Saravanan666/pypi-org huggingface dataset:
Package: b-aws-s3-backup
B.AwsS3BackupA python based package that allows you to back up, restore, and seed S3 buckets.DescriptionSometimes you want to make a small modification to your S3 bucket, however it usually requires
destruction of the bucket itself. This library allows you to do those small modifications without
any headache. You can easily backup all your data to your local computer, delete the bucket, create
a new one with desired modifications, and then simply restore it!RemarksBiomapasaims to modernise life-science
industry by sharing its IT knowledge with other companies and
the community. This is an open source library intended to be used
by anyone. Improvements and pull requests are welcome.Related technologyPython 3S3Boto3AssumptionsThe project assumes the following:You have basic knowledge in python programming.You have basic knowledge in S3.Useful sourcesRead more S3:https://docs.aws.amazon.com/AmazonS3/latest/API/Welcome.htmlInstallThe project is built and uploaded to PyPi. Install it by using pip.pip install b_aws_s3_backupOr directly install it through source.pip install .Usage & ExamplesThe library uses boto3 AWS SDK for interacting with AWS environment. Hence,
environmental credentials (such asaws_access_key_id,aws_secret_access_key, andregion_name)
are required.The library exposes cli commands. Read more about them down below.s3seedSeeds your S3 bucket with random data.>s3seedBucketNames3backBackups your whole bucket(s).# Single bucket.>s3backBucketName# All buckets.>s3backs3restRestores your whole bucket(s).# Single bucket.>s3restBucketName# All bucket.>s3restTestingThis project currently has no tests.ContributionFound a bug? Want to add or suggest a new feature?Contributions of any kind are gladly welcome. You may contact us
directly, create a pull-request or an issue in github platform.
Lets modernize the world together.Release history1.0.0Rename class UploadDb to UploadS3. This was a typo.Fix issue where item keys in S3 were full paths to local computer files.0.0.1Initial build.
Package: bawx-player
bawx-playerThis is a port of Kelvin Lawson'spykaraoke library(upgraded from Python 2 to Python 3 and packaged)Install with...pip install bawx-playerplay_karaoke.pyfrombawx_playerimportpycdgplayer=pycdg.cdgPlayer('some_song.cdg',None)player.Play()pycdg.manager.WaitForPlayer()
Package: bax
BaxBax, short for "boilerplate jax", is a small library that provides a flexible trainer
interface for Jax.Bax is rather strongly opinionated in a few ways. First, it is designed for use with theHaikuneural network library and is not
compatible with e.g.Flax. Second, Bax assumes that
data will be provided as atf.data.Dataset. The goal of this library is not to be
widely compatible and high-level (likeElegy).If you are okay with making the above assumptions, then Bax will hopefully make your
life much easier by implementing the boilerplate code involved in neural network
training loops.Please note that this library has not yet been extensively tested.InstallationYou can install Bax via pip:pip install baxUsageBelow are some simple examples that illustrate how to use Bax.MNIST Classificationimportoptaximporttensorflow_datasetsastfdsimporthaikuashkimportjax.numpyasjnpimportjaxfrombax.trainerimportTrainer# Use TensorFlow Datasets to get our MNIST data.train_ds=tfds.load("mnist",split="train").batch(32,drop_remainder=True)test_ds=tfds.load("mnist",split="test").batch(32,drop_remainder=True)# The loss function that we want to minimize.defloss_fn(step,is_training,batch):model=hk.Sequential([hk.Flatten(),hk.nets.MLP([128,128,10])])preds=model(batch["image"]/255.0)labels=jax.nn.one_hot(batch["label"],10)loss=jnp.mean(optax.softmax_cross_entropy(preds,labels))accuracy=jnp.mean(jnp.argmax(preds,axis=-1)==batch["label"])# The first returned value is the loss, which is what will be minimized by the# trainer. The second value is a dictionary that can contain other metrics you# might be interested in (or, it can just be empty).returnloss,{"accuracy":accuracy}trainer=Trainer(loss=loss_fn,optimizer=optax.adam(0.001))# Run the training loop. Metrics will be printed out each time the validation# dataset is evaluated (in this case, every 1000 steps).trainer.fit(train_ds,steps=10000,val_dataset=test_ds,validation_freq=1000)
Package: bayan
Bayan 1.0سلام . bayan یک کتابخونه برای جمع آوری اطلاعات از وبلاگ های بیان در پایتون هستش. کار با این کتابخونه بسیار ساده هستش چون اولا خیلی حجیم نیست دوما اسامی توابع کاملا واضح انتخاب شدن .Weblog Classبرای جمع آوری اطلاعات در مورد وبلاگ مورد نظر (مثلا ho55ein.blog.ir) . ابتدا یک شیء از کلاس Weblog میسازیم :‍‍>>> import bayan>>> weblog = bayan.Weblog("https://ho55ein.blog.ir")حالا خیلی راحت میتونیم از توابع زیر استفاده کنیم :getInfo()این تابع یک سری اطلاعات کلی در مورد وبلاگ مثل (عنوان وبلاگ ، تعداد دنبال کنندگان ، تعداد کامنت ها ، تعداد پست ها و...) را در قالب یک لیست بر میگرداند . البته ممکن است در بعضی از وبلاگ ها با توجه به تنظیماتی که صاحب وبلاگ اعمال کرده ، برخی از این اطلاعات در دسترس نباشد بنابراین تابع فقط اطلاعاتی که در دسترس هست رو بر میگردونه .>>> weblog.getInfo(){'TITLE': 'حسین نوشته ها ...', 'POSTS_COUNT': 114, 'COMMENTS_COUNT': 385}>>>getPages()این تابع لیستی از صفحات مستقل وبلاگ مورد نظر رو بر میگردونه.>>> weblog.getPages()[{'TITLE': 'علاقه مندی ها', 'LINK': 'https://ho55ein.blog.ir/page/who-is-hossein'}, {'TITLE': 'تماس با me :)', 'LINK': 'https://ho55ein.blog.ir/page/message-to-hossein'}, {'TITLE': 'My Playlist', 'LINK': 'https://ho55ein.blog.ir/page/My-Playlist'}]>>>getLinks()این تابع لیستی از پیوند های وبلاگ مورد نظر رو بر میگردونه .>>> weblog.getLinks()[{'TITLE': 'Mr Python (Hacking With Python)', 'LINK': 'https://mrpython.blog.ir'}, {'TITLE': 'OG150 Hacking Tutorials', 'LINK': 'http://www.og150.com/tutorials.php'}]>>>getLastPosts()این تابع لیستی از آخرین پست های وبلاگ مورد نظر رو برمیگردونه .getLastFollowers()همانطور که میدونید در وبلاگ هایی که جعبه ی دنبال کنندگان نمایش داده میشه ، لیستی از آخرین دنبال کننده ها وجود داره . این تابع در صورت وجود باکس دنبال کنندگان لیستی از آخرین دنبال کنندگان وبلاگ مورد نظر را برمیگرداند.bayan.getChanges()این تابع لیست وبلاگ های بروز شده رو بر میگردونه .همچنین هر شیء از کلاس Weblog دارای یک متغییر یا ویژگی به نام follow_link است که لینک دنبال کردن وبلاگ مورد نظر را در خود نگه میدارد :>>> print(weblog.follow_link)http://blog.ir/panel/-/followed_blogs?follow=https://ho55ein.blog.ir>>>‍
Package: bayan-address
BayanAddressBayanAddressis a Python-based address parser for Philippines. It analyzes user-provided address strings and identifies the building unit, subdivision, street, barangay, city, province, and zip code. It can also identify the island group (Luzon, Visayas, and Mindanao), ISO code, and region based on its province.Installation$python-mpipinstallbayan-addressHow to UseWith province:>>>frombayan_addressimportparse>>>parse("95 JayLee Street Sofia Subdivision Del Pilar San Fernando City Pampanga 2000"){'province':'Pampanga','iso':'PH-PAM','region':'III','island_group':'Luzon','zip_code':'2000','city':'San Fernando City','street':'JayLee Street','building':'95','subdivision':'Sofia Subdivision','barangay':'Del Pilar'}Without province:>>>parse("34 Avocado St., Brgy. Maligaya, Mariveles"){'city':'Mariveles','street':'Avocado St.','building':'34','barangay':'Brgy. Maligaya'}
Package: bay-area-mashers-member-export
bay-area-mashers-member-exportTool to query Bay Area Mashers member list and return all current members' email addressesInstallationProvision Google credentialsto query the Google SheetSave the resulting Google API credential file in~/.config/gspread/service_account.jsonInstall the bay-area-mashers-member-export toolpip install bay-area-mashers-member-exportUsagebay-area-mashers-member-export/secure/>/etc/apache2/sites-available/secure-location.include
sudoapachectlgraceful
Package: baybars
BaybarsRaison D'être:Common library that are wrappers on other third party libraries to make life easier for engineers.We want better API's to interact various infrastructure, common libraries to be more productive.We also want to share the code so that other people can reuse and make use of it.Software is meant to be used and not forgotten.FeaturesIt has great wrappers(beautiful APIs to interact with) for:Nomad(consul, vault)Azure(blob, queue, cosmosdb)KafkaFTPVarious image processing utilsVarious compression utilsVarious logging and timestamp utilsUpcomingDocumentationTests for each modules and test coverageMachine Learning Models(expect some good Tensorflow production ready models)Some common preprocessing, feature extraction modules for various data
Package: baycomp
baycompBaycomp is a library for Bayesian comparison of classifiers.Functions compare two classifiers on one or on multiple data sets. They
compute three probabilities: the probability that the first classifier has
higher scores than the second, the probability that differences are within
the region of practical equivalence (rope), or that the second classifier
has higher scores. We will refer to this probabilities asp_left,p_ropeandp_right. If the argumentropeis omitted (or set to zero), functions
return onlyp_leftandp_right.The region of practical equivalence (rope) is specified by the caller and
should correspond to what is "equivalent" in practice; for instance,
classification accuracies that differ by less than 0.5 may be called
equivalent.Similarly, whether higher scores are better or worse depends upon the type
of the score.The library can also plot the posterior distributions.The library can be used in three ways.Two shortcut functions can be used for comparison on single
and on multiple data sets. Ifnbcandj48contain a list of average
classification accuracies of naive Bayesian classifier and J48 on a
collection of data sets, we can call>>> two_on_multiple(nbc, j48, rope=1)
 (0.23124, 0.00666, 0.7621)(Actual results may differ due to Monte Carlo sampling.)With some additional arguments, the function can also plot the posterior
distribution from which these probabilities came.Tests are packed into test classes. The above call is equivalent to>>> SignedRankTest.probs(nbc, j48, rope=1)
 (0.23124, 0.00666, 0.7621)and to get a plot, we call>>> SignedRankTest.plot(nbc, j48, rope=1, names=("nbc", "j48"))To switch to another test, use another class::>>> SignTest.probs(nbc, j48, rope=1)
 (0.26508, 0.13274, 0.60218)Finally, we can construct and query sampled posterior distributions.>>> posterior = SignedRankTest(nbc, j48, rope=0.5)
 >>> posterior.probs()
 (0.23124, 0.00666, 0.7621)
 >>> posterior.plot(names=("nbc", "j48"))InstallationInstall fromPyPI:pip install baycompDocumentationUser documentation is available onhttps://baycomp.readthedocs.io/.A detailed description of the implemented methods is available inTime for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis, Alessio Benavoli, Giorgio Corani, Janez Demšar, Marco Zaffalon. Journal of Machine Learning Research, 18 (2017) 1-36.
Package: baycomp-plotting
Baycomp PlottingThebaycomp_plottingis a python package for building good-looking plots of
bayesian posteriors obtained withbaycomp.This package could be useful for scientific purposes, specially in the area of
Machine Learning.AuthorMario Juez-Gil <mariojg@ubu.es>Department of Computer ScienceUniversidad de BurgosADMIRABLE Research GroupInstallationThis package can be installed using PIP.pipinstallbaycomp_plottingBasic UsageThe package can be imported as follows:importbaycomp_plottingasbpltTwo plotting functions (tern, anddens), and one class with four matplotlib alternative colors (Color) are provided.ColorsFour alternative colors to default matplotlib colors are provided:Example:importbaycomp_plottingasbpltprint(bplt.Color.BLUE)Output:'#008ece'Density plotsFor plotting the comparison of two classifiers on a single dataset,densfunction could be used. It's parameters are the following:p: baycomp posterior.label: label of the density function.ls: line style (use a matplotlib line style) [default:-]color: density function color [default:Color.BLUE]Example:importbaycomp_plottingasbpltimportbaycompasbcposterior=bc.CorrelatedTTest(left_classifier_acc,right_classifier_acc,rope=0.01)fig=bplt.dens(posterior,label='C1',ls='-',color=bplt.Color.BLUE)Output:The output figure will have a new function namedadd_posteriorso you can add more posteriors to the figure. The parameters are the same as fordens.Example:importbaycomp_plottingasbpltimportbaycompasbcposterior=bc.CorrelatedTTest(left_classifier_1_acc,right_classifier_acc,rope=0.01)posterior_1=bc.CorrelatedTTest(left_classifier_2_acc,right_classifier_acc,rope=0.01)fig=bplt.dens(posterior,label='C1',ls='-',color=bplt.Color.BLUE)fig.add_posterior(posterior_1,label='C2',ls=(0,(5,1)),color=bplt.Color.GRAY)fig.legend()# you can show the legendOutput:Ternary plotsFor plotting the comparison of two classifiers on multiple datasets using a ternary plot,ternfunction could be used. It's parameters are the following:p: baycomp posterior.names: an array containing Left and Right region labels. [default:["L", "R"]]Example:importbaycomp_plottingasbpltimportbaycompasbcposterior=bc.HierarchicalTest(left_classifier_acc,right_classifier_acc,rope=0.01)fig=bplt.tern(posterior)Output:Comparison against baycomp default plotsDensity:Ternary:ContributeFeel free to submit any pull requests 😊AcknowlegmentsThis work was supported by the pre-doctoral grant (EDU/1100/2017) of the
Consejería de Educación of the Junta de Castilla y León, Spain, and the
European Social Fund.LicenseThis work is licensed underGNU GPL v3.Citation policyPlease, cite this work as:@software{baycomp_plotting,
  author       = {Mario Juez-Gil},
  title        = {{mjuez/baycomp_plotting}},
  month        = nov,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v1.0},
  doi          = {10.5281/zenodo.4244542},
  url          = {https://doi.org/10.5281/zenodo.4244542}
}
Package: bayerstraits_16s
# bayerstraits_16s# This is a temp version of bayerstraits_16s## Introduction* bayerstraits_16s infers traits by 16S* input: otu table (-t) and otu sequences (-s)* requirement: mafft* Optional: fasttree![alt text](https://raw.githubusercontent.com/caozhichongchong/bayerstraits_16s/master/Methodology.png)## Install`pip install bayerstraits_16s`\in preparation: `anaconda download caozhichongchong/bayerstraits_16s`## Test (any of these two commands)`bayerstraits_16s --test`\`bayerstraits_16s -t your.otu.table -s your.otu.seqs`## Availabilityin preparation: https://anaconda.org/caozhichongchong/bayerstraits_16shttps://pypi.org/project/bayerstraits_16s## How to use it1. test the bayerstraits_16s\`bayerstraits_16s --test`2. try your data\`bayerstraits_16s -t your.otu.table -s your.otu.seqs`\`bayerstraits_16s -t your.otu.table -s your.otu.seqs -top 2000`\Warning: please keep the exactly same OTU IDs in your.otu.table and your.otu.seqs!3. use your own traits\`bayerstraits_16s -t your.otu.table -s your.otu.seqs --rs your.own.reference.16s --rt your.own.reference.traits`* your.own.reference.16s is a fasta file containing the 16S sequences of your genomes\\>Genome_ID1\ATGC...\\>Genome_ID2\ATGC...* your.own.reference.traits is a metadata of whether there's trait in your genomes (0 for no and 1 for yes)\Genome_ID1   0\Genome_ID1   1## ResultsThe result dir of "Bayers_model":* `filename.infertraits.txt`: the OTUs inferring as butyrate-producing bacteria (1.0, 0.5)and non-butyrate-producing bacteria (0.0).* `filename.infertraits.abu`: the total abundance of butyrate-producing bacteria in all samples.* `filename.infertraits.otu_table`: the otu_table of butyrate-producing bacteria in all samples.The result dir of "Filtered_OTU":* Some temp files of filtered OTUs, alignment, and tree.## CopyrightCopyright: An Ni Zhang, Prof. Eric Alm, Alm Lab in MIT\Citation: Not yet, coming soon!\Contact: anniz44@mit.edu
Package: bayesbay
BayesBayBayesBay is a user-friendly Python package designed forgeneralised trans-dimensional and hierarchical Bayesian inference.
Optimised computationally through Cython, our library offers multi-processing capabilities and runs seamlessly on both standard computers and computer clusters.Distinguishing itself from existing packages, BayesBay provideshigh-level functionalities for defining complex parameterizations.
These include prior probabilities that can be specified by uniform, Gaussian, or custom density functions and may vary depending on the spatial position in a
hypothetical discretization.By default, BayesBay employsreversible-jump Markov chain Monte Carlo(MCMC) for sampling the posterior probability.
It also offers options forparallel temperingorsimulated annealing, while its low-level features enable the effortless implementation of arbitrary sampling criteria.
Utilising object-oriented programming principles, BayesBay ensures that each component of an inference problem --- such as observed data, forward function(s), and parameterization ---
is a self-contained unit. This design facilitates the integration of various forward solvers and data sets, promoting the simultaneous use of multiple data types in the
considered inverse problem.Development tipsTo set up development environment:$mambaenvcreate-fenvs/environment_dev.ymlor$python-mvenvbayesbay_dev$sourcebayesbay_dev/bin/activate$pipinstall-renvs/requirements_dev.txtTo install the package:$python-mpipinstall.Look atnoxfile.pyfor building, testing, formatting and linting.
Package: bayescache
.. raw:: html<embed><p align="center"><img width="300" src="https://github.com/yngtodd/bayescache/blob/master/img/pylibrary.png"></p></embed>--------------------------.. image:: https://badge.fury.io/py/bayescache.png:target: http://badge.fury.io/py/bayescache.. image:: https://travis-ci.org/yngtodd/bayescache.png?branch=master:target: https://travis-ci.org/yngtodd/bayescache=============================BayesCache=============================Save cache, Bae will you love you.Documentation--------------For references, tutorials, and examples check out our `documentation`_.Installation------------From Sources:You can either clone the public repository:.. code-block:: consolegit clone git://github.com/yngtodd/bayescacheOr download the `tarball`_:.. code-block:: consolecurl  -OL https://github.com/yngtodd/bayescache/tarball/masterOnce you have a copy of the source, you can install it with:.. code-block:: consolepython setup.py install.. _tarball: https://github.com/yngtodd/bayescache/tarball/master.. _documentation: https://bayescache.readthedocs.io/en/latestDocumentation-------------The full documentation is at http://bayescache.rtfd.org.History-------0.1.0 (2019-04-12)++++++++++++++++++* First release on PyPI.
Package: bayesclass
bayesclassBayesian EstimatorsTAN Tree augmented naive BayesFriedman, N., Geiger, D. & Goldszmidt, M. Bayesian Network Classifiers. Machine Learning 29, 131–163 (1997).https://doi.org/10.1023/A:1007465528199KDBMehran Sahami. 1996. Learning limited dependence Bayesian classifiers. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD'96). AAAI Press, 335–338.AODE Averaged One-Dependence EstimatorsWebb, G., Boughton, J. & Wang, Z. Not So Naive Bayes: Aggregating One-Dependence Estimators. Mach Learn 58, 5–24 (2005).https://doi.org/10.1007/s10994-005-4258-6
Package: bayesdawn
BayesDawnBayesDawn stands for Bayesian Data Augmentation for Waves and Noise. It implements an iterative Bayesian augmentation
method to handle data gaps in gravitational-wave data analysis, as described in this paper:https://arxiv.org/abs/1907.04747.InstallationBayesDawn can be installed by unzipping the source code in one directory and using this command: ::sudo python setup.py installQuick startUsing BayesDawn for your own analysis will essentially involve the datamodel.py module, which allows you to
compute the conditional distribution of missing values given the observed values of a time series.
Here is a working example that can be used.Generation of test dataTo begin with, we generate some simple time series which contains noise and signal.
To generate the noise, we start with a white, zero-mean Gaussian noise that
we then filter to obtain a stationary colored noise:# Import bayesdawn and other useful packagesfrombayesdawnimportdatamodel,psdmodelimportnumpyasnpimportrandomfromscipyimportsignal# Choose size of datan_data=2**14# Set sampling frequencyfs=1.0# Generate Gaussian white noisenoise=np.random.normal(loc=0.0,scale=1.0,size=n_data)# Apply filtering to turn it into colored noiser=0.01b,a=signal.butter(3,0.1/0.5,btype='high',analog=False)n=signal.lfilter(b,a,noise,axis=-1,zi=None)+noise*rThen we need a deterministic signal to add. We choose a sinusoid with some
frequency f0 and amplitude a0:t=np.arange(0,n_data)/fsf0=1e-2a0=5e-3s=a0*np.sin(2*np.pi*f0*t)The noisy data is theny=s+nIntroduction of data gapsNow assume that some data are missing, i.e. the time series is cut by random gaps.
The pattern is represented by a mask vector with entries equal to 1 when data
is observed, and 0 otherwise:mask=np.ones(n_data)n_gaps=30gapstarts=(n_data*np.random.random(n_gaps)).astype(int)gaplength=10gapends=(gapstarts+gaplength).astype(int)forkinrange(n_gaps):mask[gapstarts[k]:gapends[k]]=0Therefore, we do not observe the data y but its masked version, mask*y:y_masked=mask*yMissing data imputationAssuming that we know exactly the deterministic signal, we can do a crude estimation of the PSD from masked data:# Fit PSD with a spline of degree 2 and 10 knotspsd_cls=psdmodel.PSDSpline(n_data,fs,n_knots=10,d=2,fmin=fs/n_data,fmax=fs/2)psd_cls.estimate(mask*(y-s))Then, from the observed data and their model, we can reconstruct the missing data using the imputation package:# instantiate imputation classimp_cls=datamodel.GaussianStationaryProcess(s,mask,psd_cls,na=50,nb=50)# perform offline computationsimp_cls.compute_offline()# Imputation of missing datay_rec=imp_cls.draw_missing_data(y_masked)DocumentationPlease refer to thedocumentation.