Saravanan666/pypi-org huggingface dataset:
Package: congas-old
# Copy number genotyping from scRNA sequencing[![Build Status](https://travis-ci.org/Militeee/anneal.svg?branch=master)](https://travis-ci.org/Militeee/congas)
[![codecov](https://codecov.io/gh/Militeee/anneal/branch/master/graph/badge.svg)](https://codecov.io/gh/Militeee/congas)A set of Pyro models and functions to infer CNA from scRNA-seq data.
It comes with a companion [R package](https://github.com/caravagnalab/rcongas) that works as an interface and provides preprocessing, simulation and visualization routines.
We suggest to use the R package directly as this serves mosttly as a backend for computations.Currently providing:A mixture model on segments where CNV are modelled as LogNormal random variable (MixtureGaussian)A mixture model on segments where CNV are modelled as Categorical random variable (MixtureCategorical)A simple Hmm where CNVs are again categorical, but there is no clustering (SimpleHmm)To install:$ pip install congasTo run a simple analysis on the example data`python import congas as cn from congas.models import MixtureGaussian data_dict = cn.simulation_data params, loss = cn.run_analysis(data_dict,MixtureGaussian, steps=200, lr=0.05) `
Package: congers
CongerLightweight data model management for Python ecosystem.Specifically,congerprovides a management interface for easy previewing, editing, and storing data models (defined bypydanticor dataclass).InstallIf you usingrye(recommended), you can installcongeras a dependency of your project:ryeaddcongeror pip:python-mpipinstallcongerQuick StartDefine your data model (e.g.Person):fromcongerimportBaseModel,FieldclassPerson(BaseModel):name:strage:int=Field(...,gt=18,lt=100)Add this data model to flask app:fromflaskimportFlaskimportpickledbfromcongerimportCongerapp=Flask(__name__)db=pickledb.load("db.json",True)conger=Conger(app,database=db)"""your data model"""conger.add_model(Person)if__name__=="__main__":app.run()visithttp://127.0.0.1:500/conger/LICENSE MIT
Package: congler
Congler=======A small CLI tool to interface with consul services. Useful for debugging consul service discovery mechanisms... _`see the GitHub page`: https://github.com/flypenguin/python-conglerCHANGELOG=========0.5.0-----Date: 2017-01-20- FEATURE: list-filtered now has a -u switch to remove dupliate service names when not using -v0.4.0-----Date: 2017-01-20- FEATURE: *-filtered can now also filter by top level lists (i.e. tags). See github page for example.0.3.0-----Date: 2017-01-20- FEATURE: simple output filter for list-services and list-tags0.2.1-----Date: 2017-01-20- FIX: Update docs in PyPI :)0.2.0-----Date: 2017-01-20- FEATURE: Filter capability (list and delete, with {delete,list}-filtered)- FEATURE: Version output- FEATURE: Docker distribution (quay.io/flypenguin/congler)0.1.0-----Date: 2017-01-20- Initial release
Package: congress-dashboard
Horizon Plugin for CongressCongress Dashboard is an extension for OpenStack Dashboard that provides a UI
for Congress. With congress-dashboard, a user is able to easily write the
policies and rules for governance of cloud.Free software: Apache licenseSource:https://opendev.org/openstack/congress-dashboardBugs:https://bugs.launchpad.net/congressDocumentation:https://docs.openstack.org/congress-dashboard/latest/Release notes:https://docs.openstack.org/releasenotes/congress-dashboard/Enabling in DevStackAdd this repo as an external repository into yourlocal.conffile:[[local|localrc]]
enable_plugin congress https://github.com/openstack/congressManual InstallationThe following below instructions assumes that Horizon is already installed and
its installation folder is <horizon>. Detailed information on how to install
Horizon can be found athttps://docs.openstack.org/horizon/latest/contributor/quickstart.html#setup.The installation folder of Congress Dashboard will be referred to as <congress-dashboard>.Clone Congress-Dashboard$gitclonehttps://github.com/openstack/congress-dashboard.git$cdcongress-dashboardInstall requirements$sudopipinstall.Install Source code$sudopythonsetup.pyinstallAnd enable it in Horizon$ln-s<congress-dashboard>/congress_dashboard/enabled/_50_policy.py<horizon>/openstack_dashboard/local/enabled$ln-s<congress-dashboard>/congress_dashboard/enabled/_60_policies.py<horizon>/openstack_dashboard/local/enabled$ln-s<congress-dashboard>/congress_dashboard/enabled/_70_datasources.py<horizon>/openstack_dashboard/local/enabled$ln-s<congress-dashboard>/congress_dashboard/enabled/_75_monitoring.py<horizon>/openstack_dashboard/local/enabled$ln-s<congress-dashboard>/congress_dashboard/enabled/_80_library.py<horizon>/openstack_dashboard/local/enabledRestart Apache server$sudoserviceapache2restart
Package: congress-tempest-plugin
Tempest Plugin for Congress project.
This project covers Tempest tests for Congress project.Free software: Apache licenseDocumentation:https://docs.openstack.org/congress-tempest-plugin/latestRelease notes:https://docs.openstack.org/releasenotes/congress/Source:https://opendev.org/openstack/congress-tempest-pluginBugs:https://bugs.launchpad.net/congressTempest IntegrationTo list all Congress tempest cases, go to tempest directory, then run:$ testr list-tests congressTo run congress tempest plugin tests using tox, go to tempest directory, then run:$ tox -eall-plugin congressAnd, to run a specific test:$ tox -eall-plugin congress_tempest_plugin.tests.scenario.test_congress_basic_ops.TestPolicyBasicOps.test_policy_basic_op
Package: congruous
CongruousA python CLI that generates performance and accuracy reports of your OCR recognized data with the human - curated correct dataInstallationWorks on python-3 environment and requires pip3 to install . In order to avoid environment conflicts setting upvirtualenvis advisable.$ pip install congruousJust to confirm that congruous is installed correctly along with its dependency , run the--versioncomand.$ congruous --versionThis command should show you  the lastest version number without any errors. However , its
been reported that for some linux distributionpython-tkis required to run this.  Run the below  command to fix it.$ apt-get install python-tkQuick Start$ congruous --helpLists all the available commands that can be usedCongruous mainly works based on the following three sub-commands ,storematchreportCurrently supported document types :pan,aadharCommand :storeThe purpose ofstorecommand  is to handle all the database operations.  The termstoreintends to  store something or to keep hold of .  Congruous uses an in-built sqlite database to handle the store operations .Here are some of the options that can be used along with thestorecommand.--seed- feeds the database based on document type , prints number of records
seeded if successful .--head- displays the first 10 records that were seeded during the seed operation. can be used to verify if your matching against the right set of data.--tail-  displays the last 10 records that were seeded during the seed operation. can be used to verify if your matching against the right set of data.--count- displays the total number of records seeded for a document type.--drop- drops all the records for a particular document type that was seeded .Command :matchThis command performs the match operation and gives the performance of the OCR . Match can be performed either against seeded records or with another file using the options below.--ocrd-  mandatory field that passes custom built OCR parsed data as an input to the tool. By default it will be matched to seeded records--hcd- to match with a another file instead of the  seeded records  in stores . passes a file to the tool--report- generates a report on the the given current run of the congruous .Command :reportThis command is helpful to get insights about your OCR model performance over a period of time.  Right now it just displays  history - performance in the last few congruous runs . More options are to be included in the future versions--history-  displays the performance of you OCR for the last 10 runs.
Package: conib
Curation of Neurodegeneration in BEL (CONIB)This knowledge repository, developed during theHuman Brain Pharmacome project, contains documents in Biological Expression Language (BEL) describing neurodegenerative disease phenomena such as tauopathies.Installation•Usage•Contributing•LicenseContentconib: This folder contains curated BEL
documents. Most have been organized by category (protein of interest, biological process of interest, pathway, etc.).
Almost BEL documents correspond to a single publication, but a select few contain content from several due to
topic-based curation.docs: This folder contains automatically generated HTML
summaries of the content that can best be accesed throughhttps://pharmacome.github.io/conib.notebooks: This folder contains various Jupyter
notebooks associated with the repository. Since many of them use PyBEL-Jupyter, they may be best viewed usingNBViewer.Curation PlanningHere are a few links for navigating the GitHub issues and pull requests:Pull requests for curationPull requests for recuration approved by a reviewerInstallationconibcan be installed fromPyPIwith the following command:$pipinstallconibor the latest version can be installed fromGitHubwith:$pipinstallgit+https://github.com/pharmacome/conib.gitUsageThe graph can be loaded with:importconib# Get a dictionary of names to graphsgraphs=conib.get_graphs()# Get all BEL documents as a single graphgraph=conib.get_graph()# Get all knowledge as INDRA statementsstmts=conib.get_indra_statements()ContributingContributions are welcome! Please submit all pull requests tohttps://github.com/pharmacome/conib.LicenseBEL scripts in this repository are licensed under the CC0 1.0 Universal.Python source code in this repository is licensed under the MIT license.More BEL ContentSeeA Listing of Publicly Available Content in the Biological Expression Language (BEL)on Charles Tapley Hoyt's blog for more BEL content.
Package: conifer-analysis
Post-process conifer output for downstream statistical analysis.conifer-analysisusesdaskin order to analyzeconiferresults in a distributed and
out-of-memory fashion. This can be helpful when processing many such results.ExampleSay that you have a bunch ofconiferresults in a directory. You can
generate a histogram of the confidence values per file (sample) and per taxa
using the provided pipelineconfidence_hist. Even when you work locally, it
can be helpful to explicitly create a distributed client controlling the number
of workers.fromdask.distributedimportClientfromconifer_analysisimportconfidence_histclient=Client(n_workers=8)You can then visit thedefault dashboardin
your browser to observe tasks live.  Next, we run the pipeline which returns apandas.DataFrame.hist=confidence_hist("data/*.tsv")hist.info()As an example of the returned shape:<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7700 entries, 0 to 7699
Data columns (total 8 columns):#ColumnNon-NullCountDtype---  ------       --------------  -----
 0   path         7700 non-null   category
 1   name         7700 non-null   category
 2   taxonomy_id  7700 non-null   category
 3   bin          7700 non-null   interval[float64, right]
 4   midpoints    7700 non-null   float64
 5   read1_hist   7700 non-null   int64
 6   read2_hist   7700 non-null   int64
 7   avg_hist     7700 non-null   int64
dtypes: category(3), float64(1), int64(3), interval(1)
memory usage: 385.3 KBInstallIt’s as simple as:pip install conifer-analysisIf you want to observe tasks in the dask dashboard, you will need additional
dependencies.pip install conifer-analysis[dashboard]CopyrightCopyright © 2022, Moritz E. Beber.Free software distributed under theApache Software License 2.0.
Package: conjugate-bayes
Conjugate Bayesian ModelsLast update: June 2019.Lightweight Python library implementing a few conjugate Bayesian models. For details on the derivations see [1].pip3 install conjugate-bayesWe support the following:To fit distribution modelsBeta-BernoulliGamma-PoissonNormal-Inverse-GammaTo fit regression modelsLinear regression with Normal Inverse-Gamma priorLinear regression with Zellner'sg-priorFuture workDirichlet-MultinomialNormal-Inverse-WishartUsageBelow we show an example fitting a simple Bayesian linear regression with unknown beta and unknown variance.model=NIGLinearRegression(mu=np.zeros(2),v=100*np.eye(2),a=0.5,b=0.5)model.fit(x_tr,y_tr)sigma2=model.get_marginal_sigma2()beta=model.get_conditional_beta(sigma2=sigma2.mean())The above example results in the following prediction intervals.For further details theexamples/folder.References[1] P. D. Hoff, A First Course in Bayesian Statistical Methods (New York: Springer-Verlag, 2009).LicenseThis library is available under the MIT License.
Package: conjugate-models
Conjugate ModelsBayesian conjugate models in PythonInstallationpipinstallconjugate-modelsFeaturesConnection to Scipy DistributionswithdistattributeBuilt in Plottingwithplot_pdfandplot_pmfmethodsVectorized Operationsfor parameters and dataIndexing Parametersfor subsetting and slicingGeneralized Numerical Inputsfor inputs other than builtins and numpy arraysUnsupported Distributionsfor sampling from unsupported distributionsSupported ModelsMany likelihoods are supported includingBernoulli/BinomialCategorical/MultinomialPoissonNormal(including linear regression)andmany moreBasic UsageDefine prior distribution fromdistributionsmodulePass data and prior into model frommodelsmodulesAnalytics with posterior and posterior predictive distributionsfromconjugate.distributionsimportBeta,BetaBinomialfromconjugate.modelsimportbinomial_beta,binomial_beta_posterior_predictive# Observed DataX=4N=10# Analyticsprior=Beta(1,1)prior_predictive:BetaBinomial=binomial_beta_posterior_predictive(n=N,beta=prior)posterior:Beta=binomial_beta(n=N,x=X,beta_prior=prior)posterior_predictive:BetaBinomial=binomial_beta_posterior_predictive(n=N,beta=posterior)From here, do any analysis you'd like!# Figureimportmatplotlib.pyplotaspltfig,axes=plt.subplots(ncols=2)ax=axes[0]ax=posterior.plot_pdf(ax=ax,label="posterior")prior.plot_pdf(ax=ax,label="prior")ax.axvline(x=X/N,color="black",ymax=0.05,label="MLE")ax.set_title("Success Rate")ax.legend()ax=axes[1]posterior_predictive.plot_pmf(ax=ax,label="posterior predictive")prior_predictive.plot_pmf(ax=ax,label="prior predictive")ax.axvline(x=X,color="black",ymax=0.05,label="Sample")ax.set_title("Number of Successes")ax.legend()plt.show()More examples on in thedocumentation.ContributingIf you are interested in contributing, check out thecontributing guidelines
Package: conjugate-prior
Conjugate PriorPython implementation of the conjugate prior table for Bayesian StatisticsSee wikipedia page:https://en.wikipedia.org/wiki/Conjugate_prior#Table_of_conjugate_distributionsInstallation:pip install conjugate-priorSupported Models:BetaBinomial- Useful for independent trials such as click-trough-rate (ctr), web visitor conversion.BetaBernoulli- Same as above.GammaExponential- Useful for churn-rate analysis, cost, dwell-time.GammaPoisson- Useful for time passed until event, as above.NormalNormalKnownVar- Useful for modeling a centralized distribution with constant noise.NormalLogNormalKnownVar- Useful for modeling a Length of a support phone call.InvGammaNormalKnownMean- Useful for modeling the effect of a noise.InvGammaWeibullKnownShape- Useful for reasoning about particle sizes over time.DirichletMultinomial- Extension of BetaBinomial to more than 2 types of events (Limited support).Basic APImodel = GammaExponential(a, b)- A Bayesian model with anExponentiallikelihood, and aGammaprior. Whereaandbare the prior parameters.model.pdf(x)- Returns the probability-density-function of the prior function atx.model.cdf(x)- Returns the cumulative-density-function of the prior function atx.model.mean()- Returns the prior mean.model.plot(l, u)- Plots the prior distribution betweenlandu.model.posterior(l, u)- Returns the credible interval on(l,u)(equivalent tocdf(u)-cdf(l)).model.update(data)- Returns anewmodel after observingdata.model.predict(x)- Predicts the likelihood of observingx(if a posterior predictive exists).model.sample()- Draw a single sample from the posterior distribution.Coin flip example:from conjugate_prior import BetaBinomial
heads = 95
tails = 105
prior_model = BetaBinomial() # Uninformative prior
updated_model = prior_model.update(heads, tails)
credible_interval = updated_model.posterior(0.45, 0.55)
print ("There's {p:.2f}% chance that the coin is fair".format(p=credible_interval*100))
predictive = updated_model.predict(50, 50)
print ("The chance of flipping 50 Heads and 50 Tails in 100 trials is {p:.2f}%".format(p=predictive*100))Variant selection with Multi-armed-banditAssume we have10creatives (variants) we can choose for our ad campaign, at first we start with the uninformative prior.After getting feedback (i.e. clicks) from displaying the ads, we update our model.Then we sample theDirrechletMultinomialmodel for the updated distribution.from conjugate_prior import DirichletMultinomial
from collections import Counter
# Assuming we have 10 creatives
model = DirichletMultinomial(10)
mle = lambda M:[int(r.argmax()) for r in M]
selections = [v for k,v in sorted(Counter(mle(model.sample(100))).most_common())]
print("Percentage before 1000 clicks: ",selections)
# after a period of time, we got this array of clicks
clicks = [400,200,100,50,20,20,10,0,0,200]
model = model.update(clicks)
selections = [v for k,v in sorted(Counter(mle(model.sample(100))).most_common())]
print("Percentage after 1000 clicks: ",selections)
Package: conjugator
ConjugatorA conjugator for spanish verbs. You have to provide a spanish verb and the type of the desired conjugation.DescriptionInstallation:pip install conjugatorExample usage:>>> from conjugator.spanish import conjugate
>>> conjugate("buscar", "Indicative")
             Person   Present   Preterite   Imperfect  Conditional      Future
0                yo     busco      busqué     buscaba     buscaría     buscaré
1                tú    buscas    buscaste    buscabas    buscarías    buscarás
2       él/ella/Ud.     busca       buscó     buscaba     buscaría     buscará
3          nosotros  buscamos    buscamos  buscábamos  buscaríamos  buscaremos
4          vosotros   buscáis  buscasteis   buscabais   buscaríais   buscaréis
5  ellos/ellas/Uds.    buscan    buscaron    buscaban    buscarían    buscaránThe following types are supported:'Indicative',
'Subjunctive',
'Imperative',
'Continuous (Progressive)',
'Perfect',
'Perfect Subjunctive'The conjugate-method returns a pandas DataFrame object.Example:>>> df = conjugate("bailar", "Subjunctive")
>>> df[["Person", "Present"]]
             Person   Present
0                yo     baile
1                tú    bailes
2       él/ella/Ud.     baile
3          nosotros  bailemos
4          vosotros   bailéis
5  ellos/ellas/Uds.    bailenFor further reference read:https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/comparison_with_sql.htmlNoteThis project has been set up using PyScaffold 3.2.3. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: conjunto
ConjuntoDescriptionConjunto is a Django application framework usingHTMX,tabler.io,crispy-formsand more.InstallationYou can install the package by using pip (best using a virtualenv):pipinstallconjuntoUsageThere is an own section in thedocumentationavailable.Development setupFor setting up development environment you will need pytest, pre-commit and black. You can install these by running (in the project directory):pipinstall.[dev]ContributingPull requests and general ideas are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseThe Conjunto project is open-source and licensed under the MIT License. Please keep in mind that Conjunto uses many other libraries, which may be under different licenses.
Package: conjuring
ConjuringReusable globalInvoketasks that can be
merged with local project tasks.FeaturesMerge any localtasks.pyfile with global Conjuring tasksUse all global Conjuring tasks provided by this packageOnly include the global Conjuring tasks you want (opt-in mode)Use all Conjuring tasks excluding some (opt-out mode)Add your own custom tasks from Python modules or packages to global tasksDisplay your custom task modules conditionallyDisplay your custom individual tasks conditionallyMerge your project tasks with the global reusable tasksPrefix task names of your custom moduleMore details on thefeatures documentation.TasksEach module undertheconjuring/spellsdirectoryis a collection of Invoke tasks.Quick setupInstall Conjuring in an isolated virtualenv withpipx:pipxinstall--include-depsconjuringThe--include-depsflag is needed to install Invoke's apps (invokeandinv).Create atasks.pyfile on your home dir:echo-e"from conjuring import *\n\nnamespace = cast_all_spells()">~/tasks.pyYou should see the list of Conjuring tasks from any directory where you type this:invoke--list
Package: conkit
A Python Interface to Contact PredictionsNEW: Now ConKit is also compatible with residue-residue distance predictionsConKit is a Python library to provide a data object hierarchy and associated routine operations to
work and manipulate residue-residue contact prediction data. Main features shipped with this library
include:Parsers for Multiple Sequence Alignment, contact prediction and residue distance prediction filesAnalysis functions for Multiple Sequence Alignment, contact prediction data and residue distance prediction dataVisualisation of Multiple Sequence Alignment, contact prediction data and residue distance prediction dataValidation of models based on residue distance predictionsPython wrappers for the contact-prediction-related softwareFor a general overview of ConKit, watchthis video.For an overview of model validation with ConKit, watch thisvideo.Installation & UsagePlease refer toConKit’s documentation.ContributingThere are two ways by which you can contribute to ConKit:Submit any suggestions to theGitHub Issue Tracker, orFork this repository, commit your changes and submit a pull request.Found a Bug?Please use theGitHub Issue Tracker.