジトリで利用しているデータは、下記ウェブサイトで公開されているものを利用しています。ICD10対応標準病名マスター　トップページ標準病名マスター作業班　運用補助マスター
Package: jaide
AboutContributors:Nathan PrintzandGeoff RhodesFull documentation on Read The DocsJaide GithubJaide GUI GithubPypi PageOSX/Windows Compiled VersionsDescriptionThejaidepackage contains two parts: a class library for developers (here called Jaide), and a CLI tool for network engineers (referred to as the CLI tool). The function of the Jaide class is to allow an engineer or developer to create and use a Jaide object for manipulating Junos devices. Similarly, the CLI tool can be used to manipulate or retrieve information/files/output from one or many devices. Not surprisingly, the CLI tool uses the Jaide class for its internal operations. Some features of both Jaide and the CLI tool include being able to poll devices for interface errors, grab basic system information, send any operational mode commands, send and commit a file containing a list of set commands, copy files to/from devices, get a configuration diff between two devices, perform a commit check, and run shell commands. A full list of features and their usage is available in thedocumentation.There is also a GUI available that wraps the CLI tool. More on it can be found at theJaide GUI github page.Jaide, and therefore the CLI tool and the Jaide GUI, leverage several connection types to JunOS devices using python, including: ncclient, paramiko, and scp. With this base of modules, our goal is the ability to perform as many functions that you can do by directly connecting to a device from a remote interface (either Jaide object, or the CLI tool). Since we can do these remotely from one interface, these functions apply rapidly against multiple devices very easily. The CLI tool leverages multiprocessing for handling multiple connections simultaneously. Pushing code and upgrading 20 devices is quite a simple task with the Jaide tool in hand.NOTEThis tool is most beneficial to those who have a basic understanding of JUNOS. This tool can be used to perform several functions against multiple Juniper devices running Junos very easily.  Please understand the ramifications of your actions when using this script before executing it. You can push very significant changes or CPU intensive commands to a lot of devices in the network from one command or GUI execution. This tool should be used with forethought, and we are not responsible for negligence, misuse, time, outages, damages or other repercussions as a result of using this tool.InstallationThe easiest method to use to install jaide is through pip:pip install jaideYou can also download the source code fromgithuband install it manually:python setup.py installThe above command is executed from the top level directory of the source distribution.
Package: jaidegui
AboutContributors:Nathan PrintzandGeoff RhodesFull documentation on Read The DocsJaide GithubJaide GUI GithubPypi PageOSX/Windows Compiled VersionsDescriptionThejaideguipackage contains is an easy to use GUI that extends thejaidepackage. The function of Jaide is to augment an engineer’s ability to configure or manipulate multiple Junos devices at the same time. A range of functions and use cases are covered. Some features include being able to poll devices for interface errors, grab basic system information, send any operational mode commands, or send and commit a file containing a list of set commands. The Jaide GUI allows the user to easily do these operations through a graphical interface rather than a command line utility. A full list of features and their usage is available in thedocumentation.Jaide, and therefore the CLI tool and the Jaide GUI, leverage several connection types to JunOS devices using python, including: ncclient, paramiko, and scp. With this base of modules, our goal is the ability to perform as many functions that you can do by directly connecting to a device from a remote interface. Since we can do these remotely from one interface, these functions apply rapidly against multiple devices very easily. The CLI tool leverages multiprocessing for handling multiple connections simultaneously. Pushing code and upgrading 20 devices is quite a simple task with the Jaide tool in hand.NOTEThis tool is most beneficial to those who have a basic understanding of JUNOS. This tool can be used to perform several functions against multiple Juniper devices running Junos very easily.  Please understand the ramifications of your actions when using this script before executing it. You can push very significant changes or CPU intensive commands to a lot of devices in the network from one command or GUI execution. This tool should be used with forethought, and we are not responsible for negligence, misuse, time, outages, damages or other repercussions as a result of using this tool.InstallationRecommended: Use the compiled binary files for your OS (Windows or Mac) available on theJaide GUI github page.If you want to launch the Jaide GUI from a terminal window, you can also installjaideguithrough pip:pip install jaideguiOr by downloading the source code fromgithuband installing it manually:python setup.py installIf installed manually or through pip instead of the compiled versions, simply use thejaideguicommand in any new terminal window to launch the application.
Package: jail
py-jailA native Python wrapper for FreeBSD jails using libc.Usagejail_set>>>importjail>>>jiov=jail.Jiov(dict(persist=None,jid=23,path="/rescue"))>>>jail.dll.jail_set(jiov.pointer,len(jiov),1)23jail_remove>>>importjail>>>jiov=jail.Jiov(dict(persist=None,jid=23,path="/rescue"))>>>jail.dll.jail_remove(23)-1>>>jail.dll.jail_set(jiov.pointer,len(jiov),1)23>>>jail.dll.jail_set(jiov.pointer,len(jiov),1)-1>>>jiov.errmsg.valueb'jail 23 already exists'>>>jail.dll.jail_remove(23)0>>>jail.dll.jail_set(jiov.pointer,len(jiov),1)23ParametersNetworkingNon-VNET jails acceptip.addrandip6.addrparams.
Those can be defined from Pythonipaddress.IPv4Addressandipaddress.IPv6Address.ifconfigbridgecreateinet192.168.1.42/24inet6add2001:db8:10C::42/64importipaddressimportjailjiov=jail.Jiov({"persist":None,"jid":23,"path":"/rescue","ip4.addr":ipaddress.IPv4Address("192.168.1.42")"ip6.addr":ipaddress.IPv6Address("2001:db8:10C::42")})jail.dll.jail_set(jiov.pointer,len(jiov),1)DevelopmentUnit TestsUnit tests may run on FreeBSD or HardenedBSD.Static Code AnalysisThe project enforces PEP-8 code style and MyPy strong typing via flake8, that is required to pass before merging any changes.
Together with Bandit checks for common security issues the static code analysis can be ran on Linux and BSD code execution.make install-dev
make checkReleasesWe try tomanuallykeep aChangelog, following the style onchangelog.md.
New releases are tagged according toSemver, released onPyPi, and published asport.To get a port published, we need tocreate a Bugzilla Issue in the Ports category
Package: jailbreakstatus
# Jailbreak status checkerThis is a very simple checker that uses the [Can I Jailbreak?API](https://canijailbreak.com/) to see if the latest version of iOS has a jailbreak. Runjailbreakstatus. Exit code0means yes and1means no.`sh jailbreakstatus && sendmail me@me.com <<< 'Latest iOS is jailbreakable' `