Saravanan666/pypi-org huggingface dataset:
Package: fc-matplotlib4mesh
Thefc_matplotlib4meshPython package allows to display simplicial meshes or datas on simplicial meshes by usingmatplotlib(>= 2.0.0).
The simplicial meshes must be given by two arrays : the vertices array and the connectivity array.Introduction:Simplicial meshes could be:a triangular mesh in dimension 2, made with 2-simplices (ie. triangles),a tetrahedral mesh in dimension 3, made with 3-simplices (ie. tetrahedron),a triangular mesh in dimension 3 (surface mesh), made with 2-simplices,a line mesh in dimension 2 or 3 made with 1-simplices (ie. lines).A simplicial mesh is given by its vertices arrayqand its connectivity arraymeFor demonstration purpose, some simplicial meshes are given in this package. They can be load
by using the functiongetMesh2D,getMesh3DorgetMesh3Dsof thefc_meshtoolspackage.Documentation is available onfc-matplotlib4meshdedicated web page.Installation:Thefc_matplotlib4meshPython package is available from the Python Package Index, so to install/upgrade simply typepip install fc_matplotlib4mesh -UThereafter, it’s possible to run one of the demo functionsimportfc_matplotlib4meshfc_matplotlib4mesh.demos.plot3D()importfc_matplotlib4meshfc_matplotlib4mesh.demos.plot2D()Example:There is a complete source code used to represent a function on a sphereimportmatplotlib.pyplotaspltfromfc_tools.Matplotlibimportset_axes_equalimportfc_matplotlib4meshasplt4simfromfc_matplotlib4mesh.demosimportgetMesh3Dsimportnumpyasnpq2,me2=getMesh3Ds(2)q1,me1=getMesh3Ds(1)f=lambdax,y,z:np.cos(3*x-1)*np.sin(2*y-2)*np.sin(3*z)u2=f(q2[0],q2[1],q2[2])u1=f(q1[0],q1[1],q1[2])plt.ion()plt.figure(1)pp=plt4sim.plot(q2,me2,u2)plt4sim.plotmesh(q1,me1,color='Black',linewidths=2)plt.colorbar(pp)plt.axis('off')set_axes_equal()plt.figure(2)pp=plt4sim.plot(q1,me1,u1,linewidths=2,vmin=min(u2),vmax=max(u2))plt4sim.plotmesh(q2,me2,color='LightGray',alpha=0.1)plt.colorbar(pp)plt.axis('off')set_axes_equal()
Package: fc-mayavi4mesh
Thefc_mayavi4meshPython package allows to display simplicial meshes or datas on simplicial meshes by usingmayavi(>= 4.6.2)
Python package. The simplicial meshes must be given by two arrays : the vertices array and the connectivity array.Introduction:Simplicial meshes could be:a triangular mesh in dimension 2, made with 2-simplices (ie. triangles),a tetrahedral mesh in dimension 3, made with 3-simplices (ie. tetrahedron),a triangular mesh in dimension 3 (surface mesh), made with 2-simplices,a line mesh in dimension 2 or 3 made with 1-simplices (ie. lines).A simplicial mesh is given by its vertices arrayqand its connectivity arrayme.
For demonstration purpose, we used some simplicial meshes provided by thefc-meshtoolsPython package:
they can be load by using the functiongetMesh2D,getMesh3DorgetMesh3Dsof thefc_meshtools.simplicialmodule.Documentation is available onfc-mayavi4meshdedicated web page.Installation:Thefc_mayavi4meshPython package is available from the Python Package Index, so to install/upgrade simply typepip install fc_mayavi4mesh -UThereafter, it’s possible to run one of the demo functionsimportfc_mayavi4meshfc_mayavi4mesh.demos.plot2D()importfc_mayavi4meshfc_mayavi4mesh.demos.plot3D()importfc_mayavi4meshfc_mayavi4mesh.demos.streamline3D01()Example:There is a complete source code used to represent a function on a spherefrommayaviimportmlabfromfc_meshtoolsimportsimplicialasmshsimimportfc_mayavi4mesh.simplicialasmlab4simimportnumpyasnpq2,me2=mshsim.getMesh3Ds(2)[:2]q1,me1=mshsim.getMesh3Ds(1)[:2]f=lambdax,y,z:np.cos(3*x-1)*np.sin(2*y-2)*np.sin(3*z)u2=f(q2[:,0],q2[:,1],q2[:,2])u1=f(q1[:,0],q1[:,1],q1[:,2])mlab.figure(1)mlab4sim.plot(q2,me2,u2)mlab4sim.plotmesh(q1,me1,color='Black',line_width=2)mlab.colorbar()mlab.figure(2)mlab4sim.plot(q1,me1,u1,line_width=2,vmin=min(u2),vmax=max(u2))mlab4sim.plotmesh(q2,me2,color='LightGray',opacity=0.1)mlab.colorbar()
Package: fcm-client
Python client forFirebase Cloud Messaging (FCM).
Checkdocumentationto learn how to use it.The library was originally written by FCM client bySardar Yumatov.
It seems to have been abandoned around 2015 or 2016. When google announced the
move to Firebase, there was a need for a updated version of this software.Requirementsrequests- HTTP request, handles proxies etc.sixfor python 3 compatibility.AlternativesTh only alternative library known at the time of writing waspyfcm.  This library differs in the
following design decisions:Predictable execution time. Do not automatically retry request on failure.
According to Google’s recommendations, each retry has to wait exponential
back-off delay. We use an async back-end like Celery, where the best way to retry after
some delay will be scheduling the task withcountdown=delay.  Sleeping
while in Celery worker hurts your concurrency.Do not forget results if you need to retry. This sounds obvious, butpyfcmdrops important results, such as canonical ID mapping if
request needs to be (partially) retried.Clean pythonic API. No need to borrow all Java like exceptions etc.Do not hard-code validation, let FCM fail. This decision makes library
a little bit more future proof.SupportFCM client was created byJohn Loehrer,
contact me if you find any bugs or need help.
You can view outstanding issues on theFCM
Github page.
Package: fc-meshtools
Thefc_meshtoolsPython package  contains some simplicial meshes
given by their vertices arrayqand connectivity arrayme. Theses meshes can be easily used in
other Python codes for debugging or testing purpose.Introduction:A simplicial mesh is given by its vertices arrayqand its connectivity arrayme.
For demonstration purpose, some simplicial meshes are given in this package and stored in the fc_meshtools/data directory.
They can be load by using the functionsgetMesh2D,getMesh3DorgetMesh3Dsof thefc_meshtools.simplicialmodule.
Here are the kind of simplicial meshes present in this package:a triangular mesh in dimension 2, made with 2-simplices (ie. triangles),a tetrahedral mesh in dimension 3, made with 3-simplices (ie. tetrahedron),a triangular mesh in dimension 3 (surface mesh), made with 2-simplices,a line mesh in dimension 2 or 3 made with 1-simplices (ie. lines).One can go to the dedicated web pagefc-meshtoolsfor more informations.Installation:Thefc_meshtoolsPython package is available from the Python Package Index. so to install/upgrade simply typepip install fc_meshtoolsExamplesfirst exampleimportfc_meshtoolsq,me,toG=fc_meshtools.simplicial.getMesh2D(2)vols=fc_meshtools.simplicial.Volumes(q,me)print(' q:%s, me:%s, vols:%s'%(str(q.shape),str(me.shape),str(vols.shape)))The output of theprintcommand is:q:(2, 9116), me:(3, 17638), vols:(17638,)second exampleimportfc_meshtools,numpyq3,me3=fc_meshtools.simplicial.getMesh3D(3)[:2]vols3=fc_meshtools.simplicial.Volumes(q3,me3)G3=fc_meshtools.simplicial.GradBaCo(q3,me3)print(' q3:%s, me3:%s, vols3:%s, G3:%s'%(str(q3.shape),str(me3.shape),str(vols3.shape),str(G3.shape)))q2,me2,toG2=fc_meshtools.simplicial.getMesh3D(2)vols2=fc_meshtools.simplicial.Volumes(q2,me2)G2=fc_meshtools.simplicial.GradBaCo(q2,me2)print(' q2:%s, me2:%s, vols2:%s, G2:%s'%(str(q2.shape),str(me2.shape),str(vols2.shape),str(G2.shape)))E=numpy.max(numpy.abs(q2-q3[:,toG2]))print(' E=%g'%E)The output is:q3:(3, 14120), me3:(4, 67653), vols3:(67653,), G3:(67653, 3, 4)
q2:(3, 6471), me2:(3, 12950), vols2:(12950,), G2:(12950, 3, 3)
E=0.000000e+00
Package: fcm-mp
The package implements a learning method based on the Moore-Penrose inverse for hybrid Fuzzy Cognitive Maps. In this hybrid model, the user can specify how the problem features interact or let the algorithm compute that matrix from the data using unsupervised learning. The supervised learning step focuses on computing the relationships between the last hidden state of the Fuzzy Cognitive Maps and the outputs. Therefore, the model is devoted to solving multi-output regression problems where problem features are connected in non-trivial ways.
Package: fcm-mpmath
Fuzzy C-Means + mpmathProjeto desenvolvido como Trabalho de Conclusão do Curso de Anderson de Alencar BarrosEste projeto é a implementação do método Fuzzy C-Means usando pontos flutuantes de precisão arbitrária, conhecido comobigfloats, sendo útil para projetos que requerem alta precisão onde um ponto de fluante de precisão dupla poder ocasionar erros de divisão por zero ou não ser preciso o suficiente.Para explorar o projeto comece fazendo um clone do projeto,gitclonehttps://github.com/AndersonAlencarBarros/fcmOs requisitos para executar esse projeto são,PoetryPython 3.10Para instalar use o comando,pipinstallfcm-mpmathExemplofromfcm_mpmathimportFCMimportnumpyasnpX=np.array([[1,3],[2,5],[4,8],[7,9],])fcm=FCM(n_clusters=2,mu=2)fcm.fit(data=X)print(fcm.centers)ReferênciasBEZDEK, J. C.; EHRLICH, R.; FULL, W. Fcm: The fuzzy c-means clustering algorithm.
Computers & geosciences, Elsevier, v. 10, n. 2-3, p. 191–203, 1984.
Package: fcm-notifier
Сервис по отправке push-уведомлений через Firebase Cloud MessagingОбщее описаниеfcm-notifier представляет собой сервис, который отдельно запускается в окружении РИС и с заданной
периодичностью забирает накопившиеся сообщения из очереди, которые пачкой (до 500 штук) отправляет
на мобильные устройства через FCM backend используя Firebase Admin SDK.Настройка сервисаДля работы сервиса необходимо в переменных окружения задать две переменные:Путь до JSON-файла содержащего приватный ключ для доступа к сервисам Google:export GOOGLE_APPLICATION_CREDENTIALS=/opt/bars/fcm-notifier/mydiary.jsonПуть до директории содержащей файл конфигурации сервиса:export FCM_NOTIFIER_CONFIG_DIR=/opt/bars/fcm-notifier/Файл конфигурации fcm_notifier.conf:[redis]REDIS_HOST=127.0.0.1REDIS_PORT=6379REDIS_DB=12REDIS_PASSWORD=[logging]LEVEL=INFOЗапуск сервиса$ fcm-notifier workerИспользование очереди сообщений в РИСДобавление сообщения в очередь:fromfcm_notifier.helpersimportget_redis_connectionfromfcm_notifier.notificationimportNotification,NotificationPayloadfromfcm_notifier.queueimportRedisQueuequeue=RedisQueue(connection=get_redis_connection())notification=Notification(payload=NotificationPayload(title='Test message!',body='Message body',),token='dsufZwUdSFaeIGFt77aMwm:APA91bHQje7R...',)queue.enqueue_notification(notification)
Package: fcmnotify
fcmnotify is a simple Django app to conduct Web-based fcmnotify. For each
question, visitors can choose between a fixed number of answers.Detailed documentation is in the “docs” directory.Quick startAdd “fcmnotify” to your INSTALLED_APPS setting like this:pip install fcmnotifyAdd “fcmnotify” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'fcmnotify',
]Include the fcmnotify URLconf in your project urls.py like this:path('', include('fcmnotify.urls')),
 or
# url(r'^', include('fcmnotify.urls')),Runpython manage.py migrateto create the polls models.Start the development server and visithttp://127.0.0.1:8000/admin/to create Firebase information.Go into Fcmnotify > fcmsettings
Add Firebase information .
visithttp://127.0.0.1:8000/fcm-settings/To download HTML SETTINGS file
include download HTML file into base html file {% include ‘fcm_settings.html’ %}To send Notifications .Call “from fcmnotify.sender import fcm_notify”
send notification using fcm_notify
fcm_notify(sender, recipient, message, title=”DK”, dataObject=None)NOTE:Make sure you have jQuery, Your Browser should have notification permission
Package: fcmpy
FCMpy: A package for Constructing and Analysing Fuzzy Cognitive Maps in Python.The fcmpy is Python package for automatically generating causal weights for fuzzy cognitive maps based on qualitative inputs (by using fuzzy logic), optimizing the FCM connection matrix via Machine Learning Algorithms and testingwhat-ifscenarios. The package includes the following submodules:ExpertFcmSimulationInterventionMLInstallationFCMpy requires python >=3.8.1 and depends on:pandas>=1.0.3numpy>=numpy==1.18.2scikit-fuzzy>=0.4.2tqdm>=4.50.2openpyxlThe latest version can be installed by:pip install fcmpyAlternatively, you can install it from source or develop this package, you can fork and clone this repository then install FCMpy by running:py -m pip install --user --upgrade setuptools wheel
py setup.py sdist bdist_wheel
py -m pip install install e .You can run the unittest for the package as follows:py -m unittest discover unittestsDocumentation and tutorialsTutorial and documentationLearn how to use our library by following available tutorials and documentation!LicensePlease read LICENSE.txt in this directory.
Package: fcn
fcn - Fully Convolutional NetworksChainer implementation ofFully Convolutional Networks.InstallationpipinstallfcnInferenceInference is done as below:# forwaring of the networksimg_file=https://farm2.staticflickr.com/1522/26471792680_a485afb024_z_d.jpg
fcn_infer.py--img-files$img_file--gpu-1-o/tmp# cpu modefcn_infer.py--img-files$img_file--gpu0-o/tmp# gpu modeOriginal Image:https://www.flickr.com/photos/faceme/26471792680/Trainingcdexamples/voc
./download_datasets.py
./download_models.py

./train_fcn32s.py--gpu0# ./train_fcn16s.py --gpu 0# ./train_fcn8s.py --gpu 0# ./train_fcn8s_atonce.py --gpu 0The accuracy of original implementation is computed with (evaluate.py) after converting the caffe model to chainer one
usingconvert_caffe_to_chainermodel.py.You can download vgg16 model from here:vgg16_from_caffe.npz.FCN32sImplementationAccuracyAccuracy ClassMean IUFWAVACCModel FileOriginal90.481076.482463.626183.4580fcn32s_from_caffe.npzOurs (usingvgg16_from_caffe.npz)90.566876.874063.818083.5067-FCN16sImplementationAccuracyAccuracy ClassMean IUFWAVACCModel FileOriginal90.997178.071065.005084.2614fcn16s_from_caffe.npzOurs (usingfcn32s_from_caffe.npz)90.967178.061765.091184.2604-Ours (usingfcn32s_voc_iter00092000.npz)91.100977.252265.362884.3675-FCN8sImplementationAccuracyAccuracy ClassMean IUFWAVACCModel FileOriginal91.221277.614665.512684.5445fcn8s_from_caffe.npzOurs (usingfcn16s_from_caffe.npz)91.251377.149065.478984.5460-Ours (usingfcn16s_voc_iter00100000.npz)91.260878.148465.844484.6447-FCN8sAtOnceImplementationAccuracyAccuracy ClassMean IUFWAVACCModel FileOriginal91.128878.497965.399884.4326fcn8s-atonce_from_caffe.npzOurs (usingvgg16_from_caffe.npz)91.088377.352865.343384.4276-Left to right,FCN32s,FCN16sandFCN8s, which are fully trained using this repo. See above tables to see the accuracy.LicenseSeeLICENSE.Cite This ProjectIf you use this project in your research or wish to refer to the baseline results published in the README, please use the following BibTeX entry.@misc{chainer-fcn2016,author={KetaroWada},title={{fcn:ChainerImplementationofFullyConvolutionalNetworks}},howpublished={\url{https://github.com/wkentaro/fcn}},year={2016}}
Package: f-codec
PythonfCodecWrap lonesome f-strings inprint().How to install>python3-mpipinstallf-codecWhy?# -*- coding: f -*-defdictionary(inst,depth=0):ಠ_ಠ='    '*depthf'''{{'''forname,valininst.items():ifisinstance(val,dict):f'''{name}='''>ಠ_ಠdictionary(val,depth+1)else:f'''{name}={val},'''>ಠ_ಠf'''}}'''>ಠ_ಠdecl={'a':1,'b':2,'c':{'a':3,'b':4},'d':{'a':5,'b':{'a':6,'b':7},'c':8},'e':9}f'''decl ='''dictionary(decl)>python3test.pydecl={a=1,b=2,c={a=3,b=4,}d={a=5,b={a=6,b=7,}c=8,}e=9,}
Package: fcolors
colorsColors is a simple library that makes it easier to use colors in fstrings.
The standard ansi colors are (in order):BlackRedGreenYellowBluePinkCyanWhiteThese are accessible in the following variations:(f)ore(g)round(b)ack(g)round(u)nder(line)d(bold)ed(f)ore(g)round_(hi)lighted(b)ack(g)round_(hi)lighted(bold)_(hi)lightedForground, Background, Underline, and Bold can all be chained together in any order, as:f"{uline.blue}{bg.red}red underlined text{reset}"Highlighted colors must be custom-built due to a difference in how their codes are constructed.
This can be done as so:f"{all_hi(Color.Blue,Color.White,bold=0)}this is white on blue text, unbolded{reset}"While I may end up figuring out a way around this, it will not be soon. Feel free to use these features as-is.Installation$pip3installcolorsor$python3setup.pyinstallExamplesHello, World!fromfcolors.fcolorsimportfg,bg,bold,resetprint(f"{bg.blue}{bold.white}Hello, World!{reset}")A tree that talksfromfcolors.fcolorsimportfg,fg_hi,bg,bg_hi,bold,reset,all_hi,Colorcanopy=f"{fg.green}"leaves=f"{fg.green}"wood=f"{bold.yellow}"grass=f"{bg_hi.green}"text=f"{bold.white}"c=canopyl=leavesw=woodx=resett=textdefrgb(string):chars=[cforcinstring]forindexinrange(len(chars)):chars[index]=bold.colors[index%len(bold.colors)-1]+chars[index]return''.join(chars)+f'{text}'picture=(f"""\{c}__________{x}{t}______________________{l}/       )  \{x}{t}/                      \{l}| (          |{x}{t}|  Have fun using my     |{l}\________)_/{x}{t}|{rgb('fcolors')}package!    |{w}|  ||  |{x}{t}|   - Jayson             |{w}\ -- /{x}{t}\    __________________/{w}|  |{x}{t}| _/{w}|  |{x}{t}<_/{w}|  |{x}{w}|  |{x}{grass}{reset}""")print(picture)LicenseSee LICENSE.
Package: fcomments
fcommentsmanage comments from your command lineinstallationsupports Python 3.5+fcomments is onpypi$ pip install fcommentsusageDefault behavior is to switch the line. It means that if a matched line
is commented, the comment will be removed, and if the line is not
commented the comment will be added.Specifying -c flag ensures that matched lines will be commented out
after the command. The flag -u works analogically with respect to
uncommenting lines.ProvisioningfromfcommentsimportCommenterLINES=[4,5,6]c=Commenter(path='path/to/file')c.comment_file(comment=True,lines=LINES)# ensure that lines 4-6 are commented outaction()# do sth: run tests, make migrations ..c.comment_file(uncomment=True,lines=LINES)# ensure that lines 4-6 are not commentedCLI# comment all lines in a file:$fcomments--comment--allpath/to/file$fcomments-capath/to/file# comment lines 3 to 12:$fcomments--comment--lines=3-12path/to/file$fcomments-cl3-12path/to/file–helpUsage: __main__.py [options] <path>

Examples:
       __main__.py -h                  --->  see help
       __main__.py -cl1,2 path/to/file  --->  comment out lines 1 and 2
       __main__.py -ul3-6 path/to/file  --->  uncomment lines 3 to 6 (inclusive)
       __main__.py -ac path/to/file     --->  comment out all lines
       __main__.py -au path/to/file     --->  uncomment all lines

       __main__.py --start-pattern='\s+operations\s?=\s?\[' --end-pattern='\s+\]' path/to/file
         --->  comment out everything inside the `operations` list:

         1| class Migration(...):
         2|
         3|     operations = [
         4| #       migrations(
         5| #           ...
         6| #       ),
         7|     ]


  Comment or uncomment lines in a file. Default behavior: do the oposite i.e.
if a line is commented - uncomment it, and vice versa. To make sure that the
matched lines will be [un]commented out - run with -[u]c option. If you don't
specify an --output, the original file (<path>) will be overwritten.

Options:
  -h, --help            show this help message and exit
  -c, --comment         comment lines [default: False]
  -u, --uncomment       uncomment lines [default: False]
  -a, --all             apply to all lines in file; suppresses -l option
                        [default: False]
  -l LINES, --lines=LINES
                        comma separeted string of line numbers [default: None]
  -s IN_PATTERN, --start-pattern=IN_PATTERN
                        pattern to match against the line before commented
                        section [default: None]
  -e OUT_PATTERN, --end-pattern=OUT_PATTERN
                        pattern to match against the first line after
                        commented section [default: None]
  -o OUTPUT, --output=OUTPUT
                        specify a path to output file [default: None]
  --symbol=COMMENT_SYMBOL
                        specify a string to use as comment [default: '#']
Package: fcompop
fcompop is a Python library that provides function composition operators.An example of usages is following:>>> import fcompop
>>> fcompop.inject()  # adding operators to builtin&user functions
>>> func = str._ >> (lambda x: x * 2 + 'abc') >> str.upper
>>> func(123)
'123123ABC'
>>> func = (lambda x: '-' + x)._ << chr << (lambda x: x + 1) << ord
>>> func('a')
'-b'
Package: fconcrete
FConcreteConcrete beams according to NBR:6118:2014.
Usage examples inReadTheDocs Usage. Demo of use (not code) on theFConcrete AzureWebsite.Free software: MIT licenseDocumentation:https://fconcrete.readthedocs.io.Warning: This is a project in a alpha version. Much testing is needed yet.Do not use on your real life projects.A Quick IntroductionFConcrete is a python package to calculate the steel bars (longitudinal and transversal) with less material cost as possible and in a human friendy way (see default configs):n1 = fc.Node.SimpleSupport(x=0)
n2 = fc.Node.SimpleSupport(x=400)
f1 = fc.Load.UniformDistributedLoad(-0.3, x_begin=0, x_end=400)

concrete_beam = fc.ConcreteBeam(
        loads = [f1],
        nodes = [n1, n2],
        section = fc.Rectangle(30,80),
        division = 200
)It is also implemented aAnalysis Classthat can help you to get the best retangular section for your beam.
As you can see on the documentations, by the default all units are in cm, kN or combination of both.FeaturesExport plots to dxfDefine input parameters: available materials, cost, geometry definition, loads, fck, etcCalculation of efforts at any pointMoment diagram decalagedSection balance and calculation of the required steel areaAnchorage lengthRemove longitudinal barsCalculation of transversal steel bar (area per cm)Check limits and spacing per transversal bar spanCheck compliance with the steel area limitsCalculation of rotation at any pointCalculation of displacement at any pointImplement test routines comparing FtoolCheck dimensioning in E.L.S (except rupture)Associate costs with materialsProgram expense calculation functionCreate interaction functions and create table to follow the convergence of the algorithmExamples of tool usage (completion for optimized pre-dimensioning)Program expense calculation functionDocumentationDinamic calculation of d (steel height) when there is change of the expected steel positionAPI to easy connectionTODOCheck rupture (ELS)Check minimum area on the supportCorrect displacement value when there is variation of E * I along the beamPlot correctly when stirrups are not verticalPlot longitudinal bars correctly when the height or position of the beam base changes.Calculate the total length of the bar correctly when the height or position of the beam base changes.Implement compression armorInstallationTo install FConcrete, run this command in your terminal:$pipinstallfconcreteThis is the preferred method to install FConcrete, as it will always install the most recent stable release.
If you don’t havepipinstalled, thisPython installation guidecan guide
you through the process.CreditsMost of vectorized calculus made withNumpy, unit conversion withPint, all plots withMatplotlib(export to dxf withezdxf), detect peaks withpy-findpeaks,
docs made with the help ofSphinxandNumpydoc, analysis table withPandas,
this package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.1 (2020-02-15)MVP0.1.0 (2019-12-12)First release on PyPI. Updload to reserve the name.