Saravanan666/pypi-org huggingface dataset:
Package: covid-vision
Covid VisionThis project aims to develop Computer Vision algorithms to help the detection of COVID-19 virus on images.Free software: MIT licenseDocumentation:https://covid-vision.readthedocs.io.Install the projectpython setup.py installFeaturesDataLoader: Load the images dataset for the user.CovidCXR: Download CovidCXR model and predict one imageCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.4 (2020-05-25)Update the requirements for the project0.1.3 (2020-05-25)Added CovidCXR Classifier0.1.2 (2020-05-13)Configuration of pipeline to publish test package.0.1.1 (2020-05-13)DataLoader Creation.0.1.0 (2020-05-11)First release on PyPI.
Package: covimath
Python package for epidemiological models relevant to modeling Covid-19 infectionsPre-requisite: Python 3.6 and aboveTo run the following models, execute (from the top-level covimath directory):SISmodel:python3 -m covimath.models.sis N=1000 lambda=0.05 mu=0.15 gamma=0.1 I0=1 tau=30SIRmodel:python3 -m covimath.models.sir N=1000 I0=1 R0=0 beta=0.2 gamma=0.1 tau=150SEIRmodel:python3 -m covimath.models.seir N=1000 E0=1 I0=1 R0=0 beta=1.38 sigma=0.19 gamma=0.34 tau=150SEIRDmodel:python3 -m covimath.models.seird N=1000 E0=1 I0=1 R0=0 D0=0 beta=1.38 sigma=0.19 gamma=0.34 mu=0.03 tau=150To run the tests, from the top-level covimath directory, run:pytestA simple method to estimate beta for SIR model has been provided.Example code can be found in agist.Installation: To install this package, run:pip3 install covimath
Package: covimerage
covimerageGenerates code coverage information for Vim scripts.It parses the output from Vim's:profilecommand, and generates data
compatible withCoverage.py.InstallationYou can install covimerage using pip:pipinstallcovimerageSimple usageYou can usecovimerage runto wrap the call to Neovim/Vim with necessary
boilerplate:covimeragerunvim-Nutest/vimrc-c'Vader! test/**'This will write the file.coverage_covimerageby default (use--data-fileto configure it), which is compatible with Coverage.py.
A report is automatically generated (on stdout).You can then callcovimerage xmlto create acoverage.xmlfile
(Cobertura-compatible), which tools likeCodecov'scodecovtool can consume, e.g. viacodecov -f coverage.xml.Manual/advanced usage1. Generate profile information for your Vim script(s)You have to basically add the following to your tests vimrc:profilestart/tmp/vim-profile.txt
profile!file./*This makes Neovim/Vim then write a file with profiling information.2. Call covimerage on the output file(s)covimeragewrite_coverage/tmp/vim-profile.txtThis will create a file.coverage_covimerage(the default for--data-file),
with entries marked for processing by aCoverage.pyplugin (provided by
covimerage)).3. Include the covimerage plugin in .coveragercWhen usingcoverageon the generated output (data file), you need to add
thecovimerageplugin to the.coveragercfile (which Coverage.py uses).
This is basically all the.coveragercyou will need, but you could use
other settings here (for Coverage.py), e.g. to omit some files:[run]
plugins = covimerage
data_file = .coverage_covimerage4. Create the report(s)You can now call e.g.coverage report -m, and you should be able to use
coverage reporting platforms likehttps://codecov.io/orhttps://coveralls.io, which are basically usingcoverage xml.Reference implementationNeomakeis the first adopter of this.
It has an advanced test setup (including Docker based builds), and looking at
tis setup could be helpful when setting up covimerage for your
plugin/project.Neomake's coverage report on codecov.ioPR/change to integrate it in
Neomake(Neomake's test
setup is rather advanced, so do not let that scare you!)CaveatsCoverage information for a function may get wiped if the function is re-defined
after testing it. This is a known limitation that does not depend on the
implementation of covimerage, but on the vim profiler itself, since profiling
data for a function, used to extract coverage data, is reset when the function
is re-defined, even if the definition is identical.This can be an issue if the source files being checked for coverage are
reloaded withruntimebetween tests for some reasons, e.g. when mocking some
function. To prevent coverage information from being lost, one solution is to
write such tests in a different suite that is run separately, and combine the
coverage results withcovimerage run --append.LinksDiscussion in Coverage.py's issue tracker:coverage issue 607TODOLine hit counts: known to covimerage, but not supported by Coverage.py
(https://bitbucket.org/ned/coveragepy/issues/607/#comment-40048034).
Package: covira
CoVIRACoVIRA (Consensus by Voting with Iterative Re-weighting based on Agreement) is a method
to identify weights and produce consensus predictions based on a collection of results from
predictors for multiple samples. It employs a iterative recalculations of weights based on the
weighted "agreement" between the predictors, and allows the calculation of a final prediction as well.This algorithm was created to help on the integration of results from multiple prediction tools in a
reverse vaccinology study where no validation dataset was available for all features been inferred. Therefore,
we created a unsupervised way to estimate how accurate each predictor was for that particular case considering
that the more the results of a predictor is "confirmed" by the others, the higher it's accuracy.InstallingFrom PyPI$pipinstallcoviraFrom source code$gitclonehttps://github.com/fredericokremer/covira
$cdcovira
$pythonsetup.pybuild
$pythonsetup.pyinstallUsing>>>importpandasaspd>>>fromcoviraimportCovira>>>df=pd.read_csv('test_data/dataset_1.csv')>>>predictions=df.drop(['gene'],axis=1).values>>>predictions# each column represents the prediction from a different tool, while each row is a different samplearray([[1,0,0],[0,1,0],[1,1,0],[1,0,0],[1,0,0],[1,0,0],[1,0,0],[1,0,1]])>>>covira=Covira(max_iterations=1000)# max number of iterations in the weight calculation>>>covira.fit(predictions)# "fit" = calculates the weights>>>covira.predict(predictions)# calculates the consensus predictionarray([0.1875,0.375,0.5625,0.1875,0.1875,0.1875,0.1875,0.625])>>>covira.weights# weights calculated for each predictorarray([0.1875,0.375,0.4375])ReferenceGrassmann AA, Kremer FS, Dos Santos JC, Souza JD, Pinto LDS, McBride AJA.Discovery of Novel Leptospirosis Vaccine
Candidates Using Reverse and Structural Vaccinology.Front Immunol. 2017;8:463. Published 2017 Apr 27.doi:10.3389/fimmu.2017.00463
Package: covizpy
covizpycovizpyis a Python package that provides easy access to Covid-19 data fromOur World in Data, as well as functions to generate relevant Covid-19 charts and summaries easily. We aim to makecovizpysimple and easy to use. Our goal is to enable anyone with basic Python programming knowledge to access and visualize Covid-19 data, and make their own informed decisions and conclusions.There are existing Python packages that allow users to download and generate Covid-19 charts. For example,covid19pandasis a package that presents COVID-19 data from Johns Hopkins University and The New York Times in pandas dataframes, to make analysis and visualization easier in a Python environment.While other packages have more advanced plotting capabilities, we provide simpler functions that allow users to answer questions regarding the Covid-19 pandemic as quickly as possible.FeaturesThis package contains four functions:plot_metric,plot_spec,get_dataandplot_summary.plot_metric: Create a line chart presenting COVID total new cases versus another metric within a time periodplot_spec: Create a line chart presenting specific country/countries COVID information within a time periodget_data: User can retrieve the COVID data from the source as a pandas dataframe. Specific data can be retrieved by passing the date range and the list of countriesplot_summary: Create a horizontal bar chart summarising a specified variable and value within a time periodDependenciesBefore installing the package, following packages must be installed:python = "^3.9"pandas = "^1.3.5"altair = "^4.2.0"altair-saver = "^0.5.0"InstallationpipinstallcovizpyUsage and ExamplesTo use the package, import the package with following commands:fromcovizpy.get_dataimportget_datafromcovizpy.plot_summaryimportplot_summaryfromcovizpy.plot_metricimportplot_metricfromcovizpy.plot_specimportplot_specTo use the functions, see below examples:Retrieve COVID-19 data with specified date range and default all locationsdf=get_data(date_from="2022-01-01",date_to="2022-01-21")Plot summary graph (bar chart)plot_summary(df,var="location",val="new_cases",fun="sum",date_from="2022-01-01",date_to="2022-01-15",top_n=10)Plot COVID-19 cases for specific countries (line chart)plot_spec(df,location=["Canada","Turkey"],val="new_cases",date_from="2022-01-01",date_to="2022-01-07")Plot new COVID-19 cases versus another metric (line chart)plot_metric(location="Canada",metric="positive_rate",date_from="2022-01-01",date_to="2022-01-15")ContributorsRohit RawatRong LiThomas SiuTing Zhe YanContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensecovizpywas created by Rohit Rawat, Rong Li, Thomas Siu, Ting Zhe Yan . It is licensed under the terms of the MIT license.Creditscovizpywas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: covjson-pydantic
CoverageJSON PydanticThis repository contains the coveragejson-pydantic Python package. It providesPydanticmodels forCoverageJSON. This can, for example, be used to develop an API using FastAPI serving or receiving CoverageJSON.Installpipinstallcovjson-pydanticOr you can install directly from source:pipinstallgit+https://github.com/KNMI/covjson-pydantic.gitUsagefromdatetimeimportdatetime,timezonefrompydanticimportAwareDatetimefromcovjson_pydantic.coverageimportCoveragefromcovjson_pydantic.domainimportDomain,Axes,ValuesAxis,DomainTypefromcovjson_pydantic.ndarrayimportNdArrayc=Coverage(domain=Domain(domainType=DomainType.point_series,axes=Axes(x=ValuesAxis[float](values=[1.23]),y=ValuesAxis[float](values=[4.56]),t=ValuesAxis[AwareDatetime](values=[datetime.now(tz=timezone.utc)]))),ranges={"temperature":NdArray(axisNames=["x","y","t"],shape=[1,1,1],values=[42.0])})print(c.model_dump_json(exclude_none=True,indent=4))Will print{"type":"Coverage","domain":{"type":"Domain","domainType":"PointSeries","axes":{"x":{"values":[1.23]},"y":{"values":[4.56]},"t":{"values":["2023-09-14T11:54:02.151493Z"]}}},"ranges":{"temperature":{"type":"NdArray","dataType":"float","axisNames":["x","y","t"],"shape":[1,1,1],"values":[42.0]}}}ContributingMake an editable installation from within the repository rootpipinstall-e'.[test]'Running testspytesttests/Linting and typingLinting and typing (mypy) is done usingpre-commithooks.pipinstallpre-commit
pre-commitinstall
pre-commitrunRelated packagesedr-pydantic- Pydantic data models for the Environmental Data Retrieval (EDR) APIgeojson-pydantic- Pydantic data models for the GeoJSON specReal world usageThis library is used to build an OGC Environmental Data Retrieval (EDR) API, serving automatic weather data station data from The Royal Netherlands Meteorological Institute (KNMI). See theKNMI Data Platform EDR API.TODOsHelp is wanted in the following areas to fully implement the CovJSON spec:The polygon based domain types are not supported.TheTrajectoryandSectiondomain type are not supported.TheNdArrayonly supportsfloatdata.Not all requirements in the spec relating different fields are implemented.LicenseApache License, Version 2.0
Package: covmatest
covmatestGenerate covariance matrices for testing.InstallationUsing pipy:pip install covmatestFor developers:python setup.py developUsagefrom covmatest import get_covmat
n_matrices = 3
n_channels = 2
covmat = get_covmat(n_matrices, n_channels)
print(covmat)EnvironmentUbuntu, Windows, MacOsPython 3.8 and 3.9How to cite?G. Cattan, covmatest. 2021. [Online]. Available:https://github.com/gcattan/covmatest/DOI:https://doi.org/10.5281/zenodo.5574548
Package: covpred
EpidemicsRunner APICovPredis a Python library that provides various algorithms
useful for modeling population dynamics inferred from e.g. infectious diseases. Amongst others, it is used byCovPredin this exact form.ContentsInstallation NotesDescriptionInstallation NotesGeneralJust run pip-install covpredDescriptionThe following algorithms are currently implemented:NameDescriptionSIRClassical Susceptible-Infected-Recovered ODE Model augmented with a deceased classSEIRDExtended SIR model accounting for a new class (Exposed) amongst other additionsSEIRD_VARAExtended SIR model paired with variable (over time) infection rateSEIRD_CKCoupled ODE SEIRD Model to model interaction between the ODE systemsSEIRD_PDEA PDE formulation of the SEIRD model, discretized using Finite Differences
Package: covseisnet
OverviewThis package provides tools forarray signal processing, with a focus on seismic data. In addition to the classical array processing tools (e.g. beamforming, inter-station cross-correlations) we provide various tools forsource detection and location with blind source separation algorithms. The project also includes diversespectral analysistools.covseisnetmostly inherits fromobpsy(seismic-data analysis) andnumpy.DocumentationPlease see the full documentation available athttps://covseisnet.gricad-pages.univ-grenoble-alpes.fr/covseisnet.
Package: covxnet
CovXNet: A Multi-Dilation Convolutional Neural Network for Automatic COVID-19 and Other Pneumonia Detection from Chest X-ray Images with Transferable Multi-Receptive Feature OptimizationAbstract:With the recent outbreak of COVID-19, fast diagnostic testing has become one of the major challenges due to the critical shortage of test kit. Pneumonia, a major effect of COVID-19, needs to be urgently diagnosed along with its underlying reasons. In this paper, deep learning aided automated COVID-19 and other pneumonia detection scheme is proposed utilizing a small amount of COVID-19 chest X-rays. A deep convolutional neural network (CNN) based architecture, named as CovXNet, is proposed that utilizes depthwise convolution with varying dilation rates for efficiently extracting diversified features from chest X-rays. Since the chest X-ray images corresponding to COVID-19 caused pneumonia and other traditional pneumonia have significant similarities, at first, a large number of chest X-rays corresponding to normal and (viral/bacterial) pneumonia patients are used to train the proposed CovXNet. Learning of this initial training phase is transferred with some additional fine-tuning layers that are further trained with a smaller number of chest X-rays corresponding to COVID-19 and other pneumonia patients. In the proposed method, different forms of CovXNets are designed and trained with X-ray images of various resolutions and for further optimization of their predictions, a stacking algorithm is employed. Finally, a gradient-based discriminative localization is integrated to distinguish the abnormal regions of X-ray images referring to different types of pneumonia. Extensive experimentations on two different datasets provide very satisfactory detection performance and thus the new scheme can serve as an efficient tool in the current state of COVID-19 pandemic.Paper link:hereInstallationStable:pipinstallcovxnetorLatest:pipinstallgit+https://github.com/awsaf49/covxnetUsagefromcovxnetimportCovXNet128model=CovXNet128(input_shape=(128,128,3),num_classes=3)ExampleNotebook:Pneumonia Detection with CovXNetMethodVisual Abstract:Ensemble Network:Architecture:
Package: cow
Yuge heifer-like bloated CMS - now with more django, S3, Lambda friendliness!DocumentationCow lives in lambda and breathes S3, and loves to ingest your waste and
uselessly regurgitate your API calls. It provides no end to its own waste,
and offers all the bloat your money can’t buy.QuickstartInstall cow:pip install cowAdd it to yourINSTALLED_APPS:INSTALLED_APPS=(...'rest_framework','cow',...)Add cow’s URL patterns:fromcowimporturlsascow_urlsurlpatterns=[...url(r'^',include(cow_urls)),...]Migrate app:./manage.py migrate cowUsageAfter installation, add a urls endpoint, and edit menu, page, and pluginsThis should leave you with a hierarchy of pages (and their content)The API should give you a sitemap-like structure, and upon navigation,
a further API call would give all the specific content for pluginCheck examples for working samples of use!TODOUnit tests - menus and some basic view checksAdd deliciously overweight but relatable cow icon, as an endearing
and manipulating draw in for suckers wanted to support literal and
metaphorical dead weightFeaturesBig fat bloated heifer offering contentConfigure menu, page, plugin content for mobile consumptionRunning TestsDoes the code actually work?:source <YOURVIRTUALENV>/bin/activate
(myenv) $ pip install tox
(myenv) $ toxLicenseCopyright Jon Robison 2017, see LICENSE for details
Package: cow-api
Cow AssignmentDescriptionThis is a simple cow API that allows you to create, read, update, and delete cows using a RESTful API built with FastAPI.FeaturesFetch all cows or a single cow based on its ID.Create a new cow.Update the details of an existing cow.Delete a cow based on its ID.Filter cows based on their attributes.InstallationInstall the package using pip:pip install cow_apiAlternatively, you can clone the repository and install the requirements using pip:pip install -r requirements.txtUsageAfter installation, you can run the application using uvicorn:uvicorn cow_api.main:app --reloadThen navigate tohttp://localhost:8000/docsin your web browser to access the API.TestingTests are located in the tests directory. To run tests, navigate to the root directory of the application and execute the following command:pytest
Package: cowbull
Bulls and Cows (also known as Cows and Bulls or Pigs and Bulls or Bulls and Cleots) is an old code-breaking mind or paper and pencil game for two or more players, predating the similar commercially marketed board game Mastermind.
The numerical version of the game is usually played with 4 digits, but can also be played with 3 or any other number of digits.On a sheet of paper, the players each write a 4-digit secret number. The digits must be all different. Then, in turn, the players try to guess their opponent’s number who gives the number of matches. If the matching digits are in their right positions, they are “bulls”, if in different positions, they are “cows”. Example:Secret number: 4271
Opponent’s try: 1234
Answer: 1 bull and 2 cows. (The bull is “2”, the cows are “4” and “1”.)
The first one to reveal the other’s secret number wins the game. As the “first one to try” has a logical advantage, on every game the “first” player changes. In some places, the winner of the previous game will play “second”. Sometimes, if the “first” player finds the number, the “second” has one more move to make and if he also succeeds, the result is even.The game may also be played by two teams of 2–3 players. The players of every team discuss before making their move, much like in chess.A computer program moo, written in 1970 by J. M. Grochow at MIT in the PL/I computer language for the Multics operating system, was amongst the firstBulls and Cows computer implementations, inspired by a similar program written by Frank King in 1968 and running on the Cambridge University mainframe.
Because the game has simple rules, while it is difficult and entertaining, there are many computer variants; it is often included in telephones and PDAs.It is proven that any number could be solved using up to seven turns. Minimal average game length is 26274/5040=5.2131 turnsBelow Class can be used to create your own game.
Package: cowcerts-certificate-render
Certificate renderHTML5 + CSS3 template to render a certificate viewTemplates rendered with the certificate values will be included indisplayHtmlof certificates so that the certificate can be always
visualized in the original form.UsageAMakefileis present to help you developing and using the projectRunUsemake runto output a sample certificate renderizationServeUsemake serveto serve the template locally using Python 3 integrated
HTTP server.To serve the rendering of anEDS, usemake serve-edsBuild and uploadUsemake buildto create a package distribution andmake uploadto
upload it to PyPi
Package: cowdict
cowdict is a python module implementing copy-on-write pattern on dictionaries.This means that if someone change the dict-like object, the original object won’t be changed but
the differences will be tracked instead.NutshellHere’s a small example:base_dict={"foo":"bar"}cd=CowDict(base_dict)cd["foo"]="baz"print(base_dict["foo"])# will still print 'bar'print(cd["foo"])# will print 'baz'CowDict object (cdin the code above) implements theMutableMappinginterface, simply speaking it has the
same interface as thedictobject.This means that it is guaranteed that changing keys/values on this object will never cause the change of the
underlying dictionary (base_dictin the example).PhilosophyThe idea behind this module is to avoid copying dictionary where it is possible and use this wrapper class instead.
While it has some penalties on the performance on key lookup and length calculation, the memory footprint can
be kept at minimum (compared to a full copy of the dictionary).Behind the scenesBehind the scenes, new items are added to a separate directory. Keys which exist on the base dictionary are
‘deleted’ by keeping a separate set object about the the keys deleted.
Every time when a key is accessed either bygetitemoritemsor other methods,
these additional structures are involved to produce the correct result.While having an assumption of having the base dictionary read-only would make the world easier, especially
when calculating the length of the object, the library handles the situation when the base dictionary changes.
Package: cow-download
cow_download下载pip install cow_download -i https://pypi.douban.com/simple/使用一个完整的奶牛快传链接就像这样：https://xiyoucloud.cowtransfer.com/s/9f9b7c098b9049你可以根据文件的 id 来下载文件 9f9b7c098b9049。cow download 9f9b7c098b9049默认情况下会开启 20 个线程对文件进行下载，这会大大加快下载速度，不过下载速度还受到网络带宽的限制，即下载速度最快不会超过网络带宽。
Package: cowechatapi
cowechatapi这是一个发送消息到企业微信的接口工具
仅支持在python3环境下执行，支持系统:Linux、windows其他暂未验证
消息类型支持: 文本、图片、声音、视频和文件安装依赖pipinstallrequestspip安装pipinstallcowechatapi通过调用API接口发送消息调用企业微信API接口发送消息给指定用户文本消息例子:fromcowechatapi.cowechat_apiimportCoWechatAPIbot=CoWechatAPI(coid="your_company_id",secret="your_app_secret",agentid=1000001)bot.send(to_user="USER_NAME",msg_type="text",content="今天天气好吗?")文件消息例子:fromcowechatapi.cowechat_apiimportCoWechatAPIbot=CoWechatAPI(coid="your_company_id",secret="your_app_secret",agentid=1000001)bot.send(to_user="USER_NAME",msg_type="file",media_id="MEDIA_ID")图片消息例子:fromcowechatapi.cowechat_apiimportCoWechatAPIbot=CoWechatAPI(coid="your_company_id",secret="your_app_secret",agentid=1000001)bot.send(to_user="USER_NAME",msg_type="image",media_id="MEDIA_ID")media_id的获取方法是通过API的upload上传临时素材得到的，临时素材有效期为两天。上传临时素材例子:fromcowechatapi.cowechat_apiimportCoWechatAPIbot=CoWechatAPI(coid="your_company_id",secret="your_app_secret",agentid=1000001)bot.upload(filetype="image",fileurl="FILE_PATH")通过命令行方式发送消息例子:# 发送文本消息python3-mcowechat-i[your_company_id]-s[your_app_secret]-a[your_agentid]-mtext-c"content"--user[USER_NAME]# 发送图片消息python3-mcowechat-i[your_company_id]-s[your_app_secret]-a[your_agentid]-mimage--media[media_id]--user[USER_NAME]
Package: cowEncryption
cowEncryptionThe documentation of library cowEncryptionInstalltionYou can install cowEncryption by running the following command:pip install cowEncryptionUpgradeYou can upgrade cowEncryption by running the following command:pip install cowEncryption --upgradeUsagefromcowEncryptionimportsavior#your name / path file projectfile="example.py"#EncryptionEncrypt=savior.Encryption(file,jenis="file").encPrem()#check statusprint(Encrypt)FunctionTo Encrypt your project files to keep them safe from thievesProtect your project files from thievesShorten your codingLicenseThe MIT License (MIT). Please seeLicense Filefor more information.AuthorsYou can visitauthors page