Saravanan666/pypi-org huggingface dataset:
Package: kim2014convolutional
# kim2014convolutionalThis package provides a simple implementation of the models proposed inthe paper:> Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.## InstallationThis package depends on the [Keras](https://keras.io/) library. Thismeans you will need to install a backend library in order to use thismodule. Take a look to [Keras installation](https://keras.io/#installation)to get more information.After having installed the backend of yout choice, you just need toinstall this package using [pip](https://pypi.org/):pip install kim2014convolutional## UsageThis package only provides a single model. To get detailed informationon the parameters the model accepts, take a look to the documentationincluded with the module class.Here is a complete example of instantiation of the `CNN-multichannel`model proposed in the original paper using two channel of randomlyinitialized word embeddings:```pythonimport numpy as npimport numpy.random as rngvocabulary_size = 10000embedding_size = 300value = np.sqrt(6/embedding_size)weights_shape = (vocabulary_size+1, embedding_size)weights = rng.uniform(low=-value, high=value, size=weights_shape)channels = [{'weights': [weights],'trainable': False,'input_dim': vocabulary_size + 1,'output_dim': embedding_size,'name': 'random-embedding-1'},{'weights': [weights],'trainable': True,'input_dim': vocabulary_size + 1,'output_dim': embedding_size,'name': 'random-embedding-2'}]windows = [{'filters': 100,'kernel_size': 3,'activation': 'relu','name': '3-grams'},{'filters': 100,'kernel_size': 4,'activation': 'relu','name': '4-grams'},{'filters': 100,'kernel_size': 5,'activation': 'relu','name': '5-grams'}]from kim2014convolutional import Modelmodel = Model(channels=channels,windows=windows,sentence_length=37,num_classes=6,dropout_rate=0.5,maxnorm_value=3,classifier_activation='softmax',include_top=True,name='CNN-multichannel')model.summary()```
Package: kimai-python
JSON API for the Kimai 2 time-tracking software. Read more about its usage in the [API documentation](https://www.kimai.org/documentation/rest-api.html) and then download a [Swagger file](doc.json) for import e.g. in Postman. Be aware: it is not yet considered stable and BC breaks might happen, but we try to avoid them.   # noqa: E501
Package: kimariplot
KimariPlot 是一个开源的 Python 绘图脚本，用来绘制科研中的 Free Energy Profile。KimariPlot 使用简单，可以直接从命令行读取 Toml 文件绘制 Free Energy Profile，可以不需要用鼠标一直拖来拖去，是懒人绘制 Free Energy Profile 的极佳选择。安装KimariPlot 可以使用 pip 工具直接安装。pipinstallkimariplot与此同时还需要安装 matplotlib 以及 toml 库（暂时不需要安装 numpy 和 pandas 库，以后可能需要）。pipinstallmatplotlib,toml使用安装完 KimariPlot 之后，可以直接通过如下命令在命令行中运行。接着就会在当前目录下生成 figure.png 图像文件。profile.toml是一个 toml 文件，用来记录颜色、曲线格式以及绘制所需要的数据。kimariplotprofile.toml简要介绍一下 toml 文件所需要配置的内容。color为折线颜色，style为折线的格式，可以为-或--。data为数据的列表，里面可以包括很多个 3 字符串元素的列表。第一个字符串表示名字；第二个字符串表示反应的进度，一般从 1 开始；第三个字符串表示反应的 Free Energy 能垒，一般都用 kcal/mol 表示。[[path]]color="black"style="-"data=[["Name","1","0.0"],["","",""],][[path]]...使用 KimariPlot 命令时可以更改绘图的参数，KimariPlot 支持的参数如下所示。usage:kimariplot[--help][--output_typeOUTPUT][--dpiDPI][--fontFONT][--sizeSIZE][--version]input_file

Generateaenergyprofileusingkimariplot

positionalarguments:input_filePleaseinputaTomlfile

options:--help,-hShowthishelpmessageandexit--output_typeOUTPUT,-oOUTPUTTheoutputtypeofthegraph--dpiDPI,-dDPIThedpioftheoutputgraph--fontFONT,-fFONTThefontfamilyofthegraph--sizeSIZE,-sSIZEThesizeofthegraph--version,-vshowprogram'sversionnumberandexit绘制效果本例在kimariplot的examples/profile1.toml中[[path]]color="black"style="-"data=[["7","1","-0.7"],["TS1","2","22.4"],["8","3","10.9"],["TS2","4","18.0"],["9","5","-8.7"],["10", "6", "-10.0"]][[path]]color="red"style="--"data=[["7","1","-0.7"],["TS3","2","34.9"],["11","3","17.2"],["TS4","4","23.8"],["10", "6", "-10.0"]]本例在kimariplot的examples/profile2.toml中[[path]]color="black"style="-"data=[["R","1","0.0"],["","2","-2.9"],["","3","16.6"],["CP2","4","-3.6"],["P", "5", "-2.1"]][[path]]color="red"style="--"data=[["R","1","0.0"],["CP1","2","-4.4"],["TS1","3","13.5"],["","4","-1.5"],["P", "5", "-2.1"]][[path]]color="blue"style="--"data=[["","1","0.0"],["","2","1.2"],["","3","18.1"],["","4","1.3"],["", "5", "-2.1"]][[path]]color="green"style="--"data=[["","1","0.0"],["","2","2.3"],["","3","19.8"],["","4","2.5"],["", "5", "-2.1"]]小技巧：如果发现两个坐标的标签重叠了，影响观看，可以将data属性中的第二个值错开。例如example/profile3.toml：[[path]]color="black"style="-"data=[["R","1","0.0"],["CP1","3","-4.6"],["TSa","4","-3.4"],["CP2","6","-8.7"],["P","7","-5.8"],][[path]]color="blue"style="--"data=[["R","1","0.0"],["CP0","2","-3.9"],["TS0","3","4.1"],["CP2a","4","-11.8"],["P","7","-9.4"],][[path]]color="red"style="--"data=[["R","1","0.0"],["CP1s","3","-3.3"],["TSs","4","-0.88"],["CP2s","5","-9.1"],["P","7","-5.8"],]鸣谢KimariPlot 的开发离不开以下开源项目：NumPyMatplotlibSciPyToml还要感谢所有为 KimariPlot 做出贡献的开发者 Kimariyb 和用户。许可证KimariPlot 基于MIT许可证开源。这意味着您可以自由地使用、修改和分发代码。有关更多信息，请参见 LICENSE 文件。
Package: kimbo
kimboA Python micro framework for AWS lambda inspired by mixin design pattern.InstallationPython 3.6 or higher is required.python3 -m pip install -U kimboExampleimportkimboimportkimbo.mixins.api_gatewayclassHelloWorld(kimbo.mixins.api_gateway.ProxyResponseMixin,kimbo.Handler,):definit(self):self.message="Hello, world!"defperform(self):returnself.response(self.message)lambda_handler=HelloWorld()
Package: kim-convergence
kim-convergence utility moduleHow do you automatically estimate the length of the simulation required?It is desirable to simulate the minimum amount of time necessary to reach an
acceptable amount of uncertainty in the quantity of interest.How do you automatically estimate the length of the warm-up period required?Welcome tokim-convergencemodule!The kim-convergence package is designed to help in automatic equilibration
detection & run length control.Document!WORK IN PROGRESS!Installing kim-convergenceRequirementsYou need Python 3.7 or later to runkim-convergence. You can have multiple
Python versions (2.x and 3.x) installed on the same system without problems.To install Python 3 for different Linux flavors, macOS and Windows, packages
are available athttps://www.python.org/getit/Using pippipis the most popular tool for installing Python packages, and the one
included with modern versions of Python.kim-convergencecan be installed withpip:pipinstallkim-convergenceNote:Depending on your Python installation, you may need to usepip3instead ofpip.pip3installkim-convergenceDepending on your configuration, you may have to runpiplike this:python3-mpipinstallkim-convergenceUsing pip (GIT Support)pipcurrently supports cloning overgitpipinstallgit+https://github.com/openkim/kim-convergence.gitFor more information and examples, see thepip installreference.Using condacondais the package management tool for Anaconda Python installations.Installingkim-convergencefrom theconda-forgechannel can be achieved by
addingconda-forgeto your channels with:condaconfig--addchannelsconda-forgeOnce theconda-forgechannel has been enabled,kim-convergencecan be
installed with:condainstallkim-convergenceIt is possible to list all of the versions ofkim-convergenceavailable on
your platform with:condasearchkim-convergence--channelconda-forgeBasic UsageCopyrightCopyright (c) 2021, Regents of the University of Minnesota.All Rights ReservedContributingContributors:Yaser Afshar
Package: kimdb
kimdbDescriptionIMDB scraperInstallpipinstallkimdb# orpip3installkimdbUsagefromkimdbimportIMDBimdb=IMDB(debug=True)top_movies=imdb.top_movies()top_movies[0].jsonprint()top_series=imdb.top_series()top_series[0].jsonprint()most_popular_movies=imdb.most_popular_movies()most_popular_movies[0].jsonprint()most_popular_series=imdb.most_popular_movies()most_popular_series[0].jsonprint()title=imdb.get_title(top_movies[0].id)title.jsonprint()list=imdb.get_list(title.lists[0].id)list.jsonprint()user=imdb.get_user(list.owner.id)user.jsonprint()name=imdb.get_name(title.actors[0].id)name.jsonprint()Dependenciesbeautifulsoup4,jsoncodable,kcu,ksimpleapi,noraise,requests
Package: kimmdy
KIMMDYReactive MD pipeline for GROMACS using Kinetic Monte Carlo / Molecular Dynamics (KIMMDY)InstallationNote: KIMMDY requiresGROMACSto be installed.
Some reactions need a GROMACS version patched withPLUMED.
The gromacs version name should then containMODIFIEDorplumed.pipinstallkimmdyThis installation includes only the most basic functionality as no plugins and analysis tools are installed.To install the builtin reaction plugins, usepipinstallkimmdy[reactions]To install the builtin reactions and analysis tools usepipinstallkimmdy[reactions,analysis]However, this is only half the fun!KIMMDY has two exciting plugins in the making, which properly parameterize your molecules
for radicals using GrAPPa (Graph Attentional Protein Parametrization) and predict
Hydrogen Atom Transfer (HAT) rates.
Full installation instructions are availablehereDocumentationThe documentation is availablehere.Getting startedHead over to thegetting startedtutorial.DevelopmentDevelopment setupClone kimmdy and the default reaction and parameterization plugins and install requirements and kimmdy as editable viagitclonegit@github.com:hits-mbm-dev/kimmdy.git
gitclonegit@github.com:hits-mbm-dev/kimmdy-reactions.git
gitclonegit@github.com:hits-mbm-dev/kimmdy-grappa.gitcdkimmdy
python-mvenv.venvsource./venv/bin/activate
pipinstall-rrequirements.txtConventions:code style: blackdocstrings: numpyConventional commitmessages when possible for pretty release notes.Local testingFor developoment, we provide a docker image containing gromacs and multiple python versions to test against.To run the test locally, you must:install dockerinstallact, the easiest option is with github cli viagh extension install https://github.com/nektos/gh-actrun tests withgh extension exec act -j test --artifact-server-path ./artifactshtml coverage report is exported intoartifacts
Package: kimmdy-reactions
Reaction template for KIMMDYInstallationShould get installed together with kimmdy. If you want to install it separatly:Downloadpip install -e ./or for development:pip install -r requirements.txtMaking your ownImplement your reaction as a subclass ofkimmdy.reaction.ReactionRegister your Reaction class in the[options.entry_points]section in the setup.cfg. The name you give here must match the entry in the config.yml for Kimmdy!
Package: kimmy
kimmyGalactic chemical evolution in pythonOverviewkimmycontains simple tools to study chemical evolution in galaxies.AuthorJo Bovy (University of Toronto): bovy - at - astro - dot - utoronto - dot - caInstallationClone/fork/download the repository and install usingsudo python setup.py installor locally usingpython setup.py install --userUsageFor an example of usage, see theexample notebook. You can also launch aBinderinstance and directly play around with this notebook.Currently, the only implemented feature is a simple one-zone chemical model with two elementsO(for oxygen) andFe(for iron). Initialize this model asimport kimmy
oz= kimmy.OneZone()then for example compute the evolution of the default model and plot the [O/Fe] vs. [Fe/H] sequencets= numpy.linspace(0.001,10.,1001)*u.Gyr
plot(oz.Fe_H(ts),oz.O_Fe(ts))To compute the distribution of [Fe/H], do for example,FeHs= numpy.linspace(-1.525,1.225,56)
FeH_dist= [oz.Fe_H_DF(f) for f in FeHs]and similar for the distribution of [O/H] and [O/Fe]. You can directly update the main parameters of the model and the model will be re-computed. For example, to set the outflow mass-loading parameter to one and plot the [O/Fe] vs. [Fe/H] sequence, dots= numpy.linspace(0.001,10.,1001)*u.Gyr
oz.eta= 1.
plot(oz.Fe_H(ts),oz.O_Fe(ts))Keep in mind that once you change a parameter, it remains changed in the model. If you want to go back to the initial set of parameters that you used to initialize the instance, useoz.initial(); if you want to go back to the default set of parameters, useoz.default(). If you want to print the model you are using at any time, doprint(oz)which prints a nicely formatted list of all of the model parameters.
Package: kimoby
Project descriptionPython module to interact with the Kimoby REST API.Proofpoint Result APISee theREST API full documentationfor more information.Installationpip install kimobySupported endpointsCustomersList Customers-get_customers()Retrieve a Customer-get_customer()Create a Customer-create_customer()Delete a Customer-delete_customer()MessageSend a Message-send_message()
Package: kimochi
Easy to use python library for showing your emotions.Key FeaturesModern Pythonic InterfaceEasy to useInstallingPython 3.8 or higherTo install the library, run the following command#Linux/macOSpython3-mpipinstall-Ugit+https://github.com/VarMonke/kimochi#Windowspy-mpipinstall-Ugit+https://github.com/VarMonke/kimochiQuick Exampleimportasyncioimportkimochiclient=kimochi.Client()asyncdefkiss():returnawaitclient.kissprint(asyncio.run(kiss()))
Package: kimonet
KimonetA kinetic Monte Carlo code to simulate exciton dynamicsRequirementsPython 2.7.x/3.2+numpyscipymatplotlibnetworkxh5pypygraphviz (which requires graphviz-dev library) [optional]Contact infoAbel Carrerasabelcarreras83@gmail.comDonostia International Physics Center (DIPC)Donostia-San Sebastian, Euskadi (Spain)
Package: kimono
A minimal python library for theThe Kimono
APIby kimonolabs, inc. Heavily
influenced/plagiarised from thepipedrive-apiwritten by
the awesomehiway.This is a work in progress and is incomplete!UsagefromkimonoimportKimonoapi=Kimono('APIKEY','APIID')Refer to thewikifor more
detailed documentation!
Package: kimsufichecker
Kimsufi CheckerTool to checkKimsufi (OVH)availability and execute actions when a plan is available or not available.InstallTo installKimsufi CheckerfromPyPIsimply run$pip3install-U--userkimsufichecker
$kimsufi-checker--helpTo install it from the git repository, ensure you installedPoetryfirst:$pip3install-U--userpoetry
$pip3install--usergit+https://github.com/essembeh/kimsufi-checker
$kimsufi-checker--helpTo install it in avirtualenv$ pip3 install -U --user poetry
$ git clone https://github.com/essembeh/kimsufi-checker
$ cd kimsufi-checker
$ poetry install

$ poetry run kimsufi-checker --help
--or--
$ poetry shell
(.venv) $ kimsufi-checker --helpUsage$kimsufi-checker--help
usage:kimsufi-checker[-h][-sSECONDS][-zZONE][-xCOMMAND][-XCOMMAND][-1][plans[plans...]]tooltoperformactionswhenKimsufiavailabiltychanges

positionalarguments:plansplanstocheck,example1801sk13or1801sk14

optionalarguments:-h,--helpshowthishelpmessageandexit-sSECONDS,--sleepSECONDSduration(inseconds)betweenchecks,default:60-zZONE,--zoneZONEcheckavailabilityinspecificzones(example:rbxorgra)-xCOMMAND,--availableCOMMANDcommandtoexecutewhenplanbecomesavailable-XCOMMAND,--not-availableCOMMANDcommandtoexecutewhenplanisnotavailableanymore-1,--execute-on-initexecute-x/-Xactiononfirstcheck,bydefaultactionsarerunwhenplanstatuschangeExampleTo list all plan identifiers and all zone identifiers, usekimsufi-checkerwithout argument$kimsufi-checkerListofplans:150cagame1150cagame2150game1150game21623hardzone1[...]Listofzones:bhsfragra[...]If you want to be notified by SMS using the Free Mobile SMS API when plans1801sk13or1801sk14are available in France or Canada by checking every 10 minutes, use this command:$kimsufi-checker\--sleep600\--zonerbx\--zonegra\-x'curl "https://smsapi.free-mobile.fr/sendmsg?user=123456789&pass=MYPASSWORD&msg=Kimsufi%20{plan}%20available"'\-X'curl "https://smsapi.free-mobile.fr/sendmsg?user=123456789&pass=MYPASSWORD&msg=Kimsufi%20{plan}%20not%20available"'\1801sk131801sk14Note: replace123456789andMYPASSWORDwith your ownFree Mobile credentials.
Package: kinalite
Kinalite - Simple VTNAKinalite is a Python package that provides a simple API for running Variable Time Normalization
Analysis on chemical data.RequirementsPython 3.6+InstallationKinalite can be installed with pip:$ pip install kinaliteUsageTo use kinalite you will need to provide two DataFrames with experiment data to compare. Kinalite
will then use VTNA to find a best order for the first experiment.Reading dataThe first step is using the Pandas package to convert your experiment data into DataFrames:importpandasaspd# supply an absolute or relative path to CSV filesexperiment_a_data=pd.read_csv('./data/experiment_a.csv')experiment_b_data=pd.read_csv('./data/experiment_b.csv')These CSV files need to have a single header row and time in the first columns. All values, including
Time, are numbers. For example:TimeCABDcat001100.0155.45911E-050.9972740.9973280.002671750.00994541105.45909E-050.9945550.994610.00539030.00994541155.45907E-050.9918440.9918990.008101430.00994541205.45906E-050.989140.9891950.01080520.00994541Running VTNANext you can use your converted data to create anExperimentand run VTNA. You must also provide the
column indexes for the substrate and product, starting at 0 (Time is column index 0):importpandasaspdfromkinalite.experimentimportExperiment# supply an absolute or relative path to CSV filesexperiment_a_data=pd.read_csv('./data/experiment_a.csv')experiment_b_data=pd.read_csv('./data/experiment_b.csv')# create an experiment and supply the column indexes for the substrate and productexperiment=Experiment('A',[experiment_a_data,experiment_b_data],substrate_index=2,product_index=4)# run VTNA and print out the best orderresult=experiment.calculate_best_result()print('Order in A: ',result.order)Plotting dataKinalite provides some plotting methods to help visualize the results of running VTNA:fromkinalite.plotsimportplot_experiment_resultsplot_experiment_results(experiment)Example ScriptThere is also an example script with a comparison of multiple sets of data:kinalite_example/main.py
Package: kinbaku
A graph database in a single fileKinbakuis the japanese art of bondage. In a graph, ropes are replaced by edges between nodes. This library allows you to manage large graphs on disk, using only one file and without having to load the whole graph in memory. The library is written in pure Python.Source:https://github.com/kerighan/kinbakuDocumentation:https://kinbaku.readthedocs.ioTutorial:https://kinbaku.readthedocs.io/en/latest/tutorial.htmlInstallationIt is recommended to install the cityhash package:pip install cityhashTo install Kinbaku:pip install kinbakuBasic usageimportkinbakuaskn# create graph if the file doesn't already existG=kn.Graph("graph.db")# add nodesG.add_node("A")# keys must be stringsG.add_node("B")G.add_node("C")# add edgesG.add_edge("A","B")G.add_edge("A","C")# get a nodeprint(G.node("A"))print(G["A"])# get out neighborsprint(list(G.neighbors("A")))# get incoming nodesprint(list(G.predecessors("B")))# iterating through the nodesfornodeinG.nodes:print(node)# iterating through the edgesforedgeinG.edges:print(edge)Node keys must imperatively be strings with a maximum length. The maximum length can be setbeforethe graph is created using themax_key_lenkeyword argument.Using custom attributesWith Kinbaku, nodes and edges can have custom attributes. The way to proceed is to create Python dataclasses that inherit from Kinbaku structures.fromdataclassesimportdataclassimportkinbakuaskn@dataclassclassUser(kn.structure.Node):age:int=0bio:str=""@dataclassclassRelation(kn.structure.Edge):weight:float=0G=kn.Graph("graph_with_attributes.db",node_class=User,edge_class=Relation,max_str_len=40)# max string length# using the 'add_node' method:G.add_node("Mark",{"age":25,"bio":"first text"})# or using '__setitem__':G["Mary"]={"age":32,"bio":"second text"}# adding an edge with custom attributes:G.add_edge("Mark","Mary",{"weight":.1})
Package: kinda
KindaAboutKinda is a pythonic library for comparing floating point values withPython operator functions.ArgumentsThe implementation and documentation are identical tomath.isclose.ComparisonsEquality>>>importkinda>>># a == b>>>kinda.eq(1.0000,1.0000)True>>># a != b (default precision)>>>kinda.eq(0.9999,1.0001)FalsePrecisionAllmath.isclose()arguments are accepted in all functions.abs_tol: absolute tolerance (abs(a - b))rel_tol: percentage tolerance (1% = .01)>>># reduce absolute precision>>>kinda.eq(0.9999,1.0001,abs_tol=0.0002)True>>># precision: 1%>>>kinda.eq(1.0000,1.0500,rel_tol=0.01)False>>># precision: 5%>>>kinda.eq(1.0000,1.0500,rel_tol=0.05)TrueInequality>>>importkinda>>>[kinda.ne(0.9999,1.0001),kinda.ne(1.0000,1.0000)][True,False]Less/Greater Than>>>importkinda>>># [a < b, a > b]>>>[kinda.lt(1.0000,1.0001),kinda.gt(1.0001,1.0000)][True,True]Less/Greater Than or Equal To>>>importkinda>>>kinda.le(1.0000,1.0001)andkinda.ge(1.0000,1.0001)[True,False]>>>kinda.le(1.0000,1.0000)andkinda.ge(1.0000,1.0000)[True,True]>>>kinda.le(1.0000,0.9999)andkinda.ge(1.0000,0.9999)[False,True]
Package: kindabool
kindaboolLets face it, the concept of a boolean is simply too outdated for the modern world. We need something more. We need something that can represent the complexity of the human condition.We need kindabool.With kindabool, you get 3 more options:KindaTrue,Neutral, andKindaFalse.UsageYou can either use the initiated variables,KindaTrue,Neutral, andKindaFalse, or you can directly create aKindaBoolobject.fromkindaboolimport*# usually a bad idea, but its kinda essential to the libraryifTrue:print("This will always be printed")ifKindaTrue:print("This has a 75% chance of being printed")ifNeutral:print("This has a 50% chance of being printed")ifKindaFalse:print("This has a 25% chance of being printed")ifFalse:print("This will never be printed")ifkindabool.kinda_bool.KindaBool(1):# constructor takes an int from 1-5print("This will never be printed")
Package: kinde-python-sdk
Kinde Python SDKThe Kinde SDK for Python.You can also use thePython starter kit here.DocumentationFor details on integrating this SDK into your project, head over to theKinde docsand see thePython SDKdoc 👍🏼.PublishingThe core team handles publishing.ContributingPlease refer to Kinde’scontributing guidelines.LicenseBy contributing to Kinde, you agree that your contributions will be licensed under its MIT License.
Package: kindlenotes2md
kindlenotes2mdCommand line utility to convert your Kindle highlights from html to markdown.Installationpip install kindlenotes2mdUsageFrom the Kindle app, you have the option to export the highlights that you've made in the book.
The easiest way to get access to these are to email them to yourself.
These exported highlights are in html format, so are a bit tricky to use in other tools.Run this tool with the.htmlfile as a command line argument.kindlenotes2md INPUTFILENAMEHere's the documentation for the tool you get by running--help.Usage: kindlenotes2md [OPTIONS] INPUTFILENAME

Options:
  -o, --outfilename TEXT
  --help                  Show this message and exit.You have the option to specify a filename with-oor--outfilename.
This will write the markdown output to file, rather than stdout.