Saravanan666/pypi-org huggingface dataset:
Package: j-realty-api
j_realty_api: Knowing the real estate transaction prices in Japan.What is it?The j_realty_api is a wrapper for an API that allows you to retrieve real estate transaction prices in Japan, utilizing theReal Estate Transaction Price Information Retrieval APIprovided by Japan's Ministry of Land, Infrastructure, Transport and Tourism. It makes the use of the API more convenient. It supports both Japanese and English APIs.A feature of the tool is that it includes a tool to obtain prefecture codes and municipality codes, making it easier to search for data.j_realty_apiは、日本の国土交通省が出している不動産取引価格情報取得APIを使い、日本の不動産取引価格を取得できる、APIのラッパーです。APIの利用が容易になります。日本語API、英語APIの両方に対応しています。ツールの特徴は、都道府県コードと市町村コードを取得できるツールを加えて、データを探しやすくした点です。Installationインストール方法pipinstallj_realty_apipipinstallgit+https://github.com/mazarimono/j_realty_api.gitHow to Use使い方Englishsample notebook# Englishfromj_realty_apiimportj_realty_enasjrnj=jrn.CityCode('Kyoto')pref_code=j.pref_codecity_code=j.city_code('kyoto')pt=jrn.PropTransactions(pref_code,city_code,20223,20224)df=pt.get_data()# Can get Prices with forex(USD, EUR, CNY) (from ver0.2)df_forex=pt.to_forex(df,'USD')日本語sample notebookfromj_realty_apiimportj_realty_jpasjrpj=jrp.CityCode('京都')pref_code=j.pref_codecity_code=j.city_code('京都')pt=jrp.PropTransactions(pref_code,city_code,20223,20224)df=pt.get_data()LicenceMIT
Package: jref
python-jrefThis python package implements the JSON Reference and JSON Pointer
specifications in the context of processing JSON and YAML content, such as
Swagger and OpenAPI specifications.JSON ReferenceJSON Reference defines a mechanism to reference and include content from the
same or separate documents. This helps with maintenance of structured data and
documents by reducing repetition, and allowing for the organization of larger
documents across multiple files.JSON PointerJSON Pointer defines a syntax for identifying specific portions of a JSON
value.In a document, a JSON Reference takes the form of an object, mapping '$ref' to a
reference; other keys in the object are ignored. The reference itself takes the
form of a URI. As an example:{ "$ref": "http://example.com/example.json#/foo/bar" }In this implementation, the URI may reference a JSON or YAML file available in
the local filesystem, or served from the network over HTTP/HTTPS. The fragment
portion of the URI is interpreted as a JSON Pointer.Usage exampleFind the example below also asusage-example.pyin the
source repository.importosimporttextwrapimportjref.contextimportjref.pointer# All references are evaluated in a context, so start with onectx=jref.context.RemoteContext()# Reference a remote documentspec=ctx.parse_reference('https://raw.githubusercontent.com/OAI/OpenAPI-Specification/master/examples/v3.0/petstore.yaml')# Reference portions of that documentspec_title=spec.context.parse_reference('#/info/title')spec_version=spec.context.parse_reference('#/info/version')# Print out the references, not the content (which hasn't been loaded)print(textwrap.dedent('''* JSON ReferencesSpec:{}Title:{}Version:{}''').lstrip().format(spec,spec_title,spec_version))# Expand references, triggering loading of contentprint(textwrap.dedent('''* Evaluated referencesTitle:{}Version:{}''').lstrip().format(spec_title.expand(),spec_version.expand()))Expected output:* JSON References
Spec:    https://raw.githubusercontent.com/OAI/OpenAPI-Specification/master/examples/v3.0/petstore.yaml
Title:   https://raw.githubusercontent.com/OAI/OpenAPI-Specification/master/examples/v3.0/petstore.yaml#/info/title
Version: https://raw.githubusercontent.com/OAI/OpenAPI-Specification/master/examples/v3.0/petstore.yaml#/info/version

* Evaluated references
Title:   Swagger Petstore
Version: 1.0.0ReferencesJSON Reference, Internet Draft,https://tools.ietf.org/html/draft-pbryan-zyp-json-ref-03JSON Pointer, RFC 6901,https://tools.ietf.org/html/rfc6901
Package: jrender
jrenderjrenderis a command-line tool that rendersJinja template filesusing a
context built from YAML files and mappings supplied on the command line. It is primarily designed for quickly testing
template files.InstallationRequires Python version 3.9 or higher and pip.pipinstalljrenderExample# Create a template file>echo"Hello {{name}}">hello.jinja# Render the template by supplying values>jrenderhello.jinjaname=World# Values provided through the command line are always interpreted as strings. For other data types or more complex data# structures, provide the context in a YAML file.>jrenderhello.jinja-fvars.yaml
Package: j-repo-tools
This repo contains a number of tools Open edX uses for working with GitHub
repositories.oep2: Report onOEP-2compliance across repositories.tag_release: Tags multiple repos as part of the release process.Setting up GitHub authenticationMost of these make GitHub API calls, and so will need GitHub credentials in
order to not be severely rate-limited.  Edit (or create)~/.netrcso that it
has an entry like this:machine api.github.com
  login your_user_name
  password ddf9079e12042ac022c101c61c0235965851e209Change the login to your GitHub user name.  You’ll get the password value fromhttps://github.com/settings/applications.  Visit that page, click on Developer
Settings and in the section called “Personal access tokens,” click “Generate new token.”
It will prompt you for your password, then you’ll see a scary list of scopes. Check
the “repo” option and click “Generate token.” Copy the password that
appears. Paste it into your ~/.netrc.Working in the repoTo work on these tools:Use a virtualenv.Install dependencies:make dev-installRun tests:make testOlder tools were Python files run from the root of the repo.  Now we are
being more disciplined and putting code into importable modules with entry
points in setup.py.Older ToolsThere are many programs in this repo in various stages of disrepair.  A few
of them are described in this repo’solder README.mdfile.  Others are not
described at all, but may be useful, or have useful tidbits in the code.FeedbackPlease send any feedback tooscm@edx.org.
Package: jrfapp
Jrfapp PackageIntroduction:The Jrfapp stands for joint inversion of the Receiver Function and apparent velocity data. This is a python package to perform joint inversion of these datasets and outputs an estimated shear velocity model. For more info see "manuscript title".Installation:To run this code, you will need the following software and tools:Computer Program in SeismologyPython 3.8matplotlibnumpyobspyrfYou can install Computer Program in Seismology (CPS) fromhere.You need to path the binary of CPS in your .bashrc. Before proceeding to the next step make sure that this program
is installed correctly.
This package depends on the hrftn96. If you installed CPS correctly and included it in your .bashrc the output of
hrftn96 in the terminal should look like this:
`Model not specified
USAGE: hrftn96 [-P] [-S] [-2] [-r] [-z] -RAYP p -ALP alpha -DT dt -NSAMP nsamp -M model
-P           (default true )    Incident P wave
-S           (default false)    Incident S wave
-RAYP p      (default 0.05 )    Ray parameter in sec/km
-DT dt       (default 1.0  )    Sample interval for synthetic
-NSAMP nsamp (default 512  )    Number samples for synthetic
-M   model   (default none )    Earth model name
-ALP alp     (default 1.0  )    Number samples for synthetic
H(f) = exp( - (pi freq/alpha)**2)
Filter corner ~ alpha/pi
-2           (default false)    Use 2x length internally
-r           (default false)    Output radial   time series
-z           (default false)    Output vertical time series
-2  (default false) use double length FFT to
avoid FFT wrap around in convolution
-D delay     (default 5 sec)    output delay sec before t=0
-?                   Display this usage message
-h                   Display this usage message
SAC header values set by hrftn96
B     :  delay
USERO :  gwidth        KUSER0:  Rftn
USER4 :  rayp (sec/km)
USER5 :  fit in % (set at 100)
KEVNM :  Rftn          KUSER1:  hrftn96
The program creates the file names hrftn96.sac
This is the receiver fucntion, Z or R trace according to the command line flag
`All the python package requires for Jrfapp and this package can be installed bypip install jrfapp.Tip:I highly recommend creating a conda environment and installing the package in this environment.Examples:I have included four tutorials on the GitHub page that explain the main usage of the package.
Package: jrImgTools
jxImgToolsA simple toolbox for processing and visualizing medical images.FunctionsVisualizationVolSliceA tool to visualize 3D volume and segmentations in the same time.# Create fake dataimportnumpyasnpimportmatplotlib.pyplotaspltvol=np.zeros((50,128,128))vol[20:30,40:88,40:88]=1vol+=0.1*np.random.rand(*vol.shape)# plt.imshow(vol[25,:,:], cmap='gray')seg1=np.zeros((50,128,128))seg1[20:30,40:88,40:88]=1seg2=np.zeros((50,128,128))seg2[20:29,45:95,45:95]=1seg2[29,40:88,40:88]=1fig,axs=plt.subplots(1,3)axs[0].imshow(np.squeeze(vol[25,:,:]),cmap='gray')axs[1].imshow(np.squeeze(seg1[25,:,:]),cmap='gray')axs[2].imshow(np.squeeze(seg2[25,:,:]),cmap='gray')# Visualize using VolSlicerfromjximgtools.metricimportdicePerSlice,dicefromjximgtools.visualization.volSlicerimportVolSlicerdicee=dice(seg1,seg2)dices=dicePerSlice(seg1,seg2)volInfo={'Name':'Test Volume','DicePerSlice':dices}slicesInfo=[{'Dice':dices[sliceIdx]}forsliceIdxinrange(50)]VolSlicer(vol,volInfo=volInfo,segs=[seg1,seg2],slicesInfo=slicesInfo)Installationpipinstalljximgtools
Package: jrl
JrlJrl ('Jeremy's robotics library') is a robotics library containing robot models for popular robots as well as efficient, pytorch basedparallelizedimplementations of forward kinematics, inverse kinematics, and robot-robot + robot-environment collision checking. Robot models include (run withscripts/visualize_robot.pyto view):Franka PandaIiwa 7dof manipulatorFetch 8dof mobile manipulatorAvailable operations include (all part of theRobotclass):forward_kinematics_batch(): batched forward kinematicsjacobian_batch_pt(): batched manipulator jacobian calculation (change of the end effector's pose w.r.t. each joint angle)inverse_kinematics_single_step_levenburg_marquardt(): computes a single batched inverse kinematics step using Levenburg-Marquardt optimizationinverse_kinematics_single_step_batch_pt(): computes a single batched inverse kinematics step using the traditional jacobian pseudo-inverse methodself_collision_distances_batch(): batched self-collision distance calculationself_collision_distances_jacobian_batch(): batched calculation of the jacobian of the pair-wise self-collision distances w.r.t. joint anglesenv_collision_distances_batch(): batched self-environment distance calculationenv_collision_distances_jacobian_batch(): batched calculation of the jacobian of the self-environment distances w.r.t. joint anglesQuickstart code.This script will load a Panda robot model and then run forward and inverse kinematics on randomly sampled configs. See demo.py for the complete script, which includes robot-robot and robot-environment collision checking.fromjrl.robotsimportPandafromjrl.evaluationimportpose_errors_cm_degimporttorchdefassert_poses_almost_equal(poses_1,poses_2):pos_errors_cm,rot_errors_deg=pose_errors_cm_deg(poses_1,poses_2)assert(pos_errors_cm.max().item()<0.01)and(rot_errors_deg.max().item()<0.1)robot=Panda()joint_angles,poses=robot.sample_joint_angles_and_poses(n=5,return_torch=True)# sample 5 random joint angles and matching poses# Run forward-kinematicsposes_fk=robot.forward_kinematics_batch(joint_angles)assert_poses_almost_equal(poses,poses_fk)# Run inverse-kinematicsik_sols=joint_angles+0.1*torch.randn_like(joint_angles)foriinrange(5):ik_sols=robot.inverse_kinematics_single_step_levenburg_marquardt(poses,ik_sols)assert_poses_almost_equal(poses,robot.forward_kinematics_batch(ik_sols))Note: This project uses thew,x,y,zformat for quaternions.InstallationRecommended: clone the repo and install with pipgit clone https://github.com/jstmn/jrl.git && cd jrl/
pip install -e .
# or:
pip install -e ".[dev]"Second option: Install from pypi (not recomended - the pypi version will likely be out of date until this project hardens)pipinstalljrl
Package: jrm09
jrm09JRM09 model (Connerney et al. 2018) implementation using Python.InstallationInstall usingpip:pip3installjrm09--userOr by cloning this repo:gitclonehttps://github.com/mattkjames7/jrm09.gitcdjrm09#EITHER create a wheel and install (replace X.X.X with the version number):python3setup.pybdist_wheel
pip3installdist/jrm09-X.X.X-py3-none-any.whl--user#OR install directly using setup.pypython3setup.pyinstall--userUsageThe model accepts right-handed System III coordinates either in Cartesian form (jrm09.ModelCart()) or in spherical polar form (jrm09.Model()), e.g.:importjrm09#get some Cartesian field vectors (MaxDeg keyword is optional)Bx,By,Bz=jrm09.ModelCart(x,y,z,MaxDeg=10)#or spherical polar onesBr,Bt,Bp=jrm09.Model(r,theta,phi,MaxDeg=10)Please read the docstrings forjrm09.Model()andjrm09.ModelCart()usinghelpor?e.g.help(jrm09.Model).There is also a test function which requiresmatplotlibto be installed:#evaluate the model at some Rjrm09.Test(R=0.85)which produces this (based on figure 4 of Connerney et al. 2018):ReferencesConnerney, J. E. P., Kotsiaros, S., Oliversen, R. J., Espley, J. R.,  Joergensen, J. L., Joergensen, P. S., et al. (2018). A new model of Jupiter's magnetic field from Juno's first nine orbits. Geophysical Research Letters, 45, 2590– 2596.https://doi.org/10.1002/2018GL077312
Package: jrm33
jrm33JRM33 model (Connerney et al. 2022) implementation using Python.InstallationInstall usingpip:pip3installjrm33--userOr by cloning this repo:gitclonehttps://github.com/mattkjames7/jrm33.gitcdjrm09#EITHER create a wheel and install (replace X.X.X with the version number):python3setup.pybdist_wheel
pip3installdist/jrm33-X.X.X-py3-none-any.whl--user#OR install directly using setup.pypython3setup.pyinstall--userUsageThe model accepts right-handed System III coordinates either in Cartesian form (jrm33.ModelCart()) or in spherical polar form (jrm33.Model()), e.g.:importjrm33#get some Cartesian field vectors (Deg keyword is optional)Bx,By,Bz=jrm33.ModelCart(x,y,z,Deg=13)#or spherical polar onesBr,Bt,Bp=jrm33.Model(r,theta,phi,Deg=13)Please read the docstrings forjrm33.Model()andjrm33.ModelCart()usinghelpor?e.g.help(jrm33.Model).There is also a test function which requiresmatplotlibto be installed:#evaluate the model at some Rjrm33.Test(R=0.85)which produces this (based on figure 4 of Connerney et al. 2018):ReferencesConnerney, J. E. P., Kotsiaros, S., Oliversen, R. J., Espley, J. R.,  Joergensen, J. L., Joergensen, P. S., et al. (2018). A new model of Jupiter's magnetic field from Juno's first nine orbits. Geophysical Research Letters, 45, 2590– 2596.https://doi.org/10.1002/2018GL077312Connerney, J. E. P., Timmins, S., Oliversen, R. J., Espley, J. R., Joergensen, J. L., Kotsiaros, S., et al. (2022). A new model of Jupiter's magnetic field at the completion of Juno's Prime Mission.Journal of Geophysical Research: Planets, 127, e2021JE007055.A New Model of Jupiter's Magnetic Field at the Completion of Juno's Prime Mission - Connerney - 2022 - Journal of Geophysical Research: Planets - Wiley Online Library
Package: jrnl
jrnlTo get help,submit an issueon
GitHub.jrnlis a simple journal application for the command line.You can use it to easily create, search, and view journal entries. Journals are
stored as human-readable plain text, and can also be encrypted usingAES
encryption.In a NutshellTo make a new entry, just enterjrnlyesterday:Calledinsick.Usedthetimetocleanthehouseandwritemy
book.yesterday:is  interpreted byjrnlas a timestamp. Everything until the
first sentence ending (either.,?, or!) is interpreted as the title, and
the rest as the body. In your journal file, the result will look like this:[2012-03-29 09:00] Called in sick.
Used the time to clean the house and write my book.If you just calljrnl, you will be prompted to compose your entry - but you
can also configurejrnlto use your external editor.For more information, please read thedocumentation.ContributorsMaintainersOur maintainers help keep the lights on for the project:Jonathan Wren (wren)Micah Ellison (micahellison)Please thank them if you likejrnl!Code ContributorsThis project is made with love by the many fabulous people who have contributed.jrnlcouldn't exist without each and every one of you!If you'd also like to help makejrnlbetter, please see ourcontributing
documentation.Financial BackersAnother way show support is through direct financial contributions. These funds
go to covering our costs, and are a quick way to show your appreciation forjrnl.Become a financial contributorand help us sustain our community.
Package: jrnr
Job Runner for Climate Impact Lab JobsFree software: MIT licenseDocumentation:https://jrnr.readthedocs.io.jrnr is a tool to help you create, manage, and monitor your highly parallelizable jobs.FeaturesJob set-up utility for running parallel jobsCurrently only supports running slurm jobsCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: jrpyspark
Python packagesThese packages were created using the poetry python package.https://github.com/sdispater/poetryTo rebuild this package, clone the repo and install poetry.The following commands at the project root will then be usefulpoetry install- grab and install all the dependencies necessary for building the package.poetry build- build the wheels and tar for the packagepoetry publish- publish the package to pypiIf you want to build your own package from scratch, the README for poetry is a good source.
Package: jrpytests
jrpytestsOverviewThis package contains functions for CI testing of JR python packages and was created using the poetry python package, for more details seehttps://github.com/sdispater/poetry.jrpytests allows:to run pytest,to check coding style (via flake8) in python files,to extract python chunks from Rmd files and check their coding style,to check if a directory 'vignettes' exists in appropriate folder and,to count pdf files in 'vignettes' and compare to number of Rmd files.The flake8 configuration file is stored in this package, seejrpytests/flake8_config.ini.Basic usageImport the package usingimport jrpytests.Run pytestjrpytests.runpytests().Run flake8 in python filesjrpytests.runflake8pythonfiles().Run flake8 in Rmd python chunksjrpytests.runflake8rmdpychunks().Check 'vignettes' and number of pdf files in itjrpytests.checkvignettespdffiles().
Package: jrsub
JRSubУстановкаpip install jrsubИспользованиеЗагрузка ранее сгенерированной словарной базыЯРКСИyl = YarxiLoader() // экземпляр загрузчика
yd = yl.load() // словарьWarodaiwl = WarodaiLoader() // экземпляр загрузчика
wd = wl.load() // словарьПоиск в словаряхyd.lookup(lexeme: str[, reading: str, search_mode: SearchMode])→[SearchResult]возвращает списокSearchResult, включающих в себя поляlexeme,readingиtranslationиз подходящих записей;все три поля являются списками строк.yd.lookup_translations_only(lexeme: str[, reading: str, search_mode: SearchMode])→[str]возвращает значения полей "перевод" из подходящих записей в виде одномерного списка строк.ПараметрДопустимые значенияОбязательный параметрlexemeхирагана, катакана, кандзи✔️readingхирагана❌search_modeSearchMode.consecutive,SearchMode.deep_only,SearchMode.shallow_only❌SearchMode.shallow_only: поиск осуществляется только по исходным формам, дословно приведенным в словаре. Малейшие изменения в слове (например, лишние символы окуриганы или их отсутствие) приведут к пустой выдаче словаря.SearchMode.deep_only: задает "глубокий" режим поиска: при поиске в первую очередь учитываются входящие в слово иероглифы. Этот режим работает медленнее, чемSearchMode.shallow_only, но при этом он гораздо надежнее.SearchMode.consecutive(задан по умолчанию): вначале запускается "поверхностный поиск", а затем, если он не дал результатов, -- "глубокий". Это оптимальный из всех трех вариантов, лучше его не изменять.Пересборка словарных базМетоды загрузчиковДействиеМетодАргументыНачальное значениеВключение/откючение транслитерации примеров в переводах.enable_transliterationmode:boolTrue(включено)Изменение префикса к переводам, свойственным иероглифам в составе сложных слов(только для ЯРКСИ).set_compounds_prefpref:str'〈в сочет.〉'Изменение способа выделения некоторых частей перевода.set_highlightingleft:str; right:str'《';'》'Запуск перестройки словарной базы.rescanpath:str; show_progress:boolпапкаdictionaries/sourceв каталоге установки пакета;TrueДля отображения в браузере можно при помощиset_highlightingзаменить кавычки-елочки на HTML-теги:yl.set_highlighting('<i>', '</i>');Перестройка одной базы занимает около восьми минут: полосы прогресса позволяют отследить, на какой стадии база находится в данный момент.При перестройке рекомендую использовать отредактированные мной варианты исходников, доступные в репозитории в каталогеdictionaries/source(список изменений вЯркси|Вародае; в основном изменения касаются форматирования, но также было найдено и исправлено несколько опечаток).Методы полученных после сборки словарных базДействиеМетодАргументыЗначение по умолчаниюСохранение перестроенной базы.savepath:strпапкаdictionariesв каталоге установки пакетаЕсли оставить значение пути по умолчанию, база будет сохраняться в установочный каталог пакета, и при следующем запуске к ней можно будет получить доступ методом загрузчика.load()без параметров.Примеры работы со словарями (demo)cli.py-- самый простой пример взаимодействия через командную строку;app.py-- веб-интерфейс для работы через браузер;flask.Dockerfile-- docker-контейнер с приложением из файлаapp.py.
Package: jr_tools
JasperReports ToolsA collection of tools to handle Jasper Reports with pythonFree software: MIT licenseDocumentation:http://jr-tools.readthedocs.io.Tested with JasperServer CE 6.4FeaturesClient to get reports in API available formats(PDF, xls, etc)CLI: runjr_tools--helpto get the list of available commandsCLI: load resources from yaml filejr_tools load path_to_yaml_fileDevelopmentFor development there is a docker-compose based configuration to start jasper server and mysql.Use the below commands to handle the docker setup:make docker_up: this will launch docker-compose services, it’s going to take a few minutes to download the required images and setup everything.make docker_down: this will shutdown the launched containers.make mysql_shell: this will launch a mysql console to interact with the database, by default it connects todemodatabase.make mysql_shell_root: the same as above but use therootuser.Credentials:Jasper Server:username:jasperadminpassword:jasperadminMySQL:username:demopassword:demoroot password:rootdefault database:demoAfter the setup is complete you can enter tohttp://localhost:8080and login using the credentials from above.TODODjango helper to consume reports and converto to Django responsesCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.3.1 (2018-10-24)Pin Click version to 6.X to use underscore command names0.3.0 (2017-11-06)Add option to choose if a parameters must be mandatory0.2.0 (2017-10-31)Add suport to upload and configure files and reports to JasperServer using a yml file0.1.0 (2017-07-30)First release on PyPI.
Package: jrun
Run jupyter notebooks as command line scripts with variable overridesFor making variables overridable, wrap them usingjinas shown belowfromjrunimportjin# First parameter is identifier, second is default valueseed=jin("seed",1234)speed=jin("speed",45)trial_number=jin("tno",1)# Other codeRunning the notebook directly (jrun notebook.ipynb)  will use the default
parameter values. To override any of them, pass another argument to jrun
specifying the override in plain python likejrun notebook.ipynb "tno = 2; seed = 2313".