Saravanan666/pypi-org huggingface dataset:
Package: julgi-game
줄기 게임!안녕 난 줄기야!Q. 뭐하는 곳이야?A. 줄기 게임을 할 수 있어!Q. 어떻게 해?A. 일단 설치부터 해!pipinstalljulgi-game그 다음에서 터미널에서 싸우거나julgi줄기빙수줄기게임!'줄기'vs'빙수'버전:0.1.0,시드:2E3D6555[줄기]HP:14177/공격력:2864/마력:2280/스피드:2480/명중:73/극대:17[빙수]HP:7245/공격력:1521/마력:1453/스피드:2497/명중:76/극대:15[빙수]의공격!치명타![줄기]에게2444!의피해를입혔다!(남은HP:11733/14177)[줄기]의공격![빙수]에게2882의피해를입혔다!(남은HP:4363/7245)[빙수]의공격![줄기]에게1549의피해를입혔다!(남은HP:10184/14177)[줄기]는'빠르게 때리기'를사용!그러나[빙수]는회피했다![빙수]의공격!치명타![줄기]에게2080!의피해를입혔다!(남은HP:8104/14177)[줄기]의공격!그러나[빙수]는회피했다![빙수]의공격!그러나[줄기]는회피했다![줄기]의공격![빙수]에게3144의피해를입혔다!(남은HP:1219/7245)[빙수]는'두 번 베기'를사용![줄기]에게1946의피해를입혔다!(남은HP:6158/14177)[줄기]는'참수'를사용!그러나[빙수]는회피했다![빙수]의공격![줄기]에게1507의피해를입혔다!(남은HP:4651/14177)[줄기]의공격![빙수]에게3112의피해를입혔다!(남은HP:0/7245)[줄기]승!(12턴)직접 함수를 불러와서 싸워!>>>fromjulgiimportgame>>>print(game("줄기","빙수",return_as_list=False))줄기게임!'줄기'vs'빙수'버전:0.1.0,시드:DF9E4AA8[줄기]HP:14177/공격력:2864/마력:2280/스피드:2480/명중:73/극대:17[빙수]HP:7245/공격력:1521/마력:1453/스피드:2497/명중:76/극대:15[빙수]의공격![줄기]에게1607의피해를입혔다!(남은HP:12570/14177)[줄기]의공격![빙수]에게2806의피해를입혔다!(남은HP:4439/7245)[빙수]의공격!치명타![줄기]에게2278!의피해를입혔다!(남은HP:10292/14177)[줄기]의공격!치명타![빙수]에게4136!의피해를입혔다!(남은HP:303/7245)[빙수]의공격![줄기]에게1395의피해를입혔다!(남은HP:8897/14177)[줄기]의공격![빙수]에게2630의피해를입혔다!(남은HP:0/7245)[줄기]승!(6턴)줄기야 인간적으로 너 너무 쌘거 아니냐?난 잘 몰?루Q. 처음 보는 라이센스인데?A. 줄기 라이센스야!A. 스킬 왜이리 적냐Q. 미안 내가 아이디어가 부족해서...
Package: julia
PyJuliaExperimenting with developing a better interface toJulia languagethat works withPython3 and Julia v1.4+.Quick usage$python3-mpipinstalljulia# install PyJulia...                               # you may need `--user` after `install`$python3>>> import julia>>> julia.install()               # install PyCall.jl etc.>>> from julia import Base        # short demo>>> Base.sind(90)1.0See more in thedocumentation.
Package: juliacall
PythonCall & JuliaCallBringingPython®andJuliatogether in seamless harmony:Call Python code from Julia and Julia code from Python via a symmetric interface.Simple syntax, so the Python code looks like Python and the Julia code looks like Julia.Intuitive and flexible conversions between Julia and Python: anything can be converted, you are in control.Fast non-copying conversion of numeric arrays in either direction: modify Python arrays (e.g.bytes,array.array,numpy.ndarray) from Julia or Julia arrays from Python.Helpful wrappers: interpret Python sequences, dictionaries, arrays, dataframes and IO streams as their Julia counterparts, and vice versa.Beautiful stack-traces.Supports modern systems: tested on Windows, MacOS and Linux, 64-bit, Julia 1.6.1 upwards and Python 3.7 upwards.⭐ If you like this, a GitHub star would be lovely thank you. ⭐To get started, read thedocumentation.Example 1: Calling Python from JuliaIn this example, we use the Julia module PythonCall from aPlutonotebook to inspect the Iris dataset:We load the Iris dataset as a JuliaDataFrameusingRDatasets.We usepytable(df)to convert it to a PythonPandas DataFrame.We use the Python packageSeabornto produce a pair-plot, which is automatically displayed.Example 2: Calling Julia from PythonIn this example we use the Python module JuliaCall from an IPython notebook to train a simple neural network:We generate some random training data using Python's Numpy.We construct and train a neural network model using Julia's Flux.We plot some sample output from the model using Python's MatPlotLib.What about PyCall?The existing packagePyCallis another similar interface to Python. Here we note some key differences, but a more detailed comparison is in the documentation.PythonCall supports a wider range of conversions between Julia and Python, and the conversion mechanism is extensible.PythonCall by default never copies mutable objects when converting, but instead directly wraps the mutable object. This means that modifying the converted object modifies the original, and conversion is faster.PythonCall does not usually automatically convert results to Julia values, but leaves them as Python objects. This makes it easier to do Pythonic things with these objects (e.g. accessing methods) and is type-stable.PythonCall installs dependencies into a separate Conda environment for each Julia project. This means each Julia project can have an isolated set of Python dependencies.PythonCall supports Julia 1.6.1+ and Python 3.7+ whereas PyCall supports Julia 0.7+ and Python 2.7+.
Package: julialg
julialgJulia-style arrays in PythonPython library for mimicking Julia LinearAlgebra array indexing style and display formatting.Test Result:Motivation and SummaryJulia'sLinearAlgebrapackage has some nice features, some of which are easier to emulate in Python than others. When
comparing the results of Python code vs Julia code, it is annoying to have to mentally switch between the 0-indexed nature of
Python and the 1-indexed nature of Julia. Also, the Julia arrays have a prettier array.This package wraps numpy n-dimensional arrays in a new class,JulArray, and allows for 1-indexed slicing (more mathematically intuitive)
instead of 0-indexed slicing (computer science convention). Also improves the prettiness of the representation of the
array in a manner similar to Julia's LinearAlgebra package. To be clear, this is a Python package, meant to bring some
of the elegance of Julia's interface for tensors to the Python settingIndexingTheJulArrayclass wraps anumpy.ndarraybut overrides the getitem syntax to allow for 1-indexed style instead of
the default 0-indexed style. For example:>>>importnumpy,julialg# Create a numpy array>>>a=numpy.arange(1,11).reshape((2,5))# Create a JulArray from the numpy array>>>j=julialg.JulArray(a)# Index the numpy array using 0-indexed syntax>>>a[0,0:2]array([1,2])# Index the JulArray using 1-indexed syntax>>>j[1,1:3].arrayarray([1,2])Notice in the above sample that theJulArrayis able to convert both ints and slices from 1-indexed notation to
0-indexed notation to produce the same underlying numpy array.DisplayTheJulArrayoverrides the default representation of the numpy array to be more cleanly formatted (like Julia arrays).>>>julialg.JulArray(numpy.arange(1.0,11.0).reshape((2,5)))2x5Array{float64,2}1.00006.00002.00007.00003.00008.00004.00009.00005.000010.0000
Package: julian
julianjulian is a simple Python library for converting between Julian dates
and Python datetime objects.Examplesimportjulianimportdatetimemjd=54372.78dt=julian.from_jd(mjd,fmt='mjd')print(dt)2007-09-29 18:43:11.999982jd=julian.to_jd(dt+datetime.timedelta(hours=12),fmt='jd')print(jd)2454373.78Installationjulian is tested on python versions >= 3.2pip install julian
Package: juliang
简介巨量IP- API SDK - Python开始Python >= 3.6安装通过pip安装pip install juliang通过源码包安装前往gitee代码托管地址下载最新代码，解压pythonsetup.pyinstallPython 3.9以下的需要将 源码文件Juliang.py中的unquote(request.read(), "utf-8")方法替换为unquote(request.read().decode(), "utf-8")示例importjuliangip.JuliangasJuliangimportjuliangip.common.DynamicGetIpsasdgidynamic_trade_no="1135123856516518735679"dynamic_key="eb9887f742sdfasdfahewrg1e4b71bfe040a5025b55e7"defdynmaic_get_ips():m=dgi.DynamicGetIps(dynamic_key,dynamic_trade_no,"10")m.result_type="json"value=Juliang.dynamic_get_ips(m)print(value)if__name__=='__main__':dynmaic_get_ips()参考资料API文档
Package: julia-project-basic
julia_project_basicThis Python package provides functions to check if a Julia project is properly installed and ready to use.It ensures that registries and packages are installed. It ensures that PyCall.jl is
installed, built, and that the libpython of the running python interpreter is compatible with
the one used to build PyCall.jlInstallpipinstalljulia_project_basicExamplesSimplest useimportosimportjulia_project_basicos.chdir("/path/to/julia/project/")julia_project_basic.ensure_project_ready()You can also useensure_project_ready_fix_pycallwhich does everythingensure_project_readydoes and also checks whethrPyCall.jlis installed,
built and is compatible with the currently running python interpreter.PyCall.jlwill be built if it is not already.
If it is incompatible, the user will be given a choice between recompilingPyCall.jlor
installing everything to a "private" depot.In the case that the Julia project is installed and ready to use,ensure_project_readytakes about 200 micro s to run. Andensure_project_ready_fix_pycalltakes about
200 ms to run. The factor of 1000 is due to starting a julia process and running a bit
of julia code in the second case.OptionsSee the docstrings forensure_project_readyandensure_project_ready_fix_pycallfor
a description of arguments.Detailsensure_project_readydoes the followingchecks if theManifest.toml(orJuliaManifest.toml) exists and is newer thanProject.toml.
It checks if a few directories in the Julia depot are present. It optionally checks if additional
registries are installed. It optionally checks if a supplied list of packages are in theProject.toml.
If any of these checks fail, then
The following steps are taken to install registries, packages, etc. and to runPkg.instantiate.Optionally, registries are installed.Optionally, packages are added to the project (version specs are not supported)The project is instantiated.ensure_project_ready_fix_pycalladditionally checksPyCall.jland tries to fix it if necessary.
Package: julia-semver
julia_semverThis package allows you to work withJulia version specifiersin Python.
It provides three functionsversion- accepts a Julia version string and returns an instance ofsemantic_version.Version.semver_spec- accepts a Julia version specifier string and returns an instance ofsemantic_version.NpmSpec.match-match(spec, vers)returns true if vesionverssatisfies specifierspec.The tools insemantic_versioncan then be used to work with versions and version specifiers.MotivationThe packagesemantic_versioncan represent Julia versions and version specifiers. But, it does not
support the Julia syntax for constructing these representations from strings. This package provides
functions for these constructions that implement the Julia syntax exactly.Installpipinstalljulia_semverDetailsAll of theJulia version specifier format(as of julia v1.8) is supported.The syntax of the version strings and version specifier strings is exactly the same as that described in Julia docs
and implemented in Julia.Semantics of matching and comparisonThe functions and methods insemantic_versionfor comparing and matching differ in some respects from those of Julia.
In particular, in Julia, the prerelease is ignored when matching a version to a version specifier. The functionjulia_semver.matchtries to preserve the Julia semantics:semver_spec("1").match(version("1.1.2-DEV"))# Falsejulia_semver.match("1","1.1.2-DEV")# Truejulia_semver.match("1","1.1.2-DEV",strict=True)# FalseExamplesSee thetest suitefor more examples.fromjulia_semverimportsemver_spec,versionversion('1.8')insemver_spec('^1.7.2')version('0.8')notinsemver_spec('^0.7.2')
Package: juliaup
pyjuliaupA Python interface toJuliaUp, the Julia version
manager.InstallpipinstalljuliaupUsageFind JuliaUpexecutable()finds the JuliaUp executable.version()returns version of the JuliaUp executable.available()returns True if JuliaUp is available.install(interactive=True)installs JuliaUp. You don't normally need to call this as the
other API functions automatically install JuliaUp if required.meta()returns the parsed contents ofjuliaup.json, which includes information about
installed versions of Julia.Run JuliaUpstatus()print the status.add(channel)adds a channel.remove(channel)removes a channel.update(channel=None)updates all channels or the given channel.default(channel)sets the default channel.link(channel, file)links a Julia executable to a channel.self_update()updates JuliaUp itself.run(args, **kw)runs JuliaUp with the given arguments. Keyword arguments are passed on tosubprocess.run.
Package: julpyter
Jupyter Notebook to Learn Challenge===============================julpyter = julia + **Learn** + python + Rversion number: 0.0.2author: Edward BannerOverview--------A python package which provides an nbconvert exporter for converting jupyter notebooks to a Galvanize Learn challengesInstallation / Usage--------------------To install use pip:$ pip install julpyterOr clone the repo:$ git clone https://github.com/embanner/julpyter.git$ python setup.py installContributing------------TBDExample-------1. Develop your Learn challenge in a jupyter notebook called `050_lr.coding.md.ipynb`![](examples/images/ipynb.png)2. Run```bash$ jupyter nbconvert --to learn 050_lr.coding.md.ipynb```to convert the jupyter notebook to a Learn-style challenge markdown file `050_lr.coding.md`.![](examples/images/markdown.png)3. Upload `050_lr.coding.md` to Learn![](examples/images/learn.png)See more examples in [`examples/`](examples).Conventions-----------The file name format needs to follow the convention `<name>.<challenge_type>.md.ipynb` where `<challenge_type>` is one of- coding- multiple_choice- number- paragraph- rawjulpyter relies on jupyter tags to pull out sections which correspond to sections within Learn challenges.
Package: july
JulyA small library for creating pretty heatmaps of daily data.FeaturesGet rid of the eternal matplotlib tweaking every time you want to plot data in proper calendar format.Generate GitHub activity overview-like heatmaps of your daily data.Automatic handling of missing dates in input date range.Julydoes not rely only pandas (though it accepts it). Only numpy arrays and native Python data structures are used internally.Accepted date formats:datetime.datetime,datetime.date,str,pd.DatetimeIndexInstall$ pip install julyUsageimport numpy as np
import matplotlib.pyplot as plt
import july
from july.utils import date_range

dates = date_range("2020-01-01", "2020-12-31")
data = np.random.randint(0, 14, len(dates))# GitHub Activity like plot (for someone with consistently random work patterns).
july.heatmap(dates, data, title='Github Activity', cmap="github")# Here, 'osl_df' is a pandas df. 
july.heatmap(osl_df.date, osl_df.temp, cmap="golden", colorbar=True, title="Average temperatures: Oslo , Norway")# More exhaustive example using useless, but pretty colours.
july.heatmap(dates=dates, 
             data=data, 
             cmap='Pastel1',
             month_grid=True, 
             horizontal=True,
             value_label=False,
             date_label=False,
             weekday_label=True,
             month_label=True, 
             year_label=True,
             colorbar=False,
             fontfamily="monospace",
             fontsize=12,
             title=None,
             titlesize='large',
             dpi=100)# Month plot with dates.
july.month_plot(dates, data, month=5, date_label=True, ax=axes[0])
# Month plot with values.
july.month_plot(dates, data, month=5, value_label=True, ax=axes[1])# Calendar plot. 
july.calendar_plot(dates, data)Why "July"?Main reason:All the obvious names likecalplot,calmap, andcalendarplotwere all already taken by similar packages. This had me looking for a new name that wouldn't get easily mixed up with the other packages.The reasoning was roughly as follows:Heatmap+month→Hot month→July:sparkles:Also, as a summer loving person stuck in the Northern hemisphere, July is my favourite month by a light year.Release notesv0.1.0: Working build but with minimal documentation.v0.1.1: Fix relative image link in readme.v0.1.2: Remove unnecessary argument from rcmod to be compatible with matplotlib versions earlier than v3.4.x.v0.1.3: Fix week number labelling bug inmonth_plot()andcalendar_plot()TODO:Fix slight misalignment of plot and cbar whendate_gridandcolorbarare used in conjunction.Document everything...Add type hints.Add automatic date handling for strings of more types than justYYYY-MM-DD.
Package: jumandic
jumandic-pyThis packages JumanDic for MeCab for installation via PyPI.JumanDic is unmaintained and has not been updated in over ten years; this
package is provided for compatibility, but should not be used for new projects.LicenseJumanDic is copyright 2005 the University of Tokyo and is distributed under the
terms of the BSD License. See the included LICENSE.jumandic file for details.The code original to this repository is released under the MIT License.The version of JumanDic in this repository was taken from the most recent
version of MeCab.
Package: jumany
Python interface forJUMAN Morphological analysis system.RequirementPOSIX or WindowsPython 3.3 or aboveInstallationpip install jumanyHow to useimport jumany
jumany.open_lib()
jumany.analyze("吾輩は猫である")Seejumany pagefor more details in Japanese.LicensePython scripts: BSD LicenseLibrary: BSD LicensePre-built library for Windows: BSD License, additionally libraries under LGPL are linked.
Package: jumblecsv
Tool for jumbling csv data files.Currently it is expected that the CSV file used comma seperation and full-stopdecimals.Features:See the program help message below.Author:Rasmus KlitgaardFeel free to use the program - it is licenced under GPL-v3.0, sodo keep uses to within the licence.If you use the program for professional work or scientific publicationsplease provide a reference to the gitlab page.The source code is available at https://gitlab.com/RasmusKlitgaard/jumblecsv.usage: jumblecsv [-h] [-p JUMBLING_PERCENT] [-c CATEGORICAL_SWITCH_PROBABILITY][-d [DROP_COLUMNS ...]] [-l [CATEGORICAL_COLUMNS ...]][-o OUTPUT_FILE] [--not-all-categorical-parameters-present][--block-negative] [-n NUMBER_OF_HEADER_ROWS][--significant-figures SIGNIFICANT_FIGURES][--float-formatting FLOAT_FORMATTING][--int-formatting INT_FORMATTING]csv_pathTool for jumbling data, removing data and reformatting data in CSV format.positional arguments:csv_path              Path to the .csv file. Note the file has to be commaseperated with full-stop decimals , not semicolon withcomma decimal.options:-h, --help            show this help message and exit-p JUMBLING_PERCENT, --jumbling-percent JUMBLING_PERCENTPercentage to jumble non-categorical values in %-c CATEGORICAL_SWITCH_PROBABILITY, --categorical-switch-probability CATEGORICAL_SWITCH_PROBABILITYProbability to change a categorical parameter in %-d [DROP_COLUMNS ...], --drop-columns [DROP_COLUMNS ...]List of column indices to drop in the new table-l [CATEGORICAL_COLUMNS ...], --categorical-columns [CATEGORICAL_COLUMNS ...]List of column indices containing a categoricalparameter-o OUTPUT_FILE, --output-file OUTPUT_FILEWrite the resulting CSV file to this path--not-all-categorical-parameters-presentSet this if all possible values of categoricalparameters are not present in the data. If set datawill be interpolated, so we assume the outer valuesare represented.--block-negative      Caps values at a minimum of 0-n NUMBER_OF_HEADER_ROWS, --number-of-header-rows NUMBER_OF_HEADER_ROWSNumber of header rows in the input CSV file--significant-figures SIGNIFICANT_FIGURESNumber of significant figures to use when printingfloats. If neither '--significant-figures', '--float-formatting' is set, the values will be representedcentrally, as wide as the header of the column--float-formatting FLOAT_FORMATTINGFloat formatting to use when printing. E.g. '4.2f', '^8.2f'. Whatever is accepted by your pythoninterpreter 'print' function should work.--int-formatting INT_FORMATTINGInteger formatting to use when printing. E.g. '4d', '^8d'. Whatever is accepted by your python interpreter'print' function should work. If not set the valueswill be represented centrally, as wide as the headerof the column
Package: jumbo
📑 Welcome to Jumbo!Jumbo is a wrapper of the amazingpsycopg2- the
most
popularPostgreSQLdatabase adapter for the
Python programming language.Jumbo has been designed specifically for adoption in
environments where thorough widespread knowledge of SQL protocols might
be lacking and a streamlined approach to database interactions might be
needed.
Jumbo offers an intuitive and quickly deployable interface to successfully
implement a database-centred data analysis pipeline at all levels of your
team or organisation.Jumbo is intuitive yet customisable - first-time users can
easily interact with the database without worrying about handling
transactions under the hood. At the same time, experienced PostgreSQL
architects can
still
unleash the full power ofpsycopg2and exploit its more advanced
functionalities.🚀 Quick Start📚 Install jumbo like any other python package, using pip to download it
from PyPI:$ pip install jumbo🐘 Create ajumbo.envfile in the root directory of your project with
the
following structure:DATABASE_HOST = <my_database_host_address>
    DATABASE_USERNAME = <my_database_user_name>
    DATABASE_PASSWORD = <my_database_user_password>
    DATABASE_PORT = <my_database_port>
    DATABASE_NAME = <my_database_name>🐍 Test your installation running the following minimal script:importjumbo# Initialize database connectiondatabase=jumbo.database.Database()# Open a connection pool.withdatabase.open()aspool:# Get an individual connection from the pool.withpool.connect():pass📚 DocumentationTo learn more about the package head over to theofficial documentation!
Package: jumeaux
JumeauxCheck difference between two responses of API.📚 Documentationhttps://tadashi-aikawa.github.io/jumeaux/There is a 🎥demo, too.🦉 InstallSeequick start in documentation.💻 For developerRequirementspoetrymakeFlowDevelopment on master and if you need branches and issues, create themCommit with prefix emoji such as "📝", and suffix issue number like "#120"CommandsCreate environment$ poetry env use <path of python 3.8>
$ poetry installRun$ poetry run python jumeaux/main.py <args>Serve docs$ make serve-docsUnit test$ make testIntegration test$ make test-e2e📦 Release1. Update release note (mkdocs/ja/releases/*)$ git commit -m "📝 Update release note"
$ git push2. Release with GitHub Actionshttps://github.com/tadashi-aikawa/jumeaux/actions?query=workflow%3ARelease(Appendix) Another wayIf you can't or don't want to use GitHub Actions, you can release locally as following.(a1) RequirementsWindows is not supported!!!poetry (with pypi authentications)make(a2) Commandsmakereleaseversion=x.y.z🎫 LicenceMITThis software is released under the MIT License, see LICENSE.txt.
Package: jumonc-llview
Installation|Execution|Further documentation|Getting help|Acknowledgments|JuMonC-llviewA plugin forJuMonCto enable interaction with llviewInstallationjumonc-llview can be installed usingpip:pip install jumonc-llviewThis automatically registers as a JuMonC plugin, and will be started with the next JuMonC (if they are both in the same enviroment)Runtime dependenciespycryptodomeRuntime dependencies inherited from JuMonCpython3minimum version 3.6mpi4pyflaskflask-loginFlask-SQLAlchemypluggytyping-extensionin case you are using python version <=3.9ExecutionThis plugin is started withJuMonC - see execution.
The paths returned can be set usingjumonc --llview="api-paths=example/path/"when starting JuMonCGetting helpOpen anissueAcknowledgmentsJuMonC has been developed as part of theCoECproject, the CoEC project has received funding from the European Union’s Horizon 2020 research and innovation programm under grant agreement No 952181.
Package: jumonc-nekrs
Installation|Execution|Further documentation|Getting help|Acknowledgments|JuMonC-nekRSA plugin forJuMonCto enable interaction and data retrival from nekRS simulationsInstallationjumonc-nekrs can be installed usingpip:pip install jumonc-nekrsThis automatically registers as a JuMonC plugin, and will be started with the next JuMonC (if they are both in the same enviroment)Runtime dependencies inherited from JuMonCpython3minimum version 3.6mpi4pyflaskflask-loginFlask-SQLAlchemypluggytyping-extensionin case you are using python version <=3.9ExecutionThis plugin is started withJuMonC - see execution.Getting helpOpen anissueAcknowledgmentsJuMonC has been developed as part of theCoECproject, the CoEC project has received funding from the European Union’s Horizon 2020 research and innovation programm under grant agreement No 952181.