Saravanan666/pypi-org huggingface dataset:
Package: loss-checker
Loss checkerThis module helps to check udp client/server packet loss. Can be run as client or server. Client send random string to server, waits for answer and when packet will receive, compare sended string with received and count time difference (ping). If packet is loss, answer will never be received and timeout will occur.Module can be installed on multiple servers and checks on one client.Prerequisites- Python3.5 or newerInstallion$ python3 -m pip install loss_checkerOR$ python3 -m pip install git+https://github.com/jok4r/loss_checker.gitUsage / EngRun as client$ python3 -m loss_checkerRun as server$ python3 -m loss_checker --serverRun server as a service*Can be run only on Linux$ python3 -m loss_checker --server --installIf you want to answer the question automatically (that the module will be installed as a service), add argument-y:$ python3 -m loss_checker --server --install -yAnd it will be installed and run as a service. You can check status, start and stop service with following commands:$ service loss_checker status$ service loss_checker start$ service loss_checker stopEnabling and disabling service$ systemctl enable loss_checker$ systemctl disable loss_checkerUninstall service$ python3 -m loss_checker --server --uninstallYou can add-yoption to skip confirmation question:$ python3 -m loss_checker --server --uninstall -y
Package: losshub
Classification Loss Function Library.Loss Functions for Image ClassificationRmse: $y = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - y')^2}$Mse: $y = \frac{1}{n} \sum_{i=1}^{n} (y_i - y')^2$InstallationpipinstalllosshubUsagefromlosshub.lossesimportmse,rmse# outputs and labelsy_true=[1,2,3,4,5]y_pred=[1,2,3,4,5]# msemse(y_true,y_pred)# rmsermse(y_true,y_pred)ReferencesBalanced-LossRmseMse
Package: losspy
PyLoss.
├──LICENSE
├──pyloss
│├──__init__.py
│├──pixel_based
││├──__init__.py
││├──cross_entropy.py
││├──mae_mse.py
││└──total_variation.py
│├──region_based
││├──__init__.py
││├──dice_iou.py
││├──hel.py
││└──ssim.py
│└──utils
│├──__init__.py
│├──convert_onehot.py
│└──misc.py
├──pyproject.toml
└──README.mdSome loss functions for deeplearning.pipinstalllosspy
Package: losswise
This is the official Losswise Python library. This library allows for
server-side integration of Losswise.InstallationThe library can be installed using pip:pip install losswiseGetting StartedFirst create an account on the Losswise website (https://losswise.com).  This will automatically generate a unique API key.Typical usage usually looks like this:import random
import losswise

# replace with your own api key
losswise.set_api_key('your_api_key')

# replace with a string that identifies your model
session = losswise.Session(tag='my_dilated_convnet', max_iter=10, data={'num_params': 10000000})

# create empty graph for loss, keep track of minima here hence kind='min'
graph = session.graph(title='loss', kind='min')

# track artificial loss over time
for x in xrange(10):
    train_loss = 1. / (0.1 + x + 0.1 * random.random())
    test_loss = 1.5 / (0.1 + x + 0.2 * random.random())
    graph.append(x, {'train_loss': train_loss, 'test_loss': test_loss})

# mark session as complete
session.done()You can then view the visualization results on your dashboard.
Package: lossycount
Finding Frequent Items 频繁集挖掘Installapt-get install -y libboost-python-dev
pip install lossycountif cannot find -lboost_python3occurred.Then I went to/usr/lib/x86_64-linux-gnusearch and found that the library file is in different name aslibboost_python-py35.soso I made a link by following commandsudo ln -s libboost_python-py35.so libboost_python3.so
which solved my problem.Usefrom lossycount import LossyCount

# 0.001 是要统计的频率下限
lc = LossyCount(0.001)

for i in range(200):
  for j in range(100):
    for k in range(j):
      lc.incr(j)
      # lc.incr(j, 1)

for i in range(1, 100, 30):
  print(i)
  print("出现的次数(估计值)", lc.est(i))
  print(
    "estimate the worst case error in the estimate of a) particular item :",
    lc.err(i)
  )
  print("---" * 20)

result = lc.output(1000)
result.sort(key=lambda x: -x[1])
print(result)

print(lc.capacity())Thankshadjieleftheriou.com/frequent-itemsversion 1.0DescriptionThis package provides implementations of various one pass algorithms for finding frequent items in data streams. In particular it contains the following:Frequent AlgorithmLossy Counting, and variationsSpace SavingGreewald & KhannaQuantile DigestCount SketchHierarchical Count-Min SketchCombinatorial Group TestingThe code is an extension of the MassDAL library. Implementations are by Graham Cormode.CitationsFinding Frequent Items in Data StreamsG. Cormode, M. HadjieleftheriouProc. of the International Conference on Very Large Data Bases (VLDB)Auckland, New Zealand, August 2008.
Package: lostc
Home-page: UNKNOWNAuthor: majikAuthor-email: me@yamajik.comLicense: See LicenseDescription:suanpanPlatform: anyClassifier: Environment :: Web EnvironmentClassifier: Intended Audience :: DevelopersClassifier: License :: OSI Approved :: BSD LicenseClassifier: Operating System :: OS IndependentClassifier: Programming Language :: PythonClassifier: Topic :: Internet :: WWW/HTTP :: Dynamic ContentClassifier: Topic :: Software Development :: Libraries :: Python Modules
Package: lost-cat-office
lost_cat_officeA repository for the core office processors and parsersgraph LR;
   A[Folders] --> B[data]
   A --> C[docs]
   A --> F[lost_cat_office]
   A --> E[test]
   A --> J[logs]
   B --> BT>stores data files]
   C --> CT>Documents and artifacts]
   F --> G[parsers]
   G --> GT>parser modules and classes]
   G --> GW(word)
   G --> GP(pdf)
   F --> H[processors]
   H --> HT>handles the queue load]
   H --> HA(azureblob)
   H --> HW(word)
   F --> I[utils]
   I --> IT>useful helper modules and classes]
   E --> ET>test code]
   J --> JT>log files]
Package: lost-ds
LOSTDatasetThis is a library to handle datasets generated by LOST. LOSTDataset enables
directily producing semantic segmentations, anno-type transformations (i.e. polygon to bbox),
anno-style transformations (i.e. bbox xywh -> bbox x1x2y1y2), crop datasets,
visualize annotations, lost2coco and some more.Checkout some explanation and exampleshere.
Package: lostinmsh
lostinmshThe Python toolboxlostinmsh(LOcally STructured polygonal INterface MeSH), is a package using GMSH to construct locally structured triangular meshes of polygons which are useful for sign changing PDE problem.DocumentationRequirementsPython ≥ 3.10GMSH≥ 4 (andimport gmshmust work)numpyOptional requirementsmatplotlib(only use for plotting)AcknowledgementsThis research was partially supported by the National Science Foundation Grant: DMS-2009366.
Package: lostlifevalue
Lostlifevalue1 .DatasetPopulation:https://www.worldometers.info/world-population/population-by-country/VSL,GNI:https://law.vanderbilt.edu/phd/faculty/w-kip-viscusi/355_Income_Elasticities_and_Global_VSL.pdfdeaths:https://github.com/owid/covid-19-data/blob/master/public/data/jhu/total_deaths.csv2. PackageThis package is designed to calculate the value of lives lost due to Covid-19 and to determine how much damage the country has suffered in terms of GNI ratio.To calculate the value of a life, we use an index called VSL. This index stands for value of statistical life, and represents the value of a human life. Using this VSL data and the number of deaths due to Covid-19, we can calculate the value of lives lost due to Covid-19.The following indicators are used in this package.deathsVSL ($/million)GNI ($/thousand)Population(/100000)eloss_total(VSL * deaths)GNI_total(GNI * Population)eDamage_per_GNI(eloss_total/GNI_total)3. How to rum?You can use this package easily.First, use this command to download the package.$ pip install lostlifevalueNext, run this command.$ lostlifevalue Items_to_be_sorted
Package: lostruct-py
lostruct-pyThis is a reimplementation of lostruct from the original code:Lostruct. Please cite the original paperDemonstration / How to usePlease see theExample NotebookInstallationLostruct-py is available onPyPipip install lostruct-pyis the easiest way to get started.CitingPlease use our DOI to cite this specific project. Also please cite the original Lostruct paper and CyVCF2.DOI: 10.5281/zenodo.3997086Original Lostruct PaperPlease cite the original lostruct paper:Li, Han, and Peter Ralph. "Local PCA shows how the effect of population structure differs along the genome." Genetics 211.1 (2019): 289-304.CyVCF2This paper also usescyvcf2for fast VCF processing and should be cited:Brent S Pedersen, Aaron R Quinlan, cyvcf2: fast, flexible variant analysis with Python, Bioinformatics, Volume 33, Issue 12, 15 June 2017, Pages 1867–1869, https://doi.org/10.1093/bioinformatics/btx057Changes from Lostruct R packagePlease note numpy and R are different when it comes to row-major vs. column-major. Essentially, many things in the python version will be transposed from R.RequirementsPython >= 3.6 (may work with older versions). Developed on Python 3.8.5numbanumpypandasscipyskbiosklearncyvcf2CyVCF2 requires zlib-dev, libbz2-dev, libcurl-dev, liblzma-dev, and probably othersEasiest to install all of these through condaCorrelation DataUsed Medicago HapMap sister taxa chromsome 1, processed, and run with LoStructDatabcftools annotate chr1-filtered-set-2014Apr15.bcf -x INFO,FORMAT | bcftools view -a -i 'F_MISSING<=0.2' | bcftools view -q 0.05 -q 0.95 -m2 -M2 -a -Oz -o chr1-filtered.vcf.gzLostruct ProcessingRscript run_lostruct.R -t SNP -s 95 -k 10 -m 10 -i data/This generates the mds_coords.tsv that is used in the correlation comparison.FAQ / NotesFutureCurrently the end-user is expected to save the outputs. But could be good to save it in a similar way to lostruct R-code. Please open an issue if you need this.PCA, MDS, PCoAPCoA returns the same results as lostruct's MDS implementation (cmdscale). In the example Jupyter notebook you can see the correlation is R =~ 0.998. Some examples of other methods of clustering / looking at differences are included in the notebook.Casting complex values to real discards the imaginary partThis is fine.
Package: losuapi
LosuapiPython wrapper for theOsu apiV2pipinstalllosuapipoetryaddlosuapiImportantDevelopment in progressSetupRegister an Oauth application on the osuaccount settings page.Do not set an Application callback URL, the current version of this package does not need one.creating client_id and client_secret variables exampleCLIENT_ID="registerd client id"CLIENT_SECRET="registered client secret"Basic usageexample:importlosuapi# instantiate OsuApi objectapi:losuapi.OsuApi=losuapi.OsuApi(client_id=CLIENT_ID,cleint_secret=CLIENT_SECRET)# instantiate async compatible OsuApi objectasyncApi:losuapi.AsyncOsuApi=losuapi.AsyncOsuApi(client_id=CLIENT_ID,cleint_secret=CLIENT_SECRET)# lookup_beatmap method returns a losuapi.Beatmap objectbeatmap:losuapi.Beatmap=api.lookup_beatmap(beatmap_id=1920615)Working endpointsfromlosuapiimportOsuApiOsuApi.lookup_beatmap(beatmap_id,checksum,filename)OsuApi.user_beatmap_score(beatmap_id,user_id,mode,mods)OsuApi.user_beatmap_scores(beatmap_id,user_id,mode)OsuApi.beatmap_scores(beatmap_id,mode,mods,Type)OsuApi.beatmaps(beatmap_ids)OsuApi.beatmap(beatmap_id)OsuApi.beatmap_attributes(beatmap_id,modsruleset,ruleset_id)OsuApi.user_kudosu(user_id,limit,offset)OsuApi.user_scores(user_id,Type,include_fails,mode,limit,offset)OsuApi.user_beatmaps(user_id,Type,limit,offset)OsuApi.user_recent_activity(user_id,limit,offset)OsuApi.user(username,mode,key)OsuApi.users(user_ids)OsuApi.ranking(mode,Type,filter,country,cursor,spotlight_id,variant)OsuApi.spotlights()
Package: lot
python3-lotInstallationRef:https://github.com/loliot/lotsudoadd-apt-repository-yppa:loliot/ppa\&&sudoaptinstalllotsudolotinstallcppsudoaptupdate\&&sudoaptinstall-ypython3python3-devpython3-pip\&&python3-mpipinstall-U--userpipsetuptoolspython3-mpipinstall-U--userlotBlink exampleimportlotimportsysLED_PIN=13iflen(sys.argv)>1:LED_PIN=int(sys.argv[1])lot.init_time()led=lot.Gpio(LED_PIN)led.mode(lot.DOUT)whileTrue:led.toggle()lot.delay_ms(200)ChangelogRef: debian/changelogpython3-lot (0.8.1) unstable; urgency=mediumReflect lot-SBC v0.12.3-- Hyeonki Honghhk7734@gmail.comThu, 30 Jan 2020 20:53:32 +0900python3-lot (0.8.0) unstable; urgency=mediumReflect lot-SBC v0.12.0-- Hyeonki Honghhk7734@gmail.comSat, 18 Jan 2020 03:08:52 +0900python3-lot (0.7.1) unstable; urgency=mediumUpdate time/Gpio docs-- Hyeonki Honghhk7734@gmail.comFri, 17 Jan 2020 18:27:02 +0900python3-lot (0.7.0) unstable; urgency=mediumImplement Spi class-- Hyeonki Honghhk7734@gmail.comTue, 14 Jan 2020 23:13:00 +0900python3-lot (0.6.0) unstable; urgency=mediumImplement I2c class-- Hyeonki Honghhk7734@gmail.comTue, 14 Jan 2020 13:30:20 +0900python3-lot (0.5.1) unstable; urgency=mediumAdd const variables-- Hyeonki Honghhk7734@gmail.comWed, 08 Jan 2020 17:49:10 +0900python3-lot (0.5.0) unstable; urgency=mediumRename gpio.cpp to lot.cppAdd type_castersImplement Uart class-- Hyeonki Honghhk7734@gmail.comWed, 08 Jan 2020 13:49:14 +0900python3-lot (0.4.1) unstable; urgency=mediumFix crash that occurs when running 'pip install'-- Hyeonki Honghhk7734@gmail.comMon, 06 Jan 2020 19:48:55 +0900python3-lot (0.4.0) unstable; urgency=mediumUse Pybind11 instead of SwigImplement Gpio class-- Hyeonki Honghhk7734@gmail.comMon, 06 Jan 2020 18:15:49 +0900python3-lot (0.3.0) unstable; urgency=mediumImplement communication wrapper class-- Hyeonki Honghhk7734@gmail.comMon, 23 Dec 2019 14:05:06 +0900python3-lot (0.2.2) unstable; urgency=mediumFix file path error-- Hyeonki Honghhk7734@gmail.comSun, 17 Nov 2019 13:47:35 +0000python3-lot (0.2.1) unstable; urgency=mediumFix python package error-- Hyeonki Honghhk7734@gmail.comSun, 17 Nov 2019 22:21:01 +0900python3-lot (0.2.0) unstable; urgency=mediumAdd typemap to convert c++ typedefAdd lot_time.h-- Hyeonki Honghhk7734@gmail.comSun, 17 Nov 2019 19:12:19 +0900python3-lot (0.1.3) unstable; urgency=mediumFix TypeError.Move version check from setup.cfg to setup.py.-- Hyeonki Honghhk7734@gmail.comSun, 06 Oct 2019 16:56:29 +0900python3-lot (0.1.2) unstable; urgency=mediumFix include path error.-- Hyeonki Honghhk7734@gmail.comSun, 06 Oct 2019 10:27:52 +0900python3-lot (0.1.1) unstable; urgency=mediumTest basic functionMove setup description from setup.py to setup.cfg-- Hyeonki Honghhk7734@gmail.comThu, 03 Oct 2019 17:36:28 +0900python3-lot (0.1.0) unstable; urgency=mediumInitial release.-- Hyeonki Honghhk7734@gmail.comThu, 22 Aug 2019 22:03:00 +0900
Package: lotame
Lotame API WrapperWrapper Class for Lotame API.Wrapper class for Lotame REST API.https://api.lotame.com/docshttps://github.com/paulokuong/lotameRequirementsPython 3.6 (tested)GoalTo provide a generic wrapper Lotame APICode sampleInstantiatefromlotameimportApi,Credentials,FirehoseService,BehaviorServiceapi=Api(Credentials(client_id='xxx',token='yyy',access='zzz'))fs=FirehoseService(api=api)updates=fs.getUpdates(hours=1)b=BehaviorService(api=api)b.get('types')ContributorsPaulo Kuong (@pkuong)
Package: loteria-caixa
Dados de sorteios - Loteria CaixaCodigo para receber dados do sorteio atual# -*- coding: utf-8 -*-
from loteria_caixa import *

concurso = MegaSena()

print(concurso.listaDezenas())ou use um sorteio especifico# -*- coding: utf-8 -*-
from loteria_caixa import (MegaSena, LotoFacil, Quina, LotoMania, TimeMania,
                      DuplaSena, Federal, Loteca, DiadeSorte, SuperSet)

concurso = LotoFacil(2224)

print(concurso.listaDezenas())Abaixo todos os comandos# -*- coding: utf-8 -*-
from loteria_caixa import MegaSena

concurso = MegaSena()

print(concurso.todosDados())
print(concurso.tipoJogo())
print(concurso.numero())
print(concurso.nomeMunicipioUFSorteio())
print(concurso.dataApuracao())
print(concurso.valorArrecadado())
print(concurso.valorEstimadoProximoConcurso())
print(concurso.valorAcumuladoProximoConcurso())
print(concurso.valorAcumuladoConcursoEspecial())
print(concurso.valorAcumuladoConcurso_0_5())
print(concurso.acumulado())
print(concurso.indicadorConcursoEspecial())
print(concurso.dezenasSorteadasOrdemSorteio())
print(concurso.listaResultadoEquipeEsportiva())
print(concurso.numeroJogo())
print(concurso.nomeTimeCoracaoMesSorte())
print(concurso.tipoPublicacao())
print(concurso.observacao())
print(concurso.localSorteio())
print(concurso.dataProximoConcurso())
print(concurso.numeroConcursoAnterior())
print(concurso.numeroConcursoProximo())
print(concurso.valorTotalPremioFaixaUm())
print(concurso.numeroConcursoFinal_0_5())
print(concurso.listaMunicipioUFGanhadores())
print(concurso.listaRateioPremio())
print(concurso.listaDezenas())
print(concurso.listaDezenasSegundoSorteio())
print(concurso.id())
Package: lotery-drawer
lotery-drawerLotery-drawer is a simple, proven and non-falsifiable lottery draw tool.This tool can be used to draw a lotery in a provable way.
The draw is random but will always return the same result when given the same lotery datas.DocumentationInstallationPython >=3.6 is required to run this programInstall the package by running :pip install lotery-drawer --userUsageYou can simply print on the console the result of the lotery :Create your own lotery data json, and place it into theconfigdirectory for exemple.Call the main file with your json path :python3 main.py "config/dataLotery.json"Then the console will output the resultYou can also directly interface your program with the library :fromlotery_drawerimportDrawerdrawer=Drawer()result=drawer.draw(dataLotery)Here an example of the lotery data json :{"endTimestamp":1589289392,"prizes":[{"name":"1000 HXRO","nb":1},{"name":"500 HXRO","nb":1},{"name":"200 HXRO","nb":5},{"name":"100 HXRO","nb":10}],"tickets":{"player_a":10,"player_b":20,"player_c":30,"player_d":40,"player_e":150}}endTimestamp: The end date of the lotery. It is only used to generate a seed for random functionsprizes: A list of the prizes of the lotery. Each prize can have one or more winning tickets (nb) and have a name (name)tickets: A dict of the tickets at the end of the lotery. Each element of the dict is a player, the number associated is the number of tickets in his possession.The tool will draw as winning tickets as there is prizes, and return an array containing the usernames and the prizes won.
Note that if a ticket is won, it is removed from the tickets list for the next prizes.Here an exemple of the result when using the fileconfig/dataLotery.jsonin the repository :[{"prizeName":"1000 HXRO","username":"player_e"},{"prizeName":"500 HXRO","username":"player_a"},{"prizeName":"200 HXRO","username":"player_e"},{"prizeName":"200 HXRO","username":"player_e"},{"prizeName":"200 HXRO","username":"player_e"},{"prizeName":"200 HXRO","username":"player_e"},{"prizeName":"200 HXRO","username":"player_d"},{"prizeName":"100 HXRO","username":"player_d"},{"prizeName":"100 HXRO","username":"player_e"},{"prizeName":"100 HXRO","username":"player_d"},{"prizeName":"100 HXRO","username":"player_d"},{"prizeName":"100 HXRO","username":"player_b"},{"prizeName":"100 HXRO","username":"player_e"},{"prizeName":"100 HXRO","username":"player_e"},{"prizeName":"100 HXRO","username":"player_c"},{"prizeName":"100 HXRO","username":"player_c"},{"prizeName":"100 HXRO","username":"player_d"}]
Package: lotkavolterra-simulator
Lotka-Volterra simulatorSimulator of the Lotka-Volterra prey-predator system, with demographic and observational noise and biases.InstallationThis is a standard, low-weight python package, written with python 3. It is packaged athttps://pypi.organd can be installed usingpip:pip install lotkavolterra-simulatorAlternatively it is possible to clone the Github repository and to install using:pip install .DocumentationThe model is described in section III ofLeclercq (2022). The jupyter notebooksimulations.ipynbillustrates how to run the code and plot prey and predator theoretical and observed number functions.This code has been designed to illustrate concepts in simulation-based inference. It is used inpySELFIfrom version 2.0.Limited user-support may be asked from the main author, Florent Leclercq.ContributorsFlorent Leclercq,florent.leclercq@iap.frReferenceTo acknowledge the use of lotkavolterra_simulator in research papers, please cite the paperLeclercq (2022):Simulation-based inference of Bayesian hierarchical models while checking for model misspecificationF. LeclercqProceedings of the41st International Conference on Bayesian and Maximum Entropy methods in Science and Engineering (MaxEnt2022), 18-22 July 2022, Paris, FrancePhysical Sciences Forum5, 4 (2022),arXiv:2209.11057[astro-ph.CO] [ADS] [pdf]@ARTICLE{lotkavolterra_simulator,
    author = {{Leclercq}, Florent},
    title = "{Simulation-based inference of Bayesian hierarchical models while checking for model misspecification}",
    journal = {Physical Sciences Forum},
    volume = 5,
    pages = 4,
    doi = {10.3390/psf2022005004},
    keywords = {Statistics - Methodology, Astrophysics - Instrumentation and Methods for Astrophysics, Mathematics - Statistics Theory, Quantitative Biology - Populations and Evolution, Statistics - Machine Learning},
    year = 2022,
    month = sep,
    eid = {arXiv:2209.11057},
    pages = {arXiv:2209.11057},
    archivePrefix = {arXiv},
    eprint = {2209.11057},
    primaryClass = {stat.ME},
    }LicenseThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. By downloading and using lotkavolterra_simulator, you agree to theLICENSE, distributed with the source code in a text file of the same name.
Package: loto
loto : Lockout-TagoutLoto provides a simple decorator for functions
that need to access critical sections with mutex locks.When you decorate a function with@LockoutTagout(tag),
the function call will block until it can acquire a mutex lock
shared between all functions with the same tag.ExamplefromlotoimportLockoutTagoutfromthreadingimportThreadfromrandomimportrandomfromtimeimportsleepdata=[1,2,3]@LockoutTagout('data1')defreadData1(sharedData):forvalinsharedData:print(val)sleep(random())@LockoutTagout('data1')defwriteData1(sharedData):foriinrange(0,3):sharedData[i]+=1sleep(random())foriinrange(0,5):Thread(target=readData1,args=(data,)).start()Thread(target=writeData1,args=(data,)).start()These two functions will lockout-tagout with the same tag,
so they will acquire a lock managed by this decorator class before
they're executed. This means that no two functions with the same
tag can be executing concurrently.One mutex lock is managed per unique tag.InstallationYou can install this module from PyPI withpip install loto
Package: lotofacil
Dados de sorteios da LotofacilCodigo para receber dados do sorteio atual# -*- coding: utf-8 -*-
from lotofacil import Sorteio

concurso = Sorteio()

print(concurso.listaDezenas())ou use um sorteio especifico# -*- coding: utf-8 -*-
from lotofacil import Sorteio

concurso = Sorteio(2224)

print(concurso.listaDezenas())Abaixo todos os comandos# -*- coding: utf-8 -*-
from lotofacil import Sorteio

concurso = Sorteio()

print(concurso.todosDados())
print(concurso.tipoJogo())
print(concurso.numero())
print(concurso.nomeMunicipioUFSorteio())
print(concurso.dataApuracao())
print(concurso.valorArrecadado())
print(concurso.valorEstimadoProximoConcurso())
print(concurso.valorAcumuladoProximoConcurso())
print(concurso.valorAcumuladoConcursoEspecial())
print(concurso.valorAcumuladoConcurso_0_5())
print(concurso.acumulado())
print(concurso.indicadorConcursoEspecial())
print(concurso.dezenasSorteadasOrdemSorteio())
print(concurso.listaResultadoEquipeEsportiva())
print(concurso.numeroJogo())
print(concurso.nomeTimeCoracaoMesSorte())
print(concurso.tipoPublicacao())
print(concurso.observacao())
print(concurso.localSorteio())
print(concurso.dataProximoConcurso())
print(concurso.numeroConcursoAnterior())
print(concurso.numeroConcursoProximo())
print(concurso.valorTotalPremioFaixaUm())
print(concurso.numeroConcursoFinal_0_5())
print(concurso.listaMunicipioUFGanhadores())
print(concurso.listaRateioPremio())
print(concurso.listaDezenas())
print(concurso.listaDezenasSegundoSorteio())
print(concurso.id())